feat(models): support xiaomi mimo

fix(memory): CJK keyword search + vector search optimization

.github/ISSUE_TEMPLATE/1.bug.yml

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

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

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

.github/workflows/deploy-image.yml

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

.gitignore

@@ -3,16 +3,15 @@
 .vscode
 .venv
 .vs
-.wechaty/
 __pycache__/
 venv*
 *.pyc
+python
 config.json
 QR.png
 nohup.out
 tmp
 plugins.json
-itchat.pkl
 *.log
 logs/
 workspace
@@ -33,8 +32,16 @@ plugins/banwords/lib/__pycache__
 !plugins/role
 !plugins/keyword
 !plugins/linkai
-!plugins/agent
+!plugins/cow_cli
 client_config.json
 ref/
+**/.dev.vars
 .cursor/
 local/
+node_modules/
+
+# cow cli
+dist/
+build/
+*.egg-info/
+.cow.pid

README.md

@@ -1,773 +1,255 @@
-<p align="center"><img src= "https://github.com/user-attachments/assets/eca9a9ec-8534-4615-9e0f-96c5ac1d10a3" alt="Chatgpt-on-Wechat" width="550" /></p>
+<p align="center"><img src="https://github.com/user-attachments/assets/eca9a9ec-8534-4615-9e0f-96c5ac1d10a3" alt="CowAgent" width="420" /></p>
 
 <p align="center">
-   <a href="https://github.com/zhayujie/chatgpt-on-wechat/releases/latest"><img src="https://img.shields.io/github/v/release/zhayujie/chatgpt-on-wechat" alt="Latest release"></a>
-  <a href="https://github.com/zhayujie/chatgpt-on-wechat/blob/master/LICENSE"><img src="https://img.shields.io/github/license/zhayujie/chatgpt-on-wechat" alt="License: MIT"></a>
-  <a href="https://github.com/zhayujie/chatgpt-on-wechat"><img src="https://img.shields.io/github/stars/zhayujie/chatgpt-on-wechat?style=flat-square" alt="Stars"></a> <br/>
+  <a href="https://github.com/zhayujie/CowAgent/releases/latest"><img src="https://img.shields.io/github/v/release/zhayujie/CowAgent" alt="Latest release"></a>
+  <a href="https://github.com/zhayujie/CowAgent/blob/master/LICENSE"><img src="https://img.shields.io/github/license/zhayujie/CowAgent" alt="License: MIT"></a>
+  <a href="https://github.com/zhayujie/CowAgent"><img src="https://img.shields.io/github/stars/zhayujie/CowAgent?style=flat-square" alt="Stars"></a> <br/>
+  [English] | [<a href="docs/zh/README.md">中文</a>] | [<a href="docs/ja/README.md">日本語</a>]
 </p>
 
-**CowAgent** 是基于大模型的超级AI助理，能够主动思考和任务规划、操作计算机和外部资源、创造和执行Skills、拥有长期记忆并不断成长。CowAgent 支持灵活切换多种模型，能处理文本、语音、图片、文件等多模态消息，可接入网页、飞书、钉钉、企业微信应用、微信公众号中使用，7*24小时运行于你的个人电脑或服务器中。
+**CowAgent** is an open-source super AI assistant that proactively plans tasks, controls your computer and external services, creates and runs Skills, and grows alongside you through a personal knowledge base and long-term memory — a reference implementation of Agent Harness engineering.
 
-📖能力介绍：[CowAgent 2.0](/docs/agent.md)
+CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major LLM provider and run it 24/7 on a personal computer or server, across the web and all major IM platforms.
 
-# 简介
-
-> 该项目既是一个可以开箱即用的超级AI助理，也是一个支持高扩展的Agent框架，可以通过为项目扩展大模型接口、接入渠道、内置工具、Skills系统来灵活实现各种定制需求。核心能力如下：
-
--  ✅  **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、Qwen、Kimi、Doubao等国内外主流模型厂商
--  ✅  **多端部署：** 支持运行在本地计算机或服务器，可集成到网页、飞书、钉钉、微信公众号、企业微信应用中使用
--  ✅  **知识库：** 集成企业知识库能力，让Agent成为专属数字员工，基于[LinkAI](https://link-ai.tech)平台实现
-
-## 声明
-
-1. 本项目遵循 [MIT开源协议](/LICENSE)，主要用于技术研究和学习，使用本项目时需遵守所在地法律法规、相关政策以及企业章程，禁止用于任何违法或侵犯他人权益的行为。任何个人、团队和企业，无论以何种方式使用该项目、对何对象提供服务，所产生的一切后果，本项目均不承担任何责任
-2. 成本与安全：Agent模式下Token使用量高于普通对话模式，请根据效果及成本综合选择模型。Agent具有访问所在操作系统的能力，请谨慎选择项目部署环境。同时项目也会持续升级安全机制、并降低模型消耗成本
-
-## 演示
-
-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="140" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/open-community.png">
+<p align="center">
+  <a href="https://cowagent.ai/">🌐 Website</a> &nbsp;·&nbsp;
+  <a href="https://docs.cowagent.ai/en/intro/index">📖 Docs</a> &nbsp;·&nbsp;
+  <a href="https://docs.cowagent.ai/en/guide/quick-start">🚀 Quick Start</a> &nbsp;·&nbsp;
+  <a href="https://skills.cowagent.ai/">🧩 Skill Hub</a> &nbsp;·&nbsp;
+  <a href="https://link-ai.tech/cowagent/create">☁️ Try Online</a>
+</p>
 
 <br/>
 
-# 企业服务
+## 🌟 Highlights
 
-<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 目前已在智能客服、私域运营、企业效率助手等场景积累了丰富的AI解决方案，在消费、健康、文教、科技制造等各行业沉淀了大模型落地应用的最佳实践，致力于帮助更多企业和开发者拥抱 AI 生产力。
-
-**产品咨询和企业服务** 可联系产品客服：
-
-<img width="150" src="https://cdn.link-ai.tech/portal/linkai-customer-service.png">
+| Capability | Description |
+| :--- | :--- |
+| [Planning](https://docs.cowagent.ai/en/intro/architecture) | Decomposes complex tasks and executes them step by step, looping over tools until the goal is reached |
+| [Memory](https://docs.cowagent.ai/en/memory/index) | Three-tier architecture (context → daily → core), automatic Deep Dream distillation, hybrid keyword + vector retrieval |
+| [Knowledge](https://docs.cowagent.ai/en/knowledge/index) | Auto-curates structured knowledge into a Markdown wiki, builds an evolving knowledge graph with visual browsing |
+| [Skills](https://docs.cowagent.ai/en/skills/index) | One-click install from [Skill Hub](https://skills.cowagent.ai/), GitHub, ClawHub; or create custom skills via natural-language conversation |
+| [Tools](https://docs.cowagent.ai/en/tools/index) | Built-in file I/O, terminal, browser, scheduler, memory retrieval, web search, and 10+ more tools — with native MCP integration |
+| [Channels](https://docs.cowagent.ai/en/channels/index) | Integrates with Web, WeChat, Feishu, DingTalk, WeCom, QQ, and Official Accounts |
+| Multimodal | First-class support for text, images, voice, and files — recognition, generation, and delivery |
+| [Models](https://docs.cowagent.ai/en/models/index) | Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, MiniMax, Doubao, and more — swap providers from the Web console with one click |
+| [Deploy](https://docs.cowagent.ai/en/guide/quick-start) | One-line installer, unified Web console, multiple deployment modes (local, Docker, server) |
 
 <br/>
 
-# 🏷 更新日志
+## 🏗️ Architecture
 
->**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。
+<img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/architecture/en/architecture.jpg" alt="CowAgent Architecture" width="750"/>
 
->**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
+CowAgent is a complete **Agent Harness**: messages flow in through **Channels**; the **Agent Core** plans and reasons over memory, knowledge, and the available tools and skills; **Models** generate the response, which is sent back through the originating channel. Every layer is decoupled and independently extensible.
 
->**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
-
->**2024.12.13：** [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
-
->**2024.10.31：** [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
-
-更多更新历史请查看: [更新日志](/docs/release/history.md)
+Read more in [Architecture](https://docs.cowagent.ai/en/intro/architecture).
 
 <br/>
 
-# 🚀 快速开始
+## 🚀 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。
+A one-line installer takes care of dependencies, configuration, and startup:
 
-在终端执行以下命令：
+**Linux / macOS:**
 
 ```bash
-bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
+bash <(curl -fsSL https://cdn.link-ai.tech/code/cow/run.sh)
 ```
 
-脚本使用说明：[一键运行脚本](https://github.com/zhayujie/chatgpt-on-wechat/wiki/CowAgentQuickStart)
+**Windows (PowerShell):**
 
+```powershell
+irm https://cdn.link-ai.tech/code/cow/run.ps1 | iex
+```
 
-## 一、准备
-
-### 1. 模型API
-
-项目支持国内外主流厂商的模型接口，可选模型及配置说明参考：[模型说明](#模型说明)。
-
-> 注：Agent模式下推荐使用以下模型，可根据效果及成本综合选择：MiniMax-M2.5、glm-5、kimi-k2.5、qwen3.5-plus、claude-sonnet-4-6、gemini-3.1-pro-preview
-
-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.环境安装
-
-支持 Linux、MacOS、Windows 操作系统，可在个人计算机及服务器上运行，需安装 `Python`，Python版本需在3.7 ~ 3.12 之间，推荐使用3.9版本。
-
-> 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：**
+**Docker:**
 
 ```bash
-git clone https://github.com/zhayujie/chatgpt-on-wechat
-cd chatgpt-on-wechat/
+curl -O https://cdn.link-ai.tech/code/cow/docker-compose.yml
+docker compose up -d
 ```
 
-若遇到网络问题可使用国内仓库地址：https://gitee.com/zhayujie/chatgpt-on-wechat
+Once started, open `http://localhost:9899` to access the **Web console** — your one-stop hub to chat with the Agent, configure models, connect channels, and install skills.
 
-**(2) 安装核心依赖 (必选)：**
+> Deploying on a server? Set `web_host` to `0.0.0.0` in `config.json` to make the console reachable from outside, and set `web_password` to protect it. Don't forget to open port `9899` in your firewall or security group.
+
+> 📖 Detailed guides: [Quick Start](https://docs.cowagent.ai/en/guide/quick-start) · [Install from Source](https://docs.cowagent.ai/en/guide/manual-install) · [Upgrade](https://docs.cowagent.ai/en/guide/upgrade)
+
+After installation, manage the service with the [cow CLI](https://docs.cowagent.ai/en/cli/index):
 
 ```bash
-pip3 install -r requirements.txt
+cow start | stop | restart        # service control
+cow status | logs                  # status and logs
+cow update                         # pull latest code and restart
+cow skill install <name>           # install a skill
+cow install-browser                # install browser automation
 ```
 
-**(3) 拓展依赖 (可选，建议安装)：**
-
-```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后重试。
-
-## 二、配置
-
-配置文件的模板在根目录的`config-template.json`中，需复制该模板创建最终生效的 `config.json` 文件：
-
-```bash
-  cp config-template.json config.json
-```
-
-然后在`config.json`中填入配置，以下是对默认配置的说明，可根据需要进行自定义修改（注意实际使用时请去掉注释，保证JSON格式的规范）：
-
-```bash
-# config.json 文件内容示例
-{
-  "channel_type": "web",                                      # 接入渠道类型，默认为web，支持修改为:feishu,dingtalk,wechatcom_app,terminal,wechatmp,wechatmp_service
-  "model": "MiniMax-M2.5",                                    # 模型名称
-  "minimax_api_key": "",                                      # MiniMax API Key
-  "zhipu_ai_api_key": "",                                     # 智谱GLM API Key
-  "moonshot_api_key": "",                                     # Kimi/Moonshot API Key
-  "ark_api_key": "",                                          # 豆包(火山方舟) API Key
-  "dashscope_api_key": "",                                    # 百炼(通义千问)API Key
-  "claude_api_key": "",                                       # Claude API Key
-  "claude_api_base": "https://api.anthropic.com/v1",          # Claude API 地址，修改可接入三方代理平台
-  "gemini_api_key": "",                                       # Gemini API Key
-  "gemini_api_base": "https://generativelanguage.googleapis.com", # Gemini API地址
-  "open_ai_api_key": "",                                      # OpenAI API Key
-  "open_ai_api_base": "https://api.openai.com/v1",            # OpenAI API 地址
-  "linkai_api_key": "",                                       # LinkAI API Key
-  "proxy": "",                                                # 代理客户端的ip和端口，国内环境需要开启代理的可填写该项，如 "127.0.0.1:7890"
-  "speech_recognition": false,                                # 是否开启语音识别
-  "group_speech_recognition": false,                          # 是否开启群组语音识别
-  "voice_reply_voice": false,                                 # 是否使用语音回复语音
-  "use_linkai": false,                                        # 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台接口
-  "agent": true,                                              # 是否启用Agent模式，启用后拥有多轮工具决策、长期记忆、Skills能力等
-  "agent_workspace": "~/cow",                                 # Agent的工作空间路径，用于存储memory、skills、系统设定等
-  "agent_max_context_tokens": 40000,                          # Agent模式下最大上下文tokens，超出将自动丢弃最早的上下文
-  "agent_max_context_turns": 30,                              # Agent模式下最大上下文记忆轮次，每轮包括一次用户提问和AI回复
-  "agent_max_steps": 15                                       # Agent模式下单次任务的最大决策步数，超出后将停止继续调用工具
-}
-```
-
-**配置补充说明:** 
-
-<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>
-
-<details>
-<summary>2. 其他配置</summary>
-
-+ `model`: 模型名称，Agent模式下推荐使用 `MiniMax-M2.5`、`glm-5`、`kimi-k2.5`、`qwen3.5-plus`、`claude-sonnet-4-6`、`gemini-3.1-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
-+ `character_desc`：普通对话模式下的机器人系统提示词。在Agent模式下该配置不生效，由工作空间中的文件内容构成。
-+ `subscribe_msg`：订阅消息，公众号和企业微信channel中请填写，当被订阅时会自动回复， 可使用特殊占位符。目前支持的占位符有{trigger_prefix}，在程序中它会自动替换成bot的触发词。
-</details>
-
-<details>
-<summary>3. LinkAI配置</summary>
-
-+ `use_linkai`: 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台，使用知识库、工作流、插件等能力, 参考[接口文档](https://docs.link-ai.tech/platform/api/chat)
-+ `linkai_api_key`: LinkAI Api Key，可在 [控制台](https://link-ai.tech/console/interface) 创建
-+ `linkai_app_code`: LinkAI 应用或工作流的code，选填，普通对话模式中使用。
-</details>
-
-注：全部配置项说明可在 [`config.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py) 文件中查看。
-
-## 三、运行
-
-### 1.本地运行
-
-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
-```
-
-运行后默认会启动web服务，可通过访问 `http://localhost:9899/chat` 在网页端对话。
-
-如果需要接入其他应用通道只需修改 `config.json` 配置文件中的 `channel_type` 参数，详情参考：[通道说明](#通道说明)。
-
-
-### 2.服务器部署
-
-在服务器中可使用 `nohup` 命令在后台运行程序：
-
-```bash
-nohup python3 app.py & tail -f nohup.out
-```
-
-执行后程序运行于服务器后台，可通过 `ctrl+c` 关闭日志，不会影响后台程序的运行。使用 `ps -ef | grep app.py | grep -v grep` 命令可查看运行于后台的进程，如果想要重新启动程序可以先 `kill` 掉对应的进程。 日志关闭后如果想要再次打开只需输入 `tail -f nohup.out`。 
-
-此外，项目的 `scripts` 目录下有一键运行、关闭程序的脚本供使用。 运行后默认channel为web，通过可以通过修改配置文件进行切换。
-
-
-### 3.Docker部署
-
-使用docker部署无需下载源码和安装依赖，只需要获取 `docker-compose.yml` 配置文件并启动容器即可。Agent模式下更推荐使用源码进行部署，以获得更多系统访问能力。
-
-> 前提是需要安装好 `docker` 及 `docker-compose`，安装成功后执行 `docker -v` 和 `docker-compose version` (或 `docker compose version`) 可查看到版本号。安装地址为 [docker官网](https://docs.docker.com/engine/install/) 。
-
-**(1) 下载 docker-compose.yml 文件**
-
-```bash
-wget https://cdn.link-ai.tech/code/cow/docker-compose.yml
-```
-
-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         # 若docker-compose为 1.X 版本，则执行 `sudo  docker-compose up -d`
-```
-
-运行命令后，会自动取 [docker hub](https://hub.docker.com/r/zhayujie/chatgpt-on-wechat) 拉取最新release版本的镜像。当执行 `sudo docker ps` 能查看到 NAMES 为 chatgpt-on-wechat 的容器即表示运行成功。最后执行以下命令可查看容器的运行日志：
-
-```bash
-sudo docker logs -f chatgpt-on-wechat
-```
-
-**(3) 插件使用**
-
-如果需要在docker容器中修改插件配置，可通过挂载的方式完成，将 [插件配置文件](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/plugins/config.json.template)
-重命名为 `config.json`，放置于 `docker-compose.yml` 相同目录下，并在 `docker-compose.yml` 中的 `chatgpt-on-wechat` 部分下添加 `volumes` 映射:
-
-```
-volumes:
-  - ./config.json:/app/plugins/config.json
-```
-**注**：使用docker方式部署的详细教程可以参考：[docker部署CoW项目](https://www.wangpc.cc/ai/docker-deploy-cow/)
-
-
-## 模型说明
-
-以下对所有可支持的模型的配置和使用方法进行说明，模型接口实现在项目的 `models/` 目录下。
-
-<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>MiniMax</summary>
-
-方式一：官方接入，配置如下(推荐)：
-
-```json
-{
-    "model": "MiniMax-M2.5",
-    "minimax_api_key": ""
-}
-```
- - `model`: 可填写 `MiniMax-M2.5、MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
- - `minimax_api_key`：MiniMax平台的API-KEY，在 [控制台](https://platform.minimaxi.com/user-center/basic-information/interface-key) 创建
-
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "MiniMax-M2.5",
-  "open_ai_api_base": "https://api.minimaxi.com/v1",
-  "open_ai_api_key": ""
-}
-```
-- `bot_type`: OpenAI兼容方式
-- `model`: 可填 `MiniMax-M2.5、MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
-- `open_ai_api_base`: MiniMax平台API的 BASE URL
-- `open_ai_api_key`: MiniMax平台的API-KEY
-</details>
-
-<details>
-<summary>智谱AI (GLM)</summary>
-
-方式一：官方接入，配置如下(推荐)：
-
-```json
-{
-  "model": "glm-5",
-  "zhipu_ai_api_key": ""
-}
-```
- - `model`: 可填 `glm-5、glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
- - `zhipu_ai_api_key`: 智谱AI平台的 API KEY，在 [控制台](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) 创建
-
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "glm-5",
-  "open_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
-  "open_ai_api_key": ""
-}
-```
-- `bot_type`: OpenAI兼容方式
-- `model`: 可填 `glm-5、glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
-- `open_ai_api_base`: 智谱AI平台的 BASE URL
-- `open_ai_api_key`: 智谱AI平台的 API KEY
-</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": "qwen3.5-plus",
-    "dashscope_api_key": "sk-qVxxxxG"
-}
-```
- - `model`: 可填写 `qwen3.5-plus、qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
- - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建
-
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "qwen3.5-plus",
-  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
-  "open_ai_api_key": "sk-qVxxxxG"
-}
-```
-- `bot_type`: OpenAI兼容方式
-- `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>Kimi (Moonshot)</summary>
-
-方式一：官方接入，配置如下：
-
-```json
-{
-    "model": "kimi-k2.5",
-    "moonshot_api_key": ""
-}
-```
- - `model`: 可填写 `kimi-k2.5、kimi-k2、moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
- - `moonshot_api_key`: Moonshot的API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
- 
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "kimi-k2.5",
-  "open_ai_api_base": "https://api.moonshot.cn/v1",
-  "open_ai_api_key": ""
-}
-```
-- `bot_type`: OpenAI兼容方式
-- `model`: 可填写 `kimi-k2.5、kimi-k2、moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
-- `open_ai_api_base`: Moonshot的 BASE URL
-- `open_ai_api_key`: Moonshot的 API-KEY
-</details>
-
-<details>
-<summary>豆包 (Doubao)</summary>
-
-1. API Key创建：在 [火山方舟控制台](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) 创建API Key
-
-2. 填写配置
-
-```json
-{
-    "model": "doubao-seed-2-0-code-preview-260215",
-    "ark_api_key": "YOUR_API_KEY"
-}
-```
- - `model`: 可填写 `doubao-seed-2-0-code-preview-260215、doubao-seed-2-0-pro-260215、doubao-seed-2-0-lite-260215、doubao-seed-2-0-mini-260215` 等
- - `ark_api_key`: 火山方舟平台的 API Key，在 [控制台](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) 创建
- - `ark_base_url`: 可选，默认为 `https://ark.cn-beijing.volces.com/api/v3`
-</details>
-
-<details>
-<summary>Claude</summary>
-
-1. API Key创建：在 [Claude控制台](https://console.anthropic.com/settings/keys) 创建API Key
-
-2. 填写配置
-
-```json
-{
-    "model": "claude-sonnet-4-6",
-    "claude_api_key": "YOUR_API_KEY"
-}
-```
- - `model`: 参考 [官方模型ID](https://docs.anthropic.com/en/docs/about-claude/models/overview#model-aliases) ，支持 `claude-sonnet-4-6、claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
-</details>
-
-<details>
-<summary>Gemini</summary>
-
-API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn) 创建API Key ，配置如下
-```json
-{
-    "model": "gemini-3.1-pro-preview",
-    "gemini_api_key": ""
-}
-```
- - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3.1-pro-preview、gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
-</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>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
+## 🤖 Models
 
-- [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。
+CowAgent supports all mainstream LLM providers. **Chat, vision, image generation, ASR/TTS, and embeddings** can each be routed to a different vendor. Providers are configured directly in the Web console — no manual file editing required.
 
+| Provider | Featured Models | Chat | Vision | Image Gen | ASR | TTS | Embedding |
+| --- | --- | :-: | :-: | :-: | :-: | :-: | :-: |
+| [Claude](https://docs.cowagent.ai/en/models/claude) | claude-opus-4-7 | ✅ | ✅ | | | | |
+| [OpenAI](https://docs.cowagent.ai/en/models/openai) | gpt-5.5, o-series | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [Gemini](https://docs.cowagent.ai/en/models/gemini) | gemini-3.5-flash | ✅ | ✅ | ✅ | | | |
+| [DeepSeek](https://docs.cowagent.ai/en/models/deepseek) | deepseek-v4-flash / pro | ✅ | | | | | |
+| [Qwen](https://docs.cowagent.ai/en/models/qwen) | qwen3.7-max | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [GLM](https://docs.cowagent.ai/en/models/glm) | glm-5.1, glm-5v-turbo | ✅ | ✅ | | ✅ | | ✅ |
+| [Doubao](https://docs.cowagent.ai/en/models/doubao) | doubao-seed-2.0 series | ✅ | ✅ | ✅ | | | ✅ |
+| [Kimi](https://docs.cowagent.ai/en/models/kimi) | kimi-k2.6 | ✅ | ✅ | | | | |
+| [MiniMax](https://docs.cowagent.ai/en/models/minimax) | MiniMax-M2.7 | ✅ | ✅ | ✅ | | ✅ | |
+| [ERNIE](https://docs.cowagent.ai/en/models/qianfan) | ernie-5.1 | ✅ | ✅ | | | | |
+| [MiMo](https://docs.cowagent.ai/en/models/mimo) | mimo-v2.5 / pro | ✅ | ✅ | | | ✅ | |
+| [LinkAI](https://docs.cowagent.ai/en/models/linkai) | One key for 100+ models | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [Custom](https://docs.cowagent.ai/en/models/custom) | Local models / third-party proxy | ✅ | | | | | |
 
+> For details on each provider, see the [Models overview](https://docs.cowagent.ai/en/models/index).
 
-# 🔎 常见问题
+<br/>
 
-FAQs： <https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs>
+## 💬 Channels
 
-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)
+A single Agent instance can serve multiple channels in parallel. Most channels can be onboarded right from the Web console.
 
-# 🛠️ 开发
+| Channel | Text | Image | File | Voice | Group |
+| --- | :-: | :-: | :-: | :-: | :-: |
+| [Web Console](https://docs.cowagent.ai/en/channels/web) (default) | ✅ | ✅ | ✅ | ✅ | |
+| [WeChat](https://docs.cowagent.ai/en/channels/weixin) | ✅ | ✅ | ✅ | ✅ | |
+| [Feishu / Lark](https://docs.cowagent.ai/en/channels/feishu) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [DingTalk](https://docs.cowagent.ai/en/channels/dingtalk) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [WeCom Bot](https://docs.cowagent.ai/en/channels/wecom-bot) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [QQ](https://docs.cowagent.ai/en/channels/qq) | ✅ | ✅ | ✅ | | ✅ |
+| [WeCom App](https://docs.cowagent.ai/en/channels/wecom) | ✅ | ✅ | ✅ | ✅ | |
+| [WeChat Official Account](https://docs.cowagent.ai/en/channels/wechatmp) | ✅ | ✅ | | ✅ | |
 
-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)。
+> See the [Channels overview](https://docs.cowagent.ai/en/channels/index) for setup details.
 
-# ✉ 联系
+<img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/screenshots/en/web-console-chat.png" alt="CowAgent Web Console" width="800"/>
 
-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。
+*The Web console is the default channel and the unified entry point to configure models, channels, skills, memory, and more.*
 
-# 🌟 贡献者
+<br/>
 
-![cow contributors](https://contrib.rocks/image?repo=zhayujie/chatgpt-on-wechat&max=1000)
+## 🧠 Memory & Knowledge Base
+
+**Long-term memory** uses a three-tier architecture: conversation context (short-term) → daily memory (mid-term) → MEMORY.md (long-term). A nightly **Deep Dream** pass distills scattered memories into refined long-term entries and a narrative journal. See [Long-term Memory](https://docs.cowagent.ai/en/memory/index) · [Deep Dream](https://docs.cowagent.ai/en/memory/deep-dream).
+
+**Personal knowledge base** complements the time-ordered memory by organizing structured knowledge **by topic**. The Agent automatically curates valuable information from conversations, maintains cross-references and indexes, and the Web console offers an interactive knowledge-graph view. See [Personal Knowledge Base](https://docs.cowagent.ai/en/knowledge/index).
+
+<table>
+  <tr>
+    <td width="50%">
+      <img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/screenshots/en/web-console-memory.png" alt="Long-term Memory" />
+      <p align="center"><em>Long-term Memory · Three-tier architecture + Deep Dream</em></p>
+    </td>
+    <td width="50%">
+      <img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/screenshots/en/web-console-knowledge.png" alt="Personal Knowledge Base" />
+      <p align="center"><em>Knowledge Base · Auto-curated Markdown wiki</em></p>
+    </td>
+  </tr>
+</table>
+
+<br/>
+
+## 🔧 Tools & Skills
+
+**Tools** are atomic capabilities the Agent uses to interact with system resources. **Skills** are higher-level workflows defined by a manifest file that compose multiple tools to accomplish complex tasks.
+
+### Tool System
+
+**Built-in tools** cover file I/O (`read` / `write` / `edit` / `ls`), terminal (`bash`), file sending (`send`), memory retrieval (`memory`), environment variables (`env_config`), web fetching (`web_fetch`), scheduling (`scheduler`), web search (`web_search`), vision (`vision`), and browser automation (`browser`).
+
+**MCP protocol** integrates the open ecosystem of [Model Context Protocol](https://modelcontextprotocol.io) servers. A single `mcp.json` is enough — supports stdio / SSE transports, hot reload, and zero-code integration.
+
+Learn more: [Tools overview](https://docs.cowagent.ai/en/tools/index) · [MCP integration](https://docs.cowagent.ai/en/tools/mcp).
+
+### Skills System
+
+- **[Skill Hub](https://skills.cowagent.ai/)** — open skill marketplace: browse, search, install in one click
+- **GitHub / ClawHub / URL and more** — install skills from any source
+- **Conversational authoring** — generate custom skills through dialogue with `skill-creator`; turn any workflow or third-party API into a reusable skill
+
+```bash
+/skill list                   # list installed skills
+/skill search <keyword>        # search the marketplace
+/skill install <name>          # one-click install
+```
+
+Learn more: [Skills overview](https://docs.cowagent.ai/en/skills/index) · [Creating Skills](https://docs.cowagent.ai/en/skills/create).
+
+<br/>
+
+## 🏷 Changelog
+
+> **2026.05.22:** [v2.0.9](https://github.com/zhayujie/CowAgent/releases/tag/2.0.9) — Model management, MCP protocol support, persistent browser sessions, new models (gpt-5.5, gemini-3.5-flash, qwen3.7-max), deployment hardening.
+
+> **2026.05.06:** [v2.0.8](https://github.com/zhayujie/CowAgent/releases/tag/2.0.8) — Feishu channel overhaul (voice, streaming, QR onboarding), DeepSeek V4 and Baidu Qianfan support, scheduler tool upgrades.
+
+> **2026.04.22:** [v2.0.7](https://github.com/zhayujie/CowAgent/releases/tag/2.0.7) — Built-in image generation (GPT Image 2, Nano Banana), new models (Kimi K2.6, Claude Opus 4.7, GLM 5.1), memory and knowledge enhancements.
+
+> **2026.04.14:** [v2.0.6](https://github.com/zhayujie/CowAgent/releases/tag/2.0.6) — Knowledge base, Deep Dream memory distillation, smart context compression, multi-session Web console.
+
+> **2026.04.01:** [v2.0.5](https://github.com/zhayujie/CowAgent/releases/tag/2.0.5) — Cow CLI, Skill Hub open source, browser tool, WeCom Bot QR onboarding.
+
+> **2026.02.03:** [v2.0.0](https://github.com/zhayujie/CowAgent/releases/tag/2.0.0) — Major upgrade to a super Agent assistant with multi-step task planning, long-term memory, and the Skills framework.
+
+Full history: [Release Notes](https://docs.cowagent.ai/en/releases/overview)
+
+<br/>
+
+## 🤝 Community & Support
+
+[File an issue](https://github.com/zhayujie/CowAgent/issues) on GitHub, or scan the QR code below to join our WeChat community:
+
+<img width="130" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/open-community.png">
+
+<br/>
+
+## 🔗 Related Projects
+
+- **[Cow Skill Hub](https://github.com/zhayujie/cow-skill-hub)** — open skill marketplace for AI Agents; works with CowAgent, OpenClaw, Claude Code, and more
+- **[bot-on-anything](https://github.com/zhayujie/bot-on-anything)** — lightweight LLM application framework with integrations for Slack, Telegram, Discord, Gmail, and more
+- **[AgentMesh](https://github.com/MinimalFuture/AgentMesh)** — open-source multi-agent framework for solving complex problems through team collaboration
+
+<br/>
+
+## 🏢 Enterprise Services
+
+[**LinkAI**](https://link-ai.tech/) is an all-in-one AI Agent platform for enterprises and developers, offering managed hosting and enterprise-grade support for CowAgent:
+
+- **🚀 Zero-deployment hosted runtime** — spin up a [CowAgent online assistant](https://link-ai.tech/cowagent/create) in under a minute, no server required
+- **🧠 Agent infrastructure** — unified access to LLMs, knowledge bases, databases, skills, and workflows; plug-and-play building blocks that extend what CowAgent can do
+- **🏢 Team & enterprise features** — workspaces, role-based access, audit logs, and private deployment for production use cases
+
+For enterprise inquiries: sales@simple-future.tech or [scan the QR code](https://cdn.link-ai.tech/consultant.jpg) to reach our team on WeChat.
+
+<br/>
+
+## 🛠️ Development & Contributing
+
+Contributions are welcome — add a new channel by following the [Feishu channel reference](https://github.com/zhayujie/CowAgent/blob/master/channel/feishu/feishu_channel.py), or contribute new skills to [Skill Hub](https://skills.cowagent.ai/submit).
+
+⭐ Star the project to follow updates, and feel free to open PRs and Issues.
+
+## 🌟 Contributors
+
+![cow contributors](https://contrib.rocks/image?repo=zhayujie/CowAgent&max=1000)
+
+<br/>
+
+## ⚠️ Disclaimer
+
+1. This project is licensed under the [MIT License](/LICENSE) and is intended for technical research and learning. You are responsible for complying with applicable laws and regulations in your jurisdiction; the maintainers assume no liability for any consequences arising from use of this project.
+2. **Cost & safety:** Agent mode consumes substantially more tokens than regular chat — pick models that balance quality and cost. The Agent has access to your local operating system, so only deploy it in trusted environments.
+3. CowAgent is a pure open-source project and does not participate in, authorize, or issue any cryptocurrency.
+
+<br/>
+
+## 📌 Project Renaming Notice
+
+This project was previously named `chatgpt-on-wechat` and is now officially **CowAgent**. The old GitHub URL redirects automatically; existing users may optionally run `git remote set-url origin https://github.com/zhayujie/CowAgent.git` to update the local remote.

agent/chat/service.py

@@ -27,7 +27,8 @@ class ChatService:
         """
         self.agent_bridge = agent_bridge
 
-    def run(self, query: str, session_id: str, send_chunk_fn: Callable[[dict], None]):
+    def run(self, query: str, session_id: str, send_chunk_fn: Callable[[dict], None],
+            channel_type: str = ""):
         """
         Run the agent for *query* and stream results back via *send_chunk_fn*.
 
@@ -37,11 +38,17 @@ class ChatService:
         :param query: user query text
         :param session_id: session identifier for agent isolation
         :param send_chunk_fn: callable(chunk_data: dict) to send a streaming chunk
+        :param channel_type: source channel (e.g. "web", "feishu") for persistence
         """
         agent = self.agent_bridge.get_agent(session_id=session_id)
         if agent is None:
             raise RuntimeError("Failed to initialise agent for the session")
 
+        # Pass context metadata to model for downstream API requests
+        if hasattr(agent, 'model'):
+            agent.model.channel_type = channel_type or ""
+            agent.model.session_id = session_id or ""
+
         # State shared between the event callback and this method
         state = _StreamState()
 
@@ -50,7 +57,16 @@ class ChatService:
             event_type = event.get("type")
             data = event.get("data", {})
 
-            if event_type == "message_update":
+            if event_type == "reasoning_update":
+                delta = data.get("delta", "")
+                if delta:
+                    send_chunk_fn({
+                        "chunk_type": "reasoning",
+                        "delta": delta,
+                        "segment_id": state.segment_id,
+                    })
+
+            elif event_type == "message_update":
                 # Incremental text delta
                 delta = data.get("delta", "")
                 if delta:
@@ -68,9 +84,41 @@ class ChatService:
                     # a new segment; collect tool results until turn_end.
                     state.pending_tool_results = []
 
-            elif event_type == "tool_execution_end":
+            elif event_type == "file_to_send":
+                url = data.get("url") or ""
+                if url:
+                    fname = data.get("file_name") or "file"
+                    ft = data.get("file_type") or "file"
+                    if ft == "image":
+                        link = f"![{fname}]({url})"
+                    else:
+                        link = f"[{fname}]({url})"
+                    send_chunk_fn({
+                        "chunk_type": "content",
+                        "delta": "\n\n" + link + "\n\n",
+                        "segment_id": state.segment_id,
+                    })
+                    # Remove url so the model won't repeat it in its reply
+                    data.pop("url", None)
+
+            elif event_type == "tool_execution_start":
+                # Notify the client that a tool is about to run (with its input args)
                 tool_name = data.get("tool_name", "")
                 arguments = data.get("arguments", {})
+                # Cache arguments keyed by tool_call_id so tool_execution_end can include them
+                tool_call_id = data.get("tool_call_id", tool_name)
+                state.pending_tool_arguments[tool_call_id] = arguments
+                send_chunk_fn({
+                    "chunk_type": "tool_start",
+                    "tool": tool_name,
+                    "arguments": arguments,
+                })
+
+            elif event_type == "tool_execution_end":
+                tool_name = data.get("tool_name", "")
+                tool_call_id = data.get("tool_call_id", tool_name)
+                # Retrieve cached arguments from the matching tool_execution_start event
+                arguments = state.pending_tool_arguments.pop(tool_call_id, data.get("arguments", {}))
                 result = data.get("result", "")
                 status = data.get("status", "unknown")
                 execution_time = data.get("execution_time", 0)
@@ -111,7 +159,7 @@ class ChatService:
         logger.info(f"[ChatService] Starting agent run: session={session_id}, query={query[:80]}")
 
         from config import conf
-        max_context_turns = conf().get("agent_max_context_turns", 30)
+        max_context_turns = conf().get("agent_max_context_turns", 20)
 
         # Get full system prompt with skills
         full_system_prompt = agent.get_full_system_prompt()
@@ -144,10 +192,61 @@ class ChatService:
                     logger.info("[ChatService] Cleared agent message history after executor recovery")
             raise
 
-        # Append only the NEW messages from this execution (thread-safe)
+        # Sync executor messages back to agent (thread-safe).
+        # The executor may have trimmed context, making its list shorter than
+        # original_length. In that case we must replace entirely — just
+        # appending would leave stale pre-trim messages in agent.messages
+        # and cause the same trim to fire on every subsequent request.
         with agent.messages_lock:
-            new_messages = executor.messages[original_length:]
-            agent.messages.extend(new_messages)
+            trimmed = len(executor.messages) < original_length
+            if trimmed:
+                # Context was trimmed: the executor appended the new user
+                # query *before* trimming, so the new messages (user +
+                # assistant + tools) sit at the tail of the trimmed list.
+                # We cannot simply slice at original_length (it exceeds the
+                # list length).  Instead, count how many messages the
+                # executor added on top of the post-trim baseline.
+                #
+                # Timeline inside executor.run_stream:
+                #   1. messages had `original_length` items
+                #   2. append user query  → original_length + 1
+                #   3. _trim_messages()   → some smaller number (includes the
+                #      user query because it belongs to the last turn)
+                #   4. LLM replies / tool calls appended
+                #
+                # The user query message is always the first message of the
+                # last turn (it cannot be trimmed away), so we locate it to
+                # find where "new" messages begin.
+                new_start = original_length  # fallback
+                for idx in range(len(executor.messages) - 1, -1, -1):
+                    msg = executor.messages[idx]
+                    if msg.get("role") == "user":
+                        content = msg.get("content", [])
+                        is_user_query = False
+                        if isinstance(content, list):
+                            has_text = any(
+                                isinstance(b, dict) and b.get("type") == "text"
+                                for b in content
+                            )
+                            has_tool_result = any(
+                                isinstance(b, dict) and b.get("type") == "tool_result"
+                                for b in content
+                            )
+                            is_user_query = has_text and not has_tool_result
+                        elif isinstance(content, str):
+                            is_user_query = True
+                        if is_user_query:
+                            new_start = idx
+                            break
+                new_messages = list(executor.messages[new_start:])
+            else:
+                new_messages = list(executor.messages[original_length:])
+            agent.messages = list(executor.messages)
+
+        # Persist new messages to SQLite so they survive restarts and
+        # can be queried via the HISTORY interface.
+        if new_messages:
+            self._persist_messages(session_id, list(new_messages), channel_type)
 
         # Store executor reference for files_to_send access
         agent.stream_executor = executor
@@ -158,6 +257,26 @@ class ChatService:
         logger.info(f"[ChatService] Agent run completed: session={session_id}")
 
 
+
+    @staticmethod
+    def _persist_messages(session_id: str, new_messages: list, channel_type: str = ""):
+        try:
+            from config import conf
+            if not conf().get("conversation_persistence", True):
+                return
+        except Exception:
+            pass
+        try:
+            from agent.memory import get_conversation_store
+            get_conversation_store().append_messages(
+                session_id, new_messages, channel_type=channel_type
+            )
+        except Exception as e:
+            logger.warning(
+                f"[ChatService] Failed to persist messages for session={session_id}: {e}"
+            )
+
+
 class _StreamState:
     """Mutable state shared between the event callback and the run method."""
 
@@ -166,3 +285,6 @@ class _StreamState:
         # None means we are not accumulating tool results right now.
         # A list means we are in the middle of a tool-execution phase.
         self.pending_tool_results: Optional[list] = None
+        # Maps tool_call_id -> arguments captured from tool_execution_start,
+        # so that tool_execution_end can attach the correct input args.
+        self.pending_tool_arguments: dict = {}

agent/chat/session_service.py

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

agent/knowledge/service.py

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

agent/memory/__init__.py

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

agent/memory/config.py

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

agent/memory/embedding.py

@@ -1,161 +0,0 @@
-"""
-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)

agent/memory/embedding/__init__.py

@@ -0,0 +1,41 @@
+"""
+Embedding subsystem for memory.
+
+Public API:
+  create_embedding_provider, EmbeddingProvider, OpenAIEmbeddingProvider,
+  EMBEDDING_VENDORS, EmbeddingCache
+  RebuildResult, clear_index, rebuild_in_process
+  detect_index_dim, cleanup_legacy_state_file
+"""
+
+from agent.memory.embedding.provider import (
+    EMBEDDING_VENDORS,
+    DoubaoEmbeddingProvider,
+    EmbeddingCache,
+    EmbeddingProvider,
+    OpenAIEmbeddingProvider,
+    create_embedding_provider,
+)
+from agent.memory.embedding.rebuild import (
+    RebuildResult,
+    clear_index,
+    rebuild_in_process,
+)
+from agent.memory.embedding.state import (
+    cleanup_legacy_state_file,
+    detect_index_dim,
+)
+
+__all__ = [
+    "EMBEDDING_VENDORS",
+    "DoubaoEmbeddingProvider",
+    "EmbeddingCache",
+    "EmbeddingProvider",
+    "OpenAIEmbeddingProvider",
+    "create_embedding_provider",
+    "RebuildResult",
+    "clear_index",
+    "rebuild_in_process",
+    "cleanup_legacy_state_file",
+    "detect_index_dim",
+]

agent/memory/embedding/provider.py

@@ -0,0 +1,486 @@
+"""
+Embedding providers for memory
+
+Supports multiple OpenAI-compatible embedding vendors:
+  - openai     (text-embedding-3-small / large)
+  - linkai     (OpenAI-compatible passthrough)
+  - dashscope  (Aliyun Tongyi text-embedding-v4)
+  - doubao     (ByteDance Doubao Seed1.5 / large-text on Volcengine Ark)
+  - zhipu      (ZhipuAI embedding-3)
+
+Vendor keys here intentionally match the project's bot_type constants in
+common.const (OPENAI, LINKAI, QWEN_DASHSCOPE, DOUBAO, ZHIPU_AI).
+
+All providers share a single OpenAI-compatible REST client. Vendor-specific
+behaviors (truncation, query instruction prefix) are configured via metadata.
+"""
+
+import hashlib
+import math
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+# HTTP read timeout for a single embeddings request (seconds). A batch of
+# 64+ chunks can take 30-50s end-to-end from China-side networks, so 30s is
+# routinely too tight; 90s gives meaningful headroom without letting bad
+# endpoints hang forever.
+EMBEDDING_HTTP_TIMEOUT = 90
+
+
+class EmbeddingProvider(ABC):
+    """Base class for embedding providers"""
+
+    @abstractmethod
+    def embed(self, text: str) -> List[float]:
+        """Generate embedding for a single text (treated as a query by default)"""
+        pass
+
+    @abstractmethod
+    def embed_batch(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for multiple texts (treated as documents)"""
+        pass
+
+    def embed_query(self, text: str) -> List[float]:
+        """Generate embedding for a query string (may apply vendor instruction prefix)"""
+        return self.embed(text)
+
+    @property
+    @abstractmethod
+    def dimensions(self) -> int:
+        """Effective embedding dimensions"""
+        pass
+
+
+# ---------------------------------------------------------------------------
+# Vendor metadata table
+# ---------------------------------------------------------------------------
+#
+# Each entry describes how to reach a vendor's embedding endpoint. Most
+# vendors expose an OpenAI-compatible /embeddings API; the few that don't
+# (currently: doubao) set `provider_class` to pick a dedicated adapter.
+# Fields:
+#   provider_class          : optional adapter key ("doubao"); defaults to OpenAI-compat
+#   default_base_url        : default API base when not overridden by user
+#   default_model           : default embedding model name
+#   default_dimensions      : recommended unified dim when explicit path is enabled
+#   supports_dim_param      : whether the API accepts a `dimensions` request param
+#   needs_client_truncate   : whether to slice + L2-normalize on the client side
+#   needs_client_normalize  : whether to L2-normalize on the client (always safe)
+#   query_instruction       : optional prefix for asymmetric retrieval (Doubao Seed)
+#   max_batch_size          : max texts per /embeddings request; embed_batch
+#                             auto-paginates above this. Conservative defaults.
+#
+EMBEDDING_VENDORS = {
+    "openai": {
+        "default_base_url": "https://api.openai.com/v1",
+        "default_model": "text-embedding-3-small",
+        # Match the legacy default so users adding `embedding_provider: openai`
+        # to an existing index don't need to rebuild. Override via
+        # embedding_dimensions if you want 1024 / 1536 / 3072.
+        "default_dimensions": 1536,
+        "supports_dim_param": True,
+        "needs_client_truncate": False,
+        "needs_client_normalize": False,
+        "query_instruction": "",
+        # OpenAI permits up to 2048 items per request, but a single call
+        # carrying hundreds of long chunks routinely exceeds the 30s read
+        # timeout from China-side networks. 64 keeps each call well under
+        # both the token-per-request budget and a reasonable wall clock.
+        "max_batch_size": 64,
+    },
+    "linkai": {
+        "default_base_url": "https://api.link-ai.tech/v1",
+        "default_model": "text-embedding-3-small",
+        "default_dimensions": 1536,
+        "supports_dim_param": True,
+        "needs_client_truncate": False,
+        "needs_client_normalize": False,
+        "query_instruction": "",
+        "max_batch_size": 64,
+    },
+    "dashscope": {
+        "default_base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+        "default_model": "text-embedding-v4",
+        "default_dimensions": 1024,
+        "supports_dim_param": True,
+        "needs_client_truncate": False,
+        "needs_client_normalize": False,
+        "query_instruction": "",
+        "max_batch_size": 10,  # DashScope hard cap (text-embedding-v4)
+    },
+    "doubao": {
+        # Doubao no longer offers an OpenAI-compatible /v1/embeddings endpoint.
+        # Current models are unified under /api/v3/embeddings/multimodal
+        # which uses a structured `input` payload — see DoubaoEmbeddingProvider.
+        "provider_class": "doubao",
+        "default_base_url": "https://ark.cn-beijing.volces.com/api/v3",
+        "default_model": "doubao-embedding-vision-251215",
+        # Native options: 1024 or 2048. We default to 1024 to align with the
+        # other Chinese vendors (dashscope/zhipu) and keep storage footprint
+        # consistent across providers; users can still override via
+        # `embedding_dimensions: 2048` in config.
+        "default_dimensions": 1024,
+        "supports_dim_param": True,
+        "needs_client_truncate": False,
+        "needs_client_normalize": False,
+        "query_instruction": "",
+        # Multimodal endpoint produces ONE embedding per call (input list is
+        # a single document's parts, not a batch). embed_batch loops.
+        "max_batch_size": 1,
+    },
+    "zhipu": {
+        "default_base_url": "https://open.bigmodel.cn/api/paas/v4",
+        "default_model": "embedding-3",
+        "default_dimensions": 1024,
+        "supports_dim_param": True,
+        "needs_client_truncate": False,
+        "needs_client_normalize": False,
+        "query_instruction": "",
+        "max_batch_size": 64,
+    },
+}
+
+
+def _l2_normalize(vec: List[float]) -> List[float]:
+    """Normalize a vector to unit length (L2 norm). Returns input on zero vector."""
+    norm = math.sqrt(sum(v * v for v in vec))
+    if norm == 0:
+        return vec
+    return [v / norm for v in vec]
+
+
+class OpenAIEmbeddingProvider(EmbeddingProvider):
+    """
+    OpenAI-compatible embedding provider.
+
+    Used for openai/linkai/dashscope/ark/zhipu by configuring the metadata
+    fields. The legacy two-arg constructor (model, api_key, api_base) keeps
+    working, so the original OpenAI/LinkAI fallback code path is unchanged.
+    """
+
+    def __init__(
+        self,
+        model: str = "text-embedding-3-small",
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        extra_headers: Optional[dict] = None,
+        dimensions: Optional[int] = None,
+        supports_dim_param: bool = True,
+        needs_client_truncate: bool = False,
+        needs_client_normalize: bool = False,
+        query_instruction: str = "",
+        max_batch_size: int = 256,
+    ):
+        """
+        Args:
+            model: Model name (e.g. text-embedding-3-small, text-embedding-v4, embedding-3)
+            api_key: API key (required)
+            api_base: API base URL (defaults to OpenAI)
+            extra_headers: Optional extra HTTP headers
+            dimensions: Target output dimension. Required when supports_dim_param
+                is False and needs_client_truncate is True (used to slice).
+            supports_dim_param: Whether the vendor accepts a `dimensions` body param
+            needs_client_truncate: Slice the returned vector to `dimensions`
+            needs_client_normalize: L2-normalize on the client after slicing
+            query_instruction: Optional prefix prepended to query texts only
+            max_batch_size: Max items per /embeddings request; embed_batch
+                auto-paginates above this.
+        """
+        self.model = model
+        self.api_key = api_key
+        self.api_base = api_base or "https://api.openai.com/v1"
+        self.extra_headers = extra_headers or {}
+        self.supports_dim_param = supports_dim_param
+        self.needs_client_truncate = needs_client_truncate
+        self.needs_client_normalize = needs_client_normalize
+        self.query_instruction = query_instruction or ""
+        self.max_batch_size = max(1, int(max_batch_size or 1))
+
+        if not self.api_key or self.api_key in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+            raise ValueError("Embedding API key is not configured")
+
+        if dimensions is not None and dimensions > 0:
+            self._dimensions = dimensions
+        else:
+            # Legacy heuristic for OpenAI text-embedding-3-* family
+            self._dimensions = 1536 if "small" in model else 3072
+
+    def _call_api(self, input_data):
+        """Call OpenAI-compatible /embeddings endpoint"""
+        import requests
+
+        url = f"{self.api_base}/embeddings"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+            **self.extra_headers,
+        }
+        data = {
+            "input": input_data,
+            "model": self.model,
+        }
+        if self.supports_dim_param and self._dimensions:
+            data["dimensions"] = self._dimensions
+
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=EMBEDDING_HTTP_TIMEOUT)
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.ConnectionError as e:
+            raise ConnectionError(
+                f"Failed to connect to embedding API at {url}. "
+                f"Please check network and api_base. Error: {str(e)}"
+            )
+        except requests.exceptions.Timeout as e:
+            raise TimeoutError(f"Embedding API request timed out. Error: {str(e)}")
+        except requests.exceptions.HTTPError as e:
+            if e.response.status_code == 401:
+                raise ValueError("Invalid embedding API key")
+            elif e.response.status_code == 429:
+                raise ValueError("Embedding API rate limit exceeded")
+            else:
+                raise ValueError(
+                    f"Embedding API request failed: "
+                    f"{e.response.status_code} - {e.response.text}"
+                )
+
+    def _post_process(self, raw: List[float]) -> List[float]:
+        """Apply optional client-side truncation + normalization"""
+        vec = raw
+        if self.needs_client_truncate and self._dimensions and len(vec) > self._dimensions:
+            vec = vec[: self._dimensions]
+        if self.needs_client_normalize:
+            vec = _l2_normalize(vec)
+        return vec
+
+    def embed(self, text: str) -> List[float]:
+        """Generate embedding (treated as document by default)"""
+        result = self._call_api(text)
+        return self._post_process(result["data"][0]["embedding"])
+
+    def embed_query(self, text: str) -> List[float]:
+        """Generate embedding for a query (applies vendor instruction prefix if any)"""
+        if self.query_instruction:
+            text = f"{self.query_instruction}{text}"
+        return self.embed(text)
+
+    def embed_batch(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for multiple documents.
+
+        Automatically paginates by self.max_batch_size so callers can pass any
+        number of texts. Order of returned vectors matches the input order.
+        """
+        if not texts:
+            return []
+        out: List[List[float]] = []
+        step = self.max_batch_size
+        for i in range(0, len(texts), step):
+            chunk = texts[i:i + step]
+            result = self._call_api(chunk)
+            out.extend(self._post_process(item["embedding"]) for item in result["data"])
+        return out
+
+    @property
+    def dimensions(self) -> int:
+        return self._dimensions
+
+
+class DoubaoEmbeddingProvider(EmbeddingProvider):
+    """
+    Doubao (Volcengine Ark) multimodal embedding provider.
+
+    Doubao deprecated their OpenAI-compatible /v1/embeddings endpoint and
+    unified everything under /api/v3/embeddings/multimodal, which uses a
+    structured `input: [{type, text|image_url|video_url}, ...]` payload.
+
+    Notes:
+      * The endpoint produces ONE embedding per call (input list is multiple
+        modality parts of a single document, not a batch). embed_batch
+        therefore loops per-text — no native batch support.
+      * Native dimensions: 1024 or 2048 (default 1024 to align with other
+        Chinese vendors). No client-side truncation needed.
+      * Auth: Bearer ARK API key.
+    """
+
+    def __init__(
+        self,
+        model: str,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        extra_headers: Optional[dict] = None,
+        dimensions: Optional[int] = None,
+    ):
+        self.model = model
+        self.api_key = api_key
+        self.api_base = api_base or "https://ark.cn-beijing.volces.com/api/v3"
+        self.extra_headers = extra_headers or {}
+        if not self.api_key or self.api_key in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+            raise ValueError("Doubao embedding API key (ark_api_key) is not configured")
+
+        if dimensions in (1024, 2048):
+            self._dimensions = dimensions
+        elif dimensions is None:
+            self._dimensions = 1024
+        else:
+            raise ValueError(
+                f"Doubao embedding dimensions must be 1024 or 2048, got {dimensions}"
+            )
+
+    def _call_api(self, text: str) -> List[float]:
+        """One call → one embedding. multimodal endpoint takes a single
+        document represented as a list of typed parts; we send a single
+        text part."""
+        import requests
+
+        url = f"{self.api_base}/embeddings/multimodal"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+            **self.extra_headers,
+        }
+        payload = {
+            "model": self.model,
+            "input": [{"type": "text", "text": text}],
+            "dimensions": self._dimensions,
+            "encoding_format": "float",
+        }
+
+        try:
+            response = requests.post(url, headers=headers, json=payload, timeout=EMBEDDING_HTTP_TIMEOUT)
+            response.raise_for_status()
+            body = response.json()
+        except requests.exceptions.ConnectionError as e:
+            raise ConnectionError(
+                f"Failed to connect to Doubao embedding API at {url}. "
+                f"Please check network and api_base. Error: {str(e)}"
+            )
+        except requests.exceptions.Timeout as e:
+            raise TimeoutError(f"Doubao embedding API request timed out. Error: {str(e)}")
+        except requests.exceptions.HTTPError as e:
+            if e.response.status_code == 401:
+                raise ValueError("Invalid Doubao (ark) embedding API key")
+            elif e.response.status_code == 429:
+                raise ValueError("Doubao embedding API rate limit exceeded")
+            else:
+                raise ValueError(
+                    f"Doubao embedding API request failed: "
+                    f"{e.response.status_code} - {e.response.text}"
+                )
+
+        # Response shape per docs: {"data": {"embedding": [...]}}
+        data = body.get("data")
+        if isinstance(data, dict) and "embedding" in data:
+            return data["embedding"]
+        # Some providers wrap as a list of one — be defensive
+        if isinstance(data, list) and data and "embedding" in data[0]:
+            return data[0]["embedding"]
+        raise ValueError(f"Unexpected Doubao embedding response shape: {body}")
+
+    def embed(self, text: str) -> List[float]:
+        return self._call_api(text)
+
+    def embed_batch(self, texts: List[str]) -> List[List[float]]:
+        # Endpoint produces one embedding per call; loop. Order preserved.
+        return [self._call_api(t) for t in texts]
+
+    @property
+    def dimensions(self) -> int:
+        return self._dimensions
+
+
+class EmbeddingCache:
+    """In-memory cache for embeddings to avoid recomputation"""
+
+    def __init__(self):
+        self.cache = {}
+
+    def get(self, text: str, provider: str, model: str) -> Optional[List[float]]:
+        key = self._compute_key(text, provider, model)
+        return self.cache.get(key)
+
+    def put(self, text: str, provider: str, model: str, embedding: List[float]):
+        key = self._compute_key(text, provider, model)
+        self.cache[key] = embedding
+
+    @staticmethod
+    def _compute_key(text: str, provider: str, model: str) -> str:
+        content = f"{provider}:{model}:{text}"
+        return hashlib.md5(content.encode("utf-8")).hexdigest()
+
+    def clear(self):
+        self.cache.clear()
+
+
+def create_embedding_provider(
+    provider: str = "openai",
+    model: Optional[str] = None,
+    api_key: Optional[str] = None,
+    api_base: Optional[str] = None,
+    extra_headers: Optional[dict] = None,
+    dimensions: Optional[int] = None,
+) -> EmbeddingProvider:
+    """
+    Factory function to create an embedding provider.
+
+    Backward compatible: when called with provider in {"openai", "linkai"}
+    and no `dimensions` arg, behaves exactly as before (1536-dim OpenAI).
+
+    New providers ("dashscope", "doubao", "zhipu") require explicit configuration
+    and use the unified 1024-dim defaults from EMBEDDING_VENDORS.
+
+    Args:
+        provider: Vendor key (one of EMBEDDING_VENDORS)
+        model: Model name (uses vendor default if None)
+        api_key: API key (required)
+        api_base: API base URL (uses vendor default if None)
+        extra_headers: Optional extra HTTP headers
+        dimensions: Target output dimension (uses vendor default if None)
+
+    Returns:
+        EmbeddingProvider instance
+    """
+    meta = EMBEDDING_VENDORS.get(provider)
+    if meta is None:
+        raise ValueError(
+            f"Unsupported embedding provider: {provider}. "
+            f"Supported: {sorted(EMBEDDING_VENDORS.keys())}"
+        )
+
+    # Doubao uses a non-OpenAI-compatible multimodal endpoint.
+    if meta.get("provider_class") == "doubao":
+        final_dim = dimensions if (dimensions and dimensions > 0) else meta["default_dimensions"]
+        return DoubaoEmbeddingProvider(
+            model=model or meta["default_model"],
+            api_key=api_key,
+            api_base=api_base or meta["default_base_url"],
+            extra_headers=extra_headers,
+            dimensions=final_dim,
+        )
+
+    # Legacy two-arg call for openai/linkai keeps 1536-dim default behavior
+    # so existing data isn't invalidated.
+    is_legacy_call = (
+        provider in ("openai", "linkai")
+        and dimensions is None
+    )
+    if is_legacy_call:
+        return OpenAIEmbeddingProvider(
+            model=model or "text-embedding-3-small",
+            api_key=api_key,
+            api_base=api_base,
+            extra_headers=extra_headers,
+        )
+
+    final_dim = dimensions if (dimensions and dimensions > 0) else meta["default_dimensions"]
+    return OpenAIEmbeddingProvider(
+        model=model or meta["default_model"],
+        api_key=api_key,
+        api_base=api_base or meta["default_base_url"],
+        extra_headers=extra_headers,
+        dimensions=final_dim,
+        supports_dim_param=meta["supports_dim_param"],
+        needs_client_truncate=meta["needs_client_truncate"],
+        needs_client_normalize=meta["needs_client_normalize"],
+        query_instruction=meta["query_instruction"],
+        max_batch_size=meta.get("max_batch_size", 256),
+    )

agent/memory/embedding/rebuild.py

@@ -0,0 +1,191 @@
+"""
+Rebuild memory vector index.
+
+Recommended entry point (in-chat, while agent is running):
+    /memory rebuild-index
+
+Backward-compatible CLI entry (must run from project root):
+    python -m agent.memory.rebuild_index
+
+What it does:
+  1. Probes the embedding endpoint with a tiny call to fail fast on
+     bad provider/model/key — before touching the index.
+  2. Clears the SQLite chunks/files tables (workspace markdown stays intact).
+  3. Runs a fresh sync, regenerating embeddings with the currently configured
+     provider/model/dimensions.
+
+This is the only safe way to switch embedding_provider after the existing
+index has been populated by a different-dim model.
+"""
+
+from __future__ import annotations
+import asyncio
+import sys
+from dataclasses import dataclass
+from typing import Optional
+
+from common.log import logger
+from common.utils import expand_path
+
+
+@dataclass
+class RebuildResult:
+    """Outcome of a rebuild_in_process() call"""
+    ok: bool
+    removed: int = 0
+    chunks: int = 0
+    files: int = 0
+    error: Optional[str] = None
+
+
+def clear_index(db_path, storage=None) -> int:
+    """Wipe chunks/files, reset FTS5, and clean up any legacy state file.
+
+    Args:
+        db_path: Path of the index DB (also used to locate the legacy state
+            file for migration cleanup, and — when *storage* is None — to
+            open a fresh connection).
+        storage: Optional pre-opened MemoryStorage. When provided we reuse it
+            so the live connection's triggers stay in sync — opening a second
+            connection would leave the original one's triggers pointing at a
+            DROP'd chunks_fts table.
+
+    We reset (DROP+recreate) chunks_fts because its shadow tables can become
+    inconsistent across rebuild cycles, causing bm25() / ORDER BY rank to
+    raise "database disk image is malformed" even when raw MATCH still works.
+
+    Returns number of chunks removed.
+    """
+    from agent.memory.embedding.state import cleanup_legacy_state_file
+    from agent.memory.storage import MemoryStorage
+
+    owns_storage = storage is None
+    if owns_storage:
+        storage = MemoryStorage(db_path)
+    try:
+        before = storage.conn.execute("SELECT COUNT(*) FROM chunks").fetchone()[0]
+        storage.conn.execute("DELETE FROM chunks")
+        storage.conn.execute("DELETE FROM files")
+        storage.conn.commit()
+        storage.reset_fts5()
+    finally:
+        if owns_storage:
+            storage.close()
+
+    cleanup_legacy_state_file(db_path)
+    return int(before)
+
+
+def rebuild_in_process(memory_manager) -> RebuildResult:
+    """
+    Rebuild the index using an existing, fully-initialized MemoryManager.
+
+    Used by the in-chat /memory rebuild-index command. The caller already has
+    config loaded, embedding_provider built, and (optionally) the agent
+    running, so we only need to:
+      1. Clear chunks/files + state on the manager's storage.
+      2. Re-sync (force=True).
+
+    NOTE: caller must ensure memory_manager.embedding_provider is set, otherwise
+    sync() will silently skip embedding generation.
+    """
+    if memory_manager is None:
+        return RebuildResult(ok=False, error="memory_manager is None")
+    if memory_manager.embedding_provider is None:
+        return RebuildResult(ok=False, error="embedding_provider is not initialized")
+
+    # Probe the embedding endpoint BEFORE clearing the index. A bad
+    # provider/model/key would otherwise leave the user with an empty index
+    # that not even keyword search can serve.
+    try:
+        memory_manager.embedding_provider.embed_query("ping")
+    except Exception as e:
+        logger.error(f"[RebuildIndex] embedding probe failed, aborting rebuild: {e}")
+        return RebuildResult(ok=False, error=f"embedding endpoint not reachable: {e}")
+
+    db_path = memory_manager.config.get_db_path()
+    try:
+        removed = clear_index(db_path, storage=memory_manager.storage)
+    except Exception as e:
+        logger.exception("[RebuildIndex] clear_index failed")
+        return RebuildResult(ok=False, error=f"clear failed: {e}")
+
+    try:
+        asyncio.run(memory_manager.sync(force=True))
+    except RuntimeError:
+        # Already inside a running event loop (rare in chat handler thread).
+        loop = asyncio.new_event_loop()
+        try:
+            loop.run_until_complete(memory_manager.sync(force=True))
+        finally:
+            loop.close()
+    except Exception as e:
+        logger.exception("[RebuildIndex] sync failed")
+        return RebuildResult(ok=False, removed=removed, error=f"re-embed failed: {e}")
+
+    stats = memory_manager.storage.get_stats()
+    chunks = int(stats.get("chunks", 0))
+    embedded = int(stats.get("embedded", 0))
+
+    # sync() degrades to "no embeddings" on batch failure so keyword search
+    # still works at startup — but in a /rebuild-index request the user
+    # explicitly asked for vectors. Surface that as a failure.
+    if chunks > 0 and embedded == 0:
+        return RebuildResult(
+            ok=False,
+            removed=removed,
+            chunks=chunks,
+            files=int(stats.get("files", 0)),
+            error=(
+                "embedding API failed during sync; index now has chunks but no "
+                "vectors. Check embedding provider/model/key and retry."
+            ),
+        )
+
+    return RebuildResult(
+        ok=True,
+        removed=removed,
+        chunks=chunks,
+        files=int(stats.get("files", 0)),
+    )
+
+
+def main() -> int:
+    """Standalone CLI entry. Must be run from project root (relative config path)."""
+    from config import conf, load_config
+    from agent.memory import MemoryConfig, MemoryManager
+
+    load_config()
+
+    workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+    memory_config = MemoryConfig(workspace_root=workspace_root)
+
+    logger.info(f"[RebuildIndex] Workspace: {workspace_root}")
+    logger.info(f"[RebuildIndex] Index db:  {memory_config.get_db_path()}")
+
+    from bridge.agent_initializer import AgentInitializer
+
+    initializer = AgentInitializer(bridge=None, agent_bridge=None)
+    embedding_provider = initializer._init_embedding_provider(memory_config, session_id=None)
+    if embedding_provider is None:
+        logger.error(
+            "[RebuildIndex] No embedding provider could be initialized. "
+            "Check your config.json. Aborting rebuild."
+        )
+        return 1
+
+    manager = MemoryManager(memory_config, embedding_provider=embedding_provider)
+    result = rebuild_in_process(manager)
+    if not result.ok:
+        logger.error(f"[RebuildIndex] {result.error}")
+        return 1
+
+    logger.info(
+        f"[RebuildIndex] Done. removed={result.removed}, "
+        f"chunks={result.chunks}, files={result.files}"
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

agent/memory/embedding/state.py

@@ -0,0 +1,51 @@
+"""
+Embedding-related index utilities.
+
+We don't keep a sidecar state file — the SQLite index is the source of truth
+and config.json is the source of intent. The two functions below are the
+only things needing on-disk awareness:
+
+  detect_index_dim         : read the dim of stored vectors (display-only)
+  cleanup_legacy_state_file: remove old embedding_state.json from earlier
+                             versions; safe no-op when absent.
+"""
+
+from __future__ import annotations
+import json
+import os
+from pathlib import Path
+from typing import Optional, Union
+
+PathLike = Union[str, os.PathLike]
+
+
+def detect_index_dim(storage) -> Optional[int]:
+    """Return the dim of the first stored embedding, or None if the index
+    has no embeddings. Used by /memory status."""
+    try:
+        row = storage.conn.execute(
+            "SELECT embedding FROM chunks WHERE embedding IS NOT NULL LIMIT 1"
+        ).fetchone()
+    except Exception:
+        return None
+    if not row or not row["embedding"]:
+        return None
+    try:
+        raw = row["embedding"]
+        if isinstance(raw, (bytes, bytearray)):
+            # New BLOB format: 4 bytes per float32
+            return len(raw) // 4
+        emb = json.loads(raw)
+        return len(emb) if isinstance(emb, list) else None
+    except (json.JSONDecodeError, TypeError, Exception):
+        return None
+
+
+def cleanup_legacy_state_file(db_path: PathLike) -> None:
+    """Remove old embedding_state.json files from earlier versions.
+    Safe to call repeatedly; no-op if the file is absent."""
+    legacy = Path(db_path).parent / "embedding_state.json"
+    try:
+        legacy.unlink(missing_ok=True)
+    except Exception:
+        pass

agent/memory/manager.py

@@ -13,7 +13,7 @@ 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.embedding import EmbeddingProvider, EmbeddingCache
 from agent.memory.summarizer import MemoryFlushManager, create_memory_files_if_needed
 
 
@@ -50,30 +50,22 @@ class MemoryManager:
             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)")
-        
+        # Embedding provider is owned by the caller (agent_initializer is the
+        # canonical entry point and handles legacy/explicit + state validation).
+        # When None is passed, memory degrades to keyword-only search instead
+        # of silently re-initializing a vendor here, which would bypass the
+        # caller's state checks and risk corrupting the index.
+        self.embedding_provider = embedding_provider
+        if self.embedding_provider is None:
+            from common.log import logger
+            logger.info(
+                "[MemoryManager] No embedding provider; memory will use keyword search only"
+            )
+
+        # Cache for query embeddings (avoids redundant API calls within a session)
+        self._embedding_cache = EmbeddingCache()
+
+
         # Initialize memory flush manager
         workspace_dir = self.config.get_workspace()
         self.flush_manager = MemoryFlushManager(
@@ -133,12 +125,21 @@ class MemoryManager:
         if self.config.sync_on_search and self._dirty:
             await self.sync()
         
-        # Perform vector search (if embedding provider available)
+        from common.log import logger
+
+        # Perform vector search (if embedding provider available).
+        # Failures degrade silently to keyword-only — no exception is raised.
         vector_results = []
         if self.embedding_provider:
             try:
-                from common.log import logger
-                query_embedding = self.embedding_provider.embed(query)
+                provider_name = type(self.embedding_provider).__name__
+                model_name = getattr(self.embedding_provider, 'model', '')
+                cached = self._embedding_cache.get(query, provider_name, model_name)
+                if cached is not None:
+                    query_embedding = cached
+                else:
+                    query_embedding = self.embedding_provider.embed_query(query)
+                    self._embedding_cache.put(query, provider_name, model_name, query_embedding)
                 vector_results = self.storage.search_vector(
                     query_embedding=query_embedding,
                     user_id=user_id,
@@ -147,19 +148,19 @@ class MemoryManager:
                 )
                 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
+                logger.error(
+                    f"[MemoryManager] Vector search failed, falling back to keyword-only: {e}"
+                )
+
+        # Perform keyword search (also runs as fallback when vector failed)
         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,
@@ -167,7 +168,7 @@ class MemoryManager:
             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]
@@ -249,295 +250,195 @@ class MemoryManager:
     
     async def sync(self, force: bool = False):
         """
-        Synchronize memory from files
-        
+        Synchronize memory from files.
+
+        Two-pass design to amortize embedding HTTP cost:
+          1. Walk all files, chunk those whose hash changed, collect pending
+             chunks across files. No embedding calls yet.
+          2. Run a single embed_batch over the union of pending chunks (the
+             provider auto-paginates by vendor cap), then persist per-file.
+
+        For workspaces with many small files (101 files / ~1 chunk each), this
+        cuts ~100 HTTP calls down to ~ceil(total_chunks / vendor_cap).
+
         Args:
             force: Force full reindex
         """
         memory_dir = self.config.get_memory_dir()
         workspace_dir = self.config.get_workspace()
-        
-        # Scan MEMORY.md (workspace root)
+
+        files_to_scan: List[tuple] = []  # (file_path, source, scope, user_id)
+
         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)
+            files_to_scan.append((memory_file, "memory", "shared", None))
+
         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
+                rel_parts = file_path.relative_to(workspace_dir).parts
+                if any(part.startswith('.') for part in rel_parts):
+                    continue
+                # Dream diaries are narrative reflections produced by Deep
+                # Dream; their factual content has already been distilled
+                # into MEMORY.md. Indexing them adds noisy near-duplicates
+                # that crowd out the authoritative entry in retrieval.
+                if "dreams" in rel_parts:
+                    continue
+                if "daily" in rel_parts:
+                    if "users" in rel_parts or len(rel_parts) > 3:
+                        user_idx = rel_parts.index("daily") + 1
+                        user_id = rel_parts[user_idx] if user_idx < len(rel_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
+                elif "users" in rel_parts:
+                    user_idx = rel_parts.index("users") + 1
+                    user_id = rel_parts[user_idx] if user_idx < len(rel_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(encoding='utf-8')
-        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
+                files_to_scan.append((file_path, "memory", scope, user_id))
 
-**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）。
+        from config import conf
+        if conf().get("knowledge", True):
+            knowledge_dir = Path(workspace_dir) / "knowledge"
+            if knowledge_dir.exists():
+                for file_path in knowledge_dir.rglob("*.md"):
+                    files_to_scan.append((file_path, "knowledge", "shared", None))
 
-**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():
+        # Pass 1: inline chunking + change detection. Inlined (instead of
+        # calling self._prepare_file_for_sync) so this method does not depend
+        # on any sibling helpers — keeps it robust against partial reloads
+        # where the class object is older than the method's source.
+        pending: List[Dict[str, Any]] = []
+        workspace_dir_path = self.config.get_workspace()
+        for file_path, source, scope, user_id in files_to_scan:
             try:
-                content = memory_file.read_text(encoding='utf-8').strip()
-                if content:
-                    sections.append(content)
+                content = file_path.read_text(encoding='utf-8')
+            except Exception:
+                continue
+            file_hash = MemoryStorage.compute_hash(content)
+            rel_path = str(file_path.relative_to(workspace_dir_path))
+            if self.storage.get_file_hash(rel_path) == file_hash:
+                continue
+            chunks = self.chunker.chunk_text(content)
+            if not chunks:
+                continue
+            pending.append({
+                "file_path": file_path,
+                "rel_path": rel_path,
+                "source": source,
+                "scope": scope,
+                "user_id": user_id,
+                "file_hash": file_hash,
+                "chunks": chunks,
+                "texts": [c.text for c in chunks],
+            })
+
+        if not pending:
+            self._dirty = False
+            return
+
+        # Pass 2: single batched embed across all pending chunks.
+        # CRITICAL: never touch the index until we hold valid embeddings.
+        # If embed_batch fails, leave the existing index intact (chunks +
+        # file_hash) so the next sync will retry the same files. Writing
+        # NULL embeddings + updating file_hash here would mark the file as
+        # "successfully synced" and silently strand it without vectors.
+        all_texts: List[str] = []
+        for entry in pending:
+            all_texts.extend(entry["texts"])
+
+        if not self.embedding_provider:
+            # No provider configured at all (legacy keyword-only). Persist
+            # chunks without embeddings — this is the user's intent.
+            all_embeddings: List[Optional[List[float]]] = [None] * len(all_texts)
+        else:
+            try:
+                all_embeddings = self.embedding_provider.embed_batch(all_texts)
             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)
+                from common.log import logger
+                logger.error(
+                    f"[MemoryManager] Batch embedding failed for {len(all_texts)} "
+                    f"chunks across {len(pending)} files: {e}. "
+                    f"Index left untouched; will retry on next sync."
+                )
+                # Bail before touching storage. self._dirty stays True so
+                # callers know there is pending work.
+                return
+
+        # Pass 3: inline persist — same self-contained reasoning as Pass 1.
+        cursor = 0
+        for entry in pending:
+            n = len(entry["texts"])
+            entry_embeddings = all_embeddings[cursor:cursor + n]
+            cursor += n
+
+            rel_path = entry["rel_path"]
+            self.storage.delete_by_path(rel_path)
+            memory_chunks = []
+            for chunk, embedding in zip(entry["chunks"], entry_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=entry["user_id"],
+                    scope=entry["scope"],
+                    source=entry["source"],
+                    path=rel_path,
+                    start_line=chunk.start_line,
+                    end_line=chunk.end_line,
+                    text=chunk.text,
+                    embedding=embedding,
+                    hash=chunk_hash,
+                    metadata=None,
+                ))
+            self.storage.save_chunks_batch(memory_chunks)
+            stat = entry["file_path"].stat()
+            self.storage.update_file_metadata(
+                path=rel_path,
+                source=entry["source"],
+                file_hash=entry["file_hash"],
+                mtime=int(stat.st_mtime),
+                size=stat.st_size,
+            )
+
+        self._dirty = False
+
+    def flush_memory(
+        self,
+        messages: list,
+        user_id: Optional[str] = None,
+        reason: str = "threshold",
+        max_messages: int = 10,
+        context_summary_callback=None,
+    ) -> bool:
+        """
+        Flush conversation summary to daily memory file.
+
+        Args:
+            messages: Conversation message list
+            user_id: Optional user ID
+            reason: "threshold" | "overflow" | "daily_summary"
+            max_messages: Max recent messages to include (0 = all)
+            context_summary_callback: Optional callback(str) invoked with the
+                daily summary text for in-context injection
+
+        Returns:
+            True if flush was dispatched
+        """
+        success = self.flush_manager.flush_from_messages(
+            messages=messages,
+            user_id=user_id,
+            reason=reason,
+            max_messages=max_messages,
+            context_summary_callback=context_summary_callback,
+        )
+        if success:
+            self._dirty = True
+        return success
     
     def get_status(self) -> Dict[str, Any]:
         """Get memory status"""
@@ -568,6 +469,37 @@ class MemoryManager:
         content = f"{path}:{start_line}:{end_line}"
         return hashlib.md5(content.encode('utf-8')).hexdigest()
     
+    @staticmethod
+    def _compute_temporal_decay(path: str, half_life_days: float = 30.0) -> float:
+        """
+        Compute temporal decay multiplier for dated memory files.
+        
+        Inspired by OpenClaw's temporal-decay: exponential decay based on file date.
+        MEMORY.md and non-dated files are "evergreen" (no decay, multiplier=1.0).
+        Daily files like memory/2025-03-01.md decay based on age.
+        
+        Formula: multiplier = exp(-ln2/half_life * age_in_days)
+        """
+        import re
+        import math
+        
+        match = re.search(r'(\d{4})-(\d{2})-(\d{2})\.md$', path)
+        if not match:
+            return 1.0  # evergreen: MEMORY.md, non-dated files
+        
+        try:
+            file_date = datetime(
+                int(match.group(1)), int(match.group(2)), int(match.group(3))
+            )
+            age_days = (datetime.now() - file_date).days
+            if age_days <= 0:
+                return 1.0
+            
+            decay_lambda = math.log(2) / half_life_days
+            return math.exp(-decay_lambda * age_days)
+        except (ValueError, OverflowError):
+            return 1.0
+    
     def _merge_results(
         self,
         vector_results: List[SearchResult],
@@ -575,8 +507,7 @@ class MemoryManager:
         vector_weight: float,
         keyword_weight: float
     ) -> List[SearchResult]:
-        """Merge vector and keyword search results"""
-        # Create a map by (path, start_line, end_line)
+        """Merge vector and keyword search results with temporal decay for dated files"""
         merged_map = {}
         
         for result in vector_results:
@@ -598,7 +529,6 @@ class MemoryManager:
                     'keyword_score': result.score
                 }
         
-        # Calculate combined scores
         merged_results = []
         for entry in merged_map.values():
             combined_score = (
@@ -606,7 +536,11 @@ class MemoryManager:
                 keyword_weight * entry['keyword_score']
             )
             
+            # Apply temporal decay for dated memory files
             result = entry['result']
+            decay = self._compute_temporal_decay(result.path)
+            combined_score *= decay
+            
             merged_results.append(SearchResult(
                 path=result.path,
                 start_line=result.start_line,
@@ -617,6 +551,5 @@ class MemoryManager:
                 user_id=result.user_id
             ))
         
-        # Sort by score
         merged_results.sort(key=lambda r: r.score, reverse=True)
         return merged_results

agent/memory/rebuild_index.py

@@ -0,0 +1,14 @@
+"""
+Backward-compatible shim for the legacy entry point:
+    python -m agent.memory.rebuild_index
+
+The implementation now lives in agent.memory.embedding.rebuild.
+Prefer using `/memory rebuild-index` in chat going forward.
+"""
+
+from agent.memory.embedding.rebuild import main
+
+if __name__ == "__main__":
+    import sys
+
+    sys.exit(main())

agent/memory/service.py

@@ -32,68 +32,80 @@ class MemoryService:
     # ------------------------------------------------------------------
     # list — paginated file metadata
     # ------------------------------------------------------------------
-    def list_files(self, page: int = 1, page_size: int = 20) -> dict:
+    def list_files(self, page: int = 1, page_size: int = 20, category: str = "memory") -> dict:
         """
-        List all memory files with metadata (without content).
+        List memory or dream files with metadata (without content).
 
-        Returns::
-
-            {
-                "page": 1,
-                "page_size": 20,
-                "total": 15,
-                "list": [
-                    {"filename": "MEMORY.md", "type": "global", "size": 2048, "updated_at": "2026-02-20 10:00:00"},
-                    {"filename": "2026-02-20.md", "type": "daily", "size": 512, "updated_at": "2026-02-20 09:30:00"},
-                    ...
-                ]
-            }
+        Args:
+            category: ``"memory"`` (default) — MEMORY.md + daily files;
+                      ``"dream"``  — dream diary files from memory/dreams/
         """
+        if category == "dream":
+            files = self._list_dream_files()
+        else:
+            files = self._list_memory_files()
+
+        total = len(files)
+        start = (page - 1) * page_size
+        end = start + page_size
+
+        return {
+            "page": page,
+            "page_size": page_size,
+            "total": total,
+            "list": files[start:end],
+        }
+
+    def _list_memory_files(self) -> List[dict]:
+        """MEMORY.md + memory/*.md (newest first)."""
         files: List[dict] = []
 
-        # 1. Global memory — MEMORY.md in workspace root
         global_path = os.path.join(self.workspace_root, "MEMORY.md")
         if os.path.isfile(global_path):
             files.append(self._file_info(global_path, "MEMORY.md", "global"))
 
-        # 2. Daily memory files — memory/*.md (sorted newest first)
         if os.path.isdir(self.memory_dir):
             daily_files = []
             for name in os.listdir(self.memory_dir):
                 full = os.path.join(self.memory_dir, name)
                 if os.path.isfile(full) and name.endswith(".md"):
                     daily_files.append((name, full))
-            # Sort by filename descending (newest date first)
             daily_files.sort(key=lambda x: x[0], reverse=True)
             for name, full in daily_files:
                 files.append(self._file_info(full, name, "daily"))
 
-        total = len(files)
+        return files
 
-        # Paginate
-        start = (page - 1) * page_size
-        end = start + page_size
-        page_items = files[start:end]
+    def _list_dream_files(self) -> List[dict]:
+        """memory/dreams/*.md (newest first)."""
+        files: List[dict] = []
+        dreams_dir = os.path.join(self.memory_dir, "dreams")
 
-        return {
-            "page": page,
-            "page_size": page_size,
-            "total": total,
-            "list": page_items,
-        }
+        if os.path.isdir(dreams_dir):
+            entries = []
+            for name in os.listdir(dreams_dir):
+                full = os.path.join(dreams_dir, name)
+                if os.path.isfile(full) and name.endswith(".md"):
+                    entries.append((name, full))
+            entries.sort(key=lambda x: x[0], reverse=True)
+            for name, full in entries:
+                files.append(self._file_info(full, name, "dream"))
+
+        return files
 
     # ------------------------------------------------------------------
     # content — read a single file
     # ------------------------------------------------------------------
-    def get_content(self, filename: str) -> dict:
+    def get_content(self, filename: str, category: str = "memory") -> dict:
         """
-        Read the full content of a memory file.
+        Read the full content of a memory or dream file.
 
-        :param filename: File name, e.g. ``MEMORY.md`` or ``2026-02-20.md``
+        :param filename: File name, e.g. ``MEMORY.md``, ``2026-02-20.md``
+        :param category: ``"memory"`` or ``"dream"``
         :return: dict with ``filename`` and ``content``
         :raises FileNotFoundError: if the file does not exist
         """
-        path = self._resolve_path(filename)
+        path = self._resolve_path(filename, category)
         if not os.path.isfile(path):
             raise FileNotFoundError(f"Memory file not found: {filename}")
 
@@ -113,7 +125,7 @@ class MemoryService:
         Dispatch a memory management action.
 
         :param action: ``list`` or ``content``
-        :param payload: action-specific payload
+        :param payload: action-specific payload (supports ``category``: ``"memory"`` | ``"dream"``)
         :return: protocol-compatible response dict
         """
         payload = payload or {}
@@ -121,19 +133,23 @@ class MemoryService:
             if action == "list":
                 page = payload.get("page", 1)
                 page_size = payload.get("page_size", 20)
-                result_payload = self.list_files(page=page, page_size=page_size)
+                category = payload.get("category", "memory")
+                result_payload = self.list_files(page=page, page_size=page_size, category=category)
                 return {"action": action, "code": 200, "message": "success", "payload": result_payload}
 
             elif action == "content":
                 filename = payload.get("filename")
                 if not filename:
                     return {"action": action, "code": 400, "message": "filename is required", "payload": None}
-                result_payload = self.get_content(filename)
+                category = payload.get("category", "memory")
+                result_payload = self.get_content(filename, category=category)
                 return {"action": action, "code": 200, "message": "success", "payload": result_payload}
 
             else:
                 return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
 
+        except ValueError as e:
+            return {"action": action, "code": 403, "message": "invalid filename", "payload": None}
         except FileNotFoundError as e:
             return {"action": action, "code": 404, "message": str(e), "payload": None}
         except Exception as e:
@@ -143,16 +159,30 @@ class MemoryService:
     # ------------------------------------------------------------------
     # internal helpers
     # ------------------------------------------------------------------
-    def _resolve_path(self, filename: str) -> str:
+    def _resolve_path(self, filename: str, category: str = "memory") -> str:
         """
-        Resolve a filename to its absolute path.
+        Safely resolve a filename to its absolute path within the allowed directory.
 
         - ``MEMORY.md`` → ``{workspace_root}/MEMORY.md``
-        - ``2026-02-20.md`` → ``{workspace_root}/memory/2026-02-20.md``
+        - ``2026-02-20.md`` (memory) → ``{workspace_root}/memory/2026-02-20.md``
+        - ``2026-02-20.md`` (dream) → ``{workspace_root}/memory/dreams/2026-02-20.md``
+
+        Raises ValueError if the resolved path escapes the allowed directory.
         """
         if filename == "MEMORY.md":
-            return os.path.join(self.workspace_root, filename)
-        return os.path.join(self.memory_dir, filename)
+            base_dir = self.workspace_root
+        elif category == "dream":
+            base_dir = os.path.join(self.memory_dir, "dreams")
+        else:
+            base_dir = self.memory_dir
+
+        resolved = os.path.realpath(os.path.join(base_dir, filename))
+        allowed = os.path.realpath(base_dir)
+
+        if resolved != allowed and not resolved.startswith(allowed + os.sep):
+            raise ValueError(f"Invalid filename: path traversal detected")
+
+        return resolved
 
     @staticmethod
     def _file_info(path: str, filename: str, file_type: str) -> dict:

agent/memory/summarizer.py

@@ -1,225 +1,700 @@
 """
-Memory flush manager
+Memory flush manager with Deep Dream distillation
 
-Triggers memory flush before context compaction (similar to clawdbot)
+Handles memory persistence when conversation context is trimmed or overflows:
+- Uses LLM to summarize discarded messages into concise daily records
+- Writes to daily memory files (lazy creation)
+- Deduplicates trim flushes to avoid repeated writes
+- Runs summarization asynchronously to avoid blocking normal replies
+- Deep Dream: periodically distills daily memories → refined MEMORY.md + dream diary
 """
 
-from typing import Optional, Callable, Any
+import threading
+from typing import Optional, Callable, Any, List, Dict
 from pathlib import Path
 from datetime import datetime
+from common.log import logger
+
+
+SUMMARIZE_SYSTEM_PROMPT = """你是一个对话记录助手。请将对话内容归纳为当天的日常记录。
+
+## 要求
+
+按「事件」维度归纳发生的事，不要按对话轮次逐条记录：
+- 每条一行，用 "- " 开头
+- 合并同一件事的多轮对话
+- 只记录有意义的事件，忽略闲聊和问候
+- 保留关键的决策、结论和待办事项
+
+当对话没有任何记录价值（仅含问候或无意义内容），直接回复"无"。"""
+
+SUMMARIZE_USER_PROMPT = """请归纳以下对话的日常记录：
+
+{conversation}"""
+
+# ---------------------------------------------------------------------------
+# Deep Dream prompts — distill daily memories → MEMORY.md + dream diary
+# ---------------------------------------------------------------------------
+
+DREAM_SYSTEM_PROMPT = """你是一个记忆整理助手，负责定期整理用户的长期记忆。
+
+你将收到两份材料：
+1. **当前长期记忆** — MEMORY.md 的全部现有内容
+2. **今日日记** — 当天的日常记录
+
+MEMORY.md 会注入每次对话的系统提示词中，因此必须保持精炼，只存放有价值和值得记忆的内容。
+
+**重要：只能基于提供的材料进行整理，严禁编造、推测或添加材料中不存在的信息。**
+
+## 任务
+
+### Part 1: 更新后的长期记忆（[MEMORY]）
+
+在现有记忆基础上进行整理和提炼，输出完整的更新后内容：
+- **合并提炼**：将含义相近的多条合并为一条高密度表述，而非简单罗列
+- **新增萃取**：从今日日记中提取值得永久记住的新信息（偏好、决策、人物、规则、经验）
+- **冲突更新**：当新信息与旧条目矛盾时，以新信息为准，替换旧条目
+- **清理无效**：删除临时性记录、空白条目、格式残留、无意义、重复内容等
+- **删除冗余**：已被更精炼表述涵盖的旧条目应删除，避免信息重复
+- 每条一行，用 "- " 开头，不带日期前缀
+- 可用 "## 标题" 对相关条目分组，使结构更清晰
+- 目标：控制在 50 条以内，每条尽量一句话概括
+
+### Part 2: 梦境日记（[DREAM]）
+
+用简洁的叙事风格写一篇短日记，记录这次整理的发现，保持格式美观易读：
+- 发现了哪些重复或矛盾
+- 从日记中提取了什么新洞察
+- 做了哪些清理和优化
+- 整体感受和观察
+
+## 输出格式（严格遵守）
+
+```
+[MEMORY]
+- 记忆条目1
+- 记忆条目2
+...
+
+[DREAM]
+梦境日记内容...
+```"""
+
+DREAM_USER_PROMPT = """## 当前长期记忆（MEMORY.md）
+
+{memory_content}
+
+## 近期日记（最近 {days} 天）
+
+{daily_content}"""
+
 
 
 class MemoryFlushManager:
     """
-    Manages memory flush operations before context compaction
+    Manages memory flush operations.
     
-    Similar to clawdbot's memory flush mechanism:
-    - Triggers when context approaches token limit
-    - Runs a silent agent turn to write memories to disk
-    - Uses memory/YYYY-MM-DD.md for daily notes
-    - Uses MEMORY.md (workspace root) for long-term curated memories
+    Flush is triggered by agent_stream in two scenarios:
+    1. Context trim: _trim_messages discards old turns → flush discarded content
+    2. Context overflow: API rejects request → emergency flush before clearing
+    
+    Additionally, create_daily_summary() can be called by scheduler for end-of-day summaries.
     """
     
     def __init__(
         self,
         workspace_dir: Path,
-        llm_model: Optional[Any] = None
+        llm_model: Optional[Any] = None,
     ):
-        """
-        Initialize memory flush manager
-        
-        Args:
-            workspace_dir: Workspace directory
-            llm_model: LLM model for agent execution (optional)
-        """
         self.workspace_dir = workspace_dir
         self.llm_model = llm_model
         
         self.memory_dir = workspace_dir / "memory"
         self.memory_dir.mkdir(parents=True, exist_ok=True)
         
-        # Tracking
-        self.last_flush_token_count: Optional[int] = None
         self.last_flush_timestamp: Optional[datetime] = None
-        self.turn_count: int = 0  # 对话轮数计数器
+        self._trim_flushed_hashes: set = set()  # Content hashes of already-flushed messages
+        self._last_flushed_content_hash: str = ""  # Content hash at last flush, for daily dedup
+        self._last_dream_input_hash: str = ""  # "{date}:{daily_hash}" of last dream, for dedup
+        self._last_flush_thread: Optional[threading.Thread] = None
     
-    def should_flush(
-        self,
-        current_tokens: int = 0,
-        token_threshold: int = 50000,
-        turn_threshold: int = 20
-    ) -> bool:
-        """
-        Determine if memory flush should be triggered
-        
-        独立的 flush 触发机制，不依赖模型 context window:
-        - Token 阈值: 达到 50K tokens 时触发
-        - 轮次阈值: 达到 20 轮对话时触发
-        
-        Args:
-            current_tokens: Current session token count
-            token_threshold: Token threshold to trigger flush (default: 50K)
-            turn_threshold: Turn threshold to trigger flush (default: 20)
-            
-        Returns:
-            True if flush should run
-        """
-        # 检查 token 阈值
-        if current_tokens > 0 and current_tokens >= token_threshold:
-            # 避免重复 flush
-            if self.last_flush_token_count is not None:
-                if current_tokens <= self.last_flush_token_count + 5000:
-                    return False
-            return True
-        
-        # 检查轮次阈值
-        if self.turn_count >= turn_threshold:
-            return True
-        
-        return False
-    
-    def get_today_memory_file(self, user_id: Optional[str] = None) -> Path:
-        """
-        Get today's memory file path: memory/YYYY-MM-DD.md
-        
-        Args:
-            user_id: Optional user ID for user-specific memory
-            
-        Returns:
-            Path to today's memory file
-        """
+    def get_today_memory_file(self, user_id: Optional[str] = None, ensure_exists: bool = False) -> Path:
+        """Get today's memory file path: memory/YYYY-MM-DD.md"""
         today = datetime.now().strftime("%Y-%m-%d")
         
         if user_id:
             user_dir = self.memory_dir / "users" / user_id
-            user_dir.mkdir(parents=True, exist_ok=True)
-            return user_dir / f"{today}.md"
+            if ensure_exists:
+                user_dir.mkdir(parents=True, exist_ok=True)
+            today_file = user_dir / f"{today}.md"
         else:
-            return self.memory_dir / f"{today}.md"
+            today_file = self.memory_dir / f"{today}.md"
+        
+        if ensure_exists and not today_file.exists():
+            today_file.parent.mkdir(parents=True, exist_ok=True)
+            today_file.write_text(f"# Daily Memory: {today}\n\n")
+        
+        return today_file
     
     def get_main_memory_file(self, user_id: Optional[str] = None) -> Path:
-        """
-        Get main memory file path: MEMORY.md (workspace root)
-        
-        Args:
-            user_id: Optional user ID for user-specific memory
-            
-        Returns:
-            Path to main memory file
-        """
+        """Get main memory file path: MEMORY.md (workspace root)"""
         if user_id:
             user_dir = self.memory_dir / "users" / user_id
             user_dir.mkdir(parents=True, exist_ok=True)
             return user_dir / "MEMORY.md"
         else:
-            # Return workspace root MEMORY.md
             return Path(self.workspace_dir) / "MEMORY.md"
     
-    def create_flush_prompt(self) -> str:
-        """
-        Create prompt for memory flush turn
-        
-        Similar to clawdbot's DEFAULT_MEMORY_FLUSH_PROMPT
-        """
-        today = datetime.now().strftime("%Y-%m-%d")
-        return (
-            f"Pre-compaction memory flush. "
-            f"Store durable memories now (use memory/{today}.md for daily notes; "
-            f"create memory/ if needed). "
-            f"\n\n"
-            f"重要提示:\n"
-            f"- MEMORY.md: 记录最核心、最常用的信息（例如重要规则、偏好、决策、要求等）\n"
-            f"  如果 MEMORY.md 过长，可以精简或移除不再重要的内容。避免冗长描述，用关键词和要点形式记录\n"
-            f"- memory/{today}.md: 记录当天发生的事件、关键信息、经验教训、对话过程摘要等，突出重点\n"
-            f"- 如果没有重要内容需要记录，回复 NO_REPLY\n"
-        )
-    
-    def create_flush_system_prompt(self) -> str:
-        """
-        Create system prompt for memory flush turn
-        
-        Similar to clawdbot's DEFAULT_MEMORY_FLUSH_SYSTEM_PROMPT
-        """
-        return (
-            "Pre-compaction memory flush turn. "
-            "The session is near auto-compaction; capture durable memories to disk. "
-            "\n\n"
-            "记忆写入原则:\n"
-            "1. MEMORY.md 精简原则: 只记录核心信息（<2000 tokens）\n"
-            "   - 记录重要规则、偏好、决策、要求等需要长期记住的关键信息，无需记录过多细节\n"
-            "   - 如果 MEMORY.md 过长，可以根据需要精简或删除过时内容\n"
-            "\n"
-            "2. 天级记忆 (memory/YYYY-MM-DD.md):\n"
-            "   - 记录当天的重要事件、关键信息、经验教训、对话过程摘要等，确保核心信息点被完整记录\n"
-            "\n"
-            "3. 判断标准:\n"
-            "   - 这个信息未来会经常用到吗？→ MEMORY.md\n"
-            "   - 这是今天的重要事件或决策吗？→ memory/YYYY-MM-DD.md\n"
-            "   - 这是临时性的、不重要的内容吗？→ 不记录\n"
-            "\n"
-            "You may reply, but usually NO_REPLY is correct."
-        )
-    
-    async def execute_flush(
-        self,
-        agent_executor: Callable,
-        current_tokens: int,
-        user_id: Optional[str] = None,
-        **executor_kwargs
-    ) -> bool:
-        """
-        Execute memory flush by running a silent agent turn
-        
-        Args:
-            agent_executor: Function to execute agent with prompt
-            current_tokens: Current token count
-            user_id: Optional user ID
-            **executor_kwargs: Additional kwargs for agent executor
-            
-        Returns:
-            True if flush completed successfully
-        """
-        try:
-            # Create flush prompts
-            prompt = self.create_flush_prompt()
-            system_prompt = self.create_flush_system_prompt()
-            
-            # Execute agent turn (silent, no user-visible reply expected)
-            await agent_executor(
-                prompt=prompt,
-                system_prompt=system_prompt,
-                silent=True,  # NO_REPLY expected
-                **executor_kwargs
-            )
-            
-            # Track flush
-            self.last_flush_token_count = current_tokens
-            self.last_flush_timestamp = datetime.now()
-            self.turn_count = 0  # 重置轮数计数器
-            
-            return True
-            
-        except Exception as e:
-            print(f"Memory flush failed: {e}")
-            return False
-    
-    def increment_turn(self):
-        """增加对话轮数计数"""
-        self.turn_count += 1
-    
     def get_status(self) -> dict:
-        """Get memory flush status"""
         return {
-            'last_flush_tokens': self.last_flush_token_count,
             'last_flush_time': self.last_flush_timestamp.isoformat() if self.last_flush_timestamp else None,
             'today_file': str(self.get_today_memory_file()),
             'main_file': str(self.get_main_memory_file())
         }
 
+    # ---- Flush execution (called by agent_stream or scheduler) ----
+    
+    def flush_from_messages(
+        self,
+        messages: List[Dict],
+        user_id: Optional[str] = None,
+        reason: str = "trim",
+        max_messages: int = 0,
+        context_summary_callback: Optional[Callable[[str], None]] = None,
+    ) -> bool:
+        """
+        Asynchronously summarize and flush messages to daily memory.
+
+        Deduplication runs synchronously, then LLM summarization + file write
+        run in a background thread so the main reply flow is never blocked.
+
+        If *context_summary_callback* is provided, it is called with the
+        [DAILY] portion of the LLM summary once available. The caller can use
+        this to inject the summary into the live message list for context
+        continuity — one LLM call serves both disk persistence and in-context
+        injection.
+        """
+        try:
+            # Strip scheduler-injected pairs before any further processing.
+            # These messages already serve as short-term context inside the
+            # receiver session; promoting them into long-term daily memory
+            # produces low-value flat logs (e.g. "11:28 price=1013, normal /
+            # 11:58 price=1013, normal / ...") and wastes summarisation tokens.
+            messages = self._strip_scheduler_pairs(messages)
+            if not messages:
+                return False
+
+            import hashlib
+            deduped = []
+            for m in messages:
+                text = self._extract_text_from_content(m.get("content", ""))
+                if not text or not text.strip():
+                    continue
+                h = hashlib.md5(text.encode("utf-8")).hexdigest()
+                if h not in self._trim_flushed_hashes:
+                    self._trim_flushed_hashes.add(h)
+                    deduped.append(m)
+            if not deduped:
+                return False
+
+            import copy
+            snapshot = copy.deepcopy(deduped)
+            thread = threading.Thread(
+                target=self._flush_worker,
+                args=(snapshot, user_id, reason, max_messages, context_summary_callback),
+                daemon=True,
+            )
+            thread.start()
+            logger.info(f"[MemoryFlush] Async flush dispatched (reason={reason}, msgs={len(snapshot)})")
+            self._last_flush_thread = thread
+            return True
+
+        except Exception as e:
+            logger.warning(f"[MemoryFlush] Failed to dispatch flush (reason={reason}): {e}")
+            return False
+
+    def _flush_worker(
+        self,
+        messages: List[Dict],
+        user_id: Optional[str],
+        reason: str,
+        max_messages: int,
+        context_summary_callback: Optional[Callable[[str], None]] = None,
+    ):
+        """Background worker: summarize with LLM, write daily memory file."""
+        try:
+            raw_summary = self._summarize_messages(messages, max_messages)
+            if not raw_summary or not raw_summary.strip() or raw_summary.strip() == "无":
+                logger.info(f"[MemoryFlush] No valuable content to flush (reason={reason})")
+                return
+
+            # Strip legacy [DAILY]/[MEMORY] markers if model still outputs them
+            daily_part = self._clean_summary_output(raw_summary)
+            if not daily_part:
+                return
+
+            # --- Write daily memory ---
+            daily_file = ensure_daily_memory_file(self.workspace_dir, user_id)
+
+            headers = {
+                "overflow": f"## Context Overflow Recovery ({datetime.now().strftime('%H:%M')})",
+                "trim": f"## Trimmed Context ({datetime.now().strftime('%H:%M')})",
+                "daily_summary": f"## Daily Summary ({datetime.now().strftime('%H:%M')})",
+            }
+            header = headers.get(reason, f"## Session Notes ({datetime.now().strftime('%H:%M')})")
+
+            with open(daily_file, "a", encoding="utf-8") as f:
+                f.write(f"\n{header}\n\n{daily_part}\n")
+
+            logger.info(f"[MemoryFlush] Wrote daily memory to {daily_file.name} (reason={reason}, chars={len(daily_part)})")
+
+            # --- Inject context summary into live messages (if callback provided) ---
+            if context_summary_callback:
+                try:
+                    context_summary_callback(daily_part)
+                except Exception as e:
+                    logger.warning(f"[MemoryFlush] Context summary callback failed: {e}")
+
+            self.last_flush_timestamp = datetime.now()
+
+        except Exception as e:
+            logger.warning(f"[MemoryFlush] Async flush failed (reason={reason}): {e}")
+
+    @staticmethod
+    def _clean_summary_output(raw: str) -> str:
+        """Strip legacy [DAILY]/[MEMORY] markers if present, return clean daily text."""
+        raw = raw.strip()
+        if not raw or raw == "无":
+            return ""
+
+        # Strip [DAILY] marker
+        if "[DAILY]" in raw:
+            start = raw.index("[DAILY]") + len("[DAILY]")
+            end = raw.index("[MEMORY]") if "[MEMORY]" in raw else len(raw)
+            raw = raw[start:end].strip()
+
+        # Remove stray [MEMORY] section entirely
+        if "[MEMORY]" in raw:
+            raw = raw[:raw.index("[MEMORY]")].strip()
+
+        # Remove markdown code fences
+        raw = raw.replace("```", "").strip()
+
+        return raw
+
+    def create_daily_summary(
+        self,
+        messages: List[Dict],
+        user_id: Optional[str] = None
+    ) -> bool:
+        """
+        Generate end-of-day summary. Called by daily timer.
+        Skips if messages haven't changed since last flush.
+        """
+        import hashlib
+        content = "".join(
+            self._extract_text_from_content(m.get("content", ""))
+            for m in messages
+        )
+        content_hash = hashlib.md5(content.encode("utf-8")).hexdigest()
+        if content_hash == self._last_flushed_content_hash:
+            logger.debug("[MemoryFlush] Daily summary skipped: no new content since last flush")
+            return False
+        self._last_flushed_content_hash = content_hash
+        return self.flush_from_messages(
+            messages=messages,
+            user_id=user_id,
+            reason="daily_summary",
+            max_messages=0,
+        )
+
+    # ---- Deep Dream (memory distillation) ----
+
+    def deep_dream(self, user_id: Optional[str] = None, lookback_days: int = 1, force: bool = False) -> bool:
+        """
+        Distill recent daily memories into MEMORY.md and generate a dream diary.
+
+        Args:
+            lookback_days: How many days of daily files to read (default 1 for scheduled, 3 for manual)
+            force: Skip input-hash dedup check (used by manual /memory dream trigger)
+        """
+        if not self.llm_model:
+            logger.warning("[DeepDream] No LLM model available, skipping")
+            return False
+
+        logger.info(f"[DeepDream] Starting memory distillation (lookback={lookback_days} days)")
+
+        # Collect materials
+        memory_content = self._read_main_memory(user_id)
+        daily_content, has_content = self._read_recent_dailies(user_id, lookback_days)
+
+        if not has_content:
+            logger.info("[DeepDream] No recent daily records, skipping to preserve existing MEMORY.md")
+            return False
+
+        # Dedup: skip if same daily content already dreamed today.
+        # Note: only hash daily_content (not memory_content), because deep_dream
+        # itself rewrites MEMORY.md as a side effect, which would otherwise
+        # invalidate the hash on every subsequent call within the same window.
+        import hashlib
+        daily_hash = hashlib.md5(daily_content.encode("utf-8")).hexdigest()
+        today_str = datetime.now().strftime("%Y-%m-%d")
+        dedup_key = f"{today_str}:{daily_hash}"
+        if not force and dedup_key == self._last_dream_input_hash:
+            logger.info("[DeepDream] Already dreamed today with same daily content, skipping")
+            return False
+        self._last_dream_input_hash = dedup_key
+
+        logger.info(
+            f"[DeepDream] Materials collected: "
+            f"MEMORY.md={len(memory_content)} chars, "
+            f"daily={len(daily_content)} chars"
+        )
+
+        # Call LLM for distillation
+        import time as _time
+        t0 = _time.monotonic()
+        try:
+            user_msg = DREAM_USER_PROMPT.format(
+                memory_content=memory_content or "(empty)",
+                days=lookback_days,
+                daily_content=daily_content or "(no recent daily records)",
+            )
+            from agent.protocol.models import LLMRequest
+            # Scale max_tokens based on input size to avoid truncating large MEMORY.md
+            input_chars = len(memory_content) + len(daily_content)
+            dream_max_tokens = max(2000, min(input_chars, 8000))
+            request = LLMRequest(
+                messages=[{"role": "user", "content": user_msg}],
+                temperature=0.3,
+                max_tokens=dream_max_tokens,
+                stream=False,
+                system=DREAM_SYSTEM_PROMPT,
+            )
+            response = self.llm_model.call(request)
+            raw = self._extract_response_text(response)
+            elapsed = _time.monotonic() - t0
+            if not raw or not raw.strip():
+                logger.warning(f"[DeepDream] LLM returned empty response ({elapsed:.1f}s)")
+                return False
+            logger.info(f"[DeepDream] LLM distillation completed ({elapsed:.1f}s, {len(raw)} chars)")
+        except Exception as e:
+            elapsed = _time.monotonic() - t0
+            logger.warning(f"[DeepDream] LLM call failed ({elapsed:.1f}s): {e}")
+            return False
+
+        # Parse [MEMORY] and [DREAM] sections
+        new_memory, dream_diary = self._parse_dream_output(raw)
+
+        if not new_memory:
+            logger.warning("[DeepDream] No [MEMORY] section in LLM output, skipping overwrite")
+            return False
+
+        # Overwrite MEMORY.md
+        try:
+            main_file = self.get_main_memory_file(user_id)
+            old_size = len(memory_content)
+            main_file.write_text(new_memory + "\n", encoding="utf-8")
+            logger.info(
+                f"[DeepDream] Updated MEMORY.md "
+                f"({old_size} → {len(new_memory)} chars)"
+            )
+        except Exception as e:
+            logger.warning(f"[DeepDream] Failed to write MEMORY.md: {e}")
+            return False
+
+        # Write dream diary
+        if dream_diary:
+            try:
+                self._write_dream_diary(dream_diary, user_id)
+            except Exception as e:
+                logger.warning(f"[DeepDream] Failed to write dream diary: {e}")
+
+        logger.info("[DeepDream] ✅ Deep Dream completed successfully")
+        return True
+
+    def _read_main_memory(self, user_id: Optional[str] = None) -> str:
+        """Read current MEMORY.md content."""
+        main_file = self.get_main_memory_file(user_id)
+        if main_file.exists():
+            return main_file.read_text(encoding="utf-8").strip()
+        return ""
+
+    def _read_recent_dailies(
+        self, user_id: Optional[str] = None, lookback_days: int = 1
+    ) -> tuple:
+        """
+        Read recent daily memory files.
+
+        Returns:
+            (combined_text, has_content) tuple
+        """
+        from datetime import timedelta
+
+        parts = []
+        has_content = False
+        today = datetime.now().date()
+
+        for offset in range(lookback_days):
+            day = today - timedelta(days=offset)
+            date_str = day.strftime("%Y-%m-%d")
+            if user_id:
+                daily_file = self.memory_dir / "users" / user_id / f"{date_str}.md"
+            else:
+                daily_file = self.memory_dir / f"{date_str}.md"
+
+            if daily_file.exists():
+                content = daily_file.read_text(encoding="utf-8").strip()
+                if content:
+                    parts.append(f"### {date_str}\n\n{content}")
+                    has_content = True
+            else:
+                parts.append(f"### {date_str}\n\n(no records)")
+
+        return "\n\n".join(parts), has_content
+
+    @staticmethod
+    def _parse_dream_output(raw: str) -> tuple:
+        """Parse LLM output into (new_memory, dream_diary)."""
+        raw = raw.strip().replace("```", "")
+        new_memory = ""
+        dream_diary = ""
+
+        if "[MEMORY]" in raw:
+            start = raw.index("[MEMORY]") + len("[MEMORY]")
+            end = raw.index("[DREAM]") if "[DREAM]" in raw else len(raw)
+            new_memory = raw[start:end].strip()
+
+        if "[DREAM]" in raw:
+            start = raw.index("[DREAM]") + len("[DREAM]")
+            dream_diary = raw[start:].strip()
+
+        return new_memory, dream_diary
+
+    def _write_dream_diary(self, content: str, user_id: Optional[str] = None):
+        """Write dream diary to memory/dreams/YYYY-MM-DD.md."""
+        dreams_dir = self.memory_dir / "dreams"
+        if user_id:
+            dreams_dir = self.memory_dir / "users" / user_id / "dreams"
+        dreams_dir.mkdir(parents=True, exist_ok=True)
+
+        today = datetime.now().strftime("%Y-%m-%d")
+        diary_file = dreams_dir / f"{today}.md"
+        diary_file.write_text(
+            f"# Dream Diary: {today}\n\n{content}\n",
+            encoding="utf-8",
+        )
+        logger.info(f"[DeepDream] Wrote dream diary to {diary_file}")
+
+    # ---- Internal helpers ----
+    
+    def _summarize_messages(self, messages: List[Dict], max_messages: int = 0) -> str:
+        """
+        Summarize conversation messages using LLM.
+        Returns empty string if LLM deems content not worth recording.
+        Rule-based fallback only used when LLM call raises an exception.
+        """
+        conversation_text = self._format_conversation_for_summary(messages, max_messages)
+        if not conversation_text.strip():
+            return ""
+        
+        if self.llm_model:
+            try:
+                summary = self._call_llm_for_summary(conversation_text)
+                if summary and summary.strip() and summary.strip() != "无":
+                    return summary.strip()
+                logger.info("[MemoryFlush] LLM returned empty or '无', skipping write")
+                return ""
+            except Exception as e:
+                logger.warning(f"[MemoryFlush] LLM summarization failed, using fallback: {e}")
+                return self._extract_summary_fallback(messages, max_messages)
+        else:
+            logger.info("[MemoryFlush] No LLM model available, using rule-based fallback")
+            return self._extract_summary_fallback(messages, max_messages)
+
+    def _format_conversation_for_summary(self, messages: List[Dict], max_messages: int = 0) -> str:
+        """Format messages into readable conversation text for LLM summarization."""
+        msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
+        lines = []
+        for msg in msgs:
+            role = msg.get("role", "")
+            text = self._extract_text_from_content(msg.get("content", ""))
+            if not text or not text.strip():
+                continue
+            text = text.strip()
+            if role == "user":
+                lines.append(f"用户: {text[:500]}")
+            elif role == "assistant":
+                lines.append(f"助手: {text[:500]}")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _extract_response_text(response) -> str:
+        """
+        Extract text from LLM response regardless of format.
+
+        Handles:
+        - Generator (MiniMax _handle_sync_response yields Claude-format dicts)
+        - Claude format: {"role":"assistant","content":[{"type":"text","text":"..."}]}
+        - OpenAI format: {"choices":[{"message":{"content":"..."}}]}
+        - OpenAI SDK response object with .choices attribute
+        """
+        import types
+
+        # Unwrap generator — consume first yielded item
+        if isinstance(response, types.GeneratorType):
+            try:
+                response = next(response)
+            except StopIteration:
+                return ""
+
+        if not response:
+            return ""
+
+        if isinstance(response, dict):
+            # Check for error
+            if response.get("error"):
+                raise RuntimeError(response.get("message", "LLM call failed"))
+
+            # Claude format: content is a list of blocks
+            content = response.get("content")
+            if isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict) and block.get("type") == "text":
+                        return block.get("text", "")
+
+            # OpenAI format
+            choices = response.get("choices", [])
+            if choices:
+                return choices[0].get("message", {}).get("content", "")
+
+        # OpenAI SDK response object
+        if hasattr(response, "choices") and response.choices:
+            return response.choices[0].message.content or ""
+
+        return ""
+
+    def _call_llm_for_summary(self, conversation_text: str) -> str:
+        """Call LLM to generate a concise summary of the conversation."""
+        from agent.protocol.models import LLMRequest
+        
+        request = LLMRequest(
+            messages=[{"role": "user", "content": SUMMARIZE_USER_PROMPT.format(conversation=conversation_text)}],
+            temperature=0,
+            max_tokens=500,
+            stream=False,
+            system=SUMMARIZE_SYSTEM_PROMPT,
+        )
+        
+        response = self.llm_model.call(request)
+        return self._extract_response_text(response)
+
+    @staticmethod
+    def _extract_first_meaningful_line(text: str, max_len: int = 120) -> str:
+        """Extract the first meaningful line from assistant reply, skipping markdown noise."""
+        import re
+        for line in text.split("\n"):
+            line = line.strip()
+            if not line:
+                continue
+            # Skip markdown headings, horizontal rules, code fences, pure emoji/symbols
+            if re.match(r'^(#{1,4}\s|```|---|\*\*\*|[-*]\s*$|[^\w\u4e00-\u9fff]{1,5}$)', line):
+                continue
+            # Strip leading markdown bold/emoji decorations
+            cleaned = re.sub(r'^[\*#>\-\s]+', '', line).strip()
+            cleaned = re.sub(r'^[\U0001f300-\U0001f9ff\u2600-\u27bf\s]+', '', cleaned).strip()
+            if len(cleaned) >= 5:
+                return cleaned[:max_len]
+        return text.split("\n")[0].strip()[:max_len]
+
+    @staticmethod
+    def _extract_summary_fallback(messages: List[Dict], max_messages: int = 0) -> str:
+        """
+        Rule-based summary of discarded messages.
+        Format: "用户问了X; 助手回答了Y" per event, compact and readable.
+        """
+        msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
+
+        events: List[str] = []
+        current_user_text = ""
+        for msg in msgs:
+            role = msg.get("role", "")
+            text = MemoryFlushManager._extract_text_from_content(msg.get("content", ""))
+            if not text or not text.strip():
+                continue
+            text = text.strip()
+
+            if role == "user":
+                if len(text) <= 3:
+                    continue
+                current_user_text = text[:120]
+            elif role == "assistant" and current_user_text:
+                reply_summary = MemoryFlushManager._extract_first_meaningful_line(text)
+                if reply_summary:
+                    events.append(f"- 用户: {current_user_text} → 回复: {reply_summary}")
+                else:
+                    events.append(f"- 用户: {current_user_text}")
+                current_user_text = ""
+
+        if current_user_text:
+            events.append(f"- 用户: {current_user_text}")
+
+        return "\n".join(events[:10])
+    
+    @staticmethod
+    def _extract_text_from_content(content) -> str:
+        """Extract plain text from message content (string or content blocks)."""
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            parts = []
+            for block in content:
+                if isinstance(block, dict) and block.get("type") == "text":
+                    parts.append(block.get("text", ""))
+                elif isinstance(block, str):
+                    parts.append(block)
+            return "\n".join(parts)
+        return ""
+
+    @classmethod
+    def _strip_scheduler_pairs(cls, messages: List[Dict]) -> List[Dict]:
+        """Drop scheduler-injected user/assistant pairs from a flush batch.
+
+        A scheduler user message starts with the ``[SCHEDULED]`` marker
+        (written by ``AgentBridge.remember_scheduled_output``); the message
+        immediately following it (if it is an assistant turn) is its paired
+        output and is dropped together. Regular user/assistant turns and
+        any tool_use / tool_result blocks are preserved as-is.
+        """
+        if not messages:
+            return messages
+
+        SCHEDULED_PREFIX = "[SCHEDULED]"
+        result = []
+        skip_next_assistant = False
+        for msg in messages:
+            if not isinstance(msg, dict):
+                result.append(msg)
+                skip_next_assistant = False
+                continue
+            role = msg.get("role")
+            if skip_next_assistant and role == "assistant":
+                skip_next_assistant = False
+                continue
+            skip_next_assistant = False
+            if role == "user":
+                text = cls._extract_text_from_content(msg.get("content", ""))
+                if text.lstrip().startswith(SCHEDULED_PREFIX):
+                    skip_next_assistant = True
+                    continue
+            result.append(msg)
+        return result
+
 
 def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] = None):
     """
-    Create default memory files if they don't exist
+    Create essential memory files if they don't exist.
+    Only creates MEMORY.md; daily files are created lazily on first write.
     
     Args:
         workspace_dir: Workspace directory
@@ -228,7 +703,7 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
     memory_dir = workspace_dir / "memory"
     memory_dir.mkdir(parents=True, exist_ok=True)
     
-    # Create main MEMORY.md in workspace root
+    # Create main MEMORY.md in workspace root (always needed for bootstrap)
     if user_id:
         user_dir = memory_dir / "users" / user_id
         user_dir.mkdir(parents=True, exist_ok=True)
@@ -237,14 +712,28 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
         main_memory = Path(workspace_dir) / "MEMORY.md"
     
     if not main_memory.exists():
-        # Create empty file or with minimal structure (no obvious "Memory" header)
-        # Following clawdbot's approach: memories should blend naturally into context
         main_memory.write_text("")
+
+
+def ensure_daily_memory_file(workspace_dir: Path, user_id: Optional[str] = None) -> Path:
+    """
+    Ensure today's daily memory file exists, creating it only when actually needed.
+    Called lazily before first write to daily memory.
+    
+    Args:
+        workspace_dir: Workspace directory
+        user_id: Optional user ID for user-specific files
+        
+    Returns:
+        Path to today's memory file
+    """
+    memory_dir = workspace_dir / "memory"
+    memory_dir.mkdir(parents=True, exist_ok=True)
     
-    # Create today's memory file
     today = datetime.now().strftime("%Y-%m-%d")
     if user_id:
         user_dir = memory_dir / "users" / user_id
+        user_dir.mkdir(parents=True, exist_ok=True)
         today_memory = user_dir / f"{today}.md"
     else:
         today_memory = memory_dir / f"{today}.md"
@@ -252,5 +741,6 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
     if not today_memory.exists():
         today_memory.write_text(
             f"# Daily Memory: {today}\n\n"
-            f"Day-to-day notes and running context.\n\n"
         )
+    
+    return today_memory

agent/prompt/builder.py

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

agent/prompt/workspace.py

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

agent/protocol/__init__.py

@@ -3,6 +3,11 @@ from .agent_stream import AgentStreamExecutor
 from .task import Task, TaskType, TaskStatus
 from .result import AgentResult, AgentAction, AgentActionType, ToolResult
 from .models import LLMModel, LLMRequest, ModelFactory
+from .cancel import (
+    AgentCancelledError,
+    CancelTokenRegistry,
+    get_cancel_registry,
+)
 
 __all__ = [
     'Agent', 
@@ -16,5 +21,8 @@ __all__ = [
     'ToolResult',
     'LLMModel',
     'LLMRequest', 
-    'ModelFactory'
-]
+    'ModelFactory',
+    'AgentCancelledError',
+    'CancelTokenRegistry',
+    'get_cancel_registry',
+]

agent/protocol/agent.py

@@ -100,98 +100,31 @@ class Agent:
     
     def get_full_system_prompt(self, skill_filter=None) -> str:
         """
-        Get the full system prompt including skills.
+        Build the complete system prompt from scratch every time.
 
-        Note: Skills are now built into the system prompt by PromptBuilder,
-        so we just return the base prompt directly. This method is kept for
-        backward compatibility.
-
-        :param skill_filter: Optional list of skill names to include (deprecated)
-        :return: Complete system prompt
-        """
-        prompt = self.system_prompt
-
-        # Rebuild tool list section to reflect current self.tools
-        prompt = self._rebuild_tool_list_section(prompt)
-
-        # If runtime_info contains dynamic time function, rebuild runtime section
-        if self.runtime_info and callable(self.runtime_info.get('_get_current_time')):
-            prompt = self._rebuild_runtime_section(prompt)
-
-        return prompt
-    
-    def _rebuild_runtime_section(self, prompt: str) -> str:
-        """
-        Rebuild runtime info section with current time.
-        
-        This method dynamically updates the runtime info section by calling
-        the _get_current_time function from runtime_info.
-        
-        :param prompt: Original system prompt
-        :return: Updated system prompt with current runtime info
+        Re-reads AGENT.md / USER.md / RULE.md from disk, refreshes skills,
+        tools, and runtime info so any change takes effect immediately.
+        Falls back to the cached self.system_prompt on error.
         """
         try:
-            # Get current time dynamically
-            time_info = self.runtime_info['_get_current_time']()
-            
-            # Build new runtime section
-            runtime_lines = [
-                "\n## 运行时信息\n",
-                "\n",
-                f"当前时间: {time_info['time']} {time_info['weekday']} ({time_info['timezone']})\n",
-                "\n"
-            ]
-            
-            # Add other runtime info
-            runtime_parts = []
-            if self.runtime_info.get("model"):
-                runtime_parts.append(f"模型={self.runtime_info['model']}")
-            if self.runtime_info.get("workspace"):
-                # Replace backslashes with forward slashes for Windows paths
-                workspace_path = str(self.runtime_info['workspace']).replace('\\', '/')
-                runtime_parts.append(f"工作空间={workspace_path}")
-            if self.runtime_info.get("channel") and self.runtime_info.get("channel") != "web":
-                runtime_parts.append(f"渠道={self.runtime_info['channel']}")
-            
-            if runtime_parts:
-                runtime_lines.append("运行时: " + " | ".join(runtime_parts) + "\n")
-                runtime_lines.append("\n")
-            
-            new_runtime_section = "".join(runtime_lines)
-            
-            # Find and replace the runtime section
-            import re
-            pattern = r'\n## 运行时信息\s*\n.*?(?=\n##|\Z)'
-            updated_prompt = re.sub(pattern, new_runtime_section.rstrip('\n'), prompt, flags=re.DOTALL)
-            
-            return updated_prompt
+            from agent.prompt import load_context_files, PromptBuilder
+
+            if self.skill_manager:
+                self.skill_manager.refresh_skills()
+
+            context_files = load_context_files(self.workspace_dir) if self.workspace_dir else None
+
+            builder = PromptBuilder(workspace_dir=self.workspace_dir or "", language="zh")
+            return builder.build(
+                tools=self.tools,
+                context_files=context_files,
+                skill_manager=self.skill_manager,
+                memory_manager=self.memory_manager,
+                runtime_info=self.runtime_info,
+            )
         except Exception as e:
-            logger.warning(f"Failed to rebuild runtime section: {e}")
-            return prompt
-
-    def _rebuild_tool_list_section(self, prompt: str) -> str:
-        """
-        Rebuild the tool list inside the '## 工具系统' section so that it
-        always reflects the current ``self.tools`` (handles dynamic add/remove
-        of conditional tools like web_search).
-        """
-        import re
-        from agent.prompt.builder import _build_tooling_section
-
-        try:
-            if not self.tools:
-                return prompt
-
-            new_lines = _build_tooling_section(self.tools, "zh")
-            new_section = "\n".join(new_lines).rstrip("\n")
-
-            # Replace existing tooling section
-            pattern = r'## 工具系统\s*\n.*?(?=\n## |\Z)'
-            updated = re.sub(pattern, new_section, prompt, count=1, flags=re.DOTALL)
-            return updated
-        except Exception as e:
-            logger.warning(f"Failed to rebuild tool list section: {e}")
-            return prompt
+            logger.warning(f"Failed to rebuild system prompt, using cached version: {e}")
+            return self.system_prompt
 
     def refresh_skills(self):
         """Refresh the loaded skills."""
@@ -432,7 +365,8 @@ class Agent:
 
         return action
 
-    def run_stream(self, user_message: str, on_event=None, clear_history: bool = False, skill_filter=None) -> str:
+    def run_stream(self, user_message: str, on_event=None, clear_history: bool = False,
+                   skill_filter=None, cancel_event=None) -> str:
         """
         Execute single agent task with streaming (based on tool-call)
 
@@ -441,6 +375,7 @@ class Agent:
         - Multi-turn reasoning based on tool-call
         - Event callbacks
         - Persistent conversation history across calls
+        - User-initiated cancellation via ``cancel_event``
 
         Args:
             user_message: User message
@@ -448,6 +383,11 @@ class Agent:
                      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
+            cancel_event: Optional threading.Event polled at agent checkpoints.
+                When set, the loop exits at the next safe point, injects a
+                "[Interrupted by user]" assistant note, and returns the
+                partial response. ``messages`` stays in a valid state
+                (tool_use/tool_result pairs preserved).
 
         Returns:
             Final response text
@@ -480,7 +420,7 @@ class Agent:
 
         # Get max_context_turns from config
         from config import conf
-        max_context_turns = conf().get("agent_max_context_turns", 30)
+        max_context_turns = conf().get("agent_max_context_turns", 20)
         
         # Create stream executor with copied message history
         executor = AgentStreamExecutor(
@@ -491,7 +431,8 @@ class Agent:
             max_turns=self.max_steps,
             on_event=on_event,
             messages=messages_copy,  # Pass copied message history
-            max_context_turns=max_context_turns
+            max_context_turns=max_context_turns,
+            cancel_event=cancel_event,
         )
 
         # Execute
@@ -507,11 +448,15 @@ class Agent:
                     logger.info("[Agent] Cleared Agent message history after executor recovery")
             raise
 
-        # Append only the NEW messages from this execution (thread-safe)
-        # This allows concurrent requests to both contribute to history
+        # Sync executor's messages back to agent (thread-safe).
+        # If the executor trimmed context, its message list is shorter than
+        # original_length, so we must replace rather than append.
         with self.messages_lock:
-            new_messages = executor.messages[original_length:]
-            self.messages.extend(new_messages)
+            self.messages = list(executor.messages)
+            # Track messages added in this run (user query + all assistant/tool messages)
+            # original_length may exceed executor.messages length after trimming
+            trim_adjusted_start = min(original_length, len(executor.messages))
+            self._last_run_new_messages = list(executor.messages[trim_adjusted_start:])
         
         # Store executor reference for agent_bridge to access files_to_send
         self.stream_executor = executor

agent/protocol/cancel.py

@@ -0,0 +1,121 @@
+"""
+Cancel token registry for aborting in-flight agent runs.
+
+A user cancel (web Cancel button, /cancel command) sets a threading.Event
+that the agent loop polls at safe checkpoints. Tokens are keyed by
+request_id (preferred) and tracked under session_id as a fallback. Entries
+are released after the run completes to keep the registry bounded.
+
+No project deps — importable from any layer without circular imports.
+"""
+
+from __future__ import annotations
+
+import threading
+from typing import Dict, Optional
+
+
+class AgentCancelledError(Exception):
+    """Raised inside the agent loop when a stop has been requested.
+
+    The agent stream executor catches this, injects a "[Interrupted]" note
+    into the message history (preserving tool_use/tool_result integrity)
+    and returns a partial response to the caller.
+    """
+
+
+class _CancelEntry:
+    __slots__ = ("event", "session_id")
+
+    def __init__(self, session_id: Optional[str]):
+        self.event = threading.Event()
+        self.session_id = session_id
+
+
+class CancelTokenRegistry:
+    """In-process registry mapping request_id -> cancel Event.
+
+    Thread-safe. Singleton via module-level ``_registry``.
+    """
+
+    def __init__(self):
+        self._lock = threading.Lock()
+        self._by_request: Dict[str, _CancelEntry] = {}
+        # session_id -> set of request_ids currently in flight (usually 1).
+        self._by_session: Dict[str, set] = {}
+
+    def register(self, request_id: str, session_id: Optional[str] = None) -> threading.Event:
+        """Create (or return existing) cancel event for a request.
+
+        Returns the threading.Event the caller should poll via ``is_set()``.
+        """
+        if not request_id:
+            return threading.Event()
+        with self._lock:
+            entry = self._by_request.get(request_id)
+            if entry is None:
+                entry = _CancelEntry(session_id)
+                self._by_request[request_id] = entry
+                if session_id:
+                    self._by_session.setdefault(session_id, set()).add(request_id)
+            return entry.event
+
+    def get_event(self, request_id: str) -> Optional[threading.Event]:
+        if not request_id:
+            return None
+        with self._lock:
+            entry = self._by_request.get(request_id)
+            return entry.event if entry else None
+
+    def cancel_request(self, request_id: str) -> bool:
+        """Trigger cancel for a specific request. Returns True when matched."""
+        if not request_id:
+            return False
+        with self._lock:
+            entry = self._by_request.get(request_id)
+        if entry is None:
+            return False
+        entry.event.set()
+        return True
+
+    def cancel_session(self, session_id: str) -> int:
+        """Trigger cancel for every in-flight request of a session.
+
+        Returns the number of requests cancelled (0 when nothing was running).
+        """
+        if not session_id:
+            return 0
+        with self._lock:
+            request_ids = list(self._by_session.get(session_id, ()))
+            entries = [self._by_request[r] for r in request_ids if r in self._by_request]
+        for entry in entries:
+            entry.event.set()
+        return len(entries)
+
+    def unregister(self, request_id: str) -> None:
+        """Remove an entry once the agent run is done. Safe to call twice."""
+        if not request_id:
+            return
+        with self._lock:
+            entry = self._by_request.pop(request_id, None)
+            if entry and entry.session_id:
+                bucket = self._by_session.get(entry.session_id)
+                if bucket is not None:
+                    bucket.discard(request_id)
+                    if not bucket:
+                        self._by_session.pop(entry.session_id, None)
+
+    def has_active(self, session_id: str) -> bool:
+        if not session_id:
+            return False
+        with self._lock:
+            bucket = self._by_session.get(session_id)
+            return bool(bucket)
+
+
+_registry = CancelTokenRegistry()
+
+
+def get_cancel_registry() -> CancelTokenRegistry:
+    """Module-level accessor for the singleton registry."""
+    return _registry

agent/protocol/message_utils.py

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

agent/skills/config.py

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

agent/skills/formatter.py

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

agent/skills/frontmatter.py

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

agent/skills/loader.py

@@ -53,6 +53,12 @@ class SkillLoader:
         """
         Recursively load skills from a directory.
         
+        If a subdirectory contains its own SKILL.md, it is treated as a
+        self-contained skill (or skill-collection) and its children are
+        NOT scanned further. This prevents sub-skills inside a collection
+        (e.g. style-collection/style-anjing) from being listed as
+        independent top-level skills.
+        
         :param dir_path: Directory to scan
         :param source: Source identifier
         :param include_root_files: Whether to include root-level .md files
@@ -66,38 +72,41 @@ class SkillLoader:
         except Exception as e:
             diagnostics.append(f"Failed to list directory {dir_path}: {e}")
             return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
+
+        # If this directory has its own SKILL.md, load it and stop recursing.
+        # The sub-directories are internal resources of this skill.
+        if not include_root_files and 'SKILL.md' in entries:
+            skill_md_path = os.path.join(dir_path, 'SKILL.md')
+            if os.path.isfile(skill_md_path):
+                skill_result = self._load_skill_from_file(skill_md_path, source)
+                if skill_result.skills:
+                    skills.extend(skill_result.skills)
+                diagnostics.extend(skill_result.diagnostics)
+                return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
         
         for entry in entries:
-            # Skip hidden files and directories
             if entry.startswith('.'):
                 continue
             
-            # Skip common non-skill directories
             if entry in ('node_modules', '__pycache__', 'venv', '.git'):
                 continue
             
             full_path = os.path.join(dir_path, entry)
             
-            # Handle directories
             if os.path.isdir(full_path):
-                # Recursively scan subdirectories
                 sub_result = self._load_skills_recursive(full_path, source, include_root_files=False)
                 skills.extend(sub_result.skills)
                 diagnostics.extend(sub_result.diagnostics)
                 continue
             
-            # Handle files
             if not os.path.isfile(full_path):
                 continue
             
-            # Check if this is a skill file
-            is_root_md = include_root_files and entry.endswith('.md')
-            is_skill_md = not include_root_files and entry == 'SKILL.md'
+            is_root_md = include_root_files and entry.endswith('.md') and entry.upper() != 'README.MD'
             
-            if not (is_root_md or is_skill_md):
+            if not is_root_md:
                 continue
             
-            # Load the skill
             skill_result = self._load_skill_from_file(full_path, source)
             if skill_result.skills:
                 skills.extend(skill_result.skills)
@@ -184,7 +193,6 @@ class SkillLoader:
         
         config_path = os.path.join(skill_dir, "config.json")
         
-        # Without config.json, skip this skill entirely (return empty to trigger exclusion)
         if not os.path.exists(config_path):
             logger.debug(f"[SkillLoader] linkai-agent skipped: no config.json found")
             return ""

agent/skills/manager.py

@@ -84,10 +84,10 @@ class SkillManager:
         """
         Merge directory-scanned skills with the persisted config file.
 
-        - New skills discovered on disk are added with enabled=True.
+        - New skills: use metadata.default_enabled as initial enabled state.
+        - Existing skills: preserve their persisted enabled state.
         - Skills that no longer exist on disk are removed.
-        - Existing entries preserve their enabled state; name/description/source
-          are refreshed from the latest scan.
+        - name/description/source are always refreshed from the latest scan.
         """
         saved = self._load_skills_config()
         merged: Dict[str, dict] = {}
@@ -95,12 +95,24 @@ class SkillManager:
         for name, entry in self.skills.items():
             skill = entry.skill
             prev = saved.get(name, {})
-            merged[name] = {
+            category = prev.get("category", "skill")
+
+            if name in saved:
+                enabled = prev.get("enabled", True)
+            else:
+                enabled = entry.metadata.default_enabled if entry.metadata else True
+
+            entry_dict = {
                 "name": name,
                 "description": skill.description,
-                "source": skill.source,
-                "enabled": prev.get("enabled", True),
+                "source": prev.get("source") or skill.source,
+                "enabled": enabled,
+                "category": category,
             }
+            display_name = prev.get("display_name")
+            if display_name:
+                entry_dict["display_name"] = display_name
+            merged[name] = entry_dict
 
         self.skills_config = merged
         self._save_skills_config()
@@ -154,69 +166,118 @@ class SkillManager:
         """
         return list(self.skills.values())
     
+    @staticmethod
+    def _normalize_skill_filter(skill_filter: Optional[List[str]]) -> Optional[List[str]]:
+        """Normalize a skill_filter list into a flat list of stripped names."""
+        if skill_filter is None:
+            return None
+        normalized = []
+        for item in skill_filter:
+            if isinstance(item, str):
+                name = item.strip()
+                if name:
+                    normalized.append(name)
+            elif isinstance(item, list):
+                for subitem in item:
+                    if isinstance(subitem, str):
+                        name = subitem.strip()
+                        if name:
+                            normalized.append(name)
+        return normalized or None
+
     def filter_skills(
         self,
         skill_filter: Optional[List[str]] = None,
         include_disabled: bool = False,
     ) -> List[SkillEntry]:
         """
-        Filter skills based on criteria.
-
-        Simple rule: Skills are auto-enabled if requirements are met.
-        - Has required API keys -> included
-        - Missing API keys -> excluded
+        Filter skills that are eligible (enabled + requirements met).
 
         :param skill_filter: List of skill names to include (None = all)
         :param include_disabled: Whether to include disabled skills
-        :return: Filtered list of skill entries
+        :return: Filtered list of eligible skill entries
         """
         from agent.skills.config import should_include_skill
 
         entries = list(self.skills.values())
 
-        # Check requirements (platform, binaries, env vars)
         entries = [e for e in entries if should_include_skill(e, self.config)]
 
-        # Apply skill filter
-        if skill_filter is not None:
-            normalized = []
-            for item in skill_filter:
-                if isinstance(item, str):
-                    name = item.strip()
-                    if name:
-                        normalized.append(name)
-                elif isinstance(item, list):
-                    for subitem in item:
-                        if isinstance(subitem, str):
-                            name = subitem.strip()
-                            if name:
-                                normalized.append(name)
-            if normalized:
-                entries = [e for e in entries if e.skill.name in normalized]
+        normalized = self._normalize_skill_filter(skill_filter)
+        if normalized is not None:
+            entries = [e for e in entries if e.skill.name in normalized]
 
-        # Filter out disabled skills based on skills_config.json
         if not include_disabled:
             entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
 
+        from config import conf
+        if not conf().get("knowledge", True):
+            entries = [e for e in entries if e.skill.name != "knowledge-wiki"]
+
         return entries
-    
+
+    def filter_unavailable_skills(
+        self,
+        skill_filter: Optional[List[str]] = None,
+    ) -> tuple:
+        """
+        Find skills that are enabled but have unmet requirements.
+
+        :param skill_filter: Optional list of skill names to include
+        :return: Tuple of (entries, missing_map) where missing_map maps
+                 skill name to its missing requirements dict
+        """
+        from agent.skills.config import should_include_skill, get_missing_requirements
+
+        entries = list(self.skills.values())
+
+        # Only enabled skills
+        entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
+
+        normalized = self._normalize_skill_filter(skill_filter)
+        if normalized is not None:
+            entries = [e for e in entries if e.skill.name in normalized]
+
+        # Keep only those that fail should_include_skill (requirements not met)
+        unavailable = []
+        missing_map: Dict[str, dict] = {}
+        for e in entries:
+            if not should_include_skill(e, self.config):
+                missing = get_missing_requirements(e)
+                if missing:
+                    unavailable.append(e)
+                    missing_map[e.skill.name] = missing
+
+        return unavailable, missing_map
+
     def build_skills_prompt(
         self,
         skill_filter: Optional[List[str]] = None,
     ) -> str:
         """
-        Build a formatted prompt containing available skills.
-        
+        Build a formatted prompt containing available skills
+        and brief hints for unavailable ones.
+
         :param skill_filter: Optional list of skill names to include
         :return: Formatted skills prompt
         """
         from common.log import logger
-        entries = self.filter_skills(skill_filter=skill_filter, include_disabled=False)
-        logger.debug(f"[SkillManager] Filtered {len(entries)} skills for prompt (total: {len(self.skills)})")
-        if entries:
-            skill_names = [e.skill.name for e in entries]
-            logger.debug(f"[SkillManager] Skills to include: {skill_names}")
-        result = format_skill_entries_for_prompt(entries)
+        from agent.skills.formatter import format_unavailable_skills_for_prompt
+
+        eligible = self.filter_skills(skill_filter=skill_filter, include_disabled=False)
+        logger.debug(f"[SkillManager] Eligible: {len(eligible)} skills (total: {len(self.skills)})")
+        if eligible:
+            skill_names = [e.skill.name for e in eligible]
+            logger.debug(f"[SkillManager] Eligible skills: {skill_names}")
+
+        result = format_skill_entries_for_prompt(eligible)
+
+        unavailable, missing_map = self.filter_unavailable_skills(skill_filter=skill_filter)
+        if unavailable:
+            unavailable_names = [e.skill.name for e in unavailable]
+            logger.debug(f"[SkillManager] Unavailable skills (setup needed): {unavailable_names}")
+            result += format_unavailable_skills_for_prompt(unavailable, missing_map)
+
         logger.debug(f"[SkillManager] Generated prompt length: {len(result)}")
         return result
     

agent/skills/service.py

@@ -8,6 +8,8 @@ other management entry point.
 
 import os
 import shutil
+import zipfile
+import tempfile
 from typing import Dict, List, Optional
 from common.log import logger
 from agent.skills.types import Skill, SkillEntry
@@ -55,7 +57,9 @@ class SkillService:
         """
         Add (install) a skill from a remote payload.
 
-        The payload follows the socket protocol::
+        Supported payload types:
+
+        1. ``type: "url"`` – download individual files::
 
             {
                 "name": "web_search",
@@ -67,8 +71,15 @@ class SkillService:
                 ]
             }
 
-        Files are downloaded and saved under the custom skills directory
-        using *name* as the sub-directory.
+        2. ``type: "package"`` – download a zip archive and extract::
+
+            {
+                "name": "plugin-custom-tool",
+                "type": "package",
+                "category": "skills",
+                "enabled": true,
+                "files": [{"url": "https://cdn.example.com/skills/custom-tool.zip"}]
+            }
 
         :param payload: skill add payload from server
         """
@@ -76,25 +87,95 @@ class SkillService:
         if not name:
             raise ValueError("skill name is required")
 
+        payload_type = payload.get("type", "url")
+
+        if payload_type == "package":
+            self._add_package(name, payload)
+        else:
+            self._add_url(name, payload)
+
+        self.manager.refresh_skills()
+
+        category = payload.get("category")
+        if category and name in self.manager.skills_config:
+            self.manager.skills_config[name]["category"] = category
+            self.manager._save_skills_config()
+
+    def _add_url(self, name: str, payload: dict) -> None:
+        """Install a skill by downloading individual files."""
         files = payload.get("files", [])
         if not files:
             raise ValueError("skill files list is empty")
 
         skill_dir = os.path.join(self.manager.custom_dir, name)
-        os.makedirs(skill_dir, exist_ok=True)
 
-        for file_info in files:
-            url = file_info.get("url")
-            rel_path = file_info.get("path")
-            if not url or not rel_path:
-                logger.warning(f"[SkillService] add: skip invalid file entry {file_info}")
-                continue
-            dest = os.path.join(skill_dir, rel_path)
-            self._download_file(url, dest)
+        tmp_dir = skill_dir + ".tmp"
+        if os.path.exists(tmp_dir):
+            shutil.rmtree(tmp_dir)
+        os.makedirs(tmp_dir, exist_ok=True)
 
-        # Reload to pick up the new skill and sync config
-        self.manager.refresh_skills()
-        logger.info(f"[SkillService] add: skill '{name}' installed ({len(files)} files)")
+        try:
+            for file_info in files:
+                url = file_info.get("url")
+                rel_path = file_info.get("path")
+                if not url or not rel_path:
+                    logger.warning(f"[SkillService] add: skip invalid file entry {file_info}")
+                    continue
+                dest = os.path.join(tmp_dir, rel_path)
+                self._download_file(url, dest)
+        except Exception:
+            shutil.rmtree(tmp_dir, ignore_errors=True)
+            raise
+
+        if os.path.exists(skill_dir):
+            shutil.rmtree(skill_dir)
+        os.rename(tmp_dir, skill_dir)
+
+        logger.info(f"[SkillService] add: skill '{name}' installed via url ({len(files)} files)")
+
+    def _add_package(self, name: str, payload: dict) -> None:
+        """
+        Install a skill by downloading a zip archive and extracting it.
+
+        If the archive contains a single top-level directory, that directory
+        is used as the skill folder directly; otherwise a new directory named
+        after the skill is created to hold the extracted contents.
+        """
+        files = payload.get("files", [])
+        if not files or not files[0].get("url"):
+            raise ValueError("package url is required")
+
+        url = files[0]["url"]
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            zip_path = os.path.join(tmp_dir, "package.zip")
+            self._download_file(url, zip_path)
+
+            if not zipfile.is_zipfile(zip_path):
+                raise ValueError(f"downloaded file is not a valid zip archive: {url}")
+
+            extract_dir = os.path.join(tmp_dir, "extracted")
+            with zipfile.ZipFile(zip_path, "r") as zf:
+                zf.extractall(extract_dir)
+
+            # Determine the actual content root.
+            # If the zip has a single top-level directory, use its contents
+            # so the skill folder is clean (no extra nesting).
+            top_items = [
+                item for item in os.listdir(extract_dir)
+                if not item.startswith(".")
+            ]
+            if len(top_items) == 1:
+                single = os.path.join(extract_dir, top_items[0])
+                if os.path.isdir(single):
+                    extract_dir = single
+
+            if os.path.exists(skill_dir):
+                shutil.rmtree(skill_dir)
+            shutil.copytree(extract_dir, skill_dir)
+
+        logger.info(f"[SkillService] add: skill '{name}' installed via package ({url})")
 
     # ------------------------------------------------------------------
     # open / close (enable / disable)

agent/skills/types.py

@@ -29,6 +29,7 @@ class SkillInstallSpec:
 class SkillMetadata:
     """Metadata for a skill from frontmatter."""
     always: bool = False  # Always include this skill
+    default_enabled: bool = True  # Initial enabled state when first discovered
     skill_key: Optional[str] = None  # Override skill key
     primary_env: Optional[str] = None  # Primary environment variable
     emoji: Optional[str] = None

agent/tools/__init__.py

@@ -55,6 +55,24 @@ def _import_optional_tools():
     except Exception as e:
         logger.error(f"[Tools] WebSearch failed to load: {e}")
 
+    # WebFetch Tool
+    try:
+        from agent.tools.web_fetch.web_fetch import WebFetch
+        tools['WebFetch'] = WebFetch
+    except ImportError as e:
+        logger.error(f"[Tools] WebFetch not loaded - missing dependency: {e}")
+    except Exception as e:
+        logger.error(f"[Tools] WebFetch failed to load: {e}")
+
+    # Vision Tool (conditionally loaded based on API key availability)
+    try:
+        from agent.tools.vision.vision import Vision
+        tools['Vision'] = Vision
+    except ImportError as e:
+        logger.error(f"[Tools] Vision not loaded - missing dependency: {e}")
+    except Exception as e:
+        logger.error(f"[Tools] Vision failed to load: {e}")
+
     return tools
 
 # Load optional tools
@@ -62,30 +80,48 @@ _optional_tools = _import_optional_tools()
 EnvConfig = _optional_tools.get('EnvConfig')
 SchedulerTool = _optional_tools.get('SchedulerTool')
 WebSearch = _optional_tools.get('WebSearch')
+WebFetch = _optional_tools.get('WebFetch')
+Vision = _optional_tools.get('Vision')
 GoogleSearch = _optional_tools.get('GoogleSearch')
 FileSave = _optional_tools.get('FileSave')
 Terminal = _optional_tools.get('Terminal')
 
 
-# Delayed import for BrowserTool
+# BrowserTool (requires playwright)
 def _import_browser_tool():
+    from common.log import logger
     try:
         from agent.tools.browser.browser_tool import BrowserTool
         return BrowserTool
-    except ImportError:
-        # Return a placeholder class that will prompt the user to install dependencies when instantiated
-        class BrowserToolPlaceholder:
-            def __init__(self, *args, **kwargs):
-                raise ImportError(
-                    "The 'browser-use' package is required to use BrowserTool. "
-                    "Please install it with 'pip install browser-use>=0.1.40'."
-                )
+    except ImportError as e:
+        logger.info(
+            f"[Tools] BrowserTool not loaded - missing dependency: {e}\n"
+            f"  To enable browser tool, run:\n"
+            f"    pip install playwright\n"
+            f"    playwright install chromium"
+        )
+        return None
+    except Exception as e:
+        logger.error(f"[Tools] BrowserTool failed to load: {e}")
+        return None
 
-        return BrowserToolPlaceholder
+BrowserTool = _import_browser_tool()
 
+# MCP Tools (no extra dependencies, loaded on demand)
+def _import_mcp_tools():
+    """导入 MCP 工具模块（无额外依赖，按需加载）"""
+    from common.log import logger
+    try:
+        from agent.tools.mcp.mcp_tool import McpTool
+        from agent.tools.mcp.mcp_client import McpClientRegistry
+        return {'McpTool': McpTool, 'McpClientRegistry': McpClientRegistry}
+    except Exception as e:
+        logger.warning(f"[Tools] MCP tools not loaded: {e}")
+        return {}
 
-# Dynamically set BrowserTool
-# BrowserTool = _import_browser_tool()
+_mcp_tools = _import_mcp_tools()
+McpTool = _mcp_tools.get('McpTool')
+McpClientRegistry = _mcp_tools.get('McpClientRegistry')
 
 # Export all tools (including optional ones that might be None)
 __all__ = [
@@ -102,8 +138,10 @@ __all__ = [
     'EnvConfig',
     'SchedulerTool',
     'WebSearch',
-    # Optional tools (may be None if dependencies not available)
-    # 'BrowserTool'
+    'WebFetch',
+    'Vision',
+    'BrowserTool',
+    'McpTool',
 ]
 
 """

agent/tools/bash/bash.py

@@ -3,6 +3,7 @@ Bash tool - Execute bash commands
 """
 
 import os
+import re
 import sys
 import subprocess
 import tempfile
@@ -17,14 +18,18 @@ from common.utils import expand_path
 class Bash(BaseTool):
     """Tool for executing bash commands"""
 
+    _IS_WIN = sys.platform == "win32"
+
     name: str = "bash"
     description: str = f"""Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last {DEFAULT_MAX_LINES} lines or {DEFAULT_MAX_BYTES // 1024}KB (whichever is hit first). If truncated, full output is saved to a temp file.
-
+{'''
+PLATFORM: Windows (cmd.exe). Do NOT use Unix-only commands like grep, head, tail, sed, awk.
+''' if _IS_WIN else ''}
 ENVIRONMENT: All API keys from env_config are auto-injected. Use $VAR_NAME directly.
 
 SAFETY:
 - Freely create/modify/delete files within the workspace
-- For destructive and out-of-workspace commands, explain and confirm first"""
+- For destructive commands out of workspace, explain and confirm first"""
 
     params: dict = {
         "type": "object",
@@ -83,12 +88,13 @@ SAFETY:
             
             # Load environment variables from ~/.cow/.env if it exists
             env_file = expand_path("~/.cow/.env")
+            dotenv_vars = {}
             if os.path.exists(env_file):
                 try:
                     from dotenv import dotenv_values
-                    env_vars = dotenv_values(env_file)
-                    env.update(env_vars)
-                    logger.debug(f"[Bash] Loaded {len(env_vars)} variables from {env_file}")
+                    dotenv_vars = dotenv_values(env_file)
+                    env.update(dotenv_vars)
+                    logger.debug(f"[Bash] Loaded {len(dotenv_vars)} variables from {env_file}")
                 except ImportError:
                     logger.debug("[Bash] python-dotenv not installed, skipping .env loading")
                 except Exception as e:
@@ -100,7 +106,13 @@ SAFETY:
             else:
                 logger.debug(f"[Bash] Process User: {os.environ.get('USERNAME', os.environ.get('USER', 'unknown'))}")
             
-            # Execute command with inherited environment variables
+            # On Windows, convert $VAR references to %VAR% for cmd.exe
+            if self._IS_WIN:
+                env["PYTHONIOENCODING"] = "utf-8"
+                command = self._convert_env_vars_for_windows(command, dotenv_vars)
+                if command and not command.strip().lower().startswith("chcp"):
+                    command = f"chcp 65001 >nul 2>&1 && {command}"
+
             result = subprocess.run(
                 command,
                 shell=True,
@@ -108,8 +120,10 @@ SAFETY:
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
                 text=True,
+                encoding="utf-8",
+                errors="replace",
                 timeout=timeout,
-                env=env
+                env=env,
             )
             
             logger.debug(f"[Bash] Exit code: {result.returncode}")
@@ -131,6 +145,8 @@ SAFETY:
                             stdout=subprocess.PIPE,
                             stderr=subprocess.PIPE,
                             text=True,
+                            encoding="utf-8",
+                            errors="replace",
                             timeout=timeout,
                             env=env
                         )
@@ -153,10 +169,16 @@ SAFETY:
                 except Exception as retry_err:
                     logger.warning(f"[Bash] Retry failed: {retry_err}")
 
-            # Combine stdout and stderr
-            output = result.stdout
-            if result.stderr:
-                output += "\n" + result.stderr
+            # When command succeeds with stdout, keep output clean (stderr goes to server log only).
+            # When command fails or stdout is empty, include stderr so the agent can diagnose.
+            if result.returncode == 0 and result.stdout.strip():
+                output = result.stdout
+                if result.stderr:
+                    logger.info(f"[Bash] stderr (not forwarded): {result.stderr[:500]}")
+            else:
+                output = result.stdout
+                if result.stderr:
+                    output += "\n" + result.stderr
 
             # Check if we need to save full output to temp file
             temp_file_path = None
@@ -216,45 +238,58 @@ SAFETY:
 
     def _get_safety_warning(self, command: str) -> str:
         """
-        Get safety warning for potentially dangerous commands
-        Only warns about extremely dangerous system-level operations
-        
+        Get safety warning for absolutely catastrophic commands only.
+        Keep the blocklist minimal so the agent retains maximum freedom.
+
         :param command: Command to check
         :return: Warning message if dangerous, empty string if safe
         """
-        cmd_lower = command.lower().strip()
+        # Tokenize to avoid substring false positives (e.g. `rm -rf /tmp/x`
+        # must not match `rm -rf /`).
+        tokens = command.lower().split()
 
-        # 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"),
+        # `rm -rf /` or `rm -rf /*` targeting the real root.
+        for i, tok in enumerate(tokens):
+            if tok != "rm":
+                continue
+            has_rf = False
+            for j in range(i + 1, len(tokens)):
+                t = tokens[j]
+                if t.startswith("-") and "r" in t and "f" in t:
+                    has_rf = True
+                elif t in ("--recursive", "--force"):
+                    continue
+                elif t in ("/", "/*"):
+                    if has_rf:
+                        return "This command will delete the entire filesystem"
+                    break
+                else:
+                    break
 
-            # 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"),
+        # Disk wiping
+        if "if=/dev/zero" in command.lower() and "dd " in command.lower():
+            return "This command can destroy disk data"
 
-            # 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"),
-        ]
+        # Power control - match only as a standalone word (\b enforces word boundary)
+        if re.search(r'\b(shutdown|reboot|halt|poweroff)\b', command.lower()):
+            return "This command will shut down or restart the system"
 
-        for pattern, warning in dangerous_patterns:
-            if pattern in cmd_lower:
-                return warning
+        return ""
 
-        # 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"
+    @staticmethod
+    def _convert_env_vars_for_windows(command: str, dotenv_vars: dict) -> str:
+        """
+        Convert bash-style $VAR / ${VAR} references to cmd.exe %VAR% syntax.
+        Only converts variables loaded from .env (user-configured API keys etc.)
+        to avoid breaking $PATH, jq expressions, regex, etc.
+        """
+        if not dotenv_vars:
+            return command
 
-        return ""  # No warning needed
+        def replace_match(m):
+            var_name = m.group(1) or m.group(2)
+            if var_name in dotenv_vars:
+                return f"%{var_name}%"
+            return m.group(0)
+
+        return re.sub(r'\$\{(\w+)\}|\$(\w+)', replace_match, command)

agent/tools/browser/__init__.py

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

agent/tools/browser/browser_service.py

@@ -0,0 +1,947 @@
+"""
+Browser service - Playwright wrapper managing browser lifecycle and page operations.
+
+All Playwright calls run on a dedicated background thread so that callers from
+any worker thread can safely use the service.  An idle-timeout mechanism
+automatically shuts down the browser (and its thread) after a configurable
+period of inactivity to free resources.
+"""
+
+import os
+import sys
+import uuid
+import queue
+import threading
+from typing import Optional, Dict, Any, List, Callable
+
+from common.log import logger
+from common.utils import expand_path
+
+
+_DEFAULT_USER_DATA_DIR = "~/.cow/browser_profile"
+
+try:
+    from playwright.sync_api import sync_playwright, Browser, BrowserContext, Page, Playwright
+    _HAS_PLAYWRIGHT = True
+except ImportError:
+    _HAS_PLAYWRIGHT = False
+
+
+# ---------------------------------------------------------------------------
+# Snapshot DOM helpers
+# ---------------------------------------------------------------------------
+
+# Tags that typically carry useful content for an agent
+_INTERACTIVE_TAGS = {
+    "a", "button", "input", "textarea", "select", "option",
+    "label", "details", "summary",
+}
+_SEMANTIC_TAGS = {
+    "h1", "h2", "h3", "h4", "h5", "h6",
+    "p", "li", "td", "th", "caption", "figcaption", "blockquote", "pre", "code",
+    "nav", "main", "article", "section", "header", "footer", "form", "table",
+    "img", "video", "audio",
+}
+_KEEP_TAGS = _INTERACTIVE_TAGS | _SEMANTIC_TAGS
+
+_SNAPSHOT_JS = """
+() => {
+    const KEEP = new Set(%s);
+    const INTERACTIVE = new Set(%s);
+    const SKIP = new Set(["script","style","noscript","svg","path","meta","link","br","hr"]);
+    const CLICKABLE_ROLES = new Set([
+        "button","link","tab","menuitem","menuitemcheckbox","menuitemradio",
+        "option","switch","checkbox","radio","combobox","searchbox","slider",
+        "spinbutton","textbox","treeitem"
+    ]);
+    let refCounter = 0;
+    const refMap = {};
+
+    function visible(el) {
+        if (!(el instanceof HTMLElement)) return true;
+        const st = window.getComputedStyle(el);
+        if (st.display === "none" || st.visibility === "hidden") return false;
+        if (parseFloat(st.opacity) === 0) return false;
+        return true;
+    }
+
+    // Strong signals: these attributes alone are enough to mark as interactive
+    function hasStrongInteractiveSignal(el) {
+        const role = el.getAttribute("role");
+        if (role && CLICKABLE_ROLES.has(role)) return true;
+        if (el.hasAttribute("onclick") || el.hasAttribute("tabindex")) return true;
+        if (el.hasAttribute("data-click") || el.hasAttribute("data-action")) return true;
+        if (el.getAttribute("contenteditable") === "true") return true;
+        return false;
+    }
+
+    // Check if cursor:pointer is set directly (not just inherited from parent)
+    function hasOwnPointerCursor(el) {
+        try {
+            const st = window.getComputedStyle(el);
+            if (st.cursor !== "pointer") return false;
+            const parent = el.parentElement;
+            if (parent) {
+                const pst = window.getComputedStyle(parent);
+                if (pst.cursor === "pointer") return false;
+            }
+            return true;
+        } catch(e) {}
+        return false;
+    }
+
+    function hasTextOrContent(el) {
+        const t = el.textContent || "";
+        if (t.trim().length > 0) return true;
+        if (el.querySelector("img,video,audio,canvas")) return true;
+        const ariaLabel = el.getAttribute("aria-label");
+        if (ariaLabel && ariaLabel.trim()) return true;
+        const title = el.getAttribute("title");
+        if (title && title.trim()) return true;
+        return false;
+    }
+
+    function isImplicitInteractive(el) {
+        if (hasStrongInteractiveSignal(el)) return true;
+        if (hasOwnPointerCursor(el) && hasTextOrContent(el)) return true;
+        return false;
+    }
+
+    function getTextContent(el) {
+        let text = "";
+        for (const ch of el.childNodes) {
+            if (ch.nodeType === Node.TEXT_NODE) {
+                text += ch.textContent;
+            }
+        }
+        return text.trim();
+    }
+
+    function walk(node) {
+        if (node.nodeType === Node.TEXT_NODE) {
+            const t = node.textContent.trim();
+            return t ? t : null;
+        }
+        if (node.nodeType !== Node.ELEMENT_NODE) return null;
+        const tag = node.tagName.toLowerCase();
+        if (SKIP.has(tag)) return null;
+        if (!visible(node)) return null;
+
+        const children = [];
+        for (const ch of node.childNodes) {
+            const r = walk(ch);
+            if (r !== null) {
+                if (typeof r === "string") children.push(r);
+                else children.push(r);
+            }
+        }
+
+        const nativeInteractive = INTERACTIVE.has(tag);
+        const implicitInteractive = !nativeInteractive && (node instanceof HTMLElement) && isImplicitInteractive(node);
+        const keep = KEEP.has(tag) || implicitInteractive;
+
+        if (!keep) {
+            if (children.length === 0) return null;
+            if (children.length === 1) return children[0];
+            return children;
+        }
+
+        const obj = { tag };
+        if (nativeInteractive || implicitInteractive) {
+            refCounter++;
+            obj.ref = refCounter;
+            refMap[refCounter] = node;
+        }
+
+        if (implicitInteractive) {
+            const role = node.getAttribute("role");
+            if (role) obj.role = role;
+            const directText = getTextContent(node);
+            if (!directText && children.length === 0) {
+                const ariaLabel = node.getAttribute("aria-label");
+                const title = node.getAttribute("title");
+                if (ariaLabel) obj.ariaLabel = ariaLabel;
+                else if (title) obj.ariaLabel = title;
+            }
+        }
+
+        // Attributes
+        if (tag === "a" && node.href) obj.href = node.getAttribute("href");
+        if (tag === "img") {
+            obj.alt = node.alt || "";
+            obj.src = node.getAttribute("src") || "";
+        }
+        if (tag === "input" || tag === "textarea" || tag === "select") {
+            obj.type = node.type || "text";
+            obj.name = node.name || undefined;
+            obj.value = node.value || undefined;
+            obj.placeholder = node.placeholder || undefined;
+            if (node.disabled) obj.disabled = true;
+            if (tag === "input" && node.type === "checkbox") obj.checked = node.checked;
+        }
+        if (tag === "button") {
+            if (node.disabled) obj.disabled = true;
+        }
+        if (tag === "option") {
+            obj.value = node.value;
+            if (node.selected) obj.selected = true;
+        }
+        if (tag === "label" && node.htmlFor) obj.for = node.htmlFor;
+
+        // Role / aria-label for native interactive & semantic elements
+        if (!implicitInteractive) {
+            const role = node.getAttribute("role");
+            if (role) obj.role = role;
+            const ariaLabel = node.getAttribute("aria-label");
+            if (ariaLabel) obj.ariaLabel = ariaLabel;
+        }
+
+        // Children
+        if (children.length === 1 && typeof children[0] === "string") {
+            obj.text = children[0];
+        } else if (children.length > 0) {
+            obj.children = children;
+        }
+
+        return obj;
+    }
+
+    const result = walk(document.body);
+    window.__cowRefMap = refMap;
+    return { tree: result, refCount: refCounter };
+}
+""" % (
+    str(list(_KEEP_TAGS)),
+    str(list(_INTERACTIVE_TAGS)),
+)
+
+
+_BROWSER_DEAD_HINTS = (
+    "has been closed",
+    "browser has disconnected",
+    "target closed",
+    "browser closed",
+    "context or browser has been closed",
+)
+
+
+def _is_browser_dead_error(err: Exception) -> bool:
+    """Return True if *err* indicates the browser / page died out from under us."""
+    msg = str(err).lower()
+    return any(h in msg for h in _BROWSER_DEAD_HINTS)
+
+
+def _should_use_headless() -> bool:
+    """Decide headless mode: headless on Linux servers without display, headed elsewhere."""
+    if sys.platform in ("win32", "darwin"):
+        return False
+    # Linux: check for display
+    if os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY"):
+        return False
+    return True
+
+
+def _flatten_tree(node, indent=0) -> List[str]:
+    """Convert snapshot tree to compact text lines for LLM consumption."""
+    if node is None:
+        return []
+    if isinstance(node, str):
+        return [" " * indent + node]
+    if isinstance(node, list):
+        lines = []
+        for child in node:
+            lines.extend(_flatten_tree(child, indent))
+        return lines
+    if not isinstance(node, dict):
+        return []
+
+    tag = node.get("tag", "?")
+    ref = node.get("ref")
+    parts = [tag]
+    if ref:
+        parts[0] = f"[{ref}] {tag}"
+
+    # Inline attributes
+    for attr in ("type", "name", "href", "alt", "role", "ariaLabel", "placeholder", "value"):
+        val = node.get(attr)
+        if val:
+            # Truncate long values
+            s = str(val)
+            if len(s) > 80:
+                s = s[:77] + "..."
+            parts.append(f'{attr}="{s}"')
+
+    for flag in ("disabled", "checked", "selected"):
+        if node.get(flag):
+            parts.append(flag)
+
+    prefix = " " * indent
+    header = prefix + " ".join(parts)
+
+    text = node.get("text")
+    if text:
+        # Truncate long text
+        if len(text) > 120:
+            text = text[:117] + "..."
+        header += f": {text}"
+
+    lines = [header]
+    children = node.get("children", [])
+    for child in children:
+        lines.extend(_flatten_tree(child, indent + 2))
+    return lines
+
+
+class BrowserService:
+    """Manages a Playwright browser on a dedicated background thread.
+
+    All Playwright operations are dispatched to a single long-lived thread via
+    a task queue.  Callers from *any* worker thread can use the public API
+    safely.  An idle timer automatically shuts the browser down after
+    ``idle_timeout`` seconds of inactivity (default 300 = 5 min).
+    """
+
+    _IDLE_TIMEOUT_DEFAULT = 300  # seconds
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        self._config = config or {}
+        self._headless: Optional[bool] = None
+        self._screenshot_dir: Optional[str] = None
+
+        # Background thread state
+        self._thread: Optional[threading.Thread] = None
+        self._task_queue: queue.Queue = queue.Queue()
+        self._lock = threading.Lock()
+        self._alive = False
+        self._ready = threading.Event()
+
+        # Playwright objects (only accessed on the background thread)
+        self._playwright = None
+        self._browser = None
+        self._context = None
+        self._page = None
+
+        # Launch mode: one of "fresh" | "persistent" | "cdp".
+        # - cdp: connect to an externally launched Chrome via CDP endpoint.
+        # - persistent: launch with launch_persistent_context using a user_data_dir
+        #   so cookies / login state survive across runs (default).
+        # - fresh: classic launch + new_context, clean state every run.
+        cdp_endpoint = self._config.get("cdp_endpoint") or ""
+        persistent_flag = self._config.get("persistent", True)
+        user_data_dir_cfg = self._config.get("user_data_dir")
+        if user_data_dir_cfg is None:
+            user_data_dir_cfg = _DEFAULT_USER_DATA_DIR
+
+        self._cdp_endpoint: str = cdp_endpoint.strip() if isinstance(cdp_endpoint, str) else ""
+        if self._cdp_endpoint:
+            self._launch_mode = "cdp"
+            self._user_data_dir: str = ""
+        elif persistent_flag and user_data_dir_cfg:
+            self._launch_mode = "persistent"
+            self._user_data_dir = expand_path(str(user_data_dir_cfg))
+        else:
+            self._launch_mode = "fresh"
+            self._user_data_dir = ""
+
+        # Idle auto-release
+        idle_cfg = self._config.get("idle_timeout")
+        self._idle_timeout: float = float(idle_cfg) if idle_cfg is not None else self._IDLE_TIMEOUT_DEFAULT
+        self._idle_timer: Optional[threading.Timer] = None
+
+        # Set when the browser / page is detected to have died externally
+        # (e.g. user manually closed the window). The next _submit() will then
+        # tear down the stale thread and relaunch.
+        self._needs_restart = False
+
+    # ------------------------------------------------------------------
+    # Background-thread lifecycle
+    # ------------------------------------------------------------------
+
+    def _start_thread(self):
+        """Start the dedicated Playwright thread if not already running."""
+        with self._lock:
+            if self._alive and self._thread and self._thread.is_alive():
+                return
+            # Wait for old thread to fully exit before creating a new one
+            old = self._thread
+            if old and old.is_alive():
+                old.join(timeout=5)
+            # Fresh queue to avoid stale sentinels from a previous close()
+            self._task_queue = queue.Queue()
+            self._alive = True
+            self._ready = threading.Event()
+            self._thread = threading.Thread(target=self._run_loop, daemon=True, name="BrowserThread")
+            self._thread.start()
+            # Block until browser is ready (or failed)
+            self._ready.wait(timeout=30)
+
+    def _run_loop(self):
+        """Event loop running on the dedicated thread. Processes tasks until stopped."""
+        logger.info("[Browser] Background thread started")
+        try:
+            self._launch_browser()
+        except Exception as e:
+            logger.error(f"[Browser] Failed to launch browser: {e}")
+            self._alive = False
+            self._ready.set()
+            self._drain_queue(RuntimeError(f"Browser launch failed: {e}"))
+            return
+        self._ready.set()
+
+        while self._alive:
+            try:
+                task = self._task_queue.get(timeout=1.0)
+            except queue.Empty:
+                continue
+            if task is None:
+                break
+            fn, args, kwargs, result_slot = task
+            try:
+                result_slot["value"] = fn(*args, **kwargs)
+            except Exception as e:
+                result_slot["error"] = e
+                if _is_browser_dead_error(e):
+                    self._needs_restart = True
+                    logger.warning(
+                        f"[Browser] Detected closed page/context ({e}); "
+                        "will relaunch on next request."
+                    )
+            finally:
+                result_slot["event"].set()
+
+        self._shutdown_browser()
+        self._drain_queue(RuntimeError("Browser thread stopped"))
+        logger.info("[Browser] Background thread exited")
+
+    def _drain_queue(self, error: Exception):
+        """Unblock all callers waiting on the queue with an error."""
+        while True:
+            try:
+                task = self._task_queue.get_nowait()
+            except queue.Empty:
+                break
+            if task is None:
+                continue
+            _, _, _, result_slot = task
+            result_slot["error"] = error
+            result_slot["event"].set()
+
+    def _launch_browser(self):
+        """Launch / connect Chromium on the background thread."""
+        if self._headless is None:
+            headless_cfg = self._config.get("headless")
+            self._headless = headless_cfg if headless_cfg is not None else _should_use_headless()
+
+        launch_args = ["--disable-dev-shm-usage"]
+        if self._headless:
+            launch_args.append("--no-sandbox")
+
+        extra_args = self._config.get("launch_args", [])
+        if extra_args:
+            launch_args.extend(extra_args)
+
+        viewport_w = self._config.get("viewport_width", 1280)
+        viewport_h = self._config.get("viewport_height", 720)
+        viewport = {"width": viewport_w, "height": viewport_h}
+        user_agent = (
+            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
+            "AppleWebKit/537.36 (KHTML, like Gecko) "
+            "Chrome/131.0.0.0 Safari/537.36"
+        )
+
+        self._playwright = sync_playwright().start()
+
+        if self._launch_mode == "cdp":
+            self._connect_cdp(viewport)
+        elif self._launch_mode == "persistent":
+            self._launch_persistent(launch_args, viewport, user_agent)
+        else:
+            self._launch_fresh(launch_args, viewport, user_agent)
+
+        logger.info("[Browser] Browser ready")
+
+    def _launch_fresh(self, launch_args: List[str], viewport: Dict[str, int], user_agent: str):
+        """Classic launch: brand new Chromium with an empty context."""
+        logger.info(f"[Browser] Launching Chromium (fresh, headless={self._headless})")
+        self._browser = self._playwright.chromium.launch(
+            headless=self._headless,
+            args=launch_args,
+        )
+        self._context = self._browser.new_context(
+            viewport=viewport,
+            user_agent=user_agent,
+        )
+        self._page = self._context.new_page()
+        self._wire_close_listeners()
+
+    def _launch_persistent(self, launch_args: List[str], viewport: Dict[str, int], user_agent: str):
+        """Launch Chromium with a persistent user_data_dir so login state survives."""
+        os.makedirs(self._user_data_dir, exist_ok=True)
+        logger.info(
+            f"[Browser] Launching Chromium (persistent, headless={self._headless}, "
+            f"profile={self._user_data_dir})"
+        )
+        try:
+            self._context = self._playwright.chromium.launch_persistent_context(
+                user_data_dir=self._user_data_dir,
+                headless=self._headless,
+                args=launch_args,
+                viewport=viewport,
+                user_agent=user_agent,
+            )
+        except Exception as e:
+            # Profile is locked when another Chromium instance already holds it.
+            msg = str(e).lower()
+            if "singletonlock" in msg or "profile" in msg or "lock" in msg:
+                raise RuntimeError(
+                    f"Browser profile '{self._user_data_dir}' is in use by another process. "
+                    "Close the other Chromium / cow instance, or set a different "
+                    "tools.browser.user_data_dir."
+                ) from e
+            raise
+
+        # Persistent context has no parent Browser handle; reuse the auto-created page.
+        self._browser = None
+        pages = self._context.pages
+        self._page = pages[0] if pages else self._context.new_page()
+        self._wire_close_listeners()
+
+    def _connect_cdp(self, viewport: Dict[str, int]):
+        """Attach to an existing Chrome started with --remote-debugging-port."""
+        endpoint = self._cdp_endpoint
+        logger.info(f"[Browser] Connecting to existing Chrome via CDP: {endpoint}")
+        try:
+            self._browser = self._playwright.chromium.connect_over_cdp(endpoint)
+        except Exception as e:
+            msg = str(e).lower()
+            if "econnrefused" in msg or "connect" in msg or "refused" in msg:
+                raise RuntimeError(
+                    f"Cannot reach Chrome at {endpoint}. The CDP browser is not "
+                    "running. Ask the user to launch Chrome with "
+                    "--remote-debugging-port and --user-data-dir, then retry. "
+                    "Do not retry this tool until the user confirms."
+                ) from e
+            raise
+
+        contexts = self._browser.contexts
+        if contexts:
+            self._context = contexts[0]
+        else:
+            self._context = self._browser.new_context(viewport=viewport)
+
+        pages = self._context.pages
+        self._page = pages[0] if pages else self._context.new_page()
+        self._wire_close_listeners()
+
+    def _wire_close_listeners(self):
+        """Mark needs_restart whenever the browser / context / page dies externally."""
+        def _on_dead(_obj=None):
+            self._needs_restart = True
+
+        try:
+            if self._browser:
+                self._browser.on("disconnected", _on_dead)
+            if self._context:
+                self._context.on("close", _on_dead)
+            if self._page:
+                self._page.on("close", _on_dead)
+        except Exception as e:
+            logger.debug(f"[Browser] Failed to wire close listeners: {e}")
+
+    def _shutdown_browser(self):
+        """Shut down Playwright resources on the background thread.
+
+        Mode-specific behavior:
+        - cdp: only disconnect the Playwright client; leave the user's Chrome
+          and its tabs untouched (do NOT close the context).
+        - persistent: close the persistent context (no separate browser handle).
+        - fresh: close context, then browser.
+        """
+        self._cancel_idle_timer()
+
+        if self._launch_mode == "cdp":
+            # For CDP, browser.close() only detaches the Playwright client;
+            # the user's Chrome process and its tabs stay alive.
+            try:
+                if self._browser:
+                    self._browser.close()
+            except Exception as e:
+                logger.debug(f"[Browser] cdp disconnect error: {e}")
+        else:
+            for obj, label in [
+                (self._context, "context"),
+                (self._browser, "browser"),
+            ]:
+                try:
+                    if obj:
+                        obj.close()
+                except Exception as e:
+                    logger.debug(f"[Browser] {label} close error: {e}")
+
+        try:
+            if self._playwright:
+                self._playwright.stop()
+        except Exception as e:
+            logger.debug(f"[Browser] playwright stop error: {e}")
+        self._page = None
+        self._context = None
+        self._browser = None
+        self._playwright = None
+        logger.info("[Browser] Browser closed")
+
+    def _submit(self, fn: Callable, *args, **kwargs):
+        """Submit *fn* to the background thread and block until it completes."""
+        # If the browser died externally (e.g. user closed the window), tear
+        # down the stale thread first so _start_thread() will relaunch fresh.
+        if self._needs_restart:
+            logger.info("[Browser] Restarting after detecting closed browser")
+            self.close()
+            self._needs_restart = False
+
+        self._start_thread()
+
+        if not self._alive:
+            raise RuntimeError("Browser is not available")
+
+        self._reset_idle_timer()
+
+        result_slot: Dict[str, Any] = {"event": threading.Event()}
+        self._task_queue.put((fn, args, kwargs, result_slot))
+
+        # Timeout prevents permanent hang if the background thread crashes
+        completed = result_slot["event"].wait(timeout=120)
+        if not completed:
+            raise TimeoutError("Browser operation timed out (120s)")
+
+        if "error" in result_slot:
+            raise result_slot["error"]
+        return result_slot.get("value")
+
+    # ------------------------------------------------------------------
+    # Idle auto-release
+    # ------------------------------------------------------------------
+
+    def _reset_idle_timer(self):
+        self._cancel_idle_timer()
+        if self._idle_timeout > 0:
+            self._idle_timer = threading.Timer(self._idle_timeout, self._on_idle_timeout)
+            self._idle_timer.daemon = True
+            self._idle_timer.start()
+
+    def _cancel_idle_timer(self):
+        if self._idle_timer:
+            self._idle_timer.cancel()
+            self._idle_timer = None
+
+    def _on_idle_timeout(self):
+        logger.info(f"[Browser] Idle for {self._idle_timeout}s, auto-releasing browser")
+        self.close()
+
+    # ------------------------------------------------------------------
+    # Public lifecycle
+    # ------------------------------------------------------------------
+
+    def close(self):
+        """Shut down browser and background thread (safe from any thread)."""
+        self._cancel_idle_timer()
+        with self._lock:
+            if not self._alive:
+                self._needs_restart = False
+                return
+            self._alive = False
+            t = self._thread
+        if self._task_queue is not None:
+            self._task_queue.put(None)
+        if t is not None and t.is_alive():
+            t.join(timeout=10)
+        with self._lock:
+            self._thread = None
+            self._needs_restart = False
+
+    # ------------------------------------------------------------------
+    # Actions  (each method is dispatched to the background thread)
+    # ------------------------------------------------------------------
+
+    def navigate(self, url: str, timeout: int = 30000) -> Dict[str, Any]:
+        return self._submit(self._do_navigate, url, timeout)
+
+    def _do_navigate(self, url: str, timeout: int) -> Dict[str, Any]:
+        page = self._page
+        try:
+            resp = page.goto(url, wait_until="domcontentloaded", timeout=timeout)
+            status = resp.status if resp else None
+        except Exception as e:
+            return {"error": f"Navigation failed: {e}"}
+
+        try:
+            page.wait_for_load_state("networkidle", timeout=8000)
+        except Exception:
+            pass
+        page.wait_for_timeout(500)
+
+        try:
+            title = page.title()
+        except Exception:
+            title = ""
+        try:
+            current_url = page.url
+        except Exception:
+            current_url = url
+
+        return {"url": current_url, "title": title, "status": status}
+
+    def snapshot(self, selector: Optional[str] = None) -> str:
+        return self._submit(self._do_snapshot, selector)
+
+    def _do_snapshot(self, selector: Optional[str] = None) -> str:
+        page = self._page
+        try:
+            result = page.evaluate(_SNAPSHOT_JS)
+        except Exception as e:
+            return f"[Snapshot error: {e}]"
+
+        tree = result.get("tree")
+        ref_count = result.get("refCount", 0)
+        lines = _flatten_tree(tree)
+
+        try:
+            title = page.title()
+        except Exception:
+            title = ""
+        try:
+            url = page.url
+        except Exception:
+            url = ""
+
+        header = f"Page: {title}  ({url})\nInteractive elements: {ref_count}\n---"
+        body = "\n".join(lines)
+
+        max_chars = self._config.get("snapshot_max_chars", 30000)
+        if len(body) > max_chars:
+            body = body[:max_chars] + "\n... [snapshot truncated]"
+
+        return f"{header}\n{body}"
+
+    def screenshot(self, full_page: bool = False, cwd: str = "") -> str:
+        return self._submit(self._do_screenshot, full_page, cwd)
+
+    def _do_screenshot(self, full_page: bool = False, cwd: str = "") -> str:
+        page = self._page
+        save_dir = self._get_screenshot_dir(cwd)
+        filename = f"screenshot_{uuid.uuid4().hex[:8]}.png"
+        filepath = os.path.join(save_dir, filename)
+        page.screenshot(path=filepath, full_page=full_page)
+        logger.info(f"[Browser] Screenshot saved: {filepath}")
+        return filepath
+
+    def click(self, ref: Optional[int] = None, selector: Optional[str] = None,
+              timeout: int = 5000) -> Dict[str, Any]:
+        return self._submit(self._do_click, ref, selector, timeout)
+
+    def _do_click(self, ref, selector, timeout) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if ref is not None:
+                result = page.evaluate(f"""
+                    () => {{
+                        const el = window.__cowRefMap && window.__cowRefMap[{ref}];
+                        if (!el) return {{ error: "ref {ref} not found. Run snapshot first." }};
+                        el.click();
+                        return {{ clicked: true, tag: el.tagName.toLowerCase() }};
+                    }}
+                """)
+                if result.get("error"):
+                    return result
+                page.wait_for_timeout(500)
+                return result
+            elif selector:
+                page.click(selector, timeout=timeout)
+                return {"clicked": True, "selector": selector}
+            else:
+                return {"error": "Provide either ref (from snapshot) or selector"}
+        except Exception as e:
+            return {"error": f"Click failed: {e}"}
+
+    def fill(self, text: str, ref: Optional[int] = None,
+             selector: Optional[str] = None, timeout: int = 5000) -> Dict[str, Any]:
+        return self._submit(self._do_fill, text, ref, selector, timeout)
+
+    def _do_fill(self, text, ref, selector, timeout) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if ref is not None:
+                result = page.evaluate(f"""
+                    () => {{
+                        const el = window.__cowRefMap && window.__cowRefMap[{ref}];
+                        if (!el) return {{ error: "ref {ref} not found. Run snapshot first." }};
+                        el.focus();
+                        el.value = "";
+                        return {{ tag: el.tagName.toLowerCase(), name: el.name || "" }};
+                    }}
+                """)
+                if result.get("error"):
+                    return result
+                page.keyboard.type(text)
+                return {"filled": True, "ref": ref, "text": text}
+            elif selector:
+                page.fill(selector, text, timeout=timeout)
+                return {"filled": True, "selector": selector, "text": text}
+            else:
+                return {"error": "Provide either ref (from snapshot) or selector"}
+        except Exception as e:
+            return {"error": f"Fill failed: {e}"}
+
+    def select(self, value: str, ref: Optional[int] = None,
+               selector: Optional[str] = None, timeout: int = 5000) -> Dict[str, Any]:
+        return self._submit(self._do_select, value, ref, selector, timeout)
+
+    def _do_select(self, value, ref, selector, timeout) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if ref is not None:
+                result = page.evaluate(f"""
+                    () => {{
+                        const el = window.__cowRefMap && window.__cowRefMap[{ref}];
+                        if (!el || el.tagName.toLowerCase() !== "select")
+                            return {{ error: "ref {ref} is not a <select> element" }};
+                        el.value = {repr(value)};
+                        el.dispatchEvent(new Event("change", {{ bubbles: true }}));
+                        return {{ selected: true, value: el.value }};
+                    }}
+                """)
+                return result
+            elif selector:
+                page.select_option(selector, value, timeout=timeout)
+                return {"selected": True, "selector": selector, "value": value}
+            else:
+                return {"error": "Provide either ref (from snapshot) or selector"}
+        except Exception as e:
+            return {"error": f"Select failed: {e}"}
+
+    def scroll(self, direction: str = "down", amount: int = 500) -> Dict[str, Any]:
+        return self._submit(self._do_scroll, direction, amount)
+
+    def _do_scroll(self, direction, amount) -> Dict[str, Any]:
+        page = self._page
+        delta_map = {
+            "down": (0, amount),
+            "up": (0, -amount),
+            "right": (amount, 0),
+            "left": (-amount, 0),
+        }
+        dx, dy = delta_map.get(direction, (0, amount))
+        try:
+            page.mouse.wheel(dx, dy)
+            page.wait_for_timeout(300)
+            scroll_info = page.evaluate("""
+                () => ({
+                    scrollX: window.scrollX,
+                    scrollY: window.scrollY,
+                    scrollHeight: document.documentElement.scrollHeight,
+                    clientHeight: document.documentElement.clientHeight
+                })
+            """)
+            return {"scrolled": direction, "amount": amount, **scroll_info}
+        except Exception as e:
+            return {"error": f"Scroll failed: {e}"}
+
+    def wait(self, selector: Optional[str] = None, timeout: int = 5000,
+             state: str = "visible") -> Dict[str, Any]:
+        return self._submit(self._do_wait, selector, timeout, state)
+
+    def _do_wait(self, selector, timeout, state) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if selector:
+                page.wait_for_selector(selector, timeout=timeout, state=state)
+                return {"waited": True, "selector": selector, "state": state}
+            else:
+                page.wait_for_timeout(timeout)
+                return {"waited": True, "timeout_ms": timeout}
+        except Exception as e:
+            return {"error": f"Wait failed: {e}"}
+
+    def go_back(self) -> Dict[str, Any]:
+        return self._submit(self._do_go_back)
+
+    def _do_go_back(self) -> Dict[str, Any]:
+        page = self._page
+        try:
+            page.go_back(wait_until="domcontentloaded", timeout=10000)
+            try:
+                title = page.title()
+            except Exception:
+                title = ""
+            try:
+                url = page.url
+            except Exception:
+                url = ""
+            return {"url": url, "title": title}
+        except Exception as e:
+            return {"error": f"Go back failed: {e}"}
+
+    def go_forward(self) -> Dict[str, Any]:
+        return self._submit(self._do_go_forward)
+
+    def _do_go_forward(self) -> Dict[str, Any]:
+        page = self._page
+        try:
+            page.go_forward(wait_until="domcontentloaded", timeout=10000)
+            try:
+                title = page.title()
+            except Exception:
+                title = ""
+            try:
+                url = page.url
+            except Exception:
+                url = ""
+            return {"url": url, "title": title}
+        except Exception as e:
+            return {"error": f"Go forward failed: {e}"}
+
+    def get_text(self, selector: str) -> Dict[str, Any]:
+        return self._submit(self._do_get_text, selector)
+
+    def _do_get_text(self, selector) -> Dict[str, Any]:
+        page = self._page
+        try:
+            text = page.text_content(selector, timeout=5000)
+            return {"text": text or ""}
+        except Exception as e:
+            return {"error": f"Get text failed: {e}"}
+
+    def evaluate(self, script: str) -> Dict[str, Any]:
+        return self._submit(self._do_evaluate, script)
+
+    def _do_evaluate(self, script) -> Dict[str, Any]:
+        page = self._page
+        try:
+            result = page.evaluate(script)
+            return {"result": result}
+        except Exception as e:
+            return {"error": f"Evaluate failed: {e}"}
+
+    def press(self, key: str) -> Dict[str, Any]:
+        return self._submit(self._do_press, key)
+
+    def _do_press(self, key) -> Dict[str, Any]:
+        page = self._page
+        try:
+            page.keyboard.press(key)
+            page.wait_for_timeout(300)
+            return {"pressed": key}
+        except Exception as e:
+            return {"error": f"Press failed: {e}"}
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    def _get_screenshot_dir(self, cwd: str = "") -> str:
+        if self._screenshot_dir and os.path.isdir(self._screenshot_dir):
+            return self._screenshot_dir
+        base = cwd or os.getcwd()
+        d = os.path.join(base, "tmp")
+        os.makedirs(d, exist_ok=True)
+        self._screenshot_dir = d
+        return d

agent/tools/browser/browser_tool.py

@@ -0,0 +1,303 @@
+"""
+Browser tool - Control a Chromium browser for web navigation and interaction.
+
+Uses Playwright under the hood. Browser instance is lazily started on first
+use, reused across tool calls within the same session, and cleaned up via
+close().
+
+Launch modes (configured under `tools.browser` in config.json):
+  - persistent (default): Chromium runs with a persistent user_data_dir
+    (default `~/.cow/browser_profile`), so cookies and login state survive
+    across runs. The user only needs to log in once.
+  - cdp: When `cdp_endpoint` is set, attach to an externally launched Chrome
+    via the Chrome DevTools Protocol. Lets the agent reuse the user's real
+    browser (with all logins / extensions / true fingerprints).
+  - fresh: Set `persistent` to false to fall back to a clean context every run.
+"""
+
+import json
+import os
+from typing import Dict, Any, Optional
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.browser.browser_service import BrowserService
+from common.log import logger
+
+
+class BrowserTool(BaseTool):
+    """Single tool exposing all browser actions via an 'action' parameter."""
+
+    name: str = "browser"
+    description: str = (
+        "Control a browser to navigate web pages, interact with elements, and extract content. "
+        "Actions: navigate, snapshot, click, fill, select, scroll, screenshot, wait, back, forward, "
+        "get_text, press, evaluate.\n\n"
+        "Workflow: navigate (auto-includes snapshot with element refs) → click/fill/select by ref → snapshot to verify.\n\n"
+        "Use snapshot as the primary way to read pages. Use screenshot + send to show key results to the user. "
+        "For login/CAPTCHA/authorization etc., screenshot and ask the user for help. "
+        "Login state is persisted across sessions (cookies / localStorage are kept in a "
+        "user profile directory), so once the user logs in to a site, the agent can keep "
+        "using it without logging in again."
+    )
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string",
+                "description": (
+                    "The browser action to perform. One of: "
+                    "navigate, snapshot, click, fill, select, scroll, "
+                    "screenshot, wait, back, forward, get_text, press, evaluate"
+                ),
+                "enum": [
+                    "navigate", "snapshot", "click", "fill", "select", "scroll",
+                    "screenshot", "wait", "back", "forward", "get_text", "press",
+                    "evaluate"
+                ]
+            },
+            "url": {
+                "type": "string",
+                "description": "URL to navigate to (for 'navigate' action)"
+            },
+            "ref": {
+                "type": "integer",
+                "description": "Element ref number from snapshot (for click/fill/select)"
+            },
+            "selector": {
+                "type": "string",
+                "description": "CSS selector as fallback when ref is unavailable (for click/fill/select/wait/get_text)"
+            },
+            "text": {
+                "type": "string",
+                "description": "Text to type (for 'fill' action)"
+            },
+            "value": {
+                "type": "string",
+                "description": "Option value (for 'select' action)"
+            },
+            "key": {
+                "type": "string",
+                "description": "Key to press, e.g. Enter, Tab, Escape (for 'press' action)"
+            },
+            "direction": {
+                "type": "string",
+                "description": "Scroll direction: up, down, left, right (for 'scroll' action, default: down)"
+            },
+            "script": {
+                "type": "string",
+                "description": "JavaScript code to execute (for 'evaluate' action)"
+            },
+            "full_page": {
+                "type": "boolean",
+                "description": "Capture full page screenshot (for 'screenshot' action, default: false)"
+            },
+            "timeout": {
+                "type": "integer",
+                "description": "Timeout in milliseconds (optional, default varies by action)"
+            }
+        },
+        "required": ["action"]
+    }
+
+    _shared_service: Optional[BrowserService] = None
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        self._service: Optional[BrowserService] = None
+
+    def _get_service(self) -> BrowserService:
+        """Get or create the browser service, sharing across copies."""
+        if self._service is not None:
+            return self._service
+
+        # Reuse shared service across tool copies within the same session
+        if BrowserTool._shared_service is not None:
+            self._service = BrowserTool._shared_service
+            return self._service
+
+        self._service = BrowserService(self.config)
+        BrowserTool._shared_service = self._service
+        return self._service
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        action = args.get("action", "").strip().lower()
+        if not action:
+            return ToolResult.fail("Error: 'action' parameter is required")
+
+        handler = self._ACTION_MAP.get(action)
+        if not handler:
+            valid = ", ".join(sorted(self._ACTION_MAP.keys()))
+            return ToolResult.fail(f"Unknown action '{action}'. Valid actions: {valid}")
+
+        try:
+            return handler(self, args)
+        except Exception as e:
+            logger.error(f"[Browser] Action '{action}' error: {e}")
+            return ToolResult.fail(f"Browser error ({action}): {e}")
+
+    # ------------------------------------------------------------------
+    # Action handlers
+    # ------------------------------------------------------------------
+
+    def _do_navigate(self, args: Dict[str, Any]) -> ToolResult:
+        url = args.get("url", "").strip()
+        if not url:
+            return ToolResult.fail("Error: 'url' is required for navigate action")
+        # Only auto-prepend https:// for bare hosts; preserve file://, about:, data:, etc.
+        if "://" not in url and not url.startswith(("about:", "data:")):
+            url = "https://" + url
+        timeout = args.get("timeout", 30000)
+        service = self._get_service()
+        result = service.navigate(url, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        # Auto-snapshot after navigation so the agent gets page content in one call
+        snapshot_text = service.snapshot()
+        return ToolResult.success(
+            f"Navigated to: {result['url']}\nTitle: {result['title']}\nStatus: {result['status']}\n\n"
+            f"--- Page Snapshot ---\n{snapshot_text}"
+        )
+
+    def _do_snapshot(self, args: Dict[str, Any]) -> ToolResult:
+        selector = args.get("selector")
+        text = self._get_service().snapshot(selector=selector)
+        return ToolResult.success(text)
+
+    def _do_click(self, args: Dict[str, Any]) -> ToolResult:
+        ref = args.get("ref")
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        result = self._get_service().click(ref=ref, selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Clicked successfully. Use 'snapshot' to see updated page.")
+
+    def _do_fill(self, args: Dict[str, Any]) -> ToolResult:
+        text = args.get("text", "")
+        ref = args.get("ref")
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        if not text and text != "":
+            return ToolResult.fail("Error: 'text' is required for fill action")
+        result = self._get_service().fill(text, ref=ref, selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Filled text into element. Use 'snapshot' to verify.")
+
+    def _do_select(self, args: Dict[str, Any]) -> ToolResult:
+        value = args.get("value", "")
+        ref = args.get("ref")
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        if not value:
+            return ToolResult.fail("Error: 'value' is required for select action")
+        result = self._get_service().select(value, ref=ref, selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Selected option '{value}'.")
+
+    def _do_scroll(self, args: Dict[str, Any]) -> ToolResult:
+        direction = args.get("direction", "down")
+        amount = args.get("timeout", 500)  # reuse timeout field or default
+        if "amount" in args:
+            amount = args["amount"]
+        result = self._get_service().scroll(direction=direction, amount=amount)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        pos = f"scrollY={result.get('scrollY', '?')}/{result.get('scrollHeight', '?')}"
+        return ToolResult.success(f"Scrolled {direction}. Position: {pos}")
+
+    def _do_screenshot(self, args: Dict[str, Any]) -> ToolResult:
+        full_page = args.get("full_page", False)
+        filepath = self._get_service().screenshot(full_page=full_page, cwd=self.cwd)
+        return ToolResult.success(f"Screenshot saved to: {filepath}")
+
+    def _do_wait(self, args: Dict[str, Any]) -> ToolResult:
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        result = self._get_service().wait(selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Wait completed.")
+
+    def _do_back(self, args: Dict[str, Any]) -> ToolResult:
+        result = self._get_service().go_back()
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Navigated back to: {result['url']}")
+
+    def _do_forward(self, args: Dict[str, Any]) -> ToolResult:
+        result = self._get_service().go_forward()
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Navigated forward to: {result['url']}")
+
+    def _do_get_text(self, args: Dict[str, Any]) -> ToolResult:
+        selector = args.get("selector", "").strip()
+        if not selector:
+            return ToolResult.fail("Error: 'selector' is required for get_text action")
+        result = self._get_service().get_text(selector)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(result["text"])
+
+    def _do_press(self, args: Dict[str, Any]) -> ToolResult:
+        key = args.get("key", "").strip()
+        if not key:
+            return ToolResult.fail("Error: 'key' is required for press action")
+        result = self._get_service().press(key)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Pressed key: {key}")
+
+    def _do_evaluate(self, args: Dict[str, Any]) -> ToolResult:
+        script = args.get("script", "").strip()
+        if not script:
+            return ToolResult.fail("Error: 'script' is required for evaluate action")
+        result = self._get_service().evaluate(script)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        val = result.get("result")
+        if isinstance(val, (dict, list)):
+            return ToolResult.success(json.dumps(val, ensure_ascii=False, indent=2))
+        return ToolResult.success(str(val) if val is not None else "(no return value)")
+
+    # Action dispatch table
+    _ACTION_MAP = {
+        "navigate": _do_navigate,
+        "snapshot": _do_snapshot,
+        "click": _do_click,
+        "fill": _do_fill,
+        "select": _do_select,
+        "scroll": _do_scroll,
+        "screenshot": _do_screenshot,
+        "wait": _do_wait,
+        "back": _do_back,
+        "forward": _do_forward,
+        "get_text": _do_get_text,
+        "press": _do_press,
+        "evaluate": _do_evaluate,
+    }
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def copy(self):
+        """Share browser instance across tool copies (avoids re-launching)."""
+        new_tool = BrowserTool(self.config)
+        new_tool.model = self.model
+        new_tool.context = getattr(self, "context", None)
+        new_tool.cwd = self.cwd
+        new_tool._service = self._service
+        return new_tool
+
+    def close(self):
+        """Release browser resources."""
+        if self._service:
+            self._service.close()
+            self._service = None
+        BrowserTool._shared_service = None
+        logger.info("[Browser] BrowserTool closed")

agent/tools/browser_tool.py

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

agent/tools/ls/ls.py

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

agent/tools/mcp/__init__.py

@@ -0,0 +1,4 @@
+from agent.tools.mcp.mcp_client import McpClient, McpClientRegistry
+from agent.tools.mcp.mcp_tool import McpTool
+
+__all__ = ["McpClient", "McpClientRegistry", "McpTool"]

agent/tools/mcp/mcp_client.py

@@ -0,0 +1,528 @@
+"""
+MCP (Model Context Protocol) client module.
+
+Implements JSON-RPC 2.0 over stdio, SSE and Streamable HTTP transports
+without any external MCP SDK dependency.
+"""
+
+import json
+import os
+import select
+import subprocess
+import threading
+import urllib.request
+import urllib.error
+from typing import Optional
+
+from common.log import logger
+
+
+# Aliases accepted for the Streamable HTTP transport type
+_STREAMABLE_HTTP_ALIASES = {"streamable-http", "streamable_http", "streamablehttp", "http"}
+
+
+class McpClient:
+    """Single MCP Server client supporting stdio, SSE and Streamable HTTP transports."""
+
+    def __init__(self, config: dict):
+        """
+        config examples:
+          stdio:           {"name": "filesystem", "type": "stdio", "command": "npx", "args": [...]}
+          SSE:             {"name": "my-api",    "type": "sse",   "url": "http://localhost:8000/sse"}
+          streamable-http: {"name": "pubmed",    "type": "streamable-http", "url": "https://x/mcp"}
+        """
+        self.config = config
+        self.name: str = config.get("name", "unknown")
+        raw_transport: str = config.get("type", "stdio")
+        # Normalize streamable-http aliases to a single internal key
+        self.transport: str = (
+            "streamable-http"
+            if raw_transport.lower() in _STREAMABLE_HTTP_ALIASES
+            else raw_transport
+        )
+
+        # stdio state
+        self._proc: Optional[subprocess.Popen] = None
+
+        # SSE state
+        self._sse_url: Optional[str] = None
+        self._post_url: Optional[str] = None  # endpoint for sending messages (resolved from SSE)
+
+        # Streamable HTTP state
+        self._http_url: Optional[str] = None
+        self._http_headers: dict = {}  # extra headers from user config (e.g. Authorization)
+        self._http_session_id: Optional[str] = None  # Mcp-Session-Id assigned by the server
+
+        # Shared state
+        self._next_id = 1
+        self._id_lock = threading.Lock()
+        self._call_lock = threading.Lock()
+        self._initialized = False
+
+    # ------------------------------------------------------------------
+    # Public interface
+    # ------------------------------------------------------------------
+
+    def initialize(self) -> bool:
+        """Connect and perform the MCP handshake. Returns True on success."""
+        try:
+            if self.transport == "stdio":
+                return self._init_stdio()
+            elif self.transport == "sse":
+                return self._init_sse()
+            elif self.transport == "streamable-http":
+                return self._init_streamable_http()
+            else:
+                logger.warning(f"[MCP:{self.name}] Unknown transport type: {self.transport!r}")
+                return False
+        except Exception as e:
+            logger.warning(f"[MCP:{self.name}] Initialization failed: {e}")
+            return False
+
+    def list_tools(self) -> list:
+        """Return the tool list from this server.
+
+        Each item is a dict: {"name": str, "description": str, "inputSchema": dict}
+        """
+        try:
+            resp = self._send_request("tools/list", {})
+            tools = resp.get("result", {}).get("tools", [])
+            return [
+                {
+                    "name": t.get("name", ""),
+                    "description": t.get("description", ""),
+                    "inputSchema": t.get("inputSchema", {}),
+                }
+                for t in tools
+            ]
+        except Exception as e:
+            logger.warning(f"[MCP:{self.name}] list_tools failed: {e}")
+            return []
+
+    def call_tool(self, name: str, arguments: dict) -> str:
+        """Call a tool and return the result as a string."""
+        try:
+            resp = self._send_request("tools/call", {"name": name, "arguments": arguments})
+            content = resp.get("result", {}).get("content", [])
+            parts = [item.get("text", "") for item in content if item.get("type") == "text"]
+            return "\n".join(parts)
+        except Exception as e:
+            logger.warning(f"[MCP:{self.name}] call_tool({name}) failed: {e}")
+            return f"Error: {e}"
+
+    def shutdown(self):
+        """Close the connection / terminate the child process."""
+        if self._proc is not None:
+            try:
+                self._proc.stdin.close()
+            except Exception:
+                pass
+            try:
+                self._proc.terminate()
+                self._proc.wait(timeout=5)
+            except Exception:
+                try:
+                    self._proc.kill()
+                except Exception:
+                    pass
+            self._proc = None
+            logger.debug(f"[MCP:{self.name}] stdio process terminated")
+
+        # Best-effort streamable-http session termination
+        if self.transport == "streamable-http" and self._http_session_id and self._http_url:
+            try:
+                req = urllib.request.Request(
+                    self._http_url,
+                    method="DELETE",
+                    headers={"Mcp-Session-Id": self._http_session_id, **self._http_headers},
+                )
+                with urllib.request.urlopen(req, timeout=5):
+                    pass
+            except Exception:
+                pass
+            self._http_session_id = None
+
+        self._initialized = False
+
+    # ------------------------------------------------------------------
+    # stdio transport
+    # ------------------------------------------------------------------
+
+    def _init_stdio(self) -> bool:
+        command = self.config.get("command")
+        if not command:
+            logger.warning(f"[MCP:{self.name}] stdio config missing 'command'")
+            return False
+
+        args = self.config.get("args", [])
+        extra_env = self.config.get("env", None)
+        env = {**os.environ, **extra_env} if extra_env else None
+
+        self._proc = subprocess.Popen(
+            [command] + list(args),
+            stdin=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            encoding="utf-8",
+            env=env,
+        )
+        logger.debug(f"[MCP:{self.name}] stdio process started (pid={self._proc.pid})")
+
+        threading.Thread(
+            target=self._drain_stderr, daemon=True, name=f"mcp-stderr-{self.name}"
+        ).start()
+
+        return self._handshake()
+
+    def _drain_stderr(self):
+        for line in self._proc.stderr:
+            line = line.strip()
+            if line:
+                logger.debug(f"[MCP:{self.name}] stderr: {line}")
+
+    def _readline_with_timeout(self, timeout: int = 30) -> str:
+        """Read one line from stdio stdout with a hard timeout."""
+        ready, _, _ = select.select([self._proc.stdout], [], [], timeout)
+        if not ready:
+            raise TimeoutError(f"[MCP:{self.name}] stdio read timed out after {timeout}s")
+        return self._proc.stdout.readline()
+
+    def _stdio_send(self, message: dict) -> dict:
+        """Send a JSON-RPC message over stdio and read the response."""
+        raw = json.dumps(message) + "\n"
+        self._proc.stdin.write(raw)
+        self._proc.stdin.flush()
+
+        while True:
+            line = self._readline_with_timeout()
+            if not line:
+                raise IOError(f"[MCP:{self.name}] stdio process closed unexpectedly")
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                data = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+            if "id" not in data:
+                logger.debug(f"[MCP:{self.name}] notification skipped: {data.get('method', '?')}")
+                continue
+            return data
+
+    # ------------------------------------------------------------------
+    # SSE transport
+    # ------------------------------------------------------------------
+
+    def _init_sse(self) -> bool:
+        url = self.config.get("url")
+        if not url:
+            logger.warning(f"[MCP:{self.name}] SSE config missing 'url'")
+            return False
+
+        self._sse_url = url
+
+        # Read the first SSE event to discover the POST endpoint
+        try:
+            self._post_url = self._sse_discover_endpoint()
+        except Exception as e:
+            logger.warning(f"[MCP:{self.name}] SSE endpoint discovery failed: {e}")
+            return False
+
+        return self._handshake()
+
+    def _sse_discover_endpoint(self) -> str:
+        """Open SSE stream and read the 'endpoint' event to learn the POST URL."""
+        req = urllib.request.Request(
+            self._sse_url,
+            headers={"Accept": "text/event-stream"},
+        )
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            for raw_line in resp:
+                line = raw_line.decode("utf-8").rstrip("\n\r")
+                if line.startswith("data:"):
+                    data = line[len("data:"):].strip()
+                    # Some servers send JSON with a "uri" or plain path
+                    if data.startswith("{"):
+                        parsed = json.loads(data)
+                        return parsed.get("uri") or parsed.get("url") or parsed.get("endpoint")
+                    # Plain relative or absolute URL
+                    if data.startswith("http"):
+                        return data
+                    # Relative path: resolve against SSE base
+                    from urllib.parse import urljoin
+                    return urljoin(self._sse_url, data)
+        raise ValueError(f"[MCP:{self.name}] No endpoint event received from SSE stream")
+
+    def _sse_send(self, message: dict) -> dict:
+        """POST a JSON-RPC message to the server and return the response."""
+        body = json.dumps(message).encode("utf-8")
+        req = urllib.request.Request(
+            self._post_url,
+            data=body,
+            method="POST",
+            headers={"Content-Type": "application/json"},
+        )
+        with urllib.request.urlopen(req, timeout=30) as resp:
+            raw = resp.read().decode("utf-8")
+            return json.loads(raw)
+
+    # ------------------------------------------------------------------
+    # Streamable HTTP transport (MCP spec 2025-03-26)
+    # ------------------------------------------------------------------
+
+    def _init_streamable_http(self) -> bool:
+        url = self.config.get("url")
+        if not url:
+            logger.warning(f"[MCP:{self.name}] streamable-http config missing 'url'")
+            return False
+
+        self._http_url = url
+        # Allow user-provided headers (e.g. {"Authorization": "Bearer xxx"})
+        extra_headers = self.config.get("headers") or {}
+        if isinstance(extra_headers, dict):
+            self._http_headers = {str(k): str(v) for k, v in extra_headers.items()}
+
+        return self._handshake()
+
+    def _streamable_http_send(self, message: dict) -> dict:
+        """POST a JSON-RPC request and return the response (JSON or SSE-wrapped)."""
+        return self._streamable_http_post(message, expect_response=True)
+
+    def _streamable_http_post(self, message: dict, expect_response: bool) -> dict:
+        """
+        POST a JSON-RPC message over Streamable HTTP.
+
+        Per the spec, the response Content-Type can be either:
+          - application/json   -> single JSON-RPC response in body
+          - text/event-stream  -> SSE stream; we read until we get a matching response
+        """
+        body = json.dumps(message).encode("utf-8")
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "application/json, text/event-stream",
+        }
+        if self._http_session_id:
+            headers["Mcp-Session-Id"] = self._http_session_id
+        headers.update(self._http_headers)
+
+        req = urllib.request.Request(
+            self._http_url,
+            data=body,
+            method="POST",
+            headers=headers,
+        )
+
+        try:
+            resp = urllib.request.urlopen(req, timeout=30)
+        except urllib.error.HTTPError as e:
+            # Surface the server-provided error body for easier debugging
+            detail = ""
+            try:
+                detail = e.read().decode("utf-8", errors="ignore")
+            except Exception:
+                pass
+            raise IOError(
+                f"[MCP:{self.name}] streamable-http HTTP {e.code}: {detail[:200]}"
+            )
+
+        with resp:
+            # Capture session id assigned by the server (if any)
+            session_id = resp.headers.get("Mcp-Session-Id")
+            if session_id and not self._http_session_id:
+                self._http_session_id = session_id
+
+            status = resp.status if hasattr(resp, "status") else resp.getcode()
+
+            # Notifications: server may reply with 202 Accepted and no body
+            if not expect_response or status == 202:
+                try:
+                    resp.read()
+                except Exception:
+                    pass
+                return {}
+
+            content_type = (resp.headers.get("Content-Type") or "").lower()
+            expected_id = message.get("id")
+
+            if "text/event-stream" in content_type:
+                return self._read_sse_response(resp, expected_id)
+
+            raw = resp.read().decode("utf-8")
+            if not raw:
+                return {}
+            return json.loads(raw)
+
+    def _read_sse_response(self, resp, expected_id) -> dict:
+        """Read an SSE stream and return the first JSON-RPC response with matching id."""
+        data_buf: list = []
+        for raw_line in resp:
+            line = raw_line.decode("utf-8").rstrip("\n\r")
+            if line == "":
+                # End of an SSE event, attempt to parse accumulated data
+                if data_buf:
+                    payload = "\n".join(data_buf)
+                    data_buf = []
+                    try:
+                        msg = json.loads(payload)
+                    except json.JSONDecodeError:
+                        continue
+                    # Skip notifications / mismatched ids
+                    if "id" not in msg:
+                        continue
+                    if expected_id is None or msg.get("id") == expected_id:
+                        return msg
+                continue
+            if line.startswith(":"):
+                continue  # SSE comment / keepalive
+            if line.startswith("data:"):
+                data_buf.append(line[len("data:"):].lstrip())
+            # Ignore 'event:' / 'id:' lines; we only care about JSON-RPC payloads
+
+        raise IOError(f"[MCP:{self.name}] streamable-http SSE stream closed before response")
+
+    # ------------------------------------------------------------------
+    # Common JSON-RPC helpers
+    # ------------------------------------------------------------------
+
+    def _next_request_id(self) -> int:
+        with self._id_lock:
+            rid = self._next_id
+            self._next_id += 1
+        return rid
+
+    def _build_request(self, method: str, params: dict) -> dict:
+        return {
+            "jsonrpc": "2.0",
+            "id": self._next_request_id(),
+            "method": method,
+            "params": params,
+        }
+
+    def _build_notification(self, method: str, params: dict) -> dict:
+        return {"jsonrpc": "2.0", "method": method, "params": params}
+
+    def _send_request(self, method: str, params: dict) -> dict:
+        """Send a request and return the full response dict."""
+        if not self._initialized and method != "initialize":
+            raise RuntimeError(f"[MCP:{self.name}] Client not initialized")
+
+        message = self._build_request(method, params)
+
+        with self._call_lock:
+            if self.transport == "stdio":
+                return self._stdio_send(message)
+            elif self.transport == "sse":
+                return self._sse_send(message)
+            elif self.transport == "streamable-http":
+                return self._streamable_http_send(message)
+            else:
+                raise ValueError(f"[MCP:{self.name}] Unsupported transport: {self.transport}")
+
+    def _send_notification(self, method: str, params: dict):
+        """Fire-and-forget notification (no response expected)."""
+        notification = self._build_notification(method, params)
+        raw = json.dumps(notification) + "\n"
+
+        if self.transport == "stdio":
+            self._proc.stdin.write(raw)
+            self._proc.stdin.flush()
+        elif self.transport == "sse":
+            body = raw.encode("utf-8")
+            req = urllib.request.Request(
+                self._post_url,
+                data=body,
+                method="POST",
+                headers={"Content-Type": "application/json"},
+            )
+            try:
+                with urllib.request.urlopen(req, timeout=10):
+                    pass
+            except Exception:
+                pass  # notifications are fire-and-forget
+        elif self.transport == "streamable-http":
+            try:
+                self._streamable_http_post(notification, expect_response=False)
+            except Exception:
+                pass  # notifications are fire-and-forget
+
+    def _handshake(self) -> bool:
+        """Perform the MCP initialize / notifications/initialized handshake."""
+        init_params = {
+            "protocolVersion": "2024-11-05",
+            "capabilities": {},
+            "clientInfo": {"name": "CowAgent", "version": "1.0"},
+        }
+        # Temporarily mark as initialized so _send_request doesn't block
+        self._initialized = True
+        try:
+            resp = self._send_request("initialize", init_params)
+        except Exception as e:
+            self._initialized = False
+            logger.warning(f"[MCP:{self.name}] Handshake initialize failed: {e}")
+            return False
+
+        if "error" in resp:
+            self._initialized = False
+            logger.warning(f"[MCP:{self.name}] Handshake error: {resp['error']}")
+            return False
+
+        self._send_notification("notifications/initialized", {})
+        logger.debug(f"[MCP:{self.name}] Handshake complete")
+        return True
+
+
+class McpClientRegistry:
+    """Global singleton managing the lifecycle of all MCP Server clients."""
+
+    _instance = None
+    _instance_lock = threading.Lock()
+
+    def __new__(cls):
+        with cls._instance_lock:
+            if cls._instance is None:
+                obj = super().__new__(cls)
+                obj._clients: dict[str, McpClient] = {}
+                obj._registry_lock = threading.Lock()
+                cls._instance = obj
+        return cls._instance
+
+    def start_all(self, configs: list) -> None:
+        """Initialize McpClient for each config entry; skip failures with a warning."""
+        if not configs:
+            return
+
+        for cfg in configs:
+            name = cfg.get("name", "<unnamed>")
+            client = McpClient(cfg)
+            ok = client.initialize()
+            if ok:
+                with self._registry_lock:
+                    self._clients[name] = client
+                logger.info(f"[MCP] Server '{name}' initialized successfully")
+            else:
+                logger.warning(f"[MCP] Server '{name}' failed to initialize — skipping")
+
+    def get(self, server_name: str) -> Optional[McpClient]:
+        """Return the initialized client for server_name, or None."""
+        with self._registry_lock:
+            return self._clients.get(server_name)
+
+    def all_clients(self) -> dict:
+        """Return a copy of the {name: McpClient} mapping."""
+        with self._registry_lock:
+            return dict(self._clients)
+
+    def shutdown_all(self) -> None:
+        """Shut down all managed clients."""
+        with self._registry_lock:
+            clients = list(self._clients.values())
+            self._clients.clear()
+
+        for client in clients:
+            try:
+                client.shutdown()
+            except Exception as e:
+                logger.warning(f"[MCP] Error shutting down '{client.name}': {e}")
+
+        logger.info("[MCP] All servers shut down")

agent/tools/mcp/mcp_tool.py

@@ -0,0 +1,31 @@
+from agent.tools.base_tool import BaseTool, ToolResult
+from common.log import logger
+
+
+class McpTool(BaseTool):
+    """
+    将单个 MCP 工具包装为 BaseTool。
+    一个 MCP Server 可以提供多个工具，每个工具对应一个 McpTool 实例。
+    """
+
+    def __init__(self, client, tool_schema: dict, server_name: str):
+        """
+        :param client: 该工具所属的 McpClient 实例
+        :param tool_schema: MCP 返回的工具描述，格式：
+            {"name": str, "description": str, "inputSchema": dict}
+        :param server_name: Server 名称，用于日志
+        """
+        self.client = client
+        self.server_name = server_name
+        self.name = tool_schema["name"]
+        self.description = tool_schema.get("description", "")
+        self.params = tool_schema.get("inputSchema", {})
+
+    def execute(self, params: dict) -> ToolResult:
+        logger.info(f"[McpTool] server={self.server_name} tool={self.name} params={params}")
+        try:
+            result = self.client.call_tool(self.name, params)
+            return ToolResult.success(result)
+        except Exception as e:
+            logger.error(f"[McpTool] server={self.server_name} tool={self.name} error: {e}")
+            return ToolResult.fail(str(e))

agent/tools/memory/memory_get.py

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

agent/tools/memory/memory_search.py

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

agent/tools/read/read.py

@@ -48,7 +48,8 @@ class Read(BaseTool):
         self.binary_extensions = {'.exe', '.dll', '.so', '.dylib', '.bin', '.dat', '.db', '.sqlite'}
         self.archive_extensions = {'.zip', '.tar', '.gz', '.rar', '.7z', '.bz2', '.xz'}
         self.pdf_extensions = {'.pdf'}
-        
+        self.office_extensions = {'.doc', '.docx', '.xls', '.xlsx', '.ppt', '.pptx'}
+
         # Readable text formats (will be read with truncation)
         self.text_extensions = {
             '.txt', '.md', '.markdown', '.rst', '.log', '.csv', '.tsv', '.json', '.xml', '.yaml', '.yml',
@@ -57,7 +58,6 @@ class Read(BaseTool):
             '.sh', '.bash', '.zsh', '.fish', '.ps1', '.bat', '.cmd',
             '.sql', '.r', '.m', '.swift', '.kt', '.scala', '.clj', '.erl', '.ex',
             '.dockerfile', '.makefile', '.cmake', '.gradle', '.properties', '.ini', '.conf', '.cfg',
-            '.doc', '.docx', '.xls', '.xlsx', '.ppt', '.pptx'  # Office documents
         }
     
     def execute(self, args: Dict[str, Any]) -> ToolResult:
@@ -120,7 +120,11 @@ class Read(BaseTool):
         # Check if PDF
         if file_ext in self.pdf_extensions:
             return self._read_pdf(absolute_path, path, offset, limit)
-        
+
+        # Check if Office document (.docx, .xlsx, .pptx, etc.)
+        if file_ext in self.office_extensions:
+            return self._read_office(absolute_path, path, file_ext, offset, limit)
+
         # Read text file (with truncation for large files)
         return self._read_text(absolute_path, path, offset, limit)
     
@@ -240,17 +244,12 @@ class Read(BaseTool):
                     "message": f"文件过大 ({format_size(file_size)} > 50MB)，无法读取内容。文件路径: {absolute_path}"
                 })
             
-            # Read file
-            with open(absolute_path, 'r', encoding='utf-8') as f:
+            # Read file (utf-8-sig strips BOM automatically on Windows)
+            # Note: Truncation is unified via truncate_head (DEFAULT_MAX_LINES / DEFAULT_MAX_BYTES)
+            # so that offset/limit can paginate the entire file correctly.
+            with open(absolute_path, 'r', encoding='utf-8-sig') as f:
                 content = f.read()
-            
-            # Truncate content if too long (20K characters max for model context)
-            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)
             
@@ -286,11 +285,7 @@ class Read(BaseTool):
             
             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')))
@@ -337,6 +332,116 @@ class Read(BaseTool):
         except Exception as e:
             return ToolResult.fail(f"Error reading file: {str(e)}")
     
+    def _read_office(self, absolute_path: str, display_path: str, file_ext: str,
+                     offset: int = None, limit: int = None) -> ToolResult:
+        """Read Office documents (.docx, .xlsx, .pptx) using python-docx / openpyxl / python-pptx."""
+        try:
+            text = self._extract_office_text(absolute_path, file_ext)
+        except ImportError as e:
+            return ToolResult.fail(str(e))
+        except Exception as e:
+            return ToolResult.fail(f"Error reading Office document: {e}")
+
+        if not text or not text.strip():
+            return ToolResult.success({
+                "content": f"[Office file {Path(absolute_path).name}: no text content could be extracted]",
+            })
+
+        all_lines = text.split('\n')
+        total_lines = len(all_lines)
+
+        start_line = 0
+        if offset is not None:
+            if offset < 0:
+                start_line = max(0, total_lines + offset)
+            else:
+                start_line = max(0, offset - 1)
+                if start_line >= total_lines:
+                    return ToolResult.fail(
+                        f"Error: Offset {offset} is beyond end of content ({total_lines} lines total)"
+                    )
+
+        selected_content = text
+        user_limited_lines = None
+        if limit is not None:
+            end_line = min(start_line + limit, total_lines)
+            selected_content = '\n'.join(all_lines[start_line:end_line])
+            user_limited_lines = end_line - start_line
+        elif offset is not None:
+            selected_content = '\n'.join(all_lines[start_line:])
+
+        truncation = truncate_head(selected_content)
+        start_line_display = start_line + 1
+        output_text = ""
+
+        if truncation.truncated:
+            end_line_display = start_line_display + truncation.output_lines - 1
+            next_offset = end_line_display + 1
+            output_text = truncation.content
+            output_text += f"\n\n[Showing lines {start_line_display}-{end_line_display} of {total_lines}. Use offset={next_offset} to continue.]"
+        elif user_limited_lines is not None and start_line + user_limited_lines < total_lines:
+            remaining = total_lines - (start_line + user_limited_lines)
+            next_offset = start_line + user_limited_lines + 1
+            output_text = truncation.content
+            output_text += f"\n\n[{remaining} more lines in file. Use offset={next_offset} to continue.]"
+        else:
+            output_text = truncation.content
+
+        return ToolResult.success({
+            "content": output_text,
+            "total_lines": total_lines,
+            "start_line": start_line_display,
+            "output_lines": truncation.output_lines,
+        })
+
+    @staticmethod
+    def _extract_office_text(absolute_path: str, file_ext: str) -> str:
+        """Extract plain text from an Office document."""
+        if file_ext in ('.docx', '.doc'):
+            try:
+                from docx import Document
+            except ImportError:
+                raise ImportError("Error: python-docx library not installed. Install with: pip install python-docx")
+            doc = Document(absolute_path)
+            paragraphs = [p.text for p in doc.paragraphs]
+            for table in doc.tables:
+                for row in table.rows:
+                    paragraphs.append('\t'.join(cell.text for cell in row.cells))
+            return '\n'.join(paragraphs)
+
+        if file_ext in ('.xlsx', '.xls'):
+            try:
+                from openpyxl import load_workbook
+            except ImportError:
+                raise ImportError("Error: openpyxl library not installed. Install with: pip install openpyxl")
+            wb = load_workbook(absolute_path, read_only=True, data_only=True)
+            parts = []
+            for ws in wb.worksheets:
+                parts.append(f"--- Sheet: {ws.title} ---")
+                for row in ws.iter_rows(values_only=True):
+                    parts.append('\t'.join(str(c) if c is not None else '' for c in row))
+            wb.close()
+            return '\n'.join(parts)
+
+        if file_ext in ('.pptx', '.ppt'):
+            try:
+                from pptx import Presentation
+            except ImportError:
+                raise ImportError("Error: python-pptx library not installed. Install with: pip install python-pptx")
+            prs = Presentation(absolute_path)
+            parts = []
+            for i, slide in enumerate(prs.slides, 1):
+                parts.append(f"--- Slide {i} ---")
+                for shape in slide.shapes:
+                    if shape.has_text_frame:
+                        for para in shape.text_frame.paragraphs:
+                            text = para.text.strip()
+                            if text:
+                                parts.append(text)
+            return '\n'.join(parts)
+
+        return ""
+
     def _read_pdf(self, absolute_path: str, display_path: str, offset: int = None, limit: int = None) -> ToolResult:
         """
         Read PDF file content

agent/tools/scheduler/integration.py

@@ -3,6 +3,7 @@ Integration module for scheduler with AgentBridge
 """
 
 import os
+import threading
 from typing import Optional
 from config import conf
 from common.log import logger
@@ -13,65 +14,126 @@ from bridge.reply import Reply, ReplyType
 # Global scheduler service instance
 _scheduler_service = None
 _task_store = None
+# Module-level lock to guard idempotent initialization across threads
+_init_lock = threading.Lock()
 
 
 def init_scheduler(agent_bridge) -> bool:
     """
-    Initialize scheduler service
-    
+    Initialize scheduler service (idempotent).
+
+    Safe to call multiple times and from multiple threads: only the first
+    successful call creates the singleton ``SchedulerService`` + background
+    scanning thread. Subsequent calls return immediately.
+
     Args:
         agent_bridge: AgentBridge instance
-        
+
     Returns:
-        True if initialized successfully
+        True if scheduler is initialized (newly created or already running)
     """
     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 = expand_path(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")
+
+    # Fast path: already initialized and running
+    if _scheduler_service is not None and getattr(_scheduler_service, "running", False):
+        return True
+
+    with _init_lock:
+        # Re-check under the lock to avoid races where multiple threads
+        # passed the fast-path check before any of them acquired the lock.
+        if _scheduler_service is not None and getattr(_scheduler_service, "running", False):
+            return True
+
+        try:
+            from agent.tools.scheduler.task_store import TaskStore
+            from agent.tools.scheduler.scheduler_service import SchedulerService
+
+            # Get workspace from config
+            workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+            store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
+
+            # Create task store (reuse if already created)
+            if _task_store is None:
+                _task_store = TaskStore(store_path)
+                logger.debug(f"[Scheduler] Task store initialized: {store_path}")
+
+            # Create execute callback. Returns True on success, False to ask
+            # the scheduler to retry on the next tick (e.g. channel not yet
+            # ready right after process start).
+            def execute_task_callback(task: dict):
+                try:
+                    action = task.get("action", {})
+                    action_type = action.get("type")
+                    channel_type = action.get("channel_type", "unknown")
+                    receiver = action.get("receiver", "")
+
+                    if not _is_channel_ready(channel_type, receiver):
+                        logger.warning(
+                            f"[Scheduler] Task {task.get('id')}: channel "
+                            f"'{channel_type}' not ready for receiver={receiver} "
+                            f"(no inbound msg cached since restart?); deferring"
+                        )
+                        return False
+
+                    if action_type == "agent_task":
+                        return _execute_agent_task(task, agent_bridge)
+                    elif action_type == "send_message":
+                        return _execute_send_message(task, agent_bridge)
+                    elif action_type == "tool_call":
+                        return _execute_tool_call(task, agent_bridge)
+                    elif action_type == "skill_call":
+                        return _execute_skill_call(task, agent_bridge)
+                    else:
+                        logger.warning(f"[Scheduler] Unknown action type: {action_type}")
+                        return True
+                except Exception as e:
+                    logger.error(f"[Scheduler] Error executing task {task.get('id')}: {e}")
+                    return False
+
+            # Create scheduler service
+            _scheduler_service = SchedulerService(_task_store, execute_task_callback)
+            _scheduler_service.start()
+
+            logger.info("[Scheduler] Service initialized and started")
+            return True
+
+        except Exception as e:
+            logger.error(f"[Scheduler] Failed to initialize scheduler: {e}")
+            return False
+
+
+def _is_channel_ready(channel_type: str, receiver: str) -> bool:
+    """Best-effort readiness probe for outbound channels.
+
+    Returns False when we know the send will drop (e.g. weixin not yet
+    logged in, web session has no polling queue), so the scheduler can
+    defer instead of consuming the task. Unknown channels return True
+    to preserve previous behaviour.
+    """
+    if not channel_type or channel_type == "unknown":
+        return True
+    try:
+        from channel.channel_factory import create_channel
+        channel = create_channel(channel_type)
+        if channel is None:
+            return False
+
+        if channel_type == "weixin":
+            tokens = getattr(channel, "_context_tokens", None)
+            if not tokens or receiver not in tokens:
+                return False
+            return True
+
+        if channel_type == "web":
+            queues = getattr(channel, "session_queues", None)
+            if not queues or receiver not in queues:
+                return False
+            return True
+
         return True
-        
     except Exception as e:
-        logger.error(f"[Scheduler] Failed to initialize scheduler: {e}")
-        return False
+        logger.warning(f"[Scheduler] Channel readiness check failed for {channel_type}: {e}")
+        return True
 
 
 def get_task_store():
@@ -84,13 +146,53 @@ def get_scheduler_service():
     return _scheduler_service
 
 
-def _execute_agent_task(task: dict, agent_bridge):
+def _remember_delivered_output(
+    agent_bridge,
+    task: dict,
+    channel_type: str,
+    content: str,
+) -> None:
+    """Best-effort persistence of the message the scheduler sent to a user.
+
+    Uses notify_session_id (the real chat session_id stored at task creation time)
+    so that group chats correctly associate the output with the user's conversation.
+    Falls back to receiver for backward compatibility with old tasks.
+
+    Per-action-type behaviour:
+        - agent_task / tool_call / skill_call: gated by ``scheduler_inject_to_session``
+          (default True). These produce AI-generated content worth remembering.
+        - send_message: additionally gated by ``scheduler_inject_send_message``
+          (default False). Fixed reminder text rarely benefits follow-up Q&A and
+          would just consume context tokens.
     """
-    Execute an agent_task action - let Agent handle the task
-    
-    Args:
-        task: Task dictionary
-        agent_bridge: AgentBridge instance
+    if not content:
+        return
+    action = task.get("action", {})
+    action_type = action.get("type", "")
+
+    # send_message defaults to NOT being injected; explicit opt-in via config.
+    if action_type == "send_message":
+        if not conf().get("scheduler_inject_send_message", False):
+            return
+
+    session_id = action.get("notify_session_id") or action.get("receiver")
+    if not session_id:
+        return
+    try:
+        remember = getattr(agent_bridge, "remember_scheduled_output", None)
+        if remember:
+            task_desc = action.get("task_description") or action.get("content", "")
+            remember(session_id, str(content), channel_type=channel_type, task_description=task_desc)
+    except Exception as e:
+        logger.warning(
+            f"[Scheduler] Failed to remember delivered output for {session_id}: {e}"
+        )
+
+
+def _execute_agent_task(task: dict, agent_bridge) -> bool:
+    """
+    Execute an agent_task action - let Agent handle the task.
+    Returns True on successful delivery, False to retry next tick.
     """
     try:
         action = task.get("action", {})
@@ -101,11 +203,11 @@ def _execute_agent_task(task: dict, agent_bridge):
         
         if not task_description:
             logger.error(f"[Scheduler] Task {task['id']}: No task_description specified")
-            return
+            return True  # malformed task, don't loop forever
         
         if not receiver:
             logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
-            return
+            return True
         
         # Check for unsupported channels
         if channel_type == "dingtalk":
@@ -134,12 +236,13 @@ def _execute_agent_task(task: dict, agent_bridge):
         elif channel_type == "dingtalk":
             # DingTalk requires msg object, set to None for scheduled tasks
             context["msg"] = None
-            # 如果是单聊，需要传递 sender_staff_id
             if not is_group:
                 sender_staff_id = action.get("dingtalk_sender_staff_id")
                 if sender_staff_id:
                     context["dingtalk_sender_staff_id"] = sender_staff_id
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+
         # Use Agent to execute the task
         # Mark this as a scheduled task execution to prevent recursive task creation
         context["is_scheduled_task"] = True
@@ -147,50 +250,47 @@ def _execute_agent_task(task: dict, agent_bridge):
         try:
             # Don't clear history - scheduler tasks use isolated session_id so they won't pollute user conversations
             reply = agent_bridge.agent_reply(task_description, context=context, on_event=None, clear_history=False)
-            
-            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:
+
+            if not (reply and reply.content):
                 logger.error(f"[Scheduler] Task {task['id']}: No result from agent execution")
-                
+                return True  # agent ran but produced nothing; don't loop
+
+            from channel.channel_factory import create_channel
+            channel = create_channel(channel_type)
+            if not channel:
+                logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
+                return False
+
+            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
+
+            try:
+                channel.send(reply, context)
+            except Exception as e:
+                logger.error(f"[Scheduler] Failed to send result: {e}")
+                return False
+
+            _remember_delivered_output(agent_bridge, task, channel_type, reply.content)
+            logger.info(f"[Scheduler] Task {task['id']} executed successfully, result sent to {receiver}")
+            return True
+
         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()}")
-            
+            return False
+
     except Exception as e:
         logger.error(f"[Scheduler] Error in _execute_agent_task: {e}")
         import traceback
         logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+        return False
 
 
-def _execute_send_message(task: dict, agent_bridge):
-    """
-    Execute a send_message action
-    
-    Args:
-        task: Task dictionary
-        agent_bridge: AgentBridge instance
-    """
+def _execute_send_message(task: dict, agent_bridge) -> bool:
+    """Execute a send_message action. Returns True/False for delivery."""
     try:
         action = task.get("action", {})
         content = action.get("content", "")
@@ -200,7 +300,7 @@ def _execute_send_message(task: dict, agent_bridge):
         
         if not receiver:
             logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
-            return
+            return True
         
         # Create context for sending message
         context = Context(ContextType.TEXT, content)
@@ -234,174 +334,146 @@ def _execute_send_message(task: dict, agent_bridge):
                     logger.debug(f"[Scheduler] DingTalk single chat: sender_staff_id={sender_staff_id}")
                 else:
                     logger.warning(f"[Scheduler] Task {task['id']}: DingTalk single chat message missing sender_staff_id")
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+        elif channel_type == "qq":
+            context["msg"] = None
+
         # Create reply
         reply = Reply(ReplyType.TEXT, content)
         
         # Get channel and send
         from channel.channel_factory import create_channel
         
+        channel = create_channel(channel_type)
+        if not channel:
+            logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
+            return False
+
+        if channel_type == "web" and hasattr(channel, 'request_to_session'):
+            channel.request_to_session[request_id] = receiver
+
         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}")
+            channel.send(reply, context)
         except Exception as e:
             logger.error(f"[Scheduler] Failed to send message: {e}")
-            import traceback
-            logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
-            
+            return False
+
+        _remember_delivered_output(agent_bridge, task, channel_type, content)
+        logger.info(f"[Scheduler] Task {task['id']} executed: sent message to {receiver}")
+        return True
+
     except Exception as e:
         logger.error(f"[Scheduler] Error in _execute_send_message: {e}")
         import traceback
         logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+        return False
 
 
-def _execute_tool_call(task: dict, agent_bridge):
-    """
-    Execute a tool_call action
-    
-    Args:
-        task: Task dictionary
-        agent_bridge: AgentBridge instance
-    """
+def _execute_tool_call(task: dict, agent_bridge) -> bool:
+    """Execute a tool_call action. Returns True/False for delivery."""
     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
-        
+            return True
         if not receiver:
             logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
-            return
-        
-        # Get tool manager and create tool instance
+            return True
+
         from agent.tools.tool_manager import ToolManager
-        tool_manager = ToolManager()
-        tool = tool_manager.create_tool(tool_name)
-        
+        tool = ToolManager().create_tool(tool_name)
         if not tool:
             logger.error(f"[Scheduler] Task {task['id']}: Tool '{tool_name}' not found")
-            return
-        
-        # Execute tool
+            return True
+
         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
+        content = result.result if hasattr(result, 'result') else str(result)
         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
+
+        request_id = None
         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}")
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+
         reply = Reply(ReplyType.TEXT, content)
-        
-        # Get channel and send
+
         from channel.channel_factory import create_channel
-        
+        channel = create_channel(channel_type)
+        if not channel:
+            logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
+            return False
+
+        if channel_type == "web" and request_id and hasattr(channel, 'request_to_session'):
+            channel.request_to_session[request_id] = receiver
+
         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}")
+            channel.send(reply, context)
         except Exception as e:
             logger.error(f"[Scheduler] Failed to send tool result: {e}")
-            
+            return False
+
+        _remember_delivered_output(agent_bridge, task, channel_type, content)
+        logger.info(f"[Scheduler] Task {task['id']} executed: sent tool result to {receiver}")
+        return True
+
     except Exception as e:
         logger.error(f"[Scheduler] Error in _execute_tool_call: {e}")
+        return False
 
 
-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
-    """
+def _execute_skill_call(task: dict, agent_bridge) -> bool:
+    """Execute a skill_call action by asking Agent to run the skill.
+    Returns True/False for delivery."""
     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
-        
+            return True
         if not receiver:
             logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
-            return
-        
+            return True
+
         logger.info(f"[Scheduler] Task {task['id']}: Executing skill '{skill_name}' with params {skill_params}")
-        
-        # Create a unique session_id for this scheduled task to avoid polluting user's conversation
-        # Format: scheduler_<receiver>_<task_id> to ensure isolation
+
         scheduler_session_id = f"scheduler_{receiver}_{task['id']}"
-        
-        # 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"] = scheduler_session_id
-        
-        # Channel-specific setup
+
         if channel_type == "web":
             import uuid
             request_id = f"scheduler_{task['id']}_{uuid.uuid4().hex[:8]}"
@@ -409,32 +481,51 @@ def _execute_skill_call(task: dict, agent_bridge):
         elif channel_type == "feishu":
             context["receive_id_type"] = "chat_id" if is_group else "open_id"
             context["msg"] = None
-        
-        # Use Agent to execute the skill
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+
         try:
-            # Don't clear history - scheduler tasks use isolated session_id so they won't pollute user conversations
             reply = agent_bridge.agent_reply(query, context=context, on_event=None, clear_history=False)
-            
-            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()}")
-            
+            return False
+
+        if not (reply and reply.content):
+            logger.error(f"[Scheduler] Task {task['id']}: No result from skill execution")
+            return True
+
+        content = reply.content
+        if result_prefix:
+            content = f"{result_prefix}\n\n{content}"
+
+        from channel.channel_factory import create_channel
+        channel = create_channel(channel_type)
+        if not channel:
+            logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
+            return False
+
+        if channel_type == "web" and hasattr(channel, 'request_to_session'):
+            req_id = context.get("request_id")
+            if req_id:
+                channel.request_to_session[req_id] = receiver
+
+        try:
+            channel.send(Reply(ReplyType.TEXT, content), context)
+        except Exception as e:
+            logger.error(f"[Scheduler] Failed to send skill result: {e}")
+            return False
+
+        _remember_delivered_output(agent_bridge, task, channel_type, content)
+        logger.info(f"[Scheduler] Task {task['id']} executed: skill result sent to {receiver}")
+        return True
+
     except Exception as e:
         logger.error(f"[Scheduler] Error in _execute_skill_call: {e}")
         import traceback
         logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+        return False
 
 
 def attach_scheduler_to_tool(tool, context: Context = None):
@@ -451,8 +542,7 @@ def attach_scheduler_to_tool(tool, context: Context = None):
     if context:
         tool.current_context = context
         
-        # Also set channel_type from config
-        channel_type = conf().get("channel_type", "unknown")
+        channel_type = context.get("channel_type") or conf().get("channel_type", "unknown")
         if not tool.config:
             tool.config = {}
         tool.config["channel_type"] = channel_type

agent/tools/scheduler/scheduler_service.py

@@ -10,6 +10,19 @@ from croniter import croniter
 from common.log import logger
 
 
+def _parse_naive_local(iso_str: str) -> datetime:
+    """Parse an ISO datetime and coerce it to tz-naive local time.
+
+    The scheduler uses ``datetime.now()`` (tz-naive) for all comparisons,
+    so any persisted timestamp must be normalized to the same flavor —
+    otherwise comparing naive vs aware raises TypeError.
+    """
+    dt = datetime.fromisoformat(iso_str)
+    if dt.tzinfo is not None:
+        dt = dt.astimezone().replace(tzinfo=None)
+    return dt
+
+
 class SchedulerService:
     """
     Background service that executes scheduled tasks
@@ -39,7 +52,6 @@ class SchedulerService:
             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"""
@@ -54,15 +66,14 @@ class SchedulerService:
     
     def _run_loop(self):
         """Main scheduler loop"""
-        logger.debug("[Scheduler] Scheduler loop started")
+        logger.info("[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):
@@ -72,12 +83,18 @@ class SchedulerService:
         
         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
+                    ok = self._execute_task(task)
+                    if not ok:
+                        # Leave next_run_at as-is so the next loop retries.
+                        # Cron tasks within the catch-up window will keep
+                        # firing; beyond it _is_task_due will reschedule.
+                        logger.warning(
+                            f"[Scheduler] Task {task['id']} delivery failed, will retry next tick"
+                        )
+                        continue
+
                     next_run = self._calculate_next_run(task, now)
                     if next_run:
                         self.task_store.update_task(task['id'], {
@@ -85,12 +102,8 @@ class SchedulerService:
                             "last_run_at": now.isoformat()
                         })
                     else:
-                        # One-time task, disable it
-                        self.task_store.update_task(task['id'], {
-                            "enabled": False,
-                            "last_run_at": now.isoformat()
-                        })
-                        logger.info(f"[Scheduler] One-time task completed and disabled: {task['id']}")
+                        self.task_store.delete_task(task['id'])
+                        logger.info(f"[Scheduler] One-time task completed and removed: {task['id']}")
             except Exception as e:
                 logger.error(f"[Scheduler] Error processing task {task.get('id')}: {e}")
     
@@ -117,37 +130,43 @@ class SchedulerService:
             return False
         
         try:
-            next_run = datetime.fromisoformat(next_run_str)
-            
-            # Check if task is overdue (e.g., service restart)
+            next_run = _parse_naive_local(next_run_str)
+
             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}")
+                schedule = task.get("schedule", {})
+                schedule_type = schedule.get("type")
+
+                # Catch-up window: fire if we're within 10 minutes of the
+                # scheduled tick. Beyond that we'd rather skip than push a
+                # stale daily report to the user.
+                if time_diff <= 600:
+                    return True
+
+                logger.warning(
+                    f"[Scheduler] Task {task['id']} is overdue by {int(time_diff)}s, "
+                    f"skipping and scheduling next run"
+                )
+
+                if schedule_type == "once":
+                    self.task_store.delete_task(task['id'])
+                    logger.info(f"[Scheduler] One-time task {task['id']} expired, removed")
                     return False
-            
+
+                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:
+        except Exception as e:
+            logger.error(
+                f"[Scheduler] Failed to evaluate due-state for task "
+                f"{task.get('id')} (next_run_at={next_run_str!r}): {e}"
+            )
             return False
     
     def _calculate_next_run(self, task: dict, from_time: datetime) -> Optional[datetime]:
@@ -191,30 +210,34 @@ class SchedulerService:
                 return None
             
             try:
-                run_at = datetime.fromisoformat(run_at_str)
-                # Only return if in the future
+                run_at = _parse_naive_local(run_at_str)
                 if run_at > from_time:
                     return run_at
-            except:
-                pass
+            except Exception as e:
+                logger.error(
+                    f"[Scheduler] Failed to parse once-task run_at "
+                    f"{run_at_str!r}: {e}"
+                )
             return None
         
         return None
     
-    def _execute_task(self, task: dict):
+    def _execute_task(self, task: dict) -> bool:
         """
-        Execute a task
-        
-        Args:
-            task: Task dictionary
+        Execute a task.
+
+        Returns True if delivery succeeded (caller should advance state),
+        False if it failed (caller should keep next_run_at so the next
+        loop iteration retries). Callback may return None for legacy
+        behaviour, treated as success.
         """
         try:
-            # Call the execute callback
-            self.execute_callback(task)
+            result = self.execute_callback(task)
+            return False if result is False else True
         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()
             })
+            return False

agent/tools/scheduler/scheduler_tool.py

@@ -158,6 +158,11 @@ class SchedulerTool(BaseTool):
         # Create task
         task_id = str(uuid.uuid4())[:8]
         
+        # Capture the real chat session_id at task creation time so that scheduler
+        # can later inject the delivered output into the user's actual conversation
+        # (in group chats, session_id != receiver, e.g. "user_id:group_id" on feishu).
+        notify_session_id = context.get("session_id")
+
         # Build action based on message or ai_task
         if message:
             action = {
@@ -166,7 +171,8 @@ class SchedulerTool(BaseTool):
                 "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")
+                "channel_type": self.config.get("channel_type", "unknown"),
+                "notify_session_id": notify_session_id,
             }
         else:  # ai_task
             action = {
@@ -175,7 +181,8 @@ class SchedulerTool(BaseTool):
                 "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")
+                "channel_type": self.config.get("channel_type", "unknown"),
+                "notify_session_id": notify_session_id,
             }
         
         # 针对钉钉单聊，额外存储 sender_staff_id
@@ -357,9 +364,12 @@ class SchedulerTool(BaseTool):
                         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}
+                    # Absolute ISO time. Normalize to tz-naive local so it
+                    # stays comparable with the scheduler's datetime.now().
+                    parsed = datetime.fromisoformat(schedule_value)
+                    if parsed.tzinfo is not None:
+                        parsed = parsed.astimezone().replace(tzinfo=None)
+                    return {"type": "once", "run_at": parsed.isoformat()}
             
         except Exception as e:
             logger.error(f"[SchedulerTool] Invalid schedule: {e}")
@@ -424,7 +434,7 @@ class SchedulerTool(BaseTool):
             try:
                 dt = datetime.fromisoformat(run_at)
                 return f"一次性 ({dt.strftime('%Y-%m-%d %H:%M')})"
-            except:
+            except Exception:
                 return "一次性"
         
         return "未知"
@@ -438,6 +448,6 @@ class SchedulerTool(BaseTool):
                     return msg.other_user_nickname or "群聊"
                 else:
                     return msg.from_user_nickname or "用户"
-        except:
+        except Exception:
             pass
         return "未知"

agent/tools/scheduler/task_store.py

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

agent/tools/send/send.py

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

agent/tools/tool_manager.py

@@ -1,5 +1,6 @@
 import importlib
 import importlib.util
+import threading
 from pathlib import Path
 from typing import Dict, Any, Type
 from agent.tools.base_tool import BaseTool
@@ -7,6 +8,26 @@ from common.log import logger
 from config import conf
 
 
+def _normalize_mcp_configs(raw) -> list:
+    """
+    Convert MCP server config to internal list format.
+    Supports:
+      - list format (mcp_servers):  [{"name": "x", "type": "stdio", ...}]
+      - dict format (mcpServers):   {"x": {"command": "npx", ...}}
+    """
+    if isinstance(raw, list):
+        return raw
+    if isinstance(raw, dict):
+        result = []
+        for name, cfg in raw.items():
+            entry = {"name": name, **cfg}
+            if "type" not in entry:
+                entry["type"] = "sse" if "url" in entry else "stdio"
+            result.append(entry)
+        return result
+    return []
+
+
 class ToolManager:
     """
     Tool manager for managing tools.
@@ -25,6 +46,31 @@ class ToolManager:
         # Initialize only once
         if not hasattr(self, 'tool_classes'):
             self.tool_classes = {}  # Dictionary to store tool classes
+        if not hasattr(self, '_mcp_registry'):
+            self._mcp_registry = None  # Lazy init: only created when MCP servers are configured
+        if not hasattr(self, '_mcp_tool_instances'):
+            self._mcp_tool_instances: dict = {}  # tool_name -> McpTool instance
+        if not hasattr(self, '_mcp_lock'):
+            # Guards _mcp_loaded check-then-set so concurrent callers
+            # don't trigger duplicate background loaders.
+            self._mcp_lock = threading.Lock()
+        if not hasattr(self, '_mcp_loaded'):
+            # Idempotency flag. Flipped to True the moment the first loader
+            # is dispatched (synchronously, inside _mcp_lock). Subsequent
+            # _load_mcp_tools() calls become no-ops, so per-session agent
+            # initialization never re-forks MCP subprocesses.
+            self._mcp_loaded = False
+        if not hasattr(self, '_mcp_status'):
+            # server_name -> "pending" / "ready" / "failed"
+            # Useful for UI / introspection while async loading is in progress.
+            self._mcp_status: dict = {}
+        if not hasattr(self, '_mcp_signature'):
+            # (mtime, sha256) of mcp.json the last time we loaded.
+            # Used by refresh_mcp_if_changed() to skip re-parsing when nothing changed.
+            self._mcp_signature: tuple = (None, None)
+        if not hasattr(self, '_mcp_active_configs'):
+            # server_name -> normalized config dict, for diff-based reload.
+            self._mcp_active_configs: dict = {}
 
     def load_tools(self, tools_dir: str = "", config_dict=None):
         """
@@ -39,6 +85,8 @@ class ToolManager:
             self._load_tools_from_init()
             self._configure_tools_from_config(config_dict)
 
+        self._load_mcp_tools()
+
     def _load_tools_from_init(self) -> bool:
         """
         Load tool classes from tools.__init__.__all__
@@ -70,10 +118,14 @@ class ToolManager:
                                     and cls != BaseTool
                             ):
                                 try:
-                                    # Skip memory tools (they need special initialization with memory_manager)
+                                    # Skip tools that need special initialization
                                     if class_name in ["MemorySearchTool", "MemoryGetTool"]:
                                         logger.debug(f"Skipped tool {class_name} (requires memory_manager)")
                                         continue
+                                    # McpTool instances are registered dynamically via _load_mcp_tools()
+                                    if class_name == "McpTool":
+                                        logger.debug(f"Skipped tool {class_name} (registered dynamically via mcp_servers config)")
+                                        continue
                                     
                                     # Create a temporary instance to get the name
                                     temp_instance = cls()
@@ -84,11 +136,11 @@ class ToolManager:
                                 except ImportError as e:
                                     # Handle missing dependencies with helpful messages
                                     error_msg = str(e)
-                                    if "browser-use" in error_msg or "browser_use" in error_msg:
+                                    if "playwright" in error_msg:
                                         logger.warning(
                                             f"[ToolManager] Browser tool not loaded - missing dependencies.\n"
                                             f"  To enable browser tool, run:\n"
-                                            f"    pip install browser-use markdownify playwright\n"
+                                            f"    pip install playwright\n"
                                             f"    playwright install chromium"
                                         )
                                     elif "markdownify" in error_msg:
@@ -154,11 +206,11 @@ class ToolManager:
                             except ImportError as e:
                                 # Handle missing dependencies with helpful messages
                                 error_msg = str(e)
-                                if "browser-use" in error_msg or "browser_use" in error_msg:
+                                if "playwright" in error_msg:
                                     logger.warning(
                                         f"[ToolManager] Browser tool not loaded - missing dependencies.\n"
                                         f"  To enable browser tool, run:\n"
-                                        f"    pip install browser-use markdownify playwright\n"
+                                        f"    pip install playwright\n"
                                         f"    playwright install chromium"
                                     )
                                 elif "markdownify" in error_msg:
@@ -197,7 +249,7 @@ class ToolManager:
                         logger.warning(
                             f"[ToolManager] Browser tool is configured but not loaded.\n"
                             f"  To enable browser tool, run:\n"
-                            f"    pip install browser-use markdownify playwright\n"
+                            f"    pip install playwright\n"
                             f"    playwright install chromium"
                         )
                     elif tool_name == "google_search":
@@ -212,6 +264,306 @@ class ToolManager:
         except Exception as e:
             logger.error(f"Error configuring tools from config: {e}")
 
+    def _mcp_json_path(self) -> str:
+        import os
+        workspace = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        return os.path.join(workspace, "mcp.json")
+
+    def _read_mcp_json_signature(self):
+        """
+        Return (mtime, sha256_of_bytes) for ~/cow/mcp.json without parsing.
+        Returns (None, None) if the file doesn't exist or is unreadable.
+        Cheap enough (one stat + one small read) to call on every agent init.
+        """
+        import os
+        import hashlib
+        path = self._mcp_json_path()
+        try:
+            mtime = os.path.getmtime(path)
+        except OSError:
+            return (None, None)
+        try:
+            with open(path, "rb") as f:
+                digest = hashlib.sha256(f.read()).hexdigest()
+        except OSError:
+            return (mtime, None)
+        return (mtime, digest)
+
+    def _load_mcp_configs(self) -> list:
+        """
+        Load MCP server configs with priority:
+          1. ~/cow/mcp.json  (supports both mcpServers and mcp_servers keys)
+          2. config.json mcp_servers field (fallback)
+        """
+        import os
+        import json as _json
+
+        mcp_json_path = self._mcp_json_path()
+
+        if os.path.exists(mcp_json_path):
+            try:
+                with open(mcp_json_path, "r", encoding="utf-8") as f:
+                    data = _json.load(f)
+                raw = data.get("mcpServers") or data.get("mcp_servers") or data
+                logger.info(f"[ToolManager] Loading MCP config from {mcp_json_path}")
+                return _normalize_mcp_configs(raw)
+            except Exception as e:
+                logger.warning(f"[ToolManager] Failed to read {mcp_json_path}: {e}, falling back to config.json")
+
+        raw = conf().get("mcp_servers", [])
+        return _normalize_mcp_configs(raw)
+
+    def _load_mcp_tools(self):
+        """
+        Trigger MCP tool loading in a background thread (idempotent).
+
+        Returns immediately. Booting MCP servers (npx, uvx, etc.) takes
+        seconds to tens of seconds on first run, which would otherwise
+        block agent initialization and the user's first message.
+        Built-in tools work fine without MCP, so we let the agent serve
+        traffic right away and let MCP servers come online in the
+        background. Per-session agents read a snapshot of whatever is
+        ready at construction time and gracefully ignore the rest.
+        """
+        with self._mcp_lock:
+            if self._mcp_loaded:
+                return
+            mcp_servers_config = self._load_mcp_configs()
+            # Snapshot the signature now so future refresh_mcp_if_changed()
+            # calls can short-circuit when nothing has changed on disk.
+            self._mcp_signature = self._read_mcp_json_signature()
+            self._mcp_active_configs = {
+                cfg.get("name", "<unnamed>"): cfg for cfg in mcp_servers_config
+            }
+            if not mcp_servers_config:
+                # Mark as loaded even when there is nothing to load,
+                # so we don't re-read the config file on every call.
+                self._mcp_loaded = True
+                return
+
+            # Mark pending immediately so list_mcp_status() callers see
+            # the in-progress state instead of an empty dict.
+            for cfg in mcp_servers_config:
+                name = cfg.get("name", "<unnamed>")
+                self._mcp_status[name] = "pending"
+
+            self._mcp_loaded = True
+            threading.Thread(
+                target=self._load_mcp_tools_async,
+                args=(mcp_servers_config,),
+                daemon=True,
+                name="mcp-loader",
+            ).start()
+            logger.info(
+                f"[ToolManager] MCP loading started in background "
+                f"({len(mcp_servers_config)} server(s) configured)"
+            )
+
+    def refresh_mcp_if_changed(self):
+        """
+        Cheap check whether ~/cow/mcp.json has changed since last load.
+        If it has, do a diff-based reload: start newly added servers,
+        shut down removed ones, and restart any whose config was edited.
+        Untouched servers are left running.
+
+        Designed to be called on every agent creation. The fast path is
+        a single os.stat() — completely free when nothing has changed.
+        """
+        with self._mcp_lock:
+            new_sig = self._read_mcp_json_signature()
+            if new_sig == self._mcp_signature:
+                return  # no-op fast path
+
+            try:
+                new_configs = self._load_mcp_configs()
+            except Exception as e:
+                logger.warning(f"[ToolManager] MCP reload — failed to parse config: {e}")
+                return
+
+            new_by_name = {
+                cfg.get("name", "<unnamed>"): cfg for cfg in new_configs
+            }
+            old_by_name = self._mcp_active_configs
+
+            added = [n for n in new_by_name if n not in old_by_name]
+            removed = [n for n in old_by_name if n not in new_by_name]
+            changed = [
+                n for n in new_by_name
+                if n in old_by_name and new_by_name[n] != old_by_name[n]
+            ]
+
+            if not (added or removed or changed):
+                # Signature drifted but content is logically identical
+                # (e.g. user re-saved the file without edits). Just sync.
+                self._mcp_signature = new_sig
+                return
+
+            logger.info(
+                f"[ToolManager] mcp.json changed — "
+                f"adding={added}, removing={removed}, restarting={changed}"
+            )
+
+            # Tear down removed + changed servers (changed ones get restarted below)
+            for name in removed + changed:
+                self._teardown_mcp_server(name)
+
+            # Spin up newly added + changed servers in the background
+            to_start = [new_by_name[n] for n in added + changed]
+            if to_start:
+                for cfg in to_start:
+                    self._mcp_status[cfg.get("name", "<unnamed>")] = "pending"
+                threading.Thread(
+                    target=self._load_mcp_tools_async,
+                    args=(to_start,),
+                    daemon=True,
+                    name="mcp-loader-reload",
+                ).start()
+
+            self._mcp_active_configs = new_by_name
+            self._mcp_signature = new_sig
+
+    def _teardown_mcp_server(self, server_name: str):
+        """Shut down one MCP server and drop its tools from the registry."""
+        if self._mcp_registry is None:
+            return
+        client = None
+        with self._mcp_registry._registry_lock:
+            client = self._mcp_registry._clients.pop(server_name, None)
+        if client is not None:
+            try:
+                client.shutdown()
+            except Exception as e:
+                logger.warning(f"[MCP] Error shutting down '{server_name}': {e}")
+        # Drop tools that belonged to this server.
+        for tool_name in list(self._mcp_tool_instances.keys()):
+            tool = self._mcp_tool_instances.get(tool_name)
+            if tool is not None and getattr(tool, "server_name", None) == server_name:
+                self._mcp_tool_instances.pop(tool_name, None)
+        self._mcp_status.pop(server_name, None)
+
+    def _load_mcp_tools_async(self, mcp_servers_config):
+        """
+        Background worker: bring up each MCP server one-by-one and
+        publish ready tools to _mcp_tool_instances as they come online.
+
+        Server failures are isolated — one bad server cannot block
+        the others, and never raises out of the worker thread.
+        """
+        try:
+            from agent.tools.mcp.mcp_client import McpClient, McpClientRegistry
+            from agent.tools.mcp.mcp_tool import McpTool
+
+            registry = McpClientRegistry()
+            self._mcp_registry = registry
+
+            for cfg in mcp_servers_config:
+                server_name = cfg.get("name", "<unnamed>")
+                try:
+                    client = McpClient(cfg)
+                    if not client.initialize():
+                        self._mcp_status[server_name] = "failed"
+                        logger.warning(
+                            f"[MCP] Server '{server_name}' failed to initialize — skipping"
+                        )
+                        continue
+
+                    tool_schemas = client.list_tools()
+                    added = []
+                    for schema in tool_schemas:
+                        tool_name = schema.get("name", "")
+                        if not tool_name:
+                            continue
+                        mcp_tool = McpTool(client, schema, server_name)
+                        # Atomic dict assignment is GIL-safe; readers iterate
+                        # over a list() snapshot to avoid concurrent mutation.
+                        self._mcp_tool_instances[tool_name] = mcp_tool
+                        added.append(tool_name)
+
+                    # Register client into the shared registry only after its
+                    # tools are visible, so callers never see a half-loaded server.
+                    with registry._registry_lock:
+                        registry._clients[server_name] = client
+                    self._mcp_status[server_name] = "ready"
+                    logger.info(
+                        f"[MCP] Server '{server_name}' ready — "
+                        f"{len(added)} tool(s): {added}"
+                    )
+                except Exception as e:
+                    self._mcp_status[server_name] = "failed"
+                    logger.warning(f"[MCP] Server '{server_name}' load failed: {e}")
+
+            ready = sum(1 for s in self._mcp_status.values() if s == "ready")
+            total = len(self._mcp_status)
+            logger.info(
+                f"[ToolManager] MCP loading complete: "
+                f"{ready}/{total} server(s) ready, "
+                f"{len(self._mcp_tool_instances)} tool(s) available"
+            )
+        except Exception as e:
+            logger.warning(f"[ToolManager] MCP background loader crashed: {e}")
+
+    def list_mcp_status(self) -> dict:
+        """Return {server_name: status} snapshot for UI / debugging."""
+        return dict(self._mcp_status)
+
+    def sync_mcp_into_agent(self, agent) -> tuple:
+        """
+        Reconcile a live agent's tool collection with the current MCP tool registry.
+
+        Adds tools that finished loading after the agent was created,
+        and removes tools whose MCP server was torn down. Built-in tools
+        on the agent are left untouched.
+
+        Handles both representations CowAgent uses:
+          - Agent.tools: list[BaseTool]               (default Agent class)
+          - AgentStream.tools: dict[str, BaseTool]    (streaming agent)
+
+        Returns (added_names, removed_names) for logging.
+        """
+        if agent is None or not hasattr(agent, "tools"):
+            return ([], [])
+
+        from agent.tools.mcp.mcp_tool import McpTool
+        current = self._mcp_tool_instances
+        registry_names = set(current.keys())
+
+        agent_tools = agent.tools
+
+        if isinstance(agent_tools, dict):
+            agent_mcp_names = {
+                name for name, tool in agent_tools.items()
+                if isinstance(tool, McpTool)
+            }
+            added = registry_names - agent_mcp_names
+            removed = agent_mcp_names - registry_names
+            if not (added or removed):
+                return ([], [])
+            for name in added:
+                agent_tools[name] = current[name]
+            for name in removed:
+                agent_tools.pop(name, None)
+
+        elif isinstance(agent_tools, list):
+            agent_mcp_names = {
+                t.name for t in agent_tools if isinstance(t, McpTool)
+            }
+            added = registry_names - agent_mcp_names
+            removed = agent_mcp_names - registry_names
+            if not (added or removed):
+                return ([], [])
+            if removed:
+                agent.tools = [
+                    t for t in agent_tools
+                    if not (isinstance(t, McpTool) and t.name in removed)
+                ]
+            for name in added:
+                agent.tools.append(current[name])
+
+        else:
+            return ([], [])
+
+        return (sorted(added), sorted(removed))
+
     def create_tool(self, name: str) -> BaseTool:
         """
         Get a new instance of a tool by name.
@@ -229,6 +581,12 @@ class ToolManager:
                 tool_instance.config = self.tool_configs[name]
 
             return tool_instance
+
+        # Fall back to MCP tool instances
+        mcp_tool = self._mcp_tool_instances.get(name)
+        if mcp_tool:
+            return mcp_tool
+
         return None
 
     def list_tools(self) -> dict:
@@ -245,4 +603,17 @@ class ToolManager:
                 "description": temp_instance.description,
                 "parameters": temp_instance.get_json_schema()
             }
+
+        # Include MCP tool instances
+        for name, mcp_tool in self._mcp_tool_instances.items():
+            result[name] = {
+                "description": mcp_tool.description,
+                "parameters": mcp_tool.params,
+            }
+
         return result
+
+    def shutdown_mcp(self):
+        """Shut down all MCP server clients."""
+        if self._mcp_registry:
+            self._mcp_registry.shutdown_all()

agent/tools/utils/truncate.py

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

agent/tools/vision/__init__.py

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

agent/tools/vision/vision.py

@@ -0,0 +1,814 @@
+"""
+Vision tool - Analyze images using Vision API.
+Supports local files (auto base64-encoded) and HTTP URLs.
+
+Provider resolution:
+  - tools.vision.model (if set) means "prefer this model first; fall back to
+    other configured providers if it fails". The model name is mapped to its
+    native provider (e.g. doubao-* → Doubao, kimi-* → Moonshot, gpt-* →
+    OpenAI/LinkAI). That provider is tried first, then the standard auto
+    chain runs as fallback (with the preferred provider de-duplicated).
+  - Auto chain priority:
+      1. Main model via bot.call_vision — only when the main bot is known
+         to actually support vision (not just expose a call_vision method).
+      2. Other models whose API key is configured.
+      3. OpenAI / LinkAI raw HTTP.
+    When use_linkai=true, LinkAI is promoted to #1.
+"""
+
+import base64
+import os
+import subprocess
+import tempfile
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+import requests
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from common import const
+from common.log import logger
+from config import conf
+
+DEFAULT_MODEL = const.GPT_41_MINI
+DEFAULT_TIMEOUT = 60
+MAX_TOKENS = 1000
+COMPRESS_THRESHOLD = 1_048_576  # 1 MB
+
+SUPPORTED_EXTENSIONS = {
+    "jpg": "image/jpeg",
+    "jpeg": "image/jpeg",
+    "png": "image/png",
+    "gif": "image/gif",
+    "webp": "image/webp",
+}
+
+_MAIN_MODEL_PROVIDER_NAME = "MainModel"
+
+# (config_key_for_api_key, bot_type, default_vision_model, provider_display_name)
+# Auto-discovered as fallback vision providers when their API key is configured.
+# OpenAI and LinkAI are handled separately (raw HTTP providers), so not listed here.
+_DISCOVERABLE_MODELS = [
+    ("moonshot_api_key", const.MOONSHOT, const.KIMI_K2_6, "Moonshot"),
+    ("ark_api_key", const.DOUBAO, const.DOUBAO_SEED_2_PRO, "Doubao"),
+    ("dashscope_api_key", const.QWEN_DASHSCOPE, const.QWEN36_PLUS, "DashScope"),
+    ("claude_api_key", const.CLAUDEAPI, const.CLAUDE_4_6_SONNET, "Claude"),
+    ("gemini_api_key", const.GEMINI, const.GEMINI_35_FLASH, "Gemini"),
+    ("qianfan_api_key", const.QIANFAN, const.ERNIE_45_TURBO_VL, "Qianfan"),
+    ("zhipu_ai_api_key", const.ZHIPU_AI, const.GLM_4_7, "ZhipuAI"),
+    ("minimax_api_key", const.MiniMax, const.MINIMAX_M2_7, "MiniMax"),
+    ("mimo_api_key", const.MIMO, const.MIMO_V2_5_PRO, "MiMo"),
+]
+
+# Model name prefix → discoverable provider display_name.
+# Used to auto-route tools.vision.model to its native provider.
+# Matched case-insensitively; longest prefix wins.
+_MODEL_PREFIX_TO_PROVIDER = [
+    ("doubao-", "Doubao"),
+    ("kimi-", "Moonshot"),
+    ("moonshot-", "Moonshot"),
+    ("qwen", "DashScope"),       # qwen-*, qwen3-*, qwen3.6-*, etc.
+    ("claude-", "Claude"),
+    ("ernie-", "Qianfan"),
+    ("gemini-", "Gemini"),
+    ("glm-", "ZhipuAI"),
+    ("minimax-", "MiniMax"),
+    ("abab", "MiniMax"),
+    ("mimo-", "MiMo"),
+]
+
+# Model prefixes that natively belong to OpenAI / LinkAI (raw HTTP providers).
+_OPENAI_MODEL_PREFIXES = ("gpt-", "o1-", "o3-", "o4-", "chatgpt-")
+
+# Maps the UI provider id (persisted in tools.vision.provider) to the internal
+# display name used in VisionProvider.name. Keep in sync with _DISCOVERABLE_MODELS
+# and the openai/linkai branches in _route_by_model_name.
+_PROVIDER_ID_TO_DISPLAY = {
+    "openai": "OpenAI",
+    "linkai": "LinkAI",
+    "moonshot": "Moonshot",
+    "doubao": "Doubao",
+    "dashscope": "DashScope",
+    "claudeAPI": "Claude",
+    "gemini": "Gemini",
+    "qianfan": "Qianfan",
+    "zhipu": "ZhipuAI",
+    "minimax": "MiniMax",
+    "mimo": "MiMo",
+}
+
+
+@dataclass
+class VisionProvider:
+    """A single Vision API provider configuration."""
+    name: str
+    api_key: str
+    api_base: str
+    extra_headers: dict = field(default_factory=dict)
+    model_override: Optional[str] = None
+    use_bot: bool = False  # When True, call via bot.call_vision instead of raw HTTP
+    fallback_bot: Any = None  # Bot instance for non-main-model providers
+
+
+class VisionAPIError(Exception):
+    """Raised when a Vision API call fails and should trigger fallback."""
+    pass
+
+
+class Vision(BaseTool):
+    """Analyze images using Vision API"""
+
+    name: str = "vision"
+    description: str = (
+        "Analyze a local image or image URL (jpg/jpeg/png) using Vision API. "
+        "Can describe content, extract text, identify objects, colors, etc. "
+    )
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "image": {
+                "type": "string",
+                "description": "Local file path or HTTP(S) URL of the image to analyze",
+            },
+            "question": {
+                "type": "string",
+                "description": "Question to ask about the image",
+            },
+        },
+        "required": ["image", "question"],
+    }
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+
+    @staticmethod
+    def is_available() -> bool:
+        return True
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        image = args.get("image", "").strip()
+        question = args.get("question", "").strip()
+
+        if not image:
+            return ToolResult.fail("Error: 'image' parameter is required")
+        if not question:
+            return ToolResult.fail("Error: 'question' parameter is required")
+
+        providers = self._resolve_providers()
+        if not providers:
+            return ToolResult.fail(
+                "Error: No model available for Vision.\n"
+                "The main model does not support vision and no other API keys are configured.\n"
+                "Options:\n"
+                "  1. Switch to a multimodal model (e.g. ernie-4.5-turbo-vl, qwen3.6-plus, claude-sonnet-4-6, gemini-2.0-flash)\n"
+                "  2. Configure OPENAI_API_KEY: env_config(action=\"set\", key=\"OPENAI_API_KEY\", value=\"your-key\")\n"
+                "  3. Configure LINKAI_API_KEY: env_config(action=\"set\", key=\"LINKAI_API_KEY\", value=\"your-key\")"
+            )
+
+        try:
+            image_content = self._build_image_content(image)
+        except Exception as e:
+            return ToolResult.fail(f"Error: {e}")
+
+        # Default model is only used as a last-resort placeholder for providers
+        # whose VisionProvider.model_override is None (e.g. raw OpenAI provider
+        # when the user did not configure tools.vision.model).
+        return self._call_with_fallback(providers, DEFAULT_MODEL, question, image_content)
+
+    def _call_with_fallback(self, providers: List[VisionProvider], model: str,
+                            question: str, image_content: dict) -> ToolResult:
+        """Try each provider in order; fall back to the next one on failure."""
+        errors: List[str] = []
+        for i, provider in enumerate(providers):
+            use_model = provider.model_override or model
+            try:
+                logger.info(f"[Vision] Trying provider '{provider.name}' "
+                            f"with model '{use_model}' ({i + 1}/{len(providers)})")
+                if provider.use_bot:
+                    result = self._call_via_bot(use_model, question, image_content, provider)
+                else:
+                    result = self._call_api(provider, use_model, question, image_content)
+                logger.info(f"[Vision] ✅ Success via {provider.name} (model={use_model})")
+                return result
+            except VisionAPIError as e:
+                errors.append(f"[{provider.name}/{use_model}] {e}")
+                logger.warning(f"[Vision] Provider '{provider.name}' failed: {e}")
+            except requests.Timeout:
+                errors.append(f"[{provider.name}/{use_model}] Request timed out after {DEFAULT_TIMEOUT}s")
+                logger.warning(f"[Vision] Provider '{provider.name}' timed out")
+            except requests.ConnectionError:
+                errors.append(f"[{provider.name}/{use_model}] Connection failed")
+                logger.warning(f"[Vision] Provider '{provider.name}' connection failed")
+            except Exception as e:
+                errors.append(f"[{provider.name}/{use_model}] {e}")
+                logger.error(f"[Vision] Provider '{provider.name}' unexpected error: {e}", exc_info=True)
+
+        return ToolResult.fail(
+            "Error: All Vision API providers failed.\n" + "\n".join(f"  - {err}" for err in errors)
+        )
+
+    def _resolve_providers(self) -> List[VisionProvider]:
+        """
+        Build an ordered list of providers to try.
+
+        Semantics of `tools.vision.model`:
+          "Prefer this model first; fall back to other configured providers
+           if it fails."
+
+        Order:
+          1. The provider that natively serves `tools.vision.model` (if any
+             and its API key is configured) — using the user-specified model
+             name verbatim.
+          2. Auto-discovery chain as fallback:
+               - use_linkai=true → [LinkAI, MainModel?, OtherModels…, OpenAI]
+               - default         → [MainModel?, OtherModels…, OpenAI, LinkAI]
+             MainModel is only included when the main bot is known to support
+             vision (see _main_bot_supports_vision).
+
+        Providers that share the same display name as the preferred provider
+        are de-duplicated to avoid retrying the same endpoint twice.
+        """
+        user_model = self._resolve_user_vision_model()
+        user_provider = self._resolve_user_vision_provider()
+        providers: List[VisionProvider] = []
+
+        # Step 1: preferred provider — explicit `tools.vision.provider`
+        # wins so custom model names can still be routed correctly. Falls
+        # through to model-name prefix inference when provider is unset.
+        preferred = None
+        if user_provider and user_model:
+            preferred = self._route_by_provider_id(user_provider, user_model)
+        if not preferred and user_model:
+            preferred = self._route_by_model_name(user_model)
+        if preferred:
+            providers.extend(preferred)
+
+        # Step 2: auto-discovery chain as fallback
+        existing = {p.name for p in providers}
+        fallback: List[VisionProvider] = []
+        use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
+
+        if use_linkai:
+            self._append_provider(fallback, lambda: self._build_linkai_provider(user_model))
+            self._append_provider(fallback, self._build_main_model_provider)
+            self._append_other_model_providers(fallback, preferred_model=user_model)
+            self._append_provider(fallback, lambda: self._build_openai_provider(user_model))
+        else:
+            self._append_provider(fallback, self._build_main_model_provider)
+            self._append_other_model_providers(fallback, preferred_model=user_model)
+            self._append_provider(fallback, lambda: self._build_openai_provider(user_model))
+            self._append_provider(fallback, lambda: self._build_linkai_provider(user_model))
+
+        for p in fallback:
+            if p.name in existing:
+                continue
+            providers.append(p)
+            existing.add(p.name)
+
+        return providers
+
+    @staticmethod
+    def _append_provider(providers: List[VisionProvider], builder) -> None:
+        p = builder()
+        if p:
+            providers.append(p)
+
+    @staticmethod
+    def _resolve_user_vision_model() -> Optional[str]:
+        """Read tools.vision.model (singular ``tool`` kept as runtime fallback)."""
+        tools_conf = conf().get("tools") or conf().get("tool") or {}
+        if not isinstance(tools_conf, dict):
+            return None
+        vision_conf = tools_conf.get("vision", {})
+        if not isinstance(vision_conf, dict):
+            return None
+        m = vision_conf.get("model")
+        if isinstance(m, str) and m.strip():
+            return m.strip()
+        return None
+
+    @staticmethod
+    def _resolve_user_vision_provider() -> Optional[str]:
+        """Read tools.vision.provider — the UI-persisted vendor id.
+
+        Lets users pin a vendor for custom model names that prefix-inference
+        can't recognize. Returns None when unset/blank.
+        """
+        tools_conf = conf().get("tools") or conf().get("tool") or {}
+        if not isinstance(tools_conf, dict):
+            return None
+        vision_conf = tools_conf.get("vision", {})
+        if not isinstance(vision_conf, dict):
+            return None
+        p = vision_conf.get("provider")
+        if isinstance(p, str) and p.strip():
+            return p.strip()
+        return None
+
+    @staticmethod
+    def _infer_provider_from_model(model_name: str) -> Optional[str]:
+        """
+        Infer the provider display name from a model name's prefix.
+        Returns None when no rule matches (or for OpenAI-family names, which
+        are handled separately by the caller).
+        """
+        if not model_name:
+            return None
+        lower = model_name.lower()
+        # Sort by prefix length desc so e.g. "moonshot-" wins over hypothetical "moo-"
+        for prefix, display_name in sorted(_MODEL_PREFIX_TO_PROVIDER, key=lambda x: -len(x[0])):
+            if lower.startswith(prefix.lower()):
+                return display_name
+        return None
+
+    def _route_by_provider_id(self, provider_id: str, user_model: str) -> Optional[List[VisionProvider]]:
+        """Route by the UI-persisted provider id.
+
+        Returns:
+          - [provider] : provider id is known and its key is configured.
+          - None       : unknown provider id, or the bot can't be created.
+                         Caller falls through to model-name-based routing.
+        """
+        display_name = _PROVIDER_ID_TO_DISPLAY.get(provider_id)
+        if not display_name:
+            return None
+
+        # OpenAI / LinkAI use raw HTTP providers, not the discoverable bot path.
+        if provider_id == "openai":
+            p = self._build_openai_provider(user_model)
+            return [p] if p else None
+        if provider_id == "linkai":
+            p = self._build_linkai_provider(user_model)
+            return [p] if p else None
+
+        # Discoverable bot-backed providers.
+        for config_key, bot_type, _default_model, name in _DISCOVERABLE_MODELS:
+            if name != display_name:
+                continue
+            api_key = conf().get(config_key, "")
+            if not api_key or not api_key.strip():
+                logger.warning(f"[Vision] tools.vision.provider='{provider_id}' "
+                               f"but '{config_key}' is not configured. Falling back.")
+                return None
+            try:
+                from models.bot_factory import create_bot
+                bot = create_bot(bot_type)
+                if not hasattr(bot, 'call_vision'):
+                    logger.warning(f"[Vision] '{display_name}' bot does not implement call_vision.")
+                    return None
+            except Exception as e:
+                logger.warning(f"[Vision] Failed to create '{display_name}' bot: {e}")
+                return None
+            return [VisionProvider(
+                name=display_name,
+                api_key="",
+                api_base="",
+                model_override=user_model,
+                use_bot=True,
+                fallback_bot=bot,
+            )]
+        return None
+
+    def _route_by_model_name(self, user_model: str) -> Optional[List[VisionProvider]]:
+        """
+        Try to build a provider list using the user-specified model name.
+        Returns:
+          - [provider]  : matched and the provider's key is configured
+          - []          : matched but key missing → tell caller to surface this
+                          as a hard error rather than silently falling back
+          - None        : no rule matches → caller should fall through to auto
+        """
+        lower = user_model.lower()
+
+        # OpenAI / LinkAI family
+        if lower.startswith(_OPENAI_MODEL_PREFIXES):
+            providers: List[VisionProvider] = []
+            # Prefer LinkAI when explicitly enabled, else OpenAI first
+            use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
+            if use_linkai:
+                self._append_provider(providers, lambda: self._build_linkai_provider(user_model))
+                self._append_provider(providers, lambda: self._build_openai_provider(user_model))
+            else:
+                self._append_provider(providers, lambda: self._build_openai_provider(user_model))
+                self._append_provider(providers, lambda: self._build_linkai_provider(user_model))
+            if providers:
+                return providers
+            logger.warning(f"[Vision] tools.vision.model='{user_model}' looks like an OpenAI "
+                           f"model but neither OPENAI_API_KEY nor LINKAI_API_KEY is configured.")
+            return None  # fall through to auto
+
+        # Discoverable native providers (Doubao, Moonshot, etc.)
+        target_display = self._infer_provider_from_model(user_model)
+        if not target_display:
+            return None  # unknown prefix → auto
+
+        for config_key, bot_type, _default_model, display_name in _DISCOVERABLE_MODELS:
+            if display_name != target_display:
+                continue
+            api_key = conf().get(config_key, "")
+            if not api_key or not api_key.strip():
+                logger.warning(f"[Vision] tools.vision.model='{user_model}' routes to "
+                               f"'{display_name}' but '{config_key}' is not configured. "
+                               f"Falling back to auto-discovery.")
+                return None  # fall through to auto
+            try:
+                from models.bot_factory import create_bot
+                bot = create_bot(bot_type)
+                if not hasattr(bot, 'call_vision'):
+                    logger.warning(f"[Vision] '{display_name}' bot does not implement call_vision.")
+                    return None
+            except Exception as e:
+                logger.warning(f"[Vision] Failed to create '{display_name}' bot: {e}")
+                return None
+
+            return [VisionProvider(
+                name=display_name,
+                api_key="",
+                api_base="",
+                model_override=user_model,
+                use_bot=True,
+                fallback_bot=bot,
+            )]
+
+        return None
+
+    def _append_other_model_providers(self, providers: List[VisionProvider],
+                                       preferred_model: Optional[str] = None) -> None:
+        """
+        Auto-discover other models whose API key is configured.
+        Skip the main model's own bot_type (already covered by MainModel
+        provider), unless the main model itself does not support vision —
+        in that case we still want the vendor's dedicated vision model
+        as a fallback. Also skip bot_types that already appear in the
+        provider list.
+
+        If preferred_model matches a provider's family, use it instead
+        of that provider's hard-coded default model.
+        """
+        main_bot_type = None
+        main_bot_supports_vision = False
+        if self.model and hasattr(self.model, '_resolve_bot_type'):
+            main_bot_type = self.model._resolve_bot_type(conf().get("model", ""))
+            main_bot = getattr(self.model, "bot", None)
+            main_bot_supports_vision = self._main_bot_supports_vision(main_bot)
+
+        existing_names = {p.name for p in providers}
+        preferred_provider = self._infer_provider_from_model(preferred_model) if preferred_model else None
+
+        for config_key, bot_type, default_model, display_name in _DISCOVERABLE_MODELS:
+            if display_name in existing_names:
+                continue
+            # Same bot_type as the main model is normally handled by the
+            # MainModel provider; only skip it here if the main model
+            # actually supports vision. Otherwise fall through and add
+            # the vendor's dedicated vision model as a fallback.
+            if bot_type == main_bot_type and main_bot_supports_vision:
+                continue
+            api_key = conf().get(config_key, "")
+            if not api_key or not api_key.strip():
+                continue
+
+            try:
+                from models.bot_factory import create_bot
+                bot = create_bot(bot_type)
+                if not hasattr(bot, 'call_vision'):
+                    continue
+            except Exception:
+                continue
+
+            model_for_provider = (preferred_model
+                                  if preferred_provider == display_name and preferred_model
+                                  else default_model)
+
+            provider = VisionProvider(
+                name=display_name,
+                api_key="",
+                api_base="",
+                model_override=model_for_provider,
+                use_bot=True,
+                fallback_bot=bot,
+            )
+
+            # Same vendor as the main bot is the most natural fallback when
+            # the main model itself does not support vision — promote it to
+            # the front of the list instead of relying on declaration order.
+            if bot_type == main_bot_type:
+                providers.insert(0, provider)
+            else:
+                providers.append(provider)
+
+    def _main_bot_supports_vision(self, bot) -> bool:
+        """
+        Whether the main bot is known to natively support vision.
+
+        Having a `call_vision` method is necessary but not sufficient —
+        some bots implement the method against an endpoint that does not
+        actually serve vision models, which causes silent failures when a
+        vendor-foreign model name is forwarded.
+
+        Resolution order:
+          1. If the bot explicitly declares `supports_vision`, trust it.
+             This lets bots opt in or out based on their own runtime
+             configuration (e.g. the currently selected model).
+          2. Otherwise, fall back to a model-name prefix heuristic: trust
+             call_vision when the main model looks like an OpenAI family
+             model or matches a known multimodal vendor prefix.
+        """
+        if bot is None:
+            return False
+        if hasattr(bot, "supports_vision"):
+            return bool(getattr(bot, "supports_vision"))
+        main_model = (conf().get("model") or "").lower()
+        if not main_model:
+            return False
+        if main_model.startswith(_OPENAI_MODEL_PREFIXES):
+            return True
+        return self._infer_provider_from_model(main_model) is not None
+
+    def _build_main_model_provider(self) -> Optional[VisionProvider]:
+        """
+        Use the vendor's own model for vision via bot.call_vision.
+        Gated by _main_bot_supports_vision so non-vision bots (DeepSeek, etc.)
+        do not get routed vendor-foreign model names.
+        """
+        if not (self.model and hasattr(self.model, 'bot')):
+            return None
+        try:
+            bot = self.model.bot
+        except Exception:
+            return None
+        if not hasattr(bot, 'call_vision'):
+            return None
+        if not self._main_bot_supports_vision(bot):
+            return None
+
+        # Use the configured main model name; do NOT inject tools.vision.model
+        # here, because by the time we reach this branch the tools.vision.model
+        # routing has already been attempted (and either matched the main bot
+        # or failed to find a provider).
+        main_model_name = conf().get("model") or None
+
+        return VisionProvider(
+            name=_MAIN_MODEL_PROVIDER_NAME,
+            api_key="",
+            api_base="",
+            model_override=main_model_name,
+            use_bot=True,
+        )
+
+    def _build_openai_provider(self, preferred_model: Optional[str] = None) -> Optional[VisionProvider]:
+        api_key = conf().get("open_ai_api_key") or os.environ.get("OPENAI_API_KEY")
+        if not api_key:
+            return None
+        api_base = (conf().get("open_ai_api_base") or os.environ.get("OPENAI_API_BASE", "")).rstrip("/") \
+            or "https://api.openai.com/v1"
+        # Only honor preferred_model when it looks like an OpenAI-family name;
+        # otherwise the OpenAI endpoint would 400 on a vendor-specific name.
+        model_override = preferred_model if (
+            preferred_model and preferred_model.lower().startswith(_OPENAI_MODEL_PREFIXES)
+        ) else None
+        return VisionProvider(
+            name="OpenAI",
+            api_key=api_key,
+            api_base=self._ensure_v1(api_base),
+            model_override=model_override,
+        )
+
+    def _build_linkai_provider(self, preferred_model: Optional[str] = None) -> Optional[VisionProvider]:
+        api_key = conf().get("linkai_api_key") or os.environ.get("LINKAI_API_KEY")
+        if not api_key:
+            return None
+        api_base = (conf().get("linkai_api_base") or os.environ.get("LINKAI_API_BASE", "")).rstrip("/") \
+            or "https://api.link-ai.tech"
+        from common.utils import get_cloud_headers
+        extra = get_cloud_headers(api_key)
+        extra.pop("Authorization", None)
+        extra.pop("Content-Type", None)
+        # LinkAI is a multi-vendor proxy and accepts most model names, so we
+        # honor any user-configured model name here.
+        return VisionProvider(
+            name="LinkAI",
+            api_key=api_key,
+            api_base=self._ensure_v1(api_base),
+            extra_headers=extra,
+            model_override=preferred_model,
+        )
+
+    def _call_via_bot(self, model: str, question: str, image_content: dict,
+                      provider: Optional[VisionProvider] = None) -> ToolResult:
+        """
+        Call a model's call_vision with vendor-native API format.
+        Uses the provider's _fallback_bot if set, otherwise the main model bot.
+        Raises VisionAPIError on failure so fallback can proceed.
+        """
+        try:
+            bot = (provider and provider.fallback_bot) or self.model.bot
+        except Exception as e:
+            raise VisionAPIError(f"Cannot access bot: {e}")
+
+        # Extract the raw image URL from the OpenAI-format image_content block
+        image_url = image_content.get("image_url", {}).get("url", "")
+        if not image_url:
+            raise VisionAPIError("No image URL in content block")
+
+        try:
+            response = bot.call_vision(
+                image_url=image_url,
+                question=question,
+                model=model,
+                max_tokens=MAX_TOKENS,
+            )
+        except Exception as e:
+            raise VisionAPIError(f"call_vision failed: {e}")
+
+        if response is NotImplemented:
+            raise VisionAPIError("Bot does not support vision")
+
+        if isinstance(response, dict) and response.get("error"):
+            raise VisionAPIError(f"API error - {response.get('message', 'Unknown')}")
+
+        content = response.get("content", "") if isinstance(response, dict) else ""
+        if not content:
+            raise VisionAPIError("Empty response from main model")
+
+        usage_info = response.get("usage", {}) if isinstance(response, dict) else {}
+
+        # Use the actual model name from the bot response if available
+        actual_model = response.get("model", model) if isinstance(response, dict) else model
+        provider_name = provider.name if provider else _MAIN_MODEL_PROVIDER_NAME
+        return ToolResult.success({
+            "model": actual_model,
+            "provider": provider_name,
+            "content": content,
+            "usage": usage_info,
+        })
+
+    @staticmethod
+    def _ensure_v1(api_base: str) -> str:
+        """Append /v1 if the base URL doesn't already end with a versioned path."""
+        if not api_base:
+            return api_base
+        # Already has /v1 or similar version suffix
+        if api_base.rstrip("/").split("/")[-1].startswith("v"):
+            return api_base
+        return api_base.rstrip("/") + "/v1"
+
+    def _build_image_content(self, image: str) -> dict:
+        """
+        Build the image_url content block.
+        Both remote URLs and local files are converted to base64 data URLs
+        so every bot backend can consume them without extra downloads.
+        """
+        if image.startswith(("http://", "https://")):
+            return self._download_to_data_url(image)
+
+        if not os.path.isfile(image):
+            raise FileNotFoundError(f"Image file not found: {image}")
+
+        ext = image.rsplit(".", 1)[-1].lower() if "." in image else ""
+        mime_type = SUPPORTED_EXTENSIONS.get(ext)
+        if not mime_type:
+            raise ValueError(
+                f"Unsupported image format '.{ext}'. "
+                f"Supported: {', '.join(SUPPORTED_EXTENSIONS.keys())}"
+            )
+
+        file_path = self._maybe_compress(image)
+        try:
+            with open(file_path, "rb") as f:
+                b64 = base64.b64encode(f.read()).decode("ascii")
+        finally:
+            if file_path != image and os.path.exists(file_path):
+                os.remove(file_path)
+
+        data_url = f"data:{mime_type};base64,{b64}"
+        return {"type": "image_url", "image_url": {"url": data_url}}
+
+    @staticmethod
+    def _download_to_data_url(url: str) -> dict:
+        """Download a remote image and return it as a base64 data URL."""
+        resp = requests.get(url, timeout=30)
+        if resp.status_code != 200:
+            raise VisionAPIError(f"Failed to download image: HTTP {resp.status_code}")
+        content_type = resp.headers.get("Content-Type", "image/jpeg").split(";")[0].strip()
+        if not content_type.startswith("image/"):
+            content_type = "image/jpeg"
+        b64 = base64.b64encode(resp.content).decode("ascii")
+        data_url = f"data:{content_type};base64,{b64}"
+        return {"type": "image_url", "image_url": {"url": data_url}}
+
+    @staticmethod
+    def _maybe_compress(path: str) -> str:
+        """Compress image to under COMPRESS_THRESHOLD with max long-edge 1536px."""
+        file_size = os.path.getsize(path)
+        if file_size <= COMPRESS_THRESHOLD:
+            return path
+
+        tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+        tmp.close()
+
+        def _try_sips(max_dim: str, quality: str) -> bool:
+            try:
+                subprocess.run(
+                    ["sips", "-Z", max_dim, "-s", "formatOptions", quality,
+                     path, "--out", tmp.name],
+                    capture_output=True, check=True,
+                )
+                return True
+            except (FileNotFoundError, subprocess.CalledProcessError):
+                return False
+
+        def _try_convert(max_dim: str, quality: str) -> bool:
+            try:
+                subprocess.run(
+                    ["convert", path, "-resize", f"{max_dim}x{max_dim}>",
+                     "-quality", quality, tmp.name],
+                    capture_output=True, check=True,
+                )
+                return True
+            except (FileNotFoundError, subprocess.CalledProcessError):
+                return False
+
+        attempts = [
+            ("1536", "85"),
+            ("1536", "70"),
+            ("1536", "50"),
+        ]
+
+        for max_dim, quality in attempts:
+            ok = _try_sips(max_dim, quality) or _try_convert(max_dim, quality)
+            if not ok:
+                continue
+            new_size = os.path.getsize(tmp.name)
+            logger.debug(f"[Vision] Compressed image "
+                         f"({file_size // 1024}KB -> {new_size // 1024}KB, "
+                         f"max_dim={max_dim}, q={quality})")
+            if new_size <= COMPRESS_THRESHOLD:
+                return tmp.name
+
+        if os.path.exists(tmp.name) and os.path.getsize(tmp.name) > 0:
+            return tmp.name
+
+        os.remove(tmp.name)
+        return path
+
+    def _call_api(self, provider: VisionProvider, model: str,
+                  question: str, image_content: dict) -> ToolResult:
+        """
+        Call a single provider's Vision API.
+        Raises VisionAPIError on recoverable failures so the caller can try
+        the next provider.
+        """
+        payload = {
+            "model": model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": question},
+                        image_content,
+                    ],
+                }
+            ],
+        }
+
+        headers = {
+            "Authorization": f"Bearer {provider.api_key}",
+            "Content-Type": "application/json",
+            **provider.extra_headers,
+        }
+
+        resp = requests.post(
+            f"{provider.api_base}/chat/completions",
+            headers=headers,
+            json=payload,
+            timeout=DEFAULT_TIMEOUT,
+        )
+
+        if resp.status_code != 200:
+            raise VisionAPIError(f"HTTP {resp.status_code}: {resp.text[:200]}")
+
+        data = resp.json()
+
+        if "error" in data:
+            msg = data["error"].get("message", "Unknown API error")
+            raise VisionAPIError(f"API error - {msg}")
+
+        content = ""
+        choices = data.get("choices", [])
+        if choices:
+            content = choices[0].get("message", {}).get("content", "")
+
+        usage = data.get("usage", {})
+        result = {
+            "model": model,
+            "provider": provider.name,
+            "content": content,
+            "usage": {
+                "prompt_tokens": usage.get("prompt_tokens", 0),
+                "completion_tokens": usage.get("completion_tokens", 0),
+                "total_tokens": usage.get("total_tokens", 0),
+            },
+        }
+        return ToolResult.success(result)

agent/tools/web_fetch/web_fetch.py

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

agent/tools/web_search/web_search.py

@@ -1,33 +1,95 @@
-"""
-Web Search tool - Search the web using Bocha or LinkAI search API.
-Supports two backends with unified response format:
-  1. Bocha Search (primary, requires BOCHA_API_KEY)
-  2. LinkAI Search (fallback, requires LINKAI_API_KEY)
+"""Web Search tool. Supports four backends with a unified response format:
+  - bocha   (https://open.bochaai.com)
+  - zhipu   (https://docs.bigmodel.cn/cn/guide/tools/web-search)
+  - qianfan (https://cloud.baidu.com/doc/qianfan/s/2mh4su4uy)
+  - linkai  (https://link-ai.tech, fallback)
+
+Provider selection
+  - strategy 'auto' (default): pick the first configured provider in the
+    canonical order [bocha, zhipu, qianfan, linkai]. When the caller passes
+    an explicit `provider` it overrides the pick; an invalid/unconfigured
+    one silently falls back to the auto order.
+  - strategy 'fixed': use the configured provider; if its credential is
+    missing at call time, silently fall back to auto order (no card hint).
+
+Credentials
+  - bocha   : tools.web_search.bocha_api_key  ->  env BOCHA_API_KEY
+  - zhipu   : conf.zhipu_ai_api_key            ->  env ZHIPUAI_API_KEY
+  - qianfan : conf.qianfan_api_key             ->  env QIANFAN_API_KEY
+  - linkai  : conf.linkai_api_key              ->  env LINKAI_API_KEY
 """
 
-import os
 import json
-from typing import Dict, Any, Optional
+import os
+from typing import Any, Dict, List, Optional
 
 import requests
 
 from agent.tools.base_tool import BaseTool, ToolResult
 from common.log import logger
+from config import conf
 
 
-# Default timeout for API requests (seconds)
 DEFAULT_TIMEOUT = 30
 
+# Canonical fallback order. Empirically ordered by Chinese real-time
+# quality + relevance: bocha (best overall), qianfan (best for hot news),
+# zhipu (strong on long-form articles), linkai (cloud aggregator, last
+# resort).
+PROVIDER_ORDER = ("bocha", "qianfan", "zhipu", "linkai")
+
+PROVIDER_LABELS = {
+    "bocha":   "Bocha",
+    "zhipu":   "Zhipu",
+    "qianfan": "Baidu Qianfan",
+    "linkai":  "LinkAI",
+}
+
+
+def _tools_web_search_conf() -> dict:
+    """Return the tools.web_search config block (dict-like)."""
+    tools_cfg = conf().get("tools") or {}
+    if not isinstance(tools_cfg, dict):
+        return {}
+    block = tools_cfg.get("web_search") or {}
+    return block if isinstance(block, dict) else {}
+
+
+def _get_api_key(provider: str) -> str:
+    """Resolve API key for a provider, with conf -> env fallback."""
+    if provider == "bocha":
+        key = (_tools_web_search_conf().get("bocha_api_key") or "").strip()
+        return key or os.environ.get("BOCHA_API_KEY", "").strip()
+    if provider == "zhipu":
+        key = (conf().get("zhipu_ai_api_key") or "").strip()
+        return key or os.environ.get("ZHIPUAI_API_KEY", "").strip()
+    if provider == "qianfan":
+        key = (conf().get("qianfan_api_key") or "").strip()
+        return key or os.environ.get("QIANFAN_API_KEY", "").strip()
+    if provider == "linkai":
+        key = (conf().get("linkai_api_key") or "").strip()
+        return key or os.environ.get("LINKAI_API_KEY", "").strip()
+    return ""
+
+
+def configured_providers() -> List[str]:
+    """Return configured providers in canonical order."""
+    return [p for p in PROVIDER_ORDER if _get_api_key(p)]
+
+
+def _configured_strategy() -> str:
+    return (_tools_web_search_conf().get("strategy") or "auto").strip().lower()
+
+
+def _configured_provider() -> str:
+    return (_tools_web_search_conf().get("provider") or "").strip().lower()
+
 
 class WebSearch(BaseTool):
-    """Tool for searching the web using Bocha or LinkAI search API"""
+    """Tool for searching the web across multiple providers."""
 
     name: str = "web_search"
-    description: str = (
-        "Search the web for current information, news, research topics, or any real-time data. "
-        "Returns web page titles, URLs, snippets, and optional summaries. "
-        "Use this when the user asks about recent events, needs fact-checking, or wants up-to-date information."
-    )
+    description: str = "Search the web for real-time information. Returns titles, URLs, and snippets."
 
     params: dict = {
         "type": "object",
@@ -58,265 +120,368 @@ class WebSearch(BaseTool):
 
     def __init__(self, config: dict = None):
         self.config = config or {}
-        self._backend = None  # Will be resolved on first execute
 
     @staticmethod
     def is_available() -> bool:
-        """Check if web search is available (at least one API key is configured)"""
-        return bool(os.environ.get("BOCHA_API_KEY") or os.environ.get("LINKAI_API_KEY"))
+        """Tool is offered to the agent when at least one provider has a key."""
+        return bool(configured_providers())
 
-    def _resolve_backend(self) -> Optional[str]:
-        """
-        Determine which search backend to use.
-        Priority: Bocha > LinkAI
+    @classmethod
+    def get_json_schema(cls) -> dict:
+        """Augment the static schema with a `provider` field — only when the
+        user has ≥2 providers configured AND strategy is 'auto'. Otherwise
+        the backend picks silently and exposing the field would only waste
+        the agent's tokens."""
+        schema = {
+            "name": cls.name,
+            "description": cls.description,
+            "parameters": json.loads(json.dumps(cls.params)),  # deep copy
+        }
+        if _configured_strategy() != "auto":
+            return schema
+        available = configured_providers()
+        if len(available) < 2:
+            return schema
 
-        :return: 'bocha', 'linkai', or None
+        schema["parameters"]["properties"]["provider"] = {
+            "type": "string",
+            "enum": available,
+            "description": "Optional. Specifies the search backend. You may switch between providers when the user wants results from a particular source or from multiple sources.",
+        }
+        return schema
+
+    # ------------------------------------------------------------------
+    # Provider resolution
+    # ------------------------------------------------------------------
+
+    def _resolve_provider(self, requested: Optional[str]) -> Optional[str]:
+        """Pick a provider for this call.
+
+        Priority: caller-supplied (if configured) > fixed strategy (if
+        configured) > first configured in PROVIDER_ORDER. Silent fallback
+        when the desired one has no key.
         """
-        if os.environ.get("BOCHA_API_KEY"):
-            return "bocha"
-        if os.environ.get("LINKAI_API_KEY"):
-            return "linkai"
-        return None
+        available = configured_providers()
+        if not available:
+            return None
+
+        if requested:
+            req = requested.strip().lower()
+            if req in available:
+                return req
+            logger.warning(f"[WebSearch] requested provider '{requested}' unavailable, falling back")
+
+        if _configured_strategy() == "fixed":
+            pinned = _configured_provider()
+            if pinned in available:
+                return pinned
+            if pinned:
+                logger.warning(f"[WebSearch] pinned provider '{pinned}' unavailable, falling back to auto")
+
+        return available[0]
+
+    @staticmethod
+    def _resolution_reason(requested: Optional[str], chosen: str) -> str:
+        """Human-readable explanation for why `chosen` won the resolver."""
+        if requested and requested.strip().lower() == chosen:
+            return "caller-requested"
+        strategy = _configured_strategy()
+        if strategy == "fixed" and _configured_provider() == chosen:
+            return "fixed-strategy"
+        return "auto-fallback"
+
+    # ------------------------------------------------------------------
+    # Entry point
+    # ------------------------------------------------------------------
 
     def execute(self, args: Dict[str, Any]) -> ToolResult:
-        """
-        Execute web search
-
-        :param args: Search parameters (query, count, freshness, summary)
-        :return: Search results
-        """
-        query = args.get("query", "").strip()
+        query = (args.get("query") or "").strip()
         if not query:
             return ToolResult.fail("Error: 'query' parameter is required")
 
         count = args.get("count", 10)
         freshness = args.get("freshness", "noLimit")
         summary = args.get("summary", False)
-
-        # Validate count
         if not isinstance(count, int) or count < 1 or count > 50:
             count = 10
 
-        # Resolve backend
-        backend = self._resolve_backend()
-        if not backend:
+        requested = args.get("provider")
+        provider = self._resolve_provider(requested)
+        if not provider:
             return ToolResult.fail(
-                "Error: No search API key configured. "
-                "Please set BOCHA_API_KEY or LINKAI_API_KEY using env_config tool.\n"
-                "  - Bocha Search: https://open.bocha.cn\n"
-                "  - LinkAI Search: https://link-ai.tech"
+                "Error: No search provider configured. "
+                "Configure one of BOCHA_API_KEY / zhipu_ai_api_key / qianfan_api_key / linkai_api_key."
             )
 
+        # Always log the routing decision so multi-provider deployments can
+        # tell at a glance which backend served any given query.
+        available = configured_providers()
+        reason = self._resolution_reason(requested, provider)
+        q_preview = query if len(query) <= 60 else (query[:57] + "...")
+        logger.info(
+            f"[WebSearch] provider={provider} reason={reason} "
+            f"available={list(available)} query={q_preview!r} count={count} freshness={freshness}"
+        )
+
         try:
-            if backend == "bocha":
+            if provider == "bocha":
                 return self._search_bocha(query, count, freshness, summary)
-            else:
+            if provider == "zhipu":
+                return self._search_zhipu(query, count, freshness)
+            if provider == "qianfan":
+                return self._search_qianfan(query, count, freshness)
+            if provider == "linkai":
                 return self._search_linkai(query, count, freshness)
+            return ToolResult.fail(f"Error: Unknown provider '{provider}'")
         except requests.Timeout:
             return ToolResult.fail(f"Error: Search request timed out after {DEFAULT_TIMEOUT}s")
         except requests.ConnectionError:
             return ToolResult.fail("Error: Failed to connect to search API")
         except Exception as e:
-            logger.error(f"[WebSearch] Unexpected error: {e}", exc_info=True)
+            logger.error(f"[WebSearch] Unexpected error ({provider}): {e}", exc_info=True)
             return ToolResult.fail(f"Error: Search failed - {str(e)}")
 
+    # ------------------------------------------------------------------
+    # Bocha
+    # ------------------------------------------------------------------
+
     def _search_bocha(self, query: str, count: int, freshness: str, summary: bool) -> ToolResult:
-        """
-        Search using Bocha API
-
-        :param query: Search query
-        :param count: Number of results
-        :param freshness: Time range filter
-        :param summary: Whether to include summary
-        :return: Formatted search results
-        """
-        api_key = os.environ.get("BOCHA_API_KEY", "")
-        url = "https://api.bocha.cn/v1/web-search"
-
+        api_key = _get_api_key("bocha")
+        url = "https://api.bochaai.com/v1/web-search"
         headers = {
             "Authorization": f"Bearer {api_key}",
             "Content-Type": "application/json",
-            "Accept": "application/json"
+            "Accept": "application/json",
         }
+        payload = {"query": query, "count": count, "freshness": freshness, "summary": summary}
 
-        payload = {
-            "query": query,
-            "count": count,
-            "freshness": freshness,
-            "summary": summary
-        }
+        logger.debug(f"[WebSearch] bocha: query='{query}', count={count}")
+        resp = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
 
-        logger.debug(f"[WebSearch] Bocha search: query='{query}', count={count}")
+        if resp.status_code == 401:
+            return ToolResult.fail("Error: Invalid bocha API key.")
+        if resp.status_code == 403:
+            return ToolResult.fail("Error: bocha API — insufficient balance. Top up at https://open.bochaai.com")
+        if resp.status_code == 429:
+            return ToolResult.fail("Error: bocha API rate limit reached.")
+        if resp.status_code != 200:
+            return ToolResult.fail(f"Error: bocha API returned HTTP {resp.status_code}")
 
-        response = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
-
-        if response.status_code == 401:
-            return ToolResult.fail("Error: Invalid BOCHA_API_KEY. Please check your API key.")
-        if response.status_code == 403:
-            return ToolResult.fail("Error: Bocha API - insufficient balance. Please top up at https://open.bocha.cn")
-        if response.status_code == 429:
-            return ToolResult.fail("Error: Bocha API rate limit reached. Please try again later.")
-        if response.status_code != 200:
-            return ToolResult.fail(f"Error: Bocha API returned HTTP {response.status_code}")
-
-        data = response.json()
-
-        # Check API-level error code
+        data = resp.json()
         api_code = data.get("code")
         if api_code is not None and api_code != 200:
             msg = data.get("msg") or "Unknown error"
-            return ToolResult.fail(f"Error: Bocha API error (code={api_code}): {msg}")
-
-        # Extract and format results
-        return self._format_bocha_results(data, query)
-
-    def _format_bocha_results(self, data: dict, query: str) -> ToolResult:
-        """
-        Format Bocha API response into unified result structure
-
-        :param data: Raw API response
-        :param query: Original query
-        :return: Formatted ToolResult
-        """
-        search_data = data.get("data", {})
-        web_pages = search_data.get("webPages", {})
-        pages = web_pages.get("value", [])
-
-        if not pages:
-            return ToolResult.success({
-                "query": query,
-                "backend": "bocha",
-                "total": 0,
-                "results": [],
-                "message": "No results found"
-            })
+            return ToolResult.fail(f"Error: bocha API error (code={api_code}): {msg}")
 
+        pages = (data.get("data") or {}).get("webPages", {}).get("value", []) or []
         results = []
-        for page in pages:
-            result = {
-                "title": page.get("name", ""),
-                "url": page.get("url", ""),
-                "snippet": page.get("snippet", ""),
-                "siteName": page.get("siteName", ""),
-                "datePublished": page.get("datePublished") or page.get("dateLastCrawled", ""),
+        for p in pages:
+            item = {
+                "title": p.get("name", ""),
+                "url": p.get("url", ""),
+                "snippet": p.get("snippet", ""),
+                "siteName": p.get("siteName", ""),
+                "datePublished": p.get("datePublished") or p.get("dateLastCrawled", ""),
             }
-            # Include summary only if present
-            if page.get("summary"):
-                result["summary"] = page["summary"]
-            results.append(result)
-
-        total = web_pages.get("totalEstimatedMatches", len(results))
-
+            if p.get("summary"):
+                item["summary"] = p["summary"]
+            results.append(item)
+        total = (data.get("data") or {}).get("webPages", {}).get("totalEstimatedMatches", len(results))
         return ToolResult.success({
-            "query": query,
-            "backend": "bocha",
-            "total": total,
-            "count": len(results),
-            "results": results
+            "query": query, "backend": "bocha",
+            "total": total, "count": len(results), "results": results,
         })
 
-    def _search_linkai(self, query: str, count: int, freshness: str) -> ToolResult:
-        """
-        Search using LinkAI plugin API
-
-        :param query: Search query
-        :param count: Number of results
-        :param freshness: Time range filter
-        :return: Formatted search results
-        """
-        api_key = os.environ.get("LINKAI_API_KEY", "")
-        url = "https://api.link-ai.tech/v1/plugin/execute"
+    # ------------------------------------------------------------------
+    # Zhipu
+    # ------------------------------------------------------------------
 
+    def _search_zhipu(self, query: str, count: int, freshness: str) -> ToolResult:
+        api_key = _get_api_key("zhipu")
+        api_base = (conf().get("zhipu_ai_api_base") or "https://open.bigmodel.cn/api/paas/v4").rstrip("/")
+        url = f"{api_base}/web_search"
         headers = {
+            "Authorization": f"Bearer {api_key}",
             "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}"
         }
 
-        payload = {
-            "code": "web-search",
-            "args": {
-                "query": query,
-                "count": count,
-                "freshness": freshness
-            }
+        # Zhipu Web Search expects `search_query` <= 70 chars; truncate
+        # gracefully so a long agent-supplied query doesn't get rejected.
+        trimmed_query = (query or "")[:70]
+        engine = (_tools_web_search_conf().get("zhipu_search_engine") or "search_pro").strip().lower()
+        if engine not in ("search_std", "search_pro", "search_pro_sogou", "search_pro_quark"):
+            engine = "search_pro"
+
+        payload: Dict[str, Any] = {
+            "search_engine": engine,
+            "search_query": trimmed_query,
+            "search_intent": False,
+            "count": max(1, min(int(count or 10), 50)),
+            "search_recency_filter": freshness if freshness in (
+                "oneDay", "oneWeek", "oneMonth", "oneYear", "noLimit"
+            ) else "noLimit",
+        }
+        content_size = (_tools_web_search_conf().get("zhipu_content_size") or "").strip().lower()
+        if content_size in ("medium", "high"):
+            payload["content_size"] = content_size
+
+        logger.debug(f"[WebSearch] zhipu: query='{trimmed_query}', count={payload['count']}, engine={engine}")
+        resp = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
+
+        if resp.status_code == 401:
+            return ToolResult.fail("Error: Invalid Zhipu API key.")
+        if resp.status_code != 200:
+            return ToolResult.fail(f"Error: Zhipu API returned HTTP {resp.status_code}: {resp.text[:200]}")
+
+        data = resp.json()
+        # Business-level errors (1701/1702/1703 etc.) come back as
+        # {"error": {"code","message"}} even on HTTP 200.
+        if isinstance(data, dict) and data.get("error"):
+            err = data["error"] or {}
+            return ToolResult.fail(f"Error: Zhipu returned {err.get('code')}: {err.get('message','')}")
+
+        items = data.get("search_result") or (data.get("data") or {}).get("search_result") or []
+        results = []
+        for it in items:
+            results.append({
+                "title": it.get("title", ""),
+                "url": it.get("link") or it.get("url", ""),
+                "snippet": it.get("content") or it.get("snippet", ""),
+                "siteName": it.get("media") or it.get("siteName", ""),
+                "datePublished": it.get("publish_date") or it.get("datePublished", ""),
+            })
+        return ToolResult.success({
+            "query": query, "backend": "zhipu",
+            "total": len(results), "count": len(results), "results": results,
+        })
+
+    # ------------------------------------------------------------------
+    # Qianfan (Baidu)
+    # ------------------------------------------------------------------
+
+    def _search_qianfan(self, query: str, count: int, freshness: str) -> ToolResult:
+        api_key = _get_api_key("qianfan")
+        api_base = (conf().get("qianfan_api_base") or "https://qianfan.baidubce.com/v2").rstrip("/")
+        url = f"{api_base}/ai_search/web_search"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "X-Appbuilder-From": "cow",
         }
 
-        logger.debug(f"[WebSearch] LinkAI search: query='{query}', count={count}")
+        count = max(1, min(int(count or 10), 50))
+        payload: Dict[str, Any] = {
+            "messages": [{"role": "user", "content": query}],
+            "search_source": "baidu_search_v2",
+            "resource_type_filter": [{"type": "web", "top_k": count}],
+        }
 
-        response = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
+        # Baidu AI Search expects freshness as a date-range filter, not a
+        # named recency token. Translate our shared vocabulary into the
+        # underlying page_time range expected by the API.
+        search_filter = self._qianfan_build_freshness_filter(freshness)
+        if search_filter:
+            payload["search_filter"] = search_filter
 
-        if response.status_code == 401:
-            return ToolResult.fail("Error: Invalid LINKAI_API_KEY. Please check your API key.")
-        if response.status_code != 200:
-            return ToolResult.fail(f"Error: LinkAI API returned HTTP {response.status_code}")
+        logger.debug(f"[WebSearch] qianfan: query='{query}', count={count}, freshness={freshness!r}")
+        resp = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
 
-        data = response.json()
+        if resp.status_code == 401:
+            return ToolResult.fail("Error: Invalid Qianfan API key.")
+        if resp.status_code != 200:
+            return ToolResult.fail(f"Error: Qianfan API returned HTTP {resp.status_code}: {resp.text[:200]}")
 
+        data = resp.json()
+        # Even on HTTP 200 Baidu surfaces business errors as {"code","message"}.
+        if isinstance(data, dict) and data.get("code"):
+            return ToolResult.fail(f"Error: Qianfan returned {data.get('code')}: {data.get('message','')}")
+
+        refs = data.get("references") or []
+        results = []
+        for d in refs:
+            results.append({
+                "title": d.get("title", ""),
+                "url": d.get("url", ""),
+                "snippet": (d.get("content") or "")[:200],
+                "siteName": d.get("web_anchor") or d.get("website") or "",
+                "datePublished": d.get("date", ""),
+            })
+        return ToolResult.success({
+            "query": query, "backend": "qianfan",
+            "total": len(results), "count": len(results), "results": results,
+        })
+
+    @staticmethod
+    def _qianfan_build_freshness_filter(freshness: str) -> Optional[Dict[str, Any]]:
+        if not freshness or freshness == "noLimit":
+            return None
+        delta_days = {"oneDay": 1, "oneWeek": 7, "oneMonth": 30, "oneYear": 365}.get(freshness)
+        if not delta_days:
+            return None
+        from datetime import datetime, timedelta
+        now = datetime.now()
+        end_date = (now + timedelta(days=1)).strftime("%Y-%m-%d")
+        start_date = (now - timedelta(days=delta_days)).strftime("%Y-%m-%d")
+        return {"range": {"page_time": {"gte": start_date, "lt": end_date}}}
+
+    # ------------------------------------------------------------------
+    # LinkAI (plugin)
+    # ------------------------------------------------------------------
+
+    def _search_linkai(self, query: str, count: int, freshness: str) -> ToolResult:
+        api_key = _get_api_key("linkai")
+        api_base = (conf().get("linkai_api_base") or "https://api.link-ai.tech").rstrip("/")
+        url = f"{api_base}/v1/plugin/execute"
+
+        from common.utils import get_cloud_headers
+        headers = get_cloud_headers(api_key)
+
+        payload = {"code": "web-search", "args": {"query": query, "count": count, "freshness": freshness}}
+        logger.debug(f"[WebSearch] linkai: query='{query}', count={count}")
+        resp = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
+
+        if resp.status_code == 401:
+            return ToolResult.fail("Error: Invalid LinkAI API key.")
+        if resp.status_code != 200:
+            return ToolResult.fail(f"Error: LinkAI API returned HTTP {resp.status_code}")
+
+        data = resp.json()
         if not data.get("success"):
             msg = data.get("message") or "Unknown error"
             return ToolResult.fail(f"Error: LinkAI search failed: {msg}")
 
-        return self._format_linkai_results(data, query)
-
-    def _format_linkai_results(self, data: dict, query: str) -> ToolResult:
-        """
-        Format LinkAI API response into unified result structure.
-        LinkAI returns the search data in data.data field, which follows
-        the same Bing-compatible format as Bocha.
-
-        :param data: Raw API response
-        :param query: Original query
-        :return: Formatted ToolResult
-        """
-        raw_data = data.get("data", "")
-
-        # LinkAI may return data as a JSON string
-        if isinstance(raw_data, str):
+        raw = data.get("data", "")
+        if isinstance(raw, str):
             try:
-                raw_data = json.loads(raw_data)
+                raw = json.loads(raw)
             except (json.JSONDecodeError, TypeError):
-                # If data is plain text, return it as a single result
                 return ToolResult.success({
-                    "query": query,
-                    "backend": "linkai",
-                    "total": 1,
-                    "count": 1,
-                    "results": [{"content": raw_data}]
+                    "query": query, "backend": "linkai",
+                    "total": 1, "count": 1, "results": [{"content": raw}],
                 })
 
-        # If the response follows Bing-compatible structure
-        if isinstance(raw_data, dict):
-            web_pages = raw_data.get("webPages", {})
-            pages = web_pages.get("value", [])
-
+        if isinstance(raw, dict):
+            pages = (raw.get("webPages") or {}).get("value", []) or []
             if pages:
                 results = []
-                for page in pages:
-                    result = {
-                        "title": page.get("name", ""),
-                        "url": page.get("url", ""),
-                        "snippet": page.get("snippet", ""),
-                        "siteName": page.get("siteName", ""),
-                        "datePublished": page.get("datePublished") or page.get("dateLastCrawled", ""),
+                for p in pages:
+                    item = {
+                        "title": p.get("name", ""),
+                        "url": p.get("url", ""),
+                        "snippet": p.get("snippet", ""),
+                        "siteName": p.get("siteName", ""),
+                        "datePublished": p.get("datePublished") or p.get("dateLastCrawled", ""),
                     }
-                    if page.get("summary"):
-                        result["summary"] = page["summary"]
-                    results.append(result)
-
-                total = web_pages.get("totalEstimatedMatches", len(results))
+                    if p.get("summary"):
+                        item["summary"] = p["summary"]
+                    results.append(item)
+                total = (raw.get("webPages") or {}).get("totalEstimatedMatches", len(results))
                 return ToolResult.success({
-                    "query": query,
-                    "backend": "linkai",
-                    "total": total,
-                    "count": len(results),
-                    "results": results
+                    "query": query, "backend": "linkai",
+                    "total": total, "count": len(results), "results": results,
                 })
 
-        # Fallback: return raw data
         return ToolResult.success({
-            "query": query,
-            "backend": "linkai",
-            "total": 1,
-            "count": 1,
-            "results": [{"content": str(raw_data)}]
+            "query": query, "backend": "linkai",
+            "total": 1, "count": 1, "results": [{"content": str(raw)}],
         })

app.py

@@ -13,7 +13,6 @@ from plugins import *
 import threading
 
 
-# Global channel manager for restart support
 _channel_mgr = None
 
 
@@ -21,94 +20,206 @@ def get_channel_manager():
     return _channel_mgr
 
 
+def _parse_channel_type(raw) -> list:
+    """
+    Parse channel_type config value into a list of channel names.
+    Supports:
+      - single string: "feishu"
+      - comma-separated string: "feishu, dingtalk"
+      - list: ["feishu", "dingtalk"]
+    """
+    if isinstance(raw, list):
+        return [ch.strip() for ch in raw if ch.strip()]
+    if isinstance(raw, str):
+        return [ch.strip() for ch in raw.split(",") if ch.strip()]
+    return []
+
+
 class ChannelManager:
     """
-    Manage the lifecycle of a channel, supporting restart from sub-threads.
-    The channel.startup() runs in a daemon thread so that the main thread
-    remains available and a new channel can be started at any time.
+    Manage the lifecycle of multiple channels running concurrently.
+    Each channel.startup() runs in its own daemon thread.
+    The web channel is started as default console unless explicitly disabled.
     """
 
     def __init__(self):
-        self._channel = None
-        self._channel_thread = None
+        self._channels = {}        # channel_name -> channel instance
+        self._threads = {}         # channel_name -> thread
+        self._primary_channel = None
         self._lock = threading.Lock()
+        self.cloud_mode = False    # set to True when cloud client is active
 
     @property
     def channel(self):
-        return self._channel
+        """Return the primary (first non-web) channel for backward compatibility."""
+        return self._primary_channel
 
-    def start(self, channel_name: str, first_start: bool = False):
+    def get_channel(self, channel_name: str):
+        return self._channels.get(channel_name)
+
+    def start(self, channel_names: list, first_start: bool = False):
         """
-        Create and start a channel in a sub-thread.
+        Create and start one or more channels in sub-threads.
         If first_start is True, plugins and linkai client will also be initialized.
         """
         with self._lock:
-            channel = channel_factory.create_channel(channel_name)
-            self._channel = channel
+            channels = []
+            for name in channel_names:
+                ch = channel_factory.create_channel(name)
+                ch.cloud_mode = self.cloud_mode
+                self._channels[name] = ch
+                channels.append((name, ch))
+                if self._primary_channel is None and name != "web":
+                    self._primary_channel = ch
+
+            if self._primary_channel is None and channels:
+                self._primary_channel = channels[0][1]
 
             if first_start:
-                if channel_name in ["wx", "wxy", "terminal", "wechatmp", "web",
-                                    "wechatmp_service", "wechatcom_app", "wework",
-                                    const.FEISHU, const.DINGTALK]:
-                    PluginManager().load_plugins()
+                PluginManager().load_plugins()
 
-                if conf().get("use_linkai"):
+                # Cloud client is optional. It is only started when
+                # use_linkai=True AND cloud_deployment_id is set.
+                # By default neither is configured, so the app runs
+                # entirely locally without any remote connection.
+                if conf().get("use_linkai") and (
+                    os.environ.get("CLOUD_DEPLOYMENT_ID") or conf().get("cloud_deployment_id")
+                ):
                     try:
                         from common import cloud_client
-                        threading.Thread(target=cloud_client.start, args=(channel, self), daemon=True).start()
-                    except Exception as e:
+                        threading.Thread(
+                            target=cloud_client.start,
+                            args=(self._primary_channel, self),
+                            daemon=True,
+                        ).start()
+                    except Exception:
                         pass
 
-            # Run channel.startup() in a daemon thread so we can restart later
-            self._channel_thread = threading.Thread(
-                target=self._run_channel, args=(channel,), daemon=True
-            )
-            self._channel_thread.start()
-            logger.debug(f"[ChannelManager] Channel '{channel_name}' started in sub-thread")
+            # Start web console first so its logs print cleanly,
+            # then start remaining channels after a brief pause.
+            web_entry = None
+            other_entries = []
+            for entry in channels:
+                if entry[0] == "web":
+                    web_entry = entry
+                else:
+                    other_entries.append(entry)
 
-    def _run_channel(self, channel):
+            ordered = ([web_entry] if web_entry else []) + other_entries
+            for i, (name, ch) in enumerate(ordered):
+                if i > 0 and name != "web":
+                    time.sleep(0.1)
+                t = threading.Thread(target=self._run_channel, args=(name, ch), daemon=True)
+                self._threads[name] = t
+                t.start()
+                logger.debug(f"[ChannelManager] Channel '{name}' started in sub-thread")
+
+    def _run_channel(self, name: str, channel):
         try:
             channel.startup()
         except Exception as e:
-            logger.error(f"[ChannelManager] Channel startup error: {e}")
+            logger.error(f"[ChannelManager] Channel '{name}' startup error: {e}")
             logger.exception(e)
 
-    def stop(self):
+    def stop(self, channel_name: str = None):
         """
-        Stop the current channel. Since most channel startup() methods block
-        on an HTTP server or stream client, we stop by terminating the thread.
+        Stop channel(s). If channel_name is given, stop only that channel;
+        otherwise stop all channels.
         """
+        # Pop under lock, then stop outside lock to avoid deadlock
         with self._lock:
-            if self._channel is None:
+            names = [channel_name] if channel_name else list(self._channels.keys())
+            to_stop = []
+            for name in names:
+                ch = self._channels.pop(name, None)
+                th = self._threads.pop(name, None)
+                to_stop.append((name, ch, th))
+            if channel_name and self._primary_channel is self._channels.get(channel_name):
+                self._primary_channel = None
+
+        for name, ch, th in to_stop:
+            if ch is None:
+                logger.warning(f"[ChannelManager] Channel '{name}' not found in managed channels")
+                if th and th.is_alive():
+                    self._interrupt_thread(th, name)
+                continue
+            logger.info(f"[ChannelManager] Stopping channel '{name}'...")
+            graceful = False
+            if hasattr(ch, 'stop'):
+                try:
+                    ch.stop()
+                    graceful = True
+                except Exception as e:
+                    logger.warning(f"[ChannelManager] Error during channel '{name}' stop: {e}")
+            if th and th.is_alive():
+                th.join(timeout=5)
+                if th.is_alive():
+                    if graceful:
+                        logger.info(f"[ChannelManager] Channel '{name}' thread still alive after stop(), "
+                                    "leaving daemon thread to finish on its own")
+                    else:
+                        logger.warning(f"[ChannelManager] Channel '{name}' thread did not exit in 5s, forcing interrupt")
+                        self._interrupt_thread(th, name)
+
+    @staticmethod
+    def _interrupt_thread(th: threading.Thread, name: str):
+        """Raise SystemExit in target thread to break blocking loops like start_forever."""
+        import ctypes
+        try:
+            tid = th.ident
+            if tid is None:
                 return
-            channel_type = getattr(self._channel, 'channel_type', 'unknown')
-            logger.info(f"[ChannelManager] Stopping channel '{channel_type}'...")
-
-            # Try graceful stop if channel implements it
-            try:
-                if hasattr(self._channel, 'stop'):
-                    self._channel.stop()
-            except Exception as e:
-                logger.warning(f"[ChannelManager] Error during channel stop: {e}")
-
-            self._channel = None
-            self._channel_thread = None
+            res = ctypes.pythonapi.PyThreadState_SetAsyncExc(
+                ctypes.c_ulong(tid), ctypes.py_object(SystemExit)
+            )
+            if res == 1:
+                logger.info(f"[ChannelManager] Interrupted thread for channel '{name}'")
+            elif res > 1:
+                ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_ulong(tid), None)
+                logger.warning(f"[ChannelManager] Failed to interrupt thread for channel '{name}'")
+        except Exception as e:
+            logger.warning(f"[ChannelManager] Thread interrupt error for '{name}': {e}")
 
     def restart(self, new_channel_name: str):
         """
-        Restart the channel with a new channel type.
+        Restart a single channel with a new channel type.
         Can be called from any thread (e.g. linkai config callback).
         """
         logger.info(f"[ChannelManager] Restarting channel to '{new_channel_name}'...")
-        self.stop()
-
-        # Clear singleton cache so a fresh channel instance is created
+        self.stop(new_channel_name)
         _clear_singleton_cache(new_channel_name)
-
-        time.sleep(1)  # Brief pause to allow resources to release
-        self.start(new_channel_name, first_start=False)
+        time.sleep(1)
+        self.start([new_channel_name], first_start=False)
         logger.info(f"[ChannelManager] Channel restarted to '{new_channel_name}' successfully")
 
+    def add_channel(self, channel_name: str):
+        """
+        Dynamically add and start a new channel.
+        If the channel is already running, restart it instead.
+        """
+        with self._lock:
+            if channel_name in self._channels:
+                logger.info(f"[ChannelManager] Channel '{channel_name}' already exists, restarting")
+        if self._channels.get(channel_name):
+            self.restart(channel_name)
+            return
+        logger.info(f"[ChannelManager] Adding channel '{channel_name}'...")
+        _clear_singleton_cache(channel_name)
+        self.start([channel_name], first_start=False)
+        logger.info(f"[ChannelManager] Channel '{channel_name}' added successfully")
+
+    def remove_channel(self, channel_name: str):
+        """
+        Dynamically stop and remove a running channel.
+        """
+        with self._lock:
+            if channel_name not in self._channels:
+                logger.warning(f"[ChannelManager] Channel '{channel_name}' not found, nothing to remove")
+                return
+        logger.info(f"[ChannelManager] Removing channel '{channel_name}'...")
+        self.stop(channel_name)
+        logger.info(f"[ChannelManager] Channel '{channel_name}' removed successfully")
+
 
 def _clear_singleton_cache(channel_name: str):
     """
@@ -116,28 +227,25 @@ def _clear_singleton_cache(channel_name: str):
     a new instance can be created with updated config.
     """
     cls_map = {
-        "wx": "channel.wechat.wechat_channel.WechatChannel",
-        "wxy": "channel.wechat.wechaty_channel.WechatyChannel",
-        "wcf": "channel.wechat.wcf_channel.WechatfChannel",
         "web": "channel.web.web_channel.WebChannel",
         "wechatmp": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
         "wechatmp_service": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
         "wechatcom_app": "channel.wechatcom.wechatcomapp_channel.WechatComAppChannel",
-        "wework": "channel.wework.wework_channel.WeworkChannel",
         const.FEISHU: "channel.feishu.feishu_channel.FeiShuChanel",
         const.DINGTALK: "channel.dingtalk.dingtalk_channel.DingTalkChanel",
+        const.WECOM_BOT: "channel.wecom_bot.wecom_bot_channel.WecomBotChannel",
+        const.QQ: "channel.qq.qq_channel.QQChannel",
+        const.WEIXIN: "channel.weixin.weixin_channel.WeixinChannel",
+        "wx": "channel.weixin.weixin_channel.WeixinChannel",
     }
     module_path = cls_map.get(channel_name)
     if not module_path:
         return
-    # The singleton decorator stores instances in a closure dict keyed by class.
-    # We need to find the actual class and clear it from the closure.
     try:
         parts = module_path.rsplit(".", 1)
         module_name, class_name = parts[0], parts[1]
         import importlib
         module = importlib.import_module(module_name)
-        # The module-level name is the wrapper function from @singleton
         wrapper = getattr(module, class_name, None)
         if wrapper and hasattr(wrapper, '__closure__') and wrapper.__closure__:
             for cell in wrapper.__closure__:
@@ -166,6 +274,63 @@ def sigterm_handler_wrap(_signo):
     signal.signal(_signo, func)
 
 
+def _warmup_mcp_tools():
+    """
+    Kick off MCP server loading at process startup so subprocesses
+    (npx / uvx etc.) finish initializing before the first user message
+    arrives. Returns immediately — the actual work happens on a daemon
+    thread inside ToolManager. Safe to call when MCP is not configured.
+    """
+    try:
+        from agent.tools import ToolManager
+        ToolManager()._load_mcp_tools()
+    except Exception as e:
+        logger.warning(f"[App] MCP warmup failed (non-fatal): {e}")
+
+
+def _warmup_scheduler():
+    """Eager-init AgentBridge so the scheduler thread starts at process
+    boot rather than waiting for the first user message."""
+    try:
+        from bridge.bridge import Bridge
+        Bridge().get_agent_bridge()
+    except Exception as e:
+        logger.warning(f"[App] Scheduler warmup failed: {e}")
+
+
+def _sync_builtin_skills():
+    """Sync builtin skills from project skills/ to workspace skills/ on startup."""
+    import shutil
+    try:
+        workspace = conf().get("agent_workspace", "~/cow")
+        workspace = os.path.expanduser(workspace)
+        project_root = os.path.dirname(os.path.abspath(__file__))
+        builtin_dir = os.path.join(project_root, "skills")
+        custom_dir = os.path.join(workspace, "skills")
+
+        if not os.path.isdir(builtin_dir):
+            return
+
+        os.makedirs(custom_dir, exist_ok=True)
+        synced = 0
+        for name in os.listdir(builtin_dir):
+            src = os.path.join(builtin_dir, name)
+            if not os.path.isdir(src) or not os.path.isfile(os.path.join(src, "SKILL.md")):
+                continue
+            dst = os.path.join(custom_dir, name)
+            try:
+                if os.path.isdir(dst):
+                    shutil.rmtree(dst)
+                shutil.copytree(src, dst)
+                synced += 1
+            except Exception as e:
+                logger.warning(f"[App] Failed to sync builtin skill '{name}': {e}")
+        if synced:
+            logger.info(f"[App] Synced {synced} builtin skill(s) to workspace")
+    except Exception as e:
+        logger.warning(f"[App] Builtin skills sync failed: {e}")
+
+
 def run():
     global _channel_mgr
     try:
@@ -176,20 +341,39 @@ def run():
         # kill signal
         sigterm_handler_wrap(signal.SIGTERM)
 
-        # create channel
-        channel_name = conf().get("channel_type", "wx")
+        # Parse channel_type into a list
+        raw_channel = conf().get("channel_type", "web")
 
         if "--cmd" in sys.argv:
-            channel_name = "terminal"
+            channel_names = ["terminal"]
+        else:
+            channel_names = _parse_channel_type(raw_channel)
+            if not channel_names:
+                channel_names = ["web"]
 
-        if channel_name == "wxy":
-            os.environ["WECHATY_LOG"] = "warn"
+        # Auto-start web console unless explicitly disabled
+        web_console_enabled = conf().get("web_console", True)
+        if web_console_enabled and "web" not in channel_names:
+            channel_names.append("web")
+
+        # Sync builtin skills to workspace before channels start
+        _sync_builtin_skills()
+
+        # Kick off MCP server loading in the background so first-message
+        # latency isn't dominated by npx package downloads.
+        _warmup_mcp_tools()
+
+        _warmup_scheduler()
+
+        logger.info(f"[App] Starting channels: {channel_names}")
 
         _channel_mgr = ChannelManager()
-        _channel_mgr.start(channel_name, first_start=True)
+        _channel_mgr.start(channel_names, first_start=True)
 
         while True:
             time.sleep(1)
+    except KeyboardInterrupt:
+        pass
     except Exception as e:
         logger.error("App startup failed!")
         logger.exception(e)

bridge/agent_bridge.py

@@ -5,7 +5,7 @@ Agent Bridge - Integrates Agent system with existing COW bridge
 import os
 from typing import Optional, List
 
-from agent.protocol import Agent, LLMModel, LLMRequest
+from agent.protocol import Agent, LLMModel, LLMRequest, get_cancel_registry
 from bridge.agent_event_handler import AgentEventHandler
 from bridge.agent_initializer import AgentInitializer
 from bridge.bridge import Bridge
@@ -14,6 +14,7 @@ from bridge.reply import Reply, ReplyType
 from common import const
 from common.log import logger
 from common.utils import expand_path
+from config import conf
 from models.openai_compatible_bot import OpenAICompatibleBot
 
 
@@ -65,30 +66,73 @@ class AgentLLMModel(LLMModel):
     LLM Model adapter that uses COW's existing bot infrastructure
     """
 
+    _MODEL_BOT_TYPE_MAP = {
+        "wenxin": const.BAIDU, "wenxin-4": const.BAIDU,
+        "xunfei": const.XUNFEI, const.QWEN: const.QWEN_DASHSCOPE,
+        const.QIANFAN: const.QIANFAN,
+        const.MODELSCOPE: const.MODELSCOPE,
+    }
+    _MODEL_PREFIX_MAP = [
+        ("qwen", const.QWEN_DASHSCOPE), ("qwq", const.QWEN_DASHSCOPE), ("qvq", const.QWEN_DASHSCOPE),
+        ("gemini", const.GEMINI), ("glm", const.ZHIPU_AI), ("claude", const.CLAUDEAPI),
+        ("moonshot", const.MOONSHOT), ("kimi", const.MOONSHOT),
+        ("doubao", const.DOUBAO), ("deepseek", const.DEEPSEEK),
+        ("ernie", const.QIANFAN),
+    ]
+
     def __init__(self, bridge: Bridge, bot_type: str = "chat"):
-        # Get model name directly from config
-        from config import conf
-        model_name = conf().get("model", const.GPT_41)
-        super().__init__(model=model_name)
+        super().__init__(model=conf().get("model", const.GPT_41))
         self.bridge = bridge
         self.bot_type = bot_type
         self._bot = None
-        self._use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
-    
+        self._bot_model = None
+
+    @property
+    def model(self):
+        return conf().get("model", const.GPT_41)
+
+    @model.setter
+    def model(self, value):
+        pass
+
+    def _resolve_bot_type(self, model_name: str) -> str:
+        """Resolve bot type from model name, matching Bridge.__init__ logic."""
+        if conf().get("use_linkai", False) and conf().get("linkai_api_key"):
+            return const.LINKAI
+        # Support custom bot type configuration
+        configured_bot_type = conf().get("bot_type")
+        if configured_bot_type:
+            return configured_bot_type
+       
+        if not model_name or not isinstance(model_name, str):
+            return const.OPENAI
+        if model_name in self._MODEL_BOT_TYPE_MAP:
+            return self._MODEL_BOT_TYPE_MAP[model_name]
+        if model_name.lower().startswith("minimax") or model_name in ["abab6.5-chat"]:
+            return const.MiniMax
+        if model_name in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
+            return const.QWEN_DASHSCOPE
+        if model_name in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
+            return const.MOONSHOT
+        if conf().get("bot_type") == "modelscope":
+            return const.MODELSCOPE
+        lowered_model = model_name.lower()
+        for prefix, btype in self._MODEL_PREFIX_MAP:
+            if lowered_model.startswith(prefix):
+                return btype
+        return const.OPENAI
+
     @property
     def bot(self):
-        """Lazy load the bot and enhance it with tool calling if needed"""
-        if self._bot is None:
-            # If use_linkai is enabled, use LinkAI bot directly
-            if self._use_linkai:
-                self._bot = self.bridge.find_chat_bot(const.LINKAI)
-            else:
-                self._bot = self.bridge.get_bot(self.bot_type)
-                # Automatically add tool calling support if not present
-                self._bot = add_openai_compatible_support(self._bot)
-            
-            # Log bot info
-            bot_name = type(self._bot).__name__
+        """Lazy load the bot, re-create when model or bot_type changes"""
+        from models.bot_factory import create_bot
+        cur_model = self.model
+        cur_bot_type = self._resolve_bot_type(cur_model)
+        if self._bot is None or self._bot_model != cur_model or getattr(self, '_bot_type', None) != cur_bot_type:
+            self._bot = create_bot(cur_bot_type)
+            self._bot = add_openai_compatible_support(self._bot)
+            self._bot_model = cur_model
+            self._bot_type = cur_bot_type
         return self._bot
 
     def call(self, request: LLMRequest):
@@ -109,12 +153,37 @@ class AgentLLMModel(LLMModel):
                 # Only pass max_tokens if it's explicitly set
                 if request.max_tokens is not None:
                     kwargs['max_tokens'] = request.max_tokens
-                
+
                 # Extract system prompt if present
                 system_prompt = getattr(request, 'system', None)
                 if system_prompt:
                     kwargs['system'] = system_prompt
-                
+
+                # Pass context metadata to bot
+                channel_type = getattr(self, 'channel_type', None) or ''
+                if channel_type:
+                    kwargs['channel_type'] = channel_type
+                session_id = getattr(self, 'session_id', None)
+                if session_id:
+                    kwargs['session_id'] = session_id
+
+                # Thinking mode is a global toggle independent of the channel.
+                # IM channels (WeChat/WeCom/DingTalk/Feishu) won't render the
+                # reasoning trace, but still benefit from the higher answer
+                # quality the thinking pass produces.
+                from config import conf
+                thinking_enabled = bool(conf().get("enable_thinking", False))
+                kwargs['thinking'] = (
+                    {"type": "enabled"} if thinking_enabled
+                    else {"type": "disabled"}
+                )
+                # Reasoning effort is only meaningful when thinking is on.
+                # Bots that don't understand the kwarg drop it silently.
+                if thinking_enabled:
+                    effort = conf().get("reasoning_effort", "high")
+                    if effort in ("high", "max"):
+                        kwargs['reasoning_effort'] = effort
+
                 response = self.bot.call_with_tools(**kwargs)
                 return self._format_response(response)
             else:
@@ -135,7 +204,7 @@ class AgentLLMModel(LLMModel):
                 # Use tool-enabled streaming call if available
                 # Extract system prompt if present
                 system_prompt = getattr(request, 'system', None)
-                
+
                 # Build kwargs for call_with_tools
                 kwargs = {
                     'messages': request.messages,
@@ -143,15 +212,40 @@ class AgentLLMModel(LLMModel):
                     'stream': True,
                     'model': self.model  # Pass model parameter
                 }
-                
+
                 # Only pass max_tokens if explicitly set, let the bot use its default
                 if request.max_tokens is not None:
                     kwargs['max_tokens'] = request.max_tokens
-                
+
                 # Add system prompt if present
                 if system_prompt:
                     kwargs['system'] = system_prompt
-                
+
+                # Pass context metadata to bot
+                channel_type = getattr(self, 'channel_type', None) or ''
+                if channel_type:
+                    kwargs['channel_type'] = channel_type
+                session_id = getattr(self, 'session_id', None)
+                if session_id:
+                    kwargs['session_id'] = session_id
+
+                # Thinking mode is a global toggle independent of the channel.
+                # IM channels (WeChat/WeCom/DingTalk/Feishu) won't render the
+                # reasoning trace, but still benefit from the higher answer
+                # quality the thinking pass produces.
+                from config import conf
+                thinking_enabled = bool(conf().get("enable_thinking", False))
+                kwargs['thinking'] = (
+                    {"type": "enabled"} if thinking_enabled
+                    else {"type": "disabled"}
+                )
+                # Reasoning effort is only meaningful when thinking is on.
+                # Bots that don't understand the kwarg drop it silently.
+                if thinking_enabled:
+                    effort = conf().get("reasoning_effort", "high")
+                    if effort in ("high", "max"):
+                        kwargs['reasoning_effort'] = effort
+
                 stream = self.bot.call_with_tools(**kwargs)
                 
                 # Convert stream format to our expected format
@@ -191,6 +285,15 @@ class AgentBridge:
         
         # Create helper instances
         self.initializer = AgentInitializer(bridge, self)
+
+        # Eager-start the scheduler so cron tasks fire without waiting
+        # for the first user message. init_scheduler is idempotent.
+        try:
+            from agent.tools.scheduler.integration import init_scheduler
+            if init_scheduler(self):
+                self.scheduler_initialized = True
+        except Exception as e:
+            logger.warning(f"[AgentBridge] Eager scheduler init failed: {e}")
     def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent:
         """
         Create the super agent with COW integration
@@ -214,10 +317,13 @@ class AgentBridge:
             tool_manager.load_tools()
             
             tools = []
+            workspace_dir = kwargs.get("workspace_dir")
             for tool_name in tool_manager.tool_classes.keys():
                 try:
                     tool = tool_manager.create_tool(tool_name)
                     if tool:
+                        if workspace_dir and hasattr(tool, 'cwd'):
+                            tool.cwd = workspace_dir
                         tools.append(tool)
                 except Exception as e:
                     logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
@@ -230,12 +336,13 @@ class AgentBridge:
             tools=tools,
             max_steps=kwargs.get("max_steps", 15),
             output_mode=kwargs.get("output_mode", "logger"),
-            workspace_dir=kwargs.get("workspace_dir"),  # Pass workspace for skills loading
-            enable_skills=kwargs.get("enable_skills", True),  # Enable skills by default
-            memory_manager=kwargs.get("memory_manager"),  # Pass memory manager
+            workspace_dir=kwargs.get("workspace_dir"),
+            skill_manager=kwargs.get("skill_manager"),
+            enable_skills=kwargs.get("enable_skills", True),
+            memory_manager=kwargs.get("memory_manager"),
             max_context_tokens=kwargs.get("max_context_tokens"),
             context_reserve_tokens=kwargs.get("context_reserve_tokens"),
-            runtime_info=kwargs.get("runtime_info")  # Pass runtime_info for dynamic time updates
+            runtime_info=kwargs.get("runtime_info"),
         )
 
         # Log skill loading details
@@ -290,12 +397,24 @@ class AgentBridge:
         Returns:
             Reply object
         """
+        session_id = None
+        agent = None
+        request_id = None
+        cancel_event = None
         try:
             # Extract session_id from context for user isolation
-            session_id = None
             if context:
                 session_id = context.kwargs.get("session_id") or context.get("session_id")
-            
+                request_id = context.kwargs.get("request_id") or context.get("request_id")
+
+            # Register a cancel token. Prefer per-turn request_id (web),
+            # fall back to session_id (IM channels). The Event is polled by
+            # AgentStreamExecutor at safe checkpoints.
+            registry = get_cancel_registry()
+            token_key = request_id or session_id
+            if token_key:
+                cancel_event = registry.register(token_key, session_id=session_id)
+
             # Get agent for this session (will auto-initialize if needed)
             agent = self.get_agent(session_id=session_id)
             if not agent:
@@ -325,22 +444,73 @@ class AgentBridge:
                                 logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
                             break
             
+            # Pass context metadata to model for downstream API requests
+            if context and hasattr(agent, 'model'):
+                agent.model.channel_type = context.get("channel_type", "")
+                agent.model.session_id = session_id or ""
+
+            # Store session_id on agent so executor can clear DB on fatal errors
+            agent._current_session_id = session_id
+
+            # Bound the in-memory context for scheduler sessions before each run.
+            # Scheduler sessions are stable per-task and append every trigger,
+            # so without trimming they would grow unbounded across runs and
+            # blow up prompt cost. Regular user chats are not touched here —
+            # the agent's own context manager handles that path.
+            if session_id and session_id.startswith("scheduler_"):
+                from config import conf
+                scheduler_keep_turns = max(
+                    1, int(conf().get("agent_max_context_turns", 20)) // 5
+                )
+                self._trim_in_memory_to_turns(agent, scheduler_keep_turns)
+
             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
+                    clear_history=clear_history,
+                    cancel_event=cancel_event,
                 )
             finally:
                 # Restore original tools
                 if context and context.get("is_scheduled_task"):
                     agent.tools = original_tools
-                
+
                 # Log execution summary
                 event_handler.log_summary()
+
+                # Release cancel token; keep registry bounded.
+                if token_key:
+                    try:
+                        registry.unregister(token_key)
+                    except Exception:
+                        pass
+
+            # Persist new messages generated during this run
+            if session_id:
+                channel_type = (context.get("channel_type") or "") if context else ""
+                new_messages = getattr(agent, '_last_run_new_messages', [])
+                if new_messages:
+                    self._persist_messages(session_id, list(new_messages), channel_type)
+                else:
+                    with agent.messages_lock:
+                        msg_count = len(agent.messages)
+                    if msg_count == 0:
+                        try:
+                            from agent.memory import get_conversation_store
+                            get_conversation_store().clear_session(session_id)
+                            logger.info(f"[AgentBridge] Cleared DB for recovered session: {session_id}")
+                        except Exception as e:
+                            logger.warning(f"[AgentBridge] Failed to clear DB after recovery: {e}")
             
-            # Check if there are files to send (from read tool)
+            # Post-message hot-reload: detect edits to ~/cow/mcp.json and
+            # sync any new/removed MCP tools into the live agent in the
+            # background. Off the critical path so user latency is unaffected;
+            # changes take effect on the user's next message.
+            self._schedule_mcp_hot_reload(agent)
+
+            # Check if there are files to send (from send/read tool)
             if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
                 files_to_send = agent.stream_executor.files_to_send
                 if files_to_send:
@@ -358,8 +528,51 @@ class AgentBridge:
             
         except Exception as e:
             logger.error(f"Agent reply error: {e}")
+            # If the agent cleared its messages due to format error / overflow,
+            # also purge the DB so the next request starts clean.
+            if session_id and agent:
+                try:
+                    with agent.messages_lock:
+                        msg_count = len(agent.messages)
+                    if msg_count == 0:
+                        from agent.memory import get_conversation_store
+                        get_conversation_store().clear_session(session_id)
+                        logger.info(f"[AgentBridge] Cleared DB for session after error: {session_id}")
+                except Exception as db_err:
+                    logger.warning(f"[AgentBridge] Failed to clear DB after error: {db_err}")
+            # Release cancel token on error path too (idempotent).
+            if cancel_event is not None and (request_id or session_id):
+                try:
+                    get_cancel_registry().unregister(request_id or session_id)
+                except Exception:
+                    pass
             return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
     
+    def _schedule_mcp_hot_reload(self, agent):
+        """
+        Fire-and-forget: detect mcp.json edits and reconcile the agent's
+        tool dict in the background. Runs after the user's reply is sent,
+        so any cost (file stat, hash, server boot) never adds to user latency.
+        Failures are isolated and never raise into the message pipeline.
+        """
+        import threading
+        from agent.tools import ToolManager
+
+        def _run():
+            try:
+                tm = ToolManager()
+                tm.refresh_mcp_if_changed()
+                added, removed = tm.sync_mcp_into_agent(agent)
+                if added or removed:
+                    logger.info(
+                        f"[AgentBridge] Agent tools synced — "
+                        f"added={added}, removed={removed}"
+                    )
+            except Exception as e:
+                logger.warning(f"[AgentBridge] MCP hot-reload failed (non-fatal): {e}")
+
+        threading.Thread(target=_run, daemon=True, name="mcp-hot-reload").start()
+
     def _create_file_reply(self, file_info: dict, text_response: str, context: Context = None) -> Reply:
         """
         Create a reply for sending files
@@ -397,22 +610,26 @@ class AgentBridge:
                 reply.text_content = text_response
             return reply
         
-        # For other unknown file types, return text with file info
-        message = text_response or file_info.get("message", "文件已准备")
-        message += f"\n\n[文件: {file_info.get('file_name', file_path)}]"
-        return Reply(ReplyType.TEXT, message)
+        # For all other file types (tar.gz, zip, etc.), also use FILE type
+        file_url = f"file://{file_path}"
+        logger.info(f"[AgentBridge] Sending generic file: {file_url}")
+        reply = Reply(ReplyType.FILE, file_url)
+        reply.file_name = file_info.get("file_name", os.path.basename(file_path))
+        if text_response:
+            reply.text_content = text_response
+        return reply
     
     def _migrate_config_to_env(self, workspace_root: str):
         """
-        Migrate API keys from config.json to .env file if not already set
-        
+        Sync API keys from config.json to .env file.
+        Adds new keys and updates changed values on each startup.
+
         Args:
             workspace_root: Workspace directory path (not used, kept for compatibility)
         """
         from config import conf
         import os
         
-        # Mapping from config.json keys to environment variable names
         key_mapping = {
             "open_ai_api_key": "OPENAI_API_KEY",
             "open_ai_api_base": "OPENAI_API_BASE",
@@ -421,10 +638,9 @@ class AgentBridge:
             "linkai_api_key": "LINKAI_API_KEY",
         }
         
-        # Use fixed secure location for .env file
         env_file = expand_path("~/.cow/.env")
         
-        # Read existing env vars from .env file
+        # Read existing env vars (key -> value)
         existing_env_vars = {}
         if os.path.exists(env_file):
             try:
@@ -432,49 +648,300 @@ class AgentBridge:
                     for line in f:
                         line = line.strip()
                         if line and not line.startswith('#') and '=' in line:
-                            key, _ = line.split('=', 1)
-                            existing_env_vars[key.strip()] = True
+                            key, val = line.split('=', 1)
+                            existing_env_vars[key.strip()] = val.strip()
             except Exception as e:
                 logger.warning(f"[AgentBridge] Failed to read .env file: {e}")
         
-        # Check which keys need to be migrated
-        keys_to_migrate = {}
+        # Sync config.json values into .env (add/update/remove)
+        updated = False
         for config_key, env_key in key_mapping.items():
-            # Skip if already in .env file
-            if env_key in existing_env_vars:
-                continue
-            
-            # Get value from config.json
-            value = conf().get(config_key, "")
-            if value and value.strip():  # Only migrate non-empty values
-                keys_to_migrate[env_key] = value.strip()
-        
-        # Log summary if there are keys to skip
-        if existing_env_vars:
-            logger.debug(f"[AgentBridge] {len(existing_env_vars)} env vars already in .env")
-        
-        # Write new keys to .env file
-        if keys_to_migrate:
+            raw = conf().get(config_key, "")
+            value = raw.strip() if raw else ""
+            old_value = existing_env_vars.get(env_key)
+
+            if value:
+                if old_value == value:
+                    continue
+                existing_env_vars[env_key] = value
+                os.environ[env_key] = value
+                updated = True
+            else:
+                if old_value is None:
+                    continue
+                existing_env_vars.pop(env_key, None)
+                os.environ.pop(env_key, None)
+                updated = True
+            updated = True
+
+        if updated:
             try:
-                # Ensure ~/.cow directory and .env file exist
                 env_dir = os.path.dirname(env_file)
-                if not os.path.exists(env_dir):
-                    os.makedirs(env_dir, exist_ok=True)
-                if not os.path.exists(env_file):
-                    open(env_file, 'a').close()
-                
-                # Append new keys
-                with open(env_file, 'a', encoding='utf-8') as f:
-                    f.write('\n# Auto-migrated from config.json\n')
-                    for key, value in keys_to_migrate.items():
+                os.makedirs(env_dir, exist_ok=True)
+
+                with open(env_file, 'w', encoding='utf-8') as f:
+                    f.write('# Environment variables for agent\n')
+                    f.write('# Auto-managed - synced from config.json on startup\n\n')
+                    for key, value in sorted(existing_env_vars.items()):
                         f.write(f'{key}={value}\n')
-                        # Also set in current process
-                        os.environ[key] = value
-                
-                logger.info(f"[AgentBridge] Migrated {len(keys_to_migrate)} API keys from config.json to .env: {list(keys_to_migrate.keys())}")
+
+                logger.info(f"[AgentBridge] Synced API keys from config.json to .env")
             except Exception as e:
-                logger.warning(f"[AgentBridge] Failed to migrate API keys: {e}")
+                logger.warning(f"[AgentBridge] Failed to sync API keys: {e}")
     
+    def _persist_messages(
+        self, session_id: str, new_messages: list, channel_type: str = ""
+    ) -> None:
+        """
+        Persist new messages to the conversation store after each agent run.
+
+        Failures are logged but never propagate — they must not interrupt replies.
+        """
+        if not new_messages:
+            return
+        try:
+            from config import conf
+            if not conf().get("conversation_persistence", True):
+                return
+            # When deep-thinking display is disabled, strip "thinking" content
+            # blocks before persisting so they don't resurface on history reload.
+            # The in-memory message list keeps them intact for this run's
+            # multi-turn LLM context.
+            thinking_enabled = bool(conf().get("enable_thinking", False))
+        except Exception:
+            thinking_enabled = False
+
+        messages_to_store = new_messages
+        if not thinking_enabled:
+            messages_to_store = self._strip_thinking_blocks(new_messages)
+
+        try:
+            from agent.memory import get_conversation_store
+            get_conversation_store().append_messages(
+                session_id, messages_to_store, channel_type=channel_type
+            )
+        except Exception as e:
+            logger.warning(
+                f"[AgentBridge] Failed to persist messages for session={session_id}: {e}"
+            )
+
+    # Marker used to identify scheduler-injected user messages so we can apply
+    # a sliding window without touching real user turns. The legacy prefix
+    # "Scheduled task" (written by the v2 PR) is also recognised when pruning,
+    # so old data can be aged out instead of leaking forever.
+    _SCHEDULED_MARKER = "[SCHEDULED]"
+    _SCHEDULED_LEGACY_MARKERS = ("Scheduled task",)
+
+    def remember_scheduled_output(
+        self,
+        session_id: str,
+        content: str,
+        channel_type: str = "",
+        task_description: str = "",
+    ) -> None:
+        """Add the visible output of a scheduled task to the receiver's session.
+
+        Scheduled task execution uses an isolated session so internal planning and
+        tool calls do not leak into the user's chat. The final message is still
+        part of the conversation from the user's point of view, so keep a small
+        visible turn in the receiver session for follow-up questions.
+
+        Configuration:
+            scheduler_inject_to_session (bool, default True):
+                Master switch. When False, this method is a no-op.
+            scheduler_inject_max_per_session (int, default 3):
+                Maximum scheduler-injected user/assistant pairs retained per
+                session. Older injections are pruned automatically.
+
+        Content is truncated to 2000 chars to prevent a single high-volume task
+        from bloating one entry.
+        """
+        from config import conf
+        if not conf().get("scheduler_inject_to_session", True):
+            return
+        if not session_id or not content:
+            return
+
+        max_len = 2000
+        if len(content) > max_len:
+            content = content[:max_len] + "..."
+
+        user_text = self._SCHEDULED_MARKER
+        if task_description:
+            user_text = f"{self._SCHEDULED_MARKER} {task_description}"
+
+        messages = [
+            {"role": "user", "content": [{"type": "text", "text": user_text}]},
+            {"role": "assistant", "content": [{"type": "text", "text": content}]},
+        ]
+
+        # Persist first so the new pair gets a stable seq, then prune old
+        # scheduler pairs in DB, then sync the in-memory agent.messages buffer.
+        self._persist_messages(session_id, messages, channel_type)
+
+        keep_last_n = max(int(conf().get("scheduler_inject_max_per_session", 3) or 0), 0)
+        try:
+            from agent.memory import get_conversation_store
+            deleted = get_conversation_store().prune_scheduled_messages(
+                session_id, keep_last_n=keep_last_n
+            )
+            if deleted:
+                logger.debug(
+                    f"[AgentBridge] Pruned {deleted} old scheduler messages "
+                    f"for session={session_id} (keep_last_n={keep_last_n})"
+                )
+        except Exception as e:
+            logger.warning(
+                f"[AgentBridge] Failed to prune scheduled messages "
+                f"for session={session_id}: {e}"
+            )
+
+        agent = self.agents.get(session_id)
+        if agent:
+            try:
+                with agent.messages_lock:
+                    agent.messages.extend(messages)
+                    self._prune_scheduled_in_memory(agent, keep_last_n)
+            except Exception as e:
+                logger.warning(
+                    f"[AgentBridge] Failed to update in-memory scheduled output "
+                    f"for session={session_id}: {e}"
+                )
+
+    @staticmethod
+    def _trim_in_memory_to_turns(agent, keep_turns: int) -> None:
+        """Bound ``agent.messages`` to the most recent ``keep_turns`` real
+        user/assistant turns, dropping older history together with any
+        intermediate tool_use/tool_result blocks that belonged to it.
+
+        A "real" user message is any user message whose content is not solely a
+        tool_result block — matches the heuristic used elsewhere when filtering
+        history (see ``AgentInitializer._filter_text_only_messages``).
+
+        No-op when the session is already within budget. Caller does not need
+        to hold the lock; this method acquires it itself.
+        """
+        if keep_turns <= 0:
+            return
+
+        def _is_real_user(msg) -> bool:
+            if not isinstance(msg, dict) or msg.get("role") != "user":
+                return False
+            content = msg.get("content")
+            if isinstance(content, list):
+                if any(
+                    isinstance(b, dict) and b.get("type") == "tool_result"
+                    for b in content
+                ):
+                    return False
+                return any(
+                    isinstance(b, dict) and b.get("type") == "text" and b.get("text")
+                    for b in content
+                )
+            if isinstance(content, str):
+                return bool(content.strip())
+            return False
+
+        with agent.messages_lock:
+            msgs = agent.messages
+            real_user_indices = [i for i, m in enumerate(msgs) if _is_real_user(m)]
+            if len(real_user_indices) <= keep_turns:
+                return
+
+            # Cut at the (k-th from the end) real user message; keep everything
+            # from there onwards so the surviving slice is still a valid
+            # user/assistant sequence.
+            cut_idx = real_user_indices[-keep_turns]
+            if cut_idx == 0:
+                return
+
+            kept = msgs[cut_idx:]
+            msgs.clear()
+            msgs.extend(kept)
+            logger.debug(
+                f"[AgentBridge] Trimmed in-memory messages to last "
+                f"{keep_turns} turns ({len(kept)} messages remain)"
+            )
+
+    @classmethod
+    def _prune_scheduled_in_memory(cls, agent, keep_last_n: int) -> None:
+        """Mirror conversation_store.prune_scheduled_messages on agent.messages.
+
+        Caller must hold ``agent.messages_lock``.
+        """
+        if keep_last_n < 0:
+            keep_last_n = 0
+
+        markers = (cls._SCHEDULED_MARKER,) + cls._SCHEDULED_LEGACY_MARKERS
+
+        def _is_marker_user(msg) -> bool:
+            if not isinstance(msg, dict) or msg.get("role") != "user":
+                return False
+            content = msg.get("content")
+            text = ""
+            if isinstance(content, str):
+                text = content
+            elif isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict) and block.get("type") == "text":
+                        text = block.get("text", "")
+                        break
+            return any(text.startswith(m) for m in markers)
+
+        msgs = agent.messages
+        pair_indices = []  # list of (user_idx, assistant_idx_or_None)
+        for idx, msg in enumerate(msgs):
+            if not _is_marker_user(msg):
+                continue
+            assistant_idx = None
+            if idx + 1 < len(msgs):
+                nxt = msgs[idx + 1]
+                if isinstance(nxt, dict) and nxt.get("role") == "assistant":
+                    assistant_idx = idx + 1
+            pair_indices.append((idx, assistant_idx))
+
+        if len(pair_indices) <= keep_last_n:
+            return
+
+        to_drop = pair_indices[: len(pair_indices) - keep_last_n]
+        drop_set = set()
+        for u_idx, a_idx in to_drop:
+            drop_set.add(u_idx)
+            if a_idx is not None:
+                drop_set.add(a_idx)
+
+        # Rebuild the list in place to keep external references stable.
+        kept = [m for i, m in enumerate(msgs) if i not in drop_set]
+        msgs.clear()
+        msgs.extend(kept)
+
+    @staticmethod
+    def _strip_thinking_blocks(messages: list) -> list:
+        """Return a shallow copy of messages with assistant "thinking" blocks removed."""
+        cleaned = []
+        for msg in messages:
+            if not isinstance(msg, dict):
+                cleaned.append(msg)
+                continue
+            if msg.get("role") != "assistant":
+                cleaned.append(msg)
+                continue
+            content = msg.get("content")
+            if not isinstance(content, list):
+                cleaned.append(msg)
+                continue
+            filtered_blocks = [
+                b for b in content
+                if not (isinstance(b, dict) and b.get("type") == "thinking")
+            ]
+            if len(filtered_blocks) == len(content):
+                cleaned.append(msg)
+            else:
+                new_msg = dict(msg)
+                new_msg["content"] = filtered_blocks
+                cleaned.append(new_msg)
+        return cleaned
+
     def clear_session(self, session_id: str):
         """
         Clear a specific session's agent and conversation history
@@ -560,4 +1027,4 @@ class AgentBridge:
                 agent.tools = [t for t in agent.tools if t.name != "web_search"]
                 logger.info("[AgentBridge] web_search tool removed (API key no longer available)")
         except Exception as e:
-            logger.debug(f"[AgentBridge] Failed to refresh conditional tools: {e}")
+            logger.debug(f"[AgentBridge] Failed to refresh conditional tools: {e}")

bridge/agent_event_handler.py

@@ -2,114 +2,124 @@
 Agent Event Handler - Handles agent events and thinking process output
 """
 
+from common import const
 from common.log import logger
 
+# Cap intermediate thinking messages on weixin to stay within send quota.
+WEIXIN_THINKING_INSTANT_MAX = 7
+
 
 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.current_content = ""
         self.turn_number = 0
-    
+
+        channel_type = ""
+        if context and hasattr(context, "kwargs"):
+            channel_type = context.kwargs.get("channel_type", "") or ""
+        self._is_weixin = channel_type == const.WEIXIN
+        self._thinking_sent_count = 0
+        self._merged_buf: list[str] = []
+
     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 == "reasoning_update":
+            pass
         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
+        elif event_type == "agent_end":
+            self._handle_agent_end(data)
+
         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 = ""
-    
+        self.current_content = ""
+
     def _handle_message_update(self, data):
-        """Handle message update event (streaming text)"""
         delta = data.get("delta", "")
-        self.current_thinking += delta
-    
+        self.current_content += delta
+
     def _handle_message_end(self, data):
-        """Handle message end event"""
         tool_calls = data.get("tool_calls", [])
-        
-        # Only send thinking process if followed by tool calls
+
         if tool_calls:
-            if self.current_thinking.strip():
-                logger.info(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
-                # Send thinking process to channel
-                self._send_to_channel(f"{self.current_thinking.strip()}")
+            if self.current_content.strip():
+                logger.info(f"💭 {self.current_content.strip()[:200]}{'...' if len(self.current_content) > 200 else ''}")
+                self._send_to_channel(self.current_content.strip())
         else:
-            # No tool calls = final response (logged at agent_stream level)
-            if self.current_thinking.strip():
-                logger.debug(f"💬 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
-        
-        self.current_thinking = ""
-    
+            if self.current_content.strip():
+                logger.debug(f"💬 {self.current_content.strip()[:200]}{'...' if len(self.current_content) > 200 else ''}")
+            # Drain weixin buffer before final reply leaves chat_channel
+            self._flush_merged_now()
+
+        self.current_content = ""
+
+    def _handle_agent_end(self, data):
+        self._flush_merged_now()
+
     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}")
-    
+        if self.context and self.context.get("on_event"):
+            return
+        if not self.channel:
+            return
+
+        if not self._is_weixin:
+            self._do_send(message)
+            return
+
+        if self._thinking_sent_count < WEIXIN_THINKING_INSTANT_MAX:
+            self._do_send(message)
+            self._thinking_sent_count += 1
+            return
+
+        self._merged_buf.append(message)
+
+    def _flush_merged_now(self):
+        if not self._merged_buf:
+            return
+        merged = "\n\n".join(self._merged_buf)
+        count = len(self._merged_buf)
+        self._merged_buf = []
+        logger.debug(f"[AgentEventHandler] Flushing {count} merged thinking msgs, len={len(merged)}")
+        self._do_send(merged)
+        self._thinking_sent_count += 1
+
+    def _do_send(self, message):
+        try:
+            from bridge.reply import Reply, ReplyType
+            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

bridge/agent_initializer.py

@@ -5,6 +5,7 @@ Agent Initializer - Handles agent initialization logic
 import os
 import asyncio
 import datetime
+import threading
 import time
 from typing import Optional, List
 
@@ -13,6 +14,13 @@ from agent.tools import ToolManager
 from common.log import logger
 from common.utils import expand_path
 
+# Module-level lock to serialize scheduler init across concurrent sessions
+_scheduler_init_lock = threading.Lock()
+
+# Track whether the embedding model log has been printed in this process,
+# so we avoid spamming it once per session.
+_embedding_logged: bool = False
+
 
 class AgentInitializer:
     """
@@ -77,10 +85,6 @@ class AgentInitializer:
         # Initialize skill manager
         skill_manager = self._initialize_skill_manager(workspace_root, session_id)
         
-        # Check if first conversation
-        from agent.prompt.workspace import is_first_conversation, mark_conversation_started
-        is_first = is_first_conversation(workspace_root)
-        
         # Build system prompt
         prompt_builder = PromptBuilder(workspace_dir=workspace_root, language="zh")
         runtime_info = self._get_runtime_info(workspace_root)
@@ -91,12 +95,8 @@ class AgentInitializer:
             skill_manager=skill_manager,
             memory_manager=memory_manager,
             runtime_info=runtime_info,
-            is_first_conversation=is_first
         )
         
-        if is_first:
-            mark_conversation_started(workspace_root)
-        
         # Get cost control parameters
         from config import conf
         max_steps = conf().get("agent_max_steps", 20)
@@ -115,11 +115,143 @@ class AgentInitializer:
             runtime_info=runtime_info  # Pass runtime_info for dynamic time updates
         )
         
-        # Attach memory manager
+        # Attach memory manager and share LLM model for summarization
         if memory_manager:
             agent.memory_manager = memory_manager
-        
+            if hasattr(agent, 'model') and agent.model:
+                memory_manager.flush_manager.llm_model = agent.model
+
+        # Restore persisted conversation history for this session
+        if session_id:
+            self._restore_conversation_history(agent, session_id)
+
+        # Start daily memory flush timer (once, on first agent init regardless of session)
+        self._start_daily_flush_timer()
+
         return agent
+
+    def _restore_conversation_history(self, agent, session_id: str) -> None:
+        """
+        Load persisted conversation messages from SQLite and inject them
+        into the agent's in-memory message list.
+
+        Only user text and assistant text are restored. Tool call chains
+        (tool_use / tool_result) are stripped out because:
+        1. They are intermediate process, the value is already in the final
+           assistant text reply.
+        2. They consume massive context tokens (often 80%+ of history).
+        3. Different models have incompatible tool message formats, so
+           restoring tool chains across model switches causes 400 errors.
+        4. Eliminates the entire class of tool_use/tool_result pairing bugs.
+        """
+        from config import conf
+        if not conf().get("conversation_persistence", True):
+            return
+
+        try:
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            max_turns = conf().get("agent_max_context_turns", 20)
+            # Scheduler tasks run on a stable isolated session per task and
+            # can fire many times a day; a smaller restore window keeps prompt
+            # cost bounded while still letting the agent see "last few" runs
+            # for trend / dedup style logic. Regular chat sessions keep the
+            # original heuristic so user dialogues feel continuous.
+            if session_id.startswith("scheduler_"):
+                restore_turns = max(1, max_turns // 5)
+            else:
+                restore_turns = max(3, max_turns // 6)
+            saved = store.load_messages(session_id, max_turns=restore_turns)
+            if saved:
+                filtered = self._filter_text_only_messages(saved)
+                if filtered:
+                    with agent.messages_lock:
+                        agent.messages = filtered
+                    logger.debug(
+                        f"[AgentInitializer] Restored {len(filtered)} text messages "
+                        f"(from {len(saved)} total, {restore_turns} turns cap) "
+                        f"for session={session_id}"
+                    )
+        except Exception as e:
+            logger.warning(
+                f"[AgentInitializer] Failed to restore conversation history for "
+                f"session={session_id}: {e}"
+            )
+
+    @staticmethod
+    def _filter_text_only_messages(messages: list) -> list:
+        """
+        Extract clean user/assistant turn pairs from raw message history.
+
+        Groups messages into turns (each starting with a real user query),
+        then keeps only:
+        - The first user text in each turn (the actual user input)
+        - The last assistant text in each turn (the final answer)
+
+        All tool_use, tool_result, intermediate assistant thoughts, and
+        internal hint messages injected by the agent loop are discarded.
+        """
+
+        def _extract_text(content) -> str:
+            if isinstance(content, str):
+                return content.strip()
+            if isinstance(content, list):
+                parts = [
+                    b.get("text", "")
+                    for b in content
+                    if isinstance(b, dict) and b.get("type") == "text"
+                ]
+                return "\n".join(p for p in parts if p).strip()
+            return ""
+
+        def _is_real_user_msg(msg: dict) -> bool:
+            """True for actual user input, False for tool_result or internal hints."""
+            if msg.get("role") != "user":
+                return False
+            content = msg.get("content")
+            if isinstance(content, list):
+                has_tool_result = any(
+                    isinstance(b, dict) and b.get("type") == "tool_result"
+                    for b in content
+                )
+                if has_tool_result:
+                    return False
+            text = _extract_text(content)
+            return bool(text)
+
+        # Group into turns: each turn starts with a real user message
+        turns = []
+        current_turn = None
+        for msg in messages:
+            if _is_real_user_msg(msg):
+                if current_turn is not None:
+                    turns.append(current_turn)
+                current_turn = {"user": msg, "assistants": []}
+            elif current_turn is not None and msg.get("role") == "assistant":
+                text = _extract_text(msg.get("content"))
+                if text:
+                    current_turn["assistants"].append(text)
+        if current_turn is not None:
+            turns.append(current_turn)
+
+        # Build result: one user msg + one assistant msg per turn
+        filtered = []
+        for turn in turns:
+            user_text = _extract_text(turn["user"].get("content"))
+            if not user_text:
+                continue
+            filtered.append({
+                "role": "user",
+                "content": [{"type": "text", "text": user_text}]
+            })
+            if turn["assistants"]:
+                final_reply = turn["assistants"][-1]
+                filtered.append({
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": final_reply}]
+                })
+
+        return filtered
     
     def _load_env_file(self):
         """Load environment variables from .env file"""
@@ -144,37 +276,19 @@ class AgentInitializer:
         memory_tools = []
         
         try:
-            from agent.memory import MemoryManager, MemoryConfig, create_embedding_provider
+            from agent.memory import MemoryManager, MemoryConfig
             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)
+
+            embedding_provider = self._init_embedding_provider(
+                memory_config, session_id=session_id
+            )
+
             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)
@@ -187,6 +301,190 @@ class AgentInitializer:
             logger.warning(f"[AgentInitializer] Memory system not available: {e}")
         
         return memory_manager, memory_tools
+
+    def _init_embedding_provider(self, memory_config, session_id: Optional[str] = None):
+        """
+        Initialize the embedding provider for memory.
+
+        Two paths:
+          A. Default (no `embedding_provider` in config.json):
+             Auto-init OpenAI -> LinkAI fallback. Existing 1536-dim indices
+             keep working.
+          B. Explicit (`embedding_provider` is set):
+             Initialize the requested vendor with unified dim (default 1024).
+             If the index was built with a different dim, vector search will
+             quietly return no results (cosine returns 0) and keyword search
+             takes over until the user runs /memory rebuild-index.
+        """
+        from agent.memory import create_embedding_provider
+        from config import conf
+
+        explicit_provider = (conf().get("embedding_provider") or "").strip().lower()
+
+        if not explicit_provider:
+            return self._init_embedding_provider_legacy(session_id=session_id)
+
+        return self._init_embedding_provider_explicit(
+            memory_config, explicit_provider, session_id=session_id,
+        )
+
+    def _init_embedding_provider_legacy(self, session_id: Optional[str] = None):
+        """Legacy auto-init path: OpenAI -> LinkAI. Preserved verbatim for compat."""
+        from agent.memory import create_embedding_provider
+        from config import conf
+
+        embedding_provider = None
+        embedding_model = None
+
+        openai_api_key = conf().get("open_ai_api_key", "")
+        openai_api_base = conf().get("open_ai_api_base", "")
+        if openai_api_key and openai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+            try:
+                model = "text-embedding-3-small"
+                embedding_provider = create_embedding_provider(
+                    provider="openai",
+                    model=model,
+                    api_key=openai_api_key,
+                    api_base=openai_api_base or "https://api.openai.com/v1"
+                )
+                embedding_model = f"openai/{model}"
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] OpenAI embedding failed: {e}")
+
+        if embedding_provider is None:
+            linkai_api_key = conf().get("linkai_api_key", "") or os.environ.get("LINKAI_API_KEY", "")
+            linkai_api_base = conf().get("linkai_api_base", "https://api.link-ai.tech")
+            if linkai_api_key and linkai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+                try:
+                    model = "text-embedding-3-small"
+                    embedding_provider = create_embedding_provider(
+                        provider="linkai",
+                        model=model,
+                        api_key=linkai_api_key,
+                        api_base=f"{linkai_api_base}/v1"
+                    )
+                    embedding_model = f"linkai/{model}"
+                except Exception as e:
+                    logger.warning(f"[AgentInitializer] LinkAI embedding failed: {e}")
+
+        if embedding_provider is not None and embedding_model:
+            global _embedding_logged
+            if not _embedding_logged:
+                logger.info(
+                    f"[AgentInitializer] Embedding model in use: {embedding_model} "
+                    f"(dim={embedding_provider.dimensions})"
+                )
+                _embedding_logged = True
+
+        return embedding_provider
+
+    def _init_embedding_provider_explicit(
+        self,
+        memory_config,
+        provider_key: str,
+        session_id: Optional[str] = None,
+    ):
+        """Explicit-provider path: build the configured vendor.
+
+        If the index was built with a different dim, vector search will
+        silently return no results (cosine returns 0 for mismatched dims)
+        and keyword search takes over. Users switch vendors by running
+        /memory rebuild-index — see docs.
+        """
+        from agent.memory import create_embedding_provider
+        from agent.memory.embedding import EMBEDDING_VENDORS
+        from config import conf
+
+        meta = EMBEDDING_VENDORS.get(provider_key)
+        if meta is None:
+            logger.error(
+                f"[AgentInitializer] Unknown embedding_provider '{provider_key}'. "
+                f"Supported: {sorted(EMBEDDING_VENDORS.keys())}. "
+                f"Memory will run in keyword-only mode."
+            )
+            return None
+
+        api_key = self._resolve_embedding_api_key(provider_key)
+        api_base = self._resolve_embedding_api_base(provider_key, meta["default_base_url"])
+
+        if not api_key:
+            logger.error(
+                f"[AgentInitializer] embedding_provider='{provider_key}' is set but its "
+                f"API key is missing. Memory will run in keyword-only mode."
+            )
+            return None
+
+        model = (conf().get("embedding_model") or "").strip() or meta["default_model"]
+        try:
+            cfg_dim = int(conf().get("embedding_dimensions") or 0)
+        except (TypeError, ValueError):
+            cfg_dim = 0
+        dim = cfg_dim if cfg_dim > 0 else meta["default_dimensions"]
+
+        try:
+            provider = create_embedding_provider(
+                provider=provider_key,
+                model=model,
+                api_key=api_key,
+                api_base=api_base,
+                dimensions=dim,
+            )
+        except Exception as e:
+            logger.error(
+                f"[AgentInitializer] Failed to init embedding provider "
+                f"'{provider_key}/{model}': {e}"
+            )
+            return None
+
+        global _embedding_logged
+        if not _embedding_logged:
+            logger.info(
+                f"[AgentInitializer] Embedding model in use: "
+                f"{provider_key}/{model} (dim={provider.dimensions})"
+            )
+            _embedding_logged = True
+        return provider
+
+    @staticmethod
+    def _resolve_embedding_api_key(provider_key: str) -> str:
+        """Pick the API key for an explicit embedding provider from config."""
+        from config import conf
+
+        key_map = {
+            "openai":    "open_ai_api_key",
+            "linkai":    "linkai_api_key",
+            "dashscope": "dashscope_api_key",
+            "doubao":    "ark_api_key",
+            "zhipu":     "zhipu_ai_api_key",
+        }
+        field = key_map.get(provider_key)
+        if not field:
+            return ""
+        value = conf().get(field, "") or ""
+        if value in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+            return ""
+        return value
+
+    @staticmethod
+    def _resolve_embedding_api_base(provider_key: str, default_base: str) -> str:
+        """Pick the API base for an explicit embedding provider from config."""
+        from config import conf
+
+        base_map = {
+            "openai":    "open_ai_api_base",
+            "linkai":    "linkai_api_base",
+            "doubao":    "ark_base_url",
+            "zhipu":     "zhipu_ai_api_base",
+        }
+        field = base_map.get(provider_key)
+        if not field:
+            return default_base
+        value = (conf().get(field) or "").strip()
+        if not value:
+            return default_base
+        if provider_key == "linkai" and not value.rstrip("/").endswith("/v1"):
+            return f"{value.rstrip('/')}/v1"
+        return value
     
     def _sync_memory(self, memory_manager, session_id: Optional[str] = None):
         """Sync memory database"""
@@ -223,7 +521,7 @@ class AgentInitializer:
                 if tool_name == "web_search":
                     from agent.tools.web_search.web_search import WebSearch
                     if not WebSearch.is_available():
-                        logger.debug("[AgentInitializer] WebSearch skipped - no BOCHA_API_KEY or LINKAI_API_KEY")
+                        logger.debug("[AgentInitializer] WebSearch skipped - no search provider configured")
                         continue
 
                 # Special handling for EnvConfig tool
@@ -234,16 +532,33 @@ class AgentInitializer:
                     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']
+                    # Apply workspace config to file operation tools.
+                    # Merge into the existing tool.config (set by ToolManager from
+                    # config.json's `tools.<name>` section) instead of replacing
+                    # it, otherwise per-tool user configs (e.g. browser.cdp_endpoint)
+                    # would be silently dropped.
+                    if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls', 'web_fetch', 'send', 'browser']:
+                        merged_config = dict(getattr(tool, 'config', None) or {})
+                        merged_config.update(file_config)
+                        tool.config = merged_config
+                        tool.cwd = merged_config.get("cwd", getattr(tool, 'cwd', None))
+                        if 'memory_manager' in merged_config:
+                            tool.memory_manager = merged_config['memory_manager']
                     tools.append(tool)
             except Exception as e:
                 logger.warning(f"[AgentInitializer] Failed to load tool {tool_name}: {e}")
-        
+
+        # Add MCP tools (snapshot to avoid races with the background loader)
+        mcp_tools_snapshot = list(tool_manager._mcp_tool_instances.items())
+        if mcp_tools_snapshot:
+            for _, mcp_tool in mcp_tools_snapshot:
+                tools.append(mcp_tool)
+            if session_id is None:
+                names = [name for name, _ in mcp_tools_snapshot]
+                logger.info(
+                    f"[AgentInitializer] Added {len(names)} MCP tool(s): {names}"
+                )
+
         # Add memory tools
         if memory_tools:
             tools.extend(memory_tools)
@@ -256,16 +571,23 @@ class AgentInitializer:
         return tools
     
     def _initialize_scheduler(self, tools: List, session_id: Optional[str] = None):
-        """Initialize scheduler service if needed"""
+        """Initialize scheduler service if needed.
+
+        Serialize the check-and-set under a module-level lock so concurrent
+        first-time session inits cannot each create a new SchedulerService
+        (which would leak background scanning threads).
+        """
         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}")
+            with _scheduler_init_lock:
+                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:
@@ -283,7 +605,14 @@ class AgentInitializer:
                         tool.scheduler_service = scheduler_service
                         if not tool.config:
                             tool.config = {}
-                        tool.config["channel_type"] = conf().get("channel_type", "unknown")
+                        raw_ct = conf().get("channel_type", "unknown")
+                        if isinstance(raw_ct, list):
+                            ct = raw_ct[0] if raw_ct else "unknown"
+                        elif isinstance(raw_ct, str) and "," in raw_ct:
+                            ct = raw_ct.split(",")[0].strip()
+                        else:
+                            ct = raw_ct
+                        tool.config["channel_type"] = ct
             except Exception as e:
                 logger.warning(f"[AgentInitializer] Failed to inject scheduler dependencies: {e}")
     
@@ -327,10 +656,14 @@ class AgentInitializer:
                 'timezone': timezone_name
             }
         
+        def get_model():
+            """Get current model name dynamically from config"""
+            return conf().get("model", "unknown")
+
         return {
-            "model": conf().get("model", "unknown"),
+            "_get_model": get_model,
             "workspace": workspace_root,
-            "channel": conf().get("channel_type", "unknown"),
+            "channel": ", ".join(conf().get("channel_type")) if isinstance(conf().get("channel_type"), list) else conf().get("channel_type", "unknown"),
             "_get_current_time": get_current_time  # Dynamic time function
         }
     
@@ -348,7 +681,7 @@ class AgentInitializer:
         
         env_file = expand_path("~/.cow/.env")
         
-        # Read existing env vars
+        # Read existing env vars (key -> value)
         existing_env_vars = {}
         if os.path.exists(env_file):
             try:
@@ -356,35 +689,126 @@ class AgentInitializer:
                     for line in f:
                         line = line.strip()
                         if line and not line.startswith('#') and '=' in line:
-                            key, _ = line.split('=', 1)
-                            existing_env_vars[key.strip()] = True
+                            key, val = line.split('=', 1)
+                            existing_env_vars[key.strip()] = val.strip()
             except Exception as e:
                 logger.warning(f"[AgentInitializer] Failed to read .env file: {e}")
         
-        # Check which keys need migration
-        keys_to_migrate = {}
+        # Sync config.json values into .env (add/update/remove)
+        updated = False
         for config_key, env_key in key_mapping.items():
-            if env_key in existing_env_vars:
-                continue
-            value = conf().get(config_key, "")
-            if value and value.strip():
-                keys_to_migrate[env_key] = value.strip()
-        
-        # Write new keys
-        if keys_to_migrate:
+            raw = conf().get(config_key, "")
+            value = raw.strip() if raw else ""
+            old_value = existing_env_vars.get(env_key)
+
+            if value:
+                if old_value == value:
+                    continue
+                existing_env_vars[env_key] = value
+                os.environ[env_key] = value
+                updated = True
+            else:
+                if old_value is None:
+                    continue
+                existing_env_vars.pop(env_key, None)
+                os.environ.pop(env_key, None)
+                updated = True
+
+        if updated:
             try:
                 env_dir = os.path.dirname(env_file)
-                if not os.path.exists(env_dir):
-                    os.makedirs(env_dir, exist_ok=True)
-                if not os.path.exists(env_file):
-                    open(env_file, 'a').close()
-                
-                with open(env_file, 'a', encoding='utf-8') as f:
-                    f.write('\n# Auto-migrated from config.json\n')
-                    for key, value in keys_to_migrate.items():
+                os.makedirs(env_dir, exist_ok=True)
+
+                # Rewrite the entire .env file to ensure consistency
+                with open(env_file, 'w', encoding='utf-8') as f:
+                    f.write('# Environment variables for agent\n')
+                    f.write('# Auto-managed - synced from config.json on startup\n\n')
+                    for key, value in sorted(existing_env_vars.items()):
                         f.write(f'{key}={value}\n')
-                        os.environ[key] = value
-                
-                logger.info(f"[AgentInitializer] Migrated {len(keys_to_migrate)} API keys to .env: {list(keys_to_migrate.keys())}")
+
+                logger.info(f"[AgentInitializer] Synced API keys from config.json to .env")
             except Exception as e:
-                logger.warning(f"[AgentInitializer] Failed to migrate API keys: {e}")
+                logger.warning(f"[AgentInitializer] Failed to sync API keys: {e}")
+
+    def _start_daily_flush_timer(self):
+        """Start a background thread that flushes all agents' memory daily at 23:55."""
+        if getattr(self.agent_bridge, '_daily_flush_started', False):
+            return
+        self.agent_bridge._daily_flush_started = True
+
+        import threading
+
+        def _daily_flush_loop():
+            import random
+            last_run_date = None  # Track last successful run date to prevent same-day re-trigger
+            while True:
+                try:
+                    now = datetime.datetime.now()
+                    jitter_min = random.randint(50, 55)
+                    jitter_sec = random.randint(0, 59)
+                    target = now.replace(hour=23, minute=jitter_min, second=jitter_sec, microsecond=0)
+                    # Always schedule for tomorrow if we already ran today, or if target time has passed
+                    if target <= now or (last_run_date == now.date()):
+                        target += datetime.timedelta(days=1)
+                    wait_seconds = (target - now).total_seconds()
+                    logger.info(f"[DailyFlush] Next flush at {target.strftime('%Y-%m-%d %H:%M:%S')} (in {wait_seconds/3600:.1f}h)")
+                    time.sleep(wait_seconds)
+
+                    self._flush_all_agents()
+                    last_run_date = datetime.datetime.now().date()
+                except Exception as e:
+                    logger.warning(f"[DailyFlush] Error in daily flush loop: {e}")
+                    time.sleep(3600)
+
+        t = threading.Thread(target=_daily_flush_loop, daemon=True)
+        t.start()
+
+    def _flush_all_agents(self):
+        """Flush memory for all active agent sessions, then run Deep Dream."""
+        agents = []
+        if self.agent_bridge.default_agent:
+            agents.append(("default", self.agent_bridge.default_agent))
+        for sid, agent in self.agent_bridge.agents.items():
+            agents.append((sid, agent))
+
+        if not agents:
+            return
+
+        # Phase 1: flush daily summaries
+        flushed = 0
+        flush_threads = []
+        dream_candidate = None
+        for label, agent in agents:
+            try:
+                if not agent.memory_manager:
+                    continue
+                with agent.messages_lock:
+                    messages = list(agent.messages)
+                if not messages:
+                    continue
+                result = agent.memory_manager.flush_manager.create_daily_summary(messages)
+                if result:
+                    flushed += 1
+                    t = agent.memory_manager.flush_manager._last_flush_thread
+                    if t:
+                        flush_threads.append(t)
+                if dream_candidate is None:
+                    dream_candidate = agent.memory_manager.flush_manager
+            except Exception as e:
+                logger.warning(f"[DailyFlush] Failed for session {label}: {e}")
+
+        if flushed:
+            logger.info(f"[DailyFlush] Flushed {flushed}/{len(agents)} agent session(s)")
+
+        # Wait for all flush threads to finish before dreaming
+        for t in flush_threads:
+            t.join(timeout=60)
+
+        # Phase 2: Deep Dream — distill daily memories → MEMORY.md + dream diary
+        if dream_candidate:
+            try:
+                result = dream_candidate.deep_dream()
+                if result:
+                    logger.info("[DeepDream] Memory distillation completed successfully")
+            except Exception as e:
+                logger.warning(f"[DeepDream] Failed: {e}")

bridge/bridge.py

@@ -13,8 +13,10 @@ from voice.factory import create_voice
 class Bridge(object):
     def __init__(self):
         self.btype = {
-            "chat": const.CHATGPT,
-            "voice_to_text": conf().get("voice_to_text", "openai"),
+            "chat": const.OPENAI,
+            # Empty `voice_to_text` (the default in new configs) triggers
+            # the auto-pick below — see _auto_pick_voice_to_text for order.
+            "voice_to_text": conf().get("voice_to_text") or self._auto_pick_voice_to_text(),
             "text_to_voice": conf().get("text_to_voice", "google"),
             "translate": conf().get("translate", "baidu"),
         }
@@ -39,11 +41,8 @@ class Bridge(object):
                 self.btype["chat"] = const.BAIDU
             if model_type in ["xunfei"]:
                 self.btype["chat"] = const.XUNFEI
-            if model_type in [const.QWEN]:
-                self.btype["chat"] = const.QWEN
-            if model_type in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
+            if model_type in [const.QWEN, const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
                 self.btype["chat"] = const.QWEN_DASHSCOPE
-            # Support Qwen3 and other DashScope models
             if model_type and (model_type.startswith("qwen") or model_type.startswith("qwq") or model_type.startswith("qvq")):
                 self.btype["chat"] = const.QWEN_DASHSCOPE
             if model_type and model_type.startswith("gemini"):
@@ -61,6 +60,18 @@ class Bridge(object):
             if model_type and model_type.startswith("doubao"):
                 self.btype["chat"] = const.DOUBAO
 
+            if model_type and model_type.startswith("deepseek"):
+                self.btype["chat"] = const.DEEPSEEK
+
+            # 小米 MiMo 系列模型，全部以 mimo- 开头
+            if model_type and model_type.startswith("mimo-"):
+                self.btype["chat"] = const.MIMO
+
+            if model_type and isinstance(model_type, str):
+                lowered_model_type = model_type.lower()
+                if lowered_model_type == const.QIANFAN or lowered_model_type.startswith("ernie"):
+                    self.btype["chat"] = const.QIANFAN
+
             if model_type in [const.MODELSCOPE]:
                 self.btype["chat"] = const.MODELSCOPE
             
@@ -79,6 +90,46 @@ class Bridge(object):
         self.chat_bots = {}
         self._agent_bridge = None
 
+    def refresh_voice(self):
+        """Re-read voice_to_text / text_to_voice from config and drop the
+        cached voice bots so the next call picks up the new provider.
+        Used by the web console after the user edits voice settings.
+        Does NOT touch the agent_bridge / agent state.
+        """
+        new_v2t = conf().get("voice_to_text") or self._auto_pick_voice_to_text()
+        new_t2v = conf().get("text_to_voice", "google")
+        if conf().get("use_linkai") and conf().get("linkai_api_key"):
+            if not conf().get("voice_to_text") or conf().get("voice_to_text") in ["openai"]:
+                new_v2t = const.LINKAI
+            if not conf().get("text_to_voice") or conf().get("text_to_voice") in ["openai", const.TTS_1, const.TTS_1_HD]:
+                new_t2v = const.LINKAI
+        self.btype["voice_to_text"] = new_v2t
+        self.btype["text_to_voice"] = new_t2v
+        self.bots.pop("voice_to_text", None)
+        self.bots.pop("text_to_voice", None)
+        logger.info(f"[Bridge] voice refreshed: voice_to_text={new_v2t}, text_to_voice={new_t2v}")
+
+    @staticmethod
+    def _auto_pick_voice_to_text() -> str:
+        """Pick an ASR provider by configured api keys when voice_to_text is
+        unset. Order matches the web console: openai → dashscope → zhipu →
+        linkai. Falls back to 'openai' when nothing is configured so the
+        original "missing key" error is preserved.
+        """
+        def has(k: str) -> bool:
+            v = (conf().get(k) or "").strip()
+            return v != "" and v not in ("YOUR API KEY", "YOUR_API_KEY")
+
+        for key, provider in (
+            ("open_ai_api_key", "openai"),
+            ("dashscope_api_key", "dashscope"),
+            ("zhipu_ai_api_key", "zhipu"),
+            ("linkai_api_key", "linkai"),
+        ):
+            if has(key):
+                return provider
+        return "openai"
+
     # 模型对应的接口
     def get_bot(self, typename):
         if self.bots.get(typename) is None:

channel/channel.py

@@ -13,12 +13,38 @@ class Channel(object):
     channel_type = ""
     NOT_SUPPORT_REPLYTYPE = [ReplyType.VOICE, ReplyType.IMAGE]
 
+    def __init__(self):
+        import threading
+        self._startup_event = threading.Event()
+        self._startup_error = None
+        self.cloud_mode = False  # set to True by ChannelManager when running with cloud client
+
     def startup(self):
         """
         init channel
         """
         raise NotImplementedError
 
+    def report_startup_success(self):
+        self._startup_error = None
+        self._startup_event.set()
+
+    def report_startup_error(self, error: str):
+        self._startup_error = error
+        self._startup_event.set()
+
+    def wait_startup(self, timeout: float = 3) -> (bool, str):
+        """
+        Wait for channel startup result.
+        Returns (success: bool, error_msg: str).
+        """
+        ready = self._startup_event.wait(timeout=timeout)
+        if not ready:
+            return True, ""
+        if self._startup_error:
+            return False, self._startup_error
+        return True, ""
+
     def stop(self):
         """
         stop channel gracefully, called before restart
@@ -47,7 +73,7 @@ class Channel(object):
         Build reply content, using agent if enabled in config
         """
         # Check if agent mode is enabled
-        use_agent = conf().get("agent", False)
+        use_agent = conf().get("agent", True)
 
         if use_agent:
             try:
@@ -57,11 +83,14 @@ class Channel(object):
                 if context and "channel_type" not in context:
                     context["channel_type"] = self.channel_type
 
+                # Read on_event callback injected by the channel (e.g. web SSE)
+                on_event = context.get("on_event") if context else None
+
                 # Use agent bridge to handle the query
                 return Bridge().fetch_agent_reply(
                     query=query,
                     context=context,
-                    on_event=None,
+                    on_event=on_event,
                     clear_history=False
                 )
             except Exception as e:

channel/channel_factory.py

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

channel/chat_channel.py

@@ -24,11 +24,17 @@ handler_pool = ThreadPoolExecutor(max_workers=8)  # 处理消息的线程池
 class ChatChannel(Channel):
     name = None  # 登录的用户名
     user_id = None  # 登录的用户id
-    futures = {}  # 记录每个session_id提交到线程池的future对象, 用于重置会话时把没执行的future取消掉，正在执行的不会被取消
-    sessions = {}  # 用于控制并发，每个session_id同时只能有一个context在处理
-    lock = threading.Lock()  # 用于控制对sessions的访问
 
     def __init__(self):
+        super().__init__()
+        # Instance-level attributes so each channel subclass has its own
+        # independent session queue and lock. Previously these were class-level,
+        # which caused contexts from one channel (e.g. Feishu) to be consumed
+        # by another channel's consume() thread (e.g. Web), leading to errors
+        # like "No request_id found in context".
+        self.futures = {}
+        self.sessions = {}
+        self.lock = threading.Lock()
         _thread = threading.Thread(target=self.consume)
         _thread.setDaemon(True)
         _thread.start()
@@ -37,9 +43,8 @@ class ChatChannel(Channel):
     def _compose_context(self, ctype: ContextType, content, **kwargs):
         context = Context(ctype, content)
         context.kwargs = kwargs
-        # context首次传入时，origin_ctype是None,
-        # 引入的起因是：当输入语音时，会嵌套生成两个context，第一步语音转文本，第二步通过文本生成文字回复。
-        # origin_ctype用于第二步文本回复时，判断是否需要匹配前缀，如果是私聊的语音，就不需要匹配前缀
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
         if "origin_ctype" not in context:
             context["origin_ctype"] = ctype
         # context首次传入时，receiver是None，根据类型设置receiver
@@ -166,7 +171,13 @@ class ChatChannel(Channel):
             if "desire_rtype" not in context and conf().get("always_reply_voice") and ReplyType.VOICE not in self.NOT_SUPPORT_REPLYTYPE:
                 context["desire_rtype"] = ReplyType.VOICE
         elif context.type == ContextType.VOICE:
-            if "desire_rtype" not in context and conf().get("voice_reply_voice") and ReplyType.VOICE not in self.NOT_SUPPORT_REPLYTYPE:
+            # Voice input replies with voice when either voice_reply_voice
+            # (mirror voice) or the global always_reply_voice toggle is on.
+            if (
+                "desire_rtype" not in context
+                and (conf().get("voice_reply_voice") or conf().get("always_reply_voice"))
+                and ReplyType.VOICE not in self.NOT_SUPPORT_REPLYTYPE
+            ):
                 context["desire_rtype"] = ReplyType.VOICE
         return context
 
@@ -259,6 +270,8 @@ class ChatChannel(Channel):
                 if reply.type == ReplyType.TEXT:
                     reply_text = reply.content
                     if desire_rtype == ReplyType.VOICE and ReplyType.VOICE not in self.NOT_SUPPORT_REPLYTYPE:
+                        # Preserve original text for the "text-then-voice" pattern in _send_reply.
+                        context["voice_reply_text"] = reply.content
                         reply = super().build_text_to_voice(reply.content)
                         return self._decorate_reply(context, reply)
                     if context.get("isgroup", False):
@@ -292,8 +305,12 @@ class ChatChannel(Channel):
                 logger.debug("[chat_channel] sending reply: {}, context: {}".format(reply, context))
                 
                 # 如果是文本回复，尝试提取并发送图片
-                if reply.type == ReplyType.TEXT:
+                # Web channel renders images/videos inline via renderMarkdown,
+                # so skip the extract-and-send step to avoid duplicate media.
+                if reply.type == ReplyType.TEXT and context.get("channel_type") != "web":
                     self._extract_and_send_images(reply, context)
+                elif reply.type == ReplyType.TEXT:
+                    self._send(reply, context)
                 # 如果是图片回复但带有文本内容，先发文本再发图片
                 elif reply.type == ReplyType.IMAGE_URL and hasattr(reply, 'text_content') and reply.text_content:
                     # 先发送文本
@@ -302,6 +319,15 @@ class ChatChannel(Channel):
                     # 短暂延迟后发送图片
                     time.sleep(0.3)
                     self._send(reply, context)
+                # Send text bubble before voice, unless channel already streamed
+                # the text (feishu) or natively renders STT under the voice (wechatcom).
+                elif reply.type == ReplyType.VOICE and context.get("voice_reply_text") \
+                        and not context.get("feishu_streamed") \
+                        and context.get("channel_type") not in ("wechatcom_app",):
+                    text_reply = Reply(ReplyType.TEXT, context.get("voice_reply_text"))
+                    self._send(text_reply, context)
+                    time.sleep(0.3)
+                    self._send(reply, context)
                 else:
                     self._send(reply, context)
     
@@ -342,38 +368,30 @@ class ChatChannel(Channel):
         if media_items:
             logger.info(f"[chat_channel] Extracted {len(media_items)} media item(s) from reply")
             
-            # 先发送文本（保持原文本不变）
+            # Send text first (the frontend will embed video players via renderMarkdown).
             logger.info(f"[chat_channel] Sending text content before media: {reply.content[:100]}...")
             self._send(reply, context)
             logger.info(f"[chat_channel] Text sent, now sending {len(media_items)} media item(s)")
             
-            # 然后逐个发送媒体文件
             for i, (url, media_type) in enumerate(media_items):
                 try:
-                    # 判断是本地文件还是URL
+                    # Determine whether it is a remote URL or a local file.
                     if url.startswith(('http://', 'https://')):
-                        # 网络资源
                         if media_type == 'video':
-                            # 视频使用 FILE 类型发送
                             media_reply = Reply(ReplyType.FILE, url)
                             media_reply.file_name = os.path.basename(url)
                         else:
-                            # 图片使用 IMAGE_URL 类型
                             media_reply = Reply(ReplyType.IMAGE_URL, url)
                     elif os.path.exists(url):
-                        # 本地文件
                         if media_type == 'video':
-                            # 视频使用 FILE 类型，转换为 file:// URL
                             media_reply = Reply(ReplyType.FILE, f"file://{url}")
                             media_reply.file_name = os.path.basename(url)
                         else:
-                            # 图片使用 IMAGE_URL 类型，转换为 file:// URL
                             media_reply = Reply(ReplyType.IMAGE_URL, f"file://{url}")
                     else:
                         logger.warning(f"[chat_channel] Media file not found or invalid URL: {url}")
                         continue
                     
-                    # 发送媒体文件（添加小延迟避免频率限制）
                     if i > 0:
                         time.sleep(0.5)
                     self._send(media_reply, context)
@@ -420,19 +438,55 @@ class ChatChannel(Channel):
 
         return func
 
+    # Chat commands that must bypass the per-session serial queue,
+    # otherwise /cancel would queue behind the task it tries to cancel.
+    # Use /cancel (not /stop) to avoid colliding with `cow stop` CLI.
+    _BYPASS_QUEUE_COMMANDS = ("/cancel",)
+
     def produce(self, context: Context):
         session_id = context["session_id"]
+
+        # Fast path: /cancel must not enter the queue.
+        if context.type == ContextType.TEXT and context.content:
+            stripped = context.content.strip().lower()
+            if stripped in self._BYPASS_QUEUE_COMMANDS:
+                self._handle_cancel_command(context, session_id)
+                return
+
         with self.lock:
             if session_id not in self.sessions:
                 self.sessions[session_id] = [
                     Dequeue(),
-                    threading.BoundedSemaphore(conf().get("concurrency_in_session", 4)),
+                    threading.BoundedSemaphore(conf().get("concurrency_in_session", 1)),
                 ]
             if context.type == ContextType.TEXT and context.content.startswith("#"):
                 self.sessions[session_id][0].putleft(context)  # 优先处理管理命令
             else:
                 self.sessions[session_id][0].put(context)
 
+    def _handle_cancel_command(self, context: Context, session_id: str) -> None:
+        """Cancel any in-flight agent run for *session_id* and reply inline.
+
+        Runs synchronously on the caller's thread. Reply is sent through
+        _send_reply so plugins (e.g. logging) still observe it.
+        """
+        try:
+            from agent.protocol import get_cancel_registry
+            from bridge.reply import Reply, ReplyType
+
+            cancelled = get_cancel_registry().cancel_session(session_id)
+            text = (
+                "🛑 已中止"
+                if cancelled > 0
+                else "当前没有可中止的任务。"
+            )
+            logger.info(
+                f"[chat_channel] /cancel fast-path: session={session_id}, cancelled={cancelled}"
+            )
+            self._send_reply(context, Reply(ReplyType.TEXT, text))
+        except Exception as e:
+            logger.warning(f"[chat_channel] /cancel fast-path failed: {e}")
+
     # 消费者函数，单独线程，用于从消息队列中取出消息并处理
     def consume(self):
         while True:

channel/chat_message.py

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

channel/dingtalk/dingtalk_channel.py

@@ -86,6 +86,8 @@ def _check(func):
 
 @singleton
 class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
+    NOT_SUPPORT_REPLYTYPE = []
+
     dingtalk_client_id = conf().get('dingtalk_client_id')
     dingtalk_client_secret = conf().get('dingtalk_client_secret')
 
@@ -101,6 +103,8 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
         # 历史消息id暂存，用于幂等控制
         self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
         self._stream_client = None
+        self._running = False
+        self._event_loop = None
         logger.debug("[DingTalk] client_id={}, client_secret={} ".format(
             self.dingtalk_client_id, self.dingtalk_client_secret))
         # 无需群校验和前缀
@@ -113,22 +117,130 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
         # Robot code cache (extracted from incoming messages)
         self._robot_code = None
 
+    def _open_connection(self, client):
+        """
+        Open a DingTalk stream connection directly, bypassing SDK's internal error-swallowing.
+        Returns (connection_dict, error_str). On success error_str is empty; on failure
+        connection_dict is None and error_str contains a human-readable message.
+        """
+        try:
+            resp = requests.post(
+                "https://api.dingtalk.com/v1.0/gateway/connections/open",
+                headers={"Content-Type": "application/json", "Accept": "application/json"},
+                json={
+                    "clientId": client.credential.client_id,
+                    "clientSecret": client.credential.client_secret,
+                    "subscriptions": [{"type": "CALLBACK",
+                                       "topic": dingtalk_stream.chatbot.ChatbotMessage.TOPIC}],
+                    "ua": "dingtalk-sdk-python/cow",
+                    "localIp": "",
+                },
+                timeout=10,
+            )
+            body = resp.json()
+            if not resp.ok:
+                code = body.get("code", resp.status_code)
+                message = body.get("message", resp.reason)
+                return None, f"open connection failed: [{code}] {message}"
+            return body, ""
+        except Exception as e:
+            return None, f"open connection failed: {e}"
+
     def startup(self):
+        import asyncio
+        self.dingtalk_client_id = conf().get('dingtalk_client_id')
+        self.dingtalk_client_secret = conf().get('dingtalk_client_secret')
+        self._running = True
         credential = dingtalk_stream.Credential(self.dingtalk_client_id, self.dingtalk_client_secret)
         client = dingtalk_stream.DingTalkStreamClient(credential)
         self._stream_client = client
         client.register_callback_handler(dingtalk_stream.chatbot.ChatbotMessage.TOPIC, self)
-        logger.info("[DingTalk] ✅ Stream connected, ready to receive messages")
-        client.start_forever()
+        logger.info("[DingTalk] ✅ Stream client initialized, ready to receive messages")
+
+        # Run the connection loop ourselves instead of delegating to client.start(),
+        # so we can get detailed error messages and respond to stop() quickly.
+        import urllib.parse as _urlparse
+        import websockets as _ws
+        import json as _json
+        client.pre_start()
+        _first_connect = True
+        while self._running:
+            # Open connection using our own request so we get detailed error info.
+            connection, err_msg = self._open_connection(client)
+
+            if connection is None:
+                if _first_connect:
+                    logger.warning(f"[DingTalk] {err_msg}")
+                    self.report_startup_error(err_msg)
+                    _first_connect = False
+                else:
+                    logger.warning(f"[DingTalk] {err_msg}, retrying in 10s...")
+
+                # Interruptible sleep: checks _running every 100ms.
+                for _ in range(100):
+                    if not self._running:
+                        break
+                    time.sleep(0.1)
+                continue
+
+            if _first_connect:
+                logger.info("[DingTalk] ✅ Connected to DingTalk stream")
+                self.report_startup_success()
+                _first_connect = False
+            else:
+                logger.info("[DingTalk] Reconnected to DingTalk stream")
+
+            # Run the WebSocket session in an asyncio loop.
+            uri = '%s?ticket=%s' % (
+                connection['endpoint'],
+                _urlparse.quote_plus(connection['ticket'])
+            )
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            self._event_loop = loop
+            try:
+                async def _session():
+                    async with _ws.connect(uri) as websocket:
+                        client.websocket = websocket
+                        async for raw_message in websocket:
+                            json_message = _json.loads(raw_message)
+                            result = await client.route_message(json_message)
+                            if result == dingtalk_stream.DingTalkStreamClient.TAG_DISCONNECT:
+                                break
+
+                loop.run_until_complete(_session())
+            except (KeyboardInterrupt, SystemExit):
+                logger.info("[DingTalk] Session loop received stop signal, exiting")
+                break
+            except Exception as e:
+                if not self._running:
+                    break
+                logger.warning(f"[DingTalk] Stream session error: {e}, reconnecting in 3s...")
+                for _ in range(30):
+                    if not self._running:
+                        break
+                    time.sleep(0.1)
+            finally:
+                self._event_loop = None
+                try:
+                    loop.close()
+                except Exception:
+                    pass
+
+        logger.info("[DingTalk] Startup loop exited")
 
     def stop(self):
-        if self._stream_client:
+        logger.info("[DingTalk] stop() called, setting _running=False")
+        self._running = False
+        loop = self._event_loop
+        if loop and not loop.is_closed():
             try:
-                self._stream_client.stop()
-                logger.info("[DingTalk] Stream client stopped")
+                loop.call_soon_threadsafe(loop.stop)
+                logger.info("[DingTalk] Sent stop signal to event loop")
             except Exception as e:
-                logger.warning(f"[DingTalk] Error stopping stream client: {e}")
-            self._stream_client = None
+                logger.warning(f"[DingTalk] Error stopping event loop: {e}")
+        self._stream_client = None
+        logger.info("[DingTalk] stop() completed")
     
     def get_access_token(self):
         """
@@ -465,23 +577,21 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
     async def process(self, callback: dingtalk_stream.CallbackMessage):
         try:
             incoming_message = dingtalk_stream.ChatbotMessage.from_dict(callback.data)
-            
+
             # 缓存 robot_code，用于后续图片下载
             if hasattr(incoming_message, 'robot_code'):
                 self._robot_code_cache = incoming_message.robot_code
-            
-            # Debug: 打印完整的 event 数据
-            logger.debug(f"[DingTalk] ===== Incoming Message Debug =====")
-            logger.debug(f"[DingTalk] callback.data keys: {callback.data.keys() if hasattr(callback.data, 'keys') else 'N/A'}")
-            logger.debug(f"[DingTalk] incoming_message attributes: {dir(incoming_message)}")
-            logger.debug(f"[DingTalk] robot_code: {getattr(incoming_message, 'robot_code', 'N/A')}")
-            logger.debug(f"[DingTalk] chatbot_corp_id: {getattr(incoming_message, 'chatbot_corp_id', 'N/A')}")
-            logger.debug(f"[DingTalk] chatbot_user_id: {getattr(incoming_message, 'chatbot_user_id', 'N/A')}")
-            logger.debug(f"[DingTalk] conversation_id: {getattr(incoming_message, 'conversation_id', 'N/A')}")
-            logger.debug(f"[DingTalk] Raw callback.data: {callback.data}")
-            logger.debug(f"[DingTalk] =====================================")
-            
-            image_download_handler = self  # 传入方法所在的类实例
+
+            # Filter out stale messages from before channel startup (offline backlog)
+            create_at = getattr(incoming_message, 'create_at', None)
+            if create_at:
+                msg_age_s = time.time() - int(create_at) / 1000
+                if msg_age_s > 60:
+                    logger.warning(f"[DingTalk] stale msg filtered (age={msg_age_s:.0f}s), "
+                                   f"msg_id={getattr(incoming_message, 'message_id', 'N/A')}")
+                    return AckMessage.STATUS_OK, 'OK'
+
+            image_download_handler = self
             dingtalk_msg = DingTalkMessage(incoming_message, image_download_handler)
 
             if dingtalk_msg.is_group:
@@ -490,8 +600,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
                 self.handle_single(dingtalk_msg)
             return AckMessage.STATUS_OK, 'OK'
         except Exception as e:
-            logger.error(f"[DingTalk] process error: {e}")
-            logger.exception(e)  # 打印完整堆栈跟踪
+            logger.error(f"[DingTalk] process error: {e}", exc_info=True)
             return AckMessage.STATUS_SYSTEM_EXCEPTION, 'ERROR'
 
     @time_checker
@@ -763,6 +872,48 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
                     self.reply_text("抱歉，文件上传失败", incoming_message)
             return
         
+        # Native sampleAudio. Upload only accepts ogg/amr, so convert TTS mp3/wav to amr.
+        elif reply.type == ReplyType.VOICE:
+            logger.info(f"[DingTalk] Sending voice: {reply.content}")
+            access_token = self.get_access_token()
+            if not access_token:
+                logger.error("[DingTalk] Cannot get access token for voice")
+                self.reply_text("抱歉，语音发送失败（无法获取token）", incoming_message)
+                return
+
+            voice_path = reply.content
+            if voice_path.startswith("file://"):
+                voice_path = voice_path[7:]
+
+            amr_path = voice_path
+            duration_ms = 0
+            if not voice_path.lower().endswith((".amr", ".ogg")):
+                try:
+                    from voice.audio_convert import any_to_amr
+                    amr_path = os.path.splitext(voice_path)[0] + ".amr"
+                    duration_ms = int(any_to_amr(voice_path, amr_path) or 0)
+                except Exception as e:
+                    logger.error(f"[DingTalk] Failed to convert voice to amr: {e}")
+                    self.reply_text("抱歉，语音转码失败", incoming_message)
+                    return
+
+            media_id = self.upload_media(amr_path, media_type="voice")
+            if not media_id:
+                logger.error("[DingTalk] Failed to upload voice media")
+                self.reply_text("抱歉，语音上传失败", incoming_message)
+                return
+
+            msg_param = {
+                "mediaId": media_id,
+                "duration": str(duration_ms or 1000),
+            }
+            success = self._send_file_message(
+                access_token, incoming_message, "sampleAudio", msg_param, isgroup
+            )
+            if not success:
+                self.reply_text("抱歉，语音发送失败", 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)}")

channel/feishu/feishu_message.py

@@ -144,7 +144,14 @@ class FeishuMessage(ChatMessage):
             file_key = content.get("file_key")
             file_name = content.get("file_name")
 
-            self.content = TmpDir().path() + file_key + "." + utils.get_path_suffix(file_name)
+            # 落到 agent_workspace/tmp 下（绝对路径），与图片处理一致；
+            # 否则相对路径 ./tmp 在 agent 工作区里 read 时会找不到。
+            workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+            tmp_dir = os.path.join(workspace_root, "tmp")
+            os.makedirs(tmp_dir, exist_ok=True)
+            self.content = os.path.join(
+                tmp_dir, f"{file_key}.{utils.get_path_suffix(file_name)}"
+            )
 
             def _download_file():
                 # 如果响应状态码是200，则将响应内容写入本地文件
@@ -162,6 +169,42 @@ class FeishuMessage(ChatMessage):
                 else:
                     logger.info(f"[FeiShu] Failed to download file, key={file_key}, res={response.text}")
             self._prepare_fn = _download_file
+        elif msg_type == "audio":
+            # 飞书用户发送的语音消息类型为 "audio"，文件为 opus 编码格式。
+            # 映射为 ContextType.VOICE，交由 chat_channel 的语音转文字（STT）流程处理。
+            # 文件通过 _prepare_fn 延迟下载，在 chat_channel 调用 cmsg.prepare() 时才执行。
+            self.ctype = ContextType.VOICE
+            content = json.loads(msg.get("content"))
+            file_key = content.get("file_key")
+
+            # 落到 agent_workspace/tmp 下（绝对路径），保证语音 STT 流程可读到
+            workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+            tmp_dir = os.path.join(workspace_root, "tmp")
+            os.makedirs(tmp_dir, exist_ok=True)
+            self.content = os.path.join(tmp_dir, f"{file_key}.opus")
+            logger.info(f"[FeiShu] audio message: file_key={file_key}, save_path={self.content}")
+
+            def _download_audio():
+                logger.info(f"[FeiShu] downloading audio: file_key={file_key}, msg_id={self.msg_id}")
+                url = f"https://open.feishu.cn/open-apis/im/v1/messages/{self.msg_id}/resources/{file_key}"
+                headers = {
+                    "Authorization": "Bearer " + access_token,
+                }
+                params = {
+                    "type": "file"
+                }
+                try:
+                    response = requests.get(url=url, headers=headers, params=params)
+                    logger.info(f"[FeiShu] download audio response: status={response.status_code}, size={len(response.content)} bytes")
+                    if response.status_code == 200:
+                        with open(self.content, "wb") as f:
+                            f.write(response.content)
+                        logger.info(f"[FeiShu] audio saved to: {self.content}")
+                    else:
+                        logger.error(f"[FeiShu] Failed to download audio, key={file_key}, status={response.status_code}, res={response.text}")
+                except Exception as e:
+                    logger.error(f"[FeiShu] Exception downloading audio, key={file_key}: {e}", exc_info=True)
+            self._prepare_fn = _download_audio
         else:
             raise NotImplementedError("Unsupported message type: Type:{} ".format(msg_type))
 

channel/qq/qq_channel.py

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

channel/qq/qq_message.py

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

channel/web/static/logos/claudeAPI.svg

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channel/web/static/logos/custom.svg

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+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="200" height="200" fill="none" stroke="#475569" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round">
+  <!-- Horizontal slider tracks -->
+  <line x1="4" y1="7" x2="20" y2="7"/>
+  <line x1="4" y1="12" x2="20" y2="12"/>
+  <line x1="4" y1="17" x2="20" y2="17"/>
+  <!-- Knobs (filled circles) -->
+  <circle cx="9" cy="7"  r="2.2" fill="#475569" stroke="none"/>
+  <circle cx="15" cy="12" r="2.2" fill="#475569" stroke="none"/>
+  <circle cx="7" cy="17"  r="2.2" fill="#475569" stroke="none"/>
+</svg>

channel/web/static/logos/dashscope.svg

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channel/web/static/logos/doubao.svg

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channel/web/static/logos/gemini.svg

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channel/web/static/logos/minimax.svg

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channel/web/static/logos/moonshot.svg

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channel/web/static/logos/openai.svg

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channel/web/static/logos/qianfan.svg

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channel/web/static/logos/zhipu.svg

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+<?xml version="1.0" standalone="no"?><!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"><svg t="1779251419020" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="10062" xmlns:xlink="http://www.w3.org/1999/xlink" width="200" height="200"><path d="M520.063496 0v77.563152c0 269.231173-144.758953 414.054122-434.212862 434.340854L86.106618 511.968002H76.827198V255.984001l443.236298-255.984001z" fill="#5B55F6" p-id="10063"></path><path d="M520.063496 1023.936004v-77.563152c0-269.231173-144.758953-414.054122-434.212862-434.340854L86.042622 511.968002H76.827198v255.984001l443.236298 255.984001z" fill="#376AF3" p-id="10064"></path><path d="M520.063496 0v77.563152c0 269.231173 144.758953 414.054122 434.276858 434.340854L954.08437 511.968002h9.215424V255.984001L520.063496 0z" fill="#5B55F6" p-id="10065"></path><path d="M520.063496 1023.936004v-77.563152c0-269.231173 144.758953-414.054122 434.276858-434.340854L954.08437 511.968002h9.27942v255.984001l-443.236298 255.984001z" fill="#376AF3" p-id="10066"></path></svg>

channel/web/static/vendor/README.md

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+# Vendor assets
+
+Third-party frontend assets bundled locally so the Web Console can run in
+fully offline / air-gapped environments (no requests to cloudflare, jsdelivr,
+googleapis, gstatic, etc.).
+
+All files here are vendored copies of upstream releases. Do not edit them by
+hand; re-download from the official source if upgrading.
+
+## Manifest
+
+| Path                                                | Source                                                                                            | Version |
+| --------------------------------------------------- | ------------------------------------------------------------------------------------------------- | ------- |
+| `fontawesome/css/all.min.css`                       | https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css                         | 6.4.0   |
+| `fontawesome/webfonts/fa-{brands,regular,solid,v4compatibility}-*.woff2` | https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/webfonts/              | 6.4.0   |
+| `fonts/inter/inter-latin.woff2`                     | https://fonts.gstatic.com/s/inter/v20/UcC73FwrK3iLTeHuS_nVMrMxCp50SjIa1ZL7.woff2                  | v20     |
+| `fonts/inter/inter.css`                             | Hand-written `@font-face` declaration that maps Inter weights 300-700 to the local woff2          | -       |
+| `tailwind/tailwind.min.js`                          | https://cdn.tailwindcss.com (Play CDN runtime, JIT engine for the browser)                        | latest  |
+| `markdown-it/markdown-it.min.js`                    | https://cdn.jsdelivr.net/npm/markdown-it@13.0.1/dist/markdown-it.min.js                           | 13.0.1  |
+| `highlightjs/highlight.min.js`                      | https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js                       | 11.9.0  |
+| `highlightjs/styles/github{,-dark}.min.css`         | https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/                                | 11.9.0  |
+| `highlightjs/languages/{python,javascript,java,go,bash}.min.js` | https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/languages/                  | 11.9.0  |
+| `d3/d3.min.js`                                      | https://cdn.jsdelivr.net/npm/d3@7/dist/d3.min.js (loaded lazily for the knowledge graph view)     | 7.x     |
+
+Notes:
+
+- The Inter font only ships the latin subset (CJK characters fall back to the
+  system sans-serif via the font-family chain in `tailwind.config`).
+- Only `woff2` font files are shipped (no `ttf` fallback). woff2 is supported
+  by all browsers released since 2014-2018 (Chrome 36+, Firefox 39+, Safari
+  12+, Edge, Opera 26+). The only mainstream browser that lacks woff2 support
+  is IE 11, which cannot run the rest of the console anyway. `all.min.css`
+  still references the ttf paths as a `src:` fallback — those 404s are
+  harmless and ignored by the browser once the woff2 loads.
+- `tailwind.min.js` is the official Tailwind Play CDN build (an in-browser JIT
+  engine). It must be served as JS to keep the existing `tailwind.config = {}`
+  customization working.
+- One external script remains in `channel/web/static/js/console.js`:
+  `wwcdn.weixin.qq.com/.../wecom-aibot-sdk` — Tencent requires the WeCom Bot
+  SDK to be loaded from their CDN, and it is only fetched when the user opens
+  the WeCom Bot QR-login flow.

channel/web/static/vendor/fonts/inter/inter.css

@@ -0,0 +1,16 @@
+/* Inter font (latin subset only).
+ * Single variable font woff2 that covers weights 300/400/500/600/700.
+ * Non-latin scripts (CJK, etc.) fall back to system sans-serif via the
+ * font-family chain defined in tailwind.config (Inter, system-ui, ...).
+ * Source: Google Fonts (Inter v20), redistributed locally to avoid runtime
+ * dependency on fonts.googleapis.com / fonts.gstatic.com.
+ */
+
+@font-face {
+    font-family: 'Inter';
+    font-style: normal;
+    font-weight: 300 700;
+    font-display: swap;
+    src: url('./inter-latin.woff2') format('woff2');
+    unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}