feat: docs update

docs: init docs
Merge branch 'master' of github.com:zhayujie/chatgpt-on-wechat
2026-06-02 09:48:22 +08:00 · 2026-02-27 16:03:47 +08:00 · 2026-02-27 12:10:16 +08:00 · 2026-02-26 18:57:43 +08:00 · 2026-02-26 18:57:19 +08:00 · 2026-02-26 18:57:08 +08:00
182 changed files with 11214 additions and 2988 deletions
--- a/README.md
+++ b/README.md
@@ -12,20 +12,21 @@

 # 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FTS5 not available, using LIKE-based keyword searc度扩展的Agent框架，可以通过为项目扩展大模型接口、接入渠道、内置工具、Skills系统来灵活实现各种定制需求。核心能力如下：
+> 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、通义千问, Kimi等国内外主流模型厂商
+-  ✅  **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
+1. 本项目遵循 [MIT开源协议](/LICENSE)，主要用于技术研究和学习，使用本项目时需遵守所在地法律法规、相关政策以及企业章程，禁止用于任何违法或侵犯他人权益的行为。任何个人、团队和企业，无论以何种方式使用该项目、对何对象提供服务，所产生的一切后果，本项目均不承担任何责任。
+2. 成本与安全：Agent模式下Token使用量高于普通对话模式，请根据效果及成本综合选择模型。Agent具有访问所在操作系统的能力，请谨慎选择项目部署环境。同时项目也会持续升级安全机制、并降低模型消耗成本。
+3. CowAgent项目专注于开源技术开发，不会参与、授权或发行任何加密货币。

 ## 演示

@@ -90,7 +91,7 @@ bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)

 项目支持国内外主流厂商的模型接口，可选模型及配置说明参考：[模型说明](#模型说明)。

-> 注：Agent模式下推荐使用以下模型，可根据效果及成本综合选择： Claude(claude-sonnet-4-5、claude-sonnet-4-0)、Gemini(gemini-3-flash-preview、gemini-3-pro-preview)、GLM(glm-4.7)、MiniMAx(MiniMax-M2.1)、Qwen(qwen3-max)
+> 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)。

@@ -136,16 +137,18 @@ pip3 install -r requirements-optional.txt
 # 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": "claude-sonnet-4-5",                               # 模型名称
+  "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
-  "open_ai_api_key": "",                                      # OpenAI API Key
-  "open_ai_api_base": "https://api.openai.com/v1",            # OpenAI API 地址
  "gemini_api_key": "",                                       # Gemini API Key
  "gemini_api_base": "https://generativelanguage.googleapis.com", # Gemini API地址
-  "zhipu_ai_api_key": "",                                     # 智谱GLM API Key
-  "minimax_api_key": "",                                      # MiniMax API Key
-  "dashscope_api_key": "",                                    # 百炼(通义千问)API Key
+  "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
@@ -173,13 +176,13 @@ pip3 install -r requirements-optional.txt
 <details>
 <summary>2. 其他配置</summary>

-+ `model`: 模型名称，Agent模式下推荐使用 `claude-sonnet-4-5`、`claude-sonnet-4-0`、`gemini-3-flash-preview`、`gemini-3-pro-preview`、`glm-4.7`、`MiniMax-M2.1`、`qwen3-max`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
+ `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>5. LinkAI配置</summary>
+<summary>3. LinkAI配置</summary>

 + `use_linkai`: 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台，使用知识库、工作流、插件等能力, 参考[接口文档](https://docs.link-ai.tech/platform/api/chat)
 + `linkai_api_key`: LinkAI Api Key，可在 [控制台](https://link-ai.tech/console/interface) 创建
@@ -302,6 +305,140 @@ volumes:
 + `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>

@@ -311,11 +448,11 @@ volumes:

 ```json
 {
-    "model": "claude-sonnet-4-5",
+    "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-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
+ - `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>
@@ -324,11 +461,11 @@ volumes:
 API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn) 创建API Key ，配置如下
 ```json
 {
-    "model": "gemini-3-flash-preview",
+    "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-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
+ - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3.1-pro-preview、gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
 </details>

 <details>
@@ -354,122 +491,6 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 - `open_ai_api_base`: DeepSeek平台 BASE URL
 </details>

-<details>
-<summary>通义千问 (Qwen)</summary>
-
-方式一：官方SDK接入，配置如下(推荐)：
-
-```json
-{
-    "model": "qwen3-max",
-    "dashscope_api_key": "sk-qVxxxxG"
-}
-```
- - `model`: 可填写 `qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
- - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建
- 
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "qwen3-max",
-  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
-  "open_ai_api_key": "sk-qVxxxxG"
-}
-```
- `bot_type`: OpenAI兼容方式
- `model`: 支持官方所有模型，参考[模型列表](https://help.aliyun.com/zh/model-studio/models?spm=a2c4g.11186623.0.0.78d84823Kth5on#9f8890ce29g5u)
- `open_ai_api_base`: 通义千问API的 BASE URL
- `open_ai_api_key`: 通义千问的 API-KEY
-</details>
-
-<details>
-<summary>MiniMax</summary>
-
-方式一：官方接入，配置如下(推荐)：
-
-```json
-{
-    "model": "MiniMax-M2.1",
-    "minimax_api_key": ""
-}
-```
- - `model`: 可填写 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
- - `minimax_api_key`：MiniMax平台的API-KEY，在 [控制台](https://platform.minimaxi.com/user-center/basic-information/interface-key) 创建
- 
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "MiniMax-M2.1",
-  "open_ai_api_base": "https://api.minimaxi.com/v1",
-  "open_ai_api_key": ""
-}
-```
- `bot_type`: OpenAI兼容方式
- `model`: 可填 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
- `open_ai_api_base`: MiniMax平台API的 BASE URL
- `open_ai_api_key`: MiniMax平台的API-KEY
-</details>
-
-<details>
-<summary>智谱AI (GLM)</summary>
-
-方式一：官方接入，配置如下(推荐)：
-
-```json
-{
-  "model": "glm-4.7",
-  "zhipu_ai_api_key": ""
-}
-```
- - `model`: 可填 `glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm-4系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
- - `zhipu_ai_api_key`: 智谱AI平台的 API KEY，在 [控制台](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) 创建
- 
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "glm-4.7",
-  "open_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
-  "open_ai_api_key": ""
-}
-```
- `bot_type`: OpenAI兼容方式
- `model`: 可填 `glm-4.7、glm-4.6、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
- `open_ai_api_base`: 智谱AI平台的 BASE URL
- `open_ai_api_key`: 智谱AI平台的 API KEY
-</details>
-
-<details>
-<summary>Kimi (Moonshot)</summary>
-
-方式一：官方接入，配置如下：
-
-```json
-{
-    "model": "moonshot-v1-128k",
-    "moonshot_api_key": ""
-}
-```
- - `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
- - `moonshot_api_key`: Moonshot的API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
- 
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "moonshot-v1-128k",
-  "open_ai_api_base": "https://api.moonshot.cn/v1",
-  "open_ai_api_key": ""
-}
-```
- `bot_type`: OpenAI兼容方式
- `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
- `open_ai_api_base`: Moonshot的 BASE URL
- `open_ai_api_key`: Moonshot的 API-KEY
-</details>
-
 <details>
 <summary>Azure</summary>

@@ -587,10 +608,12 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)

 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。

+支持同时可接入多个通道，配置时可通过逗号进行分割，例如 `"channel_type": "feishu,dingtalk"`。
+
 <details>
 <summary>1. Web</summary>

-项目启动后默认运行Web通道，配置如下：
+项目启动后会默认运行Web控制台，配置如下：

 ```json
 {
--- a/agent/chat/init.py
+++ b/agent/chat/init.py
@@ -0,0 +1,3 @@
+from agent.chat.service import ChatService
+
+__all__ = ["ChatService"]
--- a/agent/chat/service.py
+++ b/agent/chat/service.py
@@ -0,0 +1,169 @@
+"""
+ChatService - Wraps the Agent stream execution to produce CHAT protocol chunks.
+
+Translates agent events (message_update, message_end, tool_execution_end, etc.)
+into the CHAT socket protocol format (content chunks with segment_id, tool_calls chunks).
+"""
+
+import time
+from typing import Callable, Optional
+
+from common.log import logger
+
+
+class ChatService:
+    """
+    High-level service that runs an Agent for a given query and streams
+    the results as CHAT protocol chunks via a callback.
+
+    Usage:
+        svc = ChatService(agent_bridge)
+        svc.run(query, session_id, send_chunk_fn)
+    """
+
+    def __init__(self, agent_bridge):
+        """
+        :param agent_bridge: AgentBridge instance (manages agent lifecycle)
+        """
+        self.agent_bridge = agent_bridge
+
+    def run(self, query: str, session_id: str, send_chunk_fn: Callable[[dict], None]):
+        """
+        Run the agent for *query* and stream results back via *send_chunk_fn*.
+
+        The method blocks until the agent finishes. After it returns the SDK
+        will automatically send the final (streaming=false) message.
+
+        :param query: user query text
+        :param session_id: session identifier for agent isolation
+        :param send_chunk_fn: callable(chunk_data: dict) to send a streaming chunk
+        """
+        agent = self.agent_bridge.get_agent(session_id=session_id)
+        if agent is None:
+            raise RuntimeError("Failed to initialise agent for the session")
+
+        # State shared between the event callback and this method
+        state = _StreamState()
+
+        def on_event(event: dict):
+            """Translate agent events into CHAT protocol chunks."""
+            event_type = event.get("type")
+            data = event.get("data", {})
+
+            if event_type == "message_update":
+                # Incremental text delta
+                delta = data.get("delta", "")
+                if delta:
+                    send_chunk_fn({
+                        "chunk_type": "content",
+                        "delta": delta,
+                        "segment_id": state.segment_id,
+                    })
+
+            elif event_type == "message_end":
+                # A content segment finished.
+                tool_calls = data.get("tool_calls", [])
+                if tool_calls:
+                    # After tool_calls are executed the next content will be
+                    # a new segment; collect tool results until turn_end.
+                    state.pending_tool_results = []
+
+            elif event_type == "tool_execution_end":
+                tool_name = data.get("tool_name", "")
+                arguments = data.get("arguments", {})
+                result = data.get("result", "")
+                status = data.get("status", "unknown")
+                execution_time = data.get("execution_time", 0)
+                elapsed_str = f"{execution_time:.2f}s"
+
+                # Serialise result to string if needed
+                if not isinstance(result, str):
+                    import json
+                    try:
+                        result = json.dumps(result, ensure_ascii=False)
+                    except Exception:
+                        result = str(result)
+
+                tool_info = {
+                    "name": tool_name,
+                    "arguments": arguments,
+                    "result": result,
+                    "status": status,
+                    "elapsed": elapsed_str,
+                }
+
+                if state.pending_tool_results is not None:
+                    state.pending_tool_results.append(tool_info)
+
+            elif event_type == "turn_end":
+                has_tool_calls = data.get("has_tool_calls", False)
+                if has_tool_calls and state.pending_tool_results:
+                    # Flush collected tool results as a single tool_calls chunk
+                    send_chunk_fn({
+                        "chunk_type": "tool_calls",
+                        "tool_calls": state.pending_tool_results,
+                    })
+                    state.pending_tool_results = None
+                    # Next content belongs to a new segment
+                    state.segment_id += 1
+
+        # Run the agent with our event callback ---------------------------
+        logger.info(f"[ChatService] Starting agent run: session={session_id}, query={query[:80]}")
+
+        from config import conf
+        max_context_turns = conf().get("agent_max_context_turns", 30)
+
+        # Get full system prompt with skills
+        full_system_prompt = agent.get_full_system_prompt()
+
+        # Create a copy of messages for this execution
+        with agent.messages_lock:
+            messages_copy = agent.messages.copy()
+            original_length = len(agent.messages)
+
+        from agent.protocol.agent_stream import AgentStreamExecutor
+
+        executor = AgentStreamExecutor(
+            agent=agent,
+            model=agent.model,
+            system_prompt=full_system_prompt,
+            tools=agent.tools,
+            max_turns=agent.max_steps,
+            on_event=on_event,
+            messages=messages_copy,
+            max_context_turns=max_context_turns,
+        )
+
+        try:
+            response = executor.run_stream(query)
+        except Exception:
+            # If executor cleared messages (context overflow), sync back
+            if len(executor.messages) == 0:
+                with agent.messages_lock:
+                    agent.messages.clear()
+                    logger.info("[ChatService] Cleared agent message history after executor recovery")
+            raise
+
+        # Append only the NEW messages from this execution (thread-safe)
+        with agent.messages_lock:
+            new_messages = executor.messages[original_length:]
+            agent.messages.extend(new_messages)
+
+        # Store executor reference for files_to_send access
+        agent.stream_executor = executor
+
+        # Execute post-process tools
+        agent._execute_post_process_tools()
+
+        logger.info(f"[ChatService] Agent run completed: session={session_id}")
+
+
+
+class _StreamState:
+    """Mutable state shared between the event callback and the run method."""
+
+    def __init__(self):
+        self.segment_id: int = 0
+        # None means we are not accumulating tool results right now.
+        # A list means we are in the middle of a tool-execution phase.
+        self.pending_tool_results: Optional[list] = None
--- a/agent/memory/init.py
+++ b/agent/memory/init.py
@@ -1,11 +1,21 @@
 """
 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

-__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',
+]
--- a/agent/memory/config.py
+++ b/agent/memory/config.py
@@ -11,12 +11,18 @@ from typing import Optional, List
 from pathlib import Path


+def _default_workspace():
+    """Get default workspace path with proper Windows support"""
+    from common.utils import expand_path
+    return expand_path("~/cow")
+
+
@dataclass
 class MemoryConfig:
    """Configuration for memory storage and search"""
    
    # Storage paths (default: ~/cow)
-    workspace_root: str = field(default_factory=lambda: os.path.expanduser("~/cow"))
+    workspace_root: str = field(default_factory=_default_workspace)
    
    # Embedding config
    embedding_provider: str = "openai"  # "openai" | "local"
--- a/agent/memory/conversation_store.py
+++ b/agent/memory/conversation_store.py
@@ -0,0 +1,618 @@
+"""
+Conversation history persistence using SQLite.
+
+Design:
+- sessions table: per-session metadata (channel_type, last_active, msg_count)
+- messages table: individual messages stored as JSON, append-only
+- Pruning: age-based only (sessions not updated within N days are deleted)
+- Thread-safe via a single in-process lock
+
+Storage path: ~/cow/sessions/conversations.db
+"""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from common.log import logger
+
+
+# ---------------------------------------------------------------------------
+# Schema
+# ---------------------------------------------------------------------------
+
+_DDL = """
+CREATE TABLE IF NOT EXISTS sessions (
+    session_id   TEXT    PRIMARY KEY,
+    channel_type TEXT    NOT NULL DEFAULT '',
+    created_at   INTEGER NOT NULL,
+    last_active  INTEGER NOT NULL,
+    msg_count    INTEGER NOT NULL DEFAULT 0
+);
+
+CREATE TABLE IF NOT EXISTS messages (
+    id           INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id   TEXT    NOT NULL,
+    seq          INTEGER NOT NULL,
+    role         TEXT    NOT NULL,
+    content      TEXT    NOT NULL,
+    created_at   INTEGER NOT NULL,
+    UNIQUE (session_id, seq)
+);
+
+CREATE INDEX IF NOT EXISTS idx_messages_session
+    ON messages (session_id, seq);
+
+CREATE INDEX IF NOT EXISTS idx_sessions_last_active
+    ON sessions (last_active);
+"""
+
+# Migration: add channel_type column to existing databases that predate it.
+_MIGRATION_ADD_CHANNEL_TYPE = """
+ALTER TABLE sessions ADD COLUMN channel_type TEXT NOT NULL DEFAULT '';
+"""
+
+DEFAULT_MAX_AGE_DAYS: int = 30
+
+
+def _is_visible_user_message(content: Any) -> bool:
+    """
+    Return True when a user-role message represents actual user input
+    (not an internal tool_result injected by the agent loop).
+    """
+    if isinstance(content, str):
+        return bool(content.strip())
+    if isinstance(content, list):
+        return any(
+            isinstance(b, dict) and b.get("type") == "text"
+            for b in content
+        )
+    return False
+
+
+def _extract_display_text(content: Any) -> str:
+    """
+    Extract the human-readable text portion from a message content value.
+    Returns an empty string for tool_use / tool_result blocks.
+    """
+    if isinstance(content, str):
+        return content.strip()
+    if isinstance(content, list):
+        parts = [
+            b.get("text", "")
+            for b in content
+            if isinstance(b, dict) and b.get("type") == "text"
+        ]
+        return "\n".join(p for p in parts if p).strip()
+    return ""
+
+
+def _extract_tool_calls(content: Any) -> List[Dict[str, Any]]:
+    """
+    Extract tool_use blocks from an assistant message content.
+    Returns a list of {name, arguments} dicts (result filled in later).
+    """
+    if not isinstance(content, list):
+        return []
+    return [
+        {"id": b.get("id", ""), "name": b.get("name", ""), "arguments": b.get("input", {})}
+        for b in content
+        if isinstance(b, dict) and b.get("type") == "tool_use"
+    ]
+
+
+def _extract_tool_results(content: Any) -> Dict[str, str]:
+    """
+    Extract tool_result blocks from a user message, keyed by tool_use_id.
+    """
+    if not isinstance(content, list):
+        return {}
+    results = {}
+    for b in content:
+        if not isinstance(b, dict) or b.get("type") != "tool_result":
+            continue
+        tool_id = b.get("tool_use_id", "")
+        result_content = b.get("content", "")
+        if isinstance(result_content, list):
+            result_content = "\n".join(
+                rb.get("text", "") for rb in result_content
+                if isinstance(rb, dict) and rb.get("type") == "text"
+            )
+        results[tool_id] = str(result_content)
+    return results
+
+
+def _group_into_display_turns(
+    rows: List[tuple],
+) -> List[Dict[str, Any]]:
+    """
+    Convert raw (role, content_json, created_at) DB rows into display turns.
+
+    One display turn = one visible user message  +  one merged assistant reply.
+    All intermediate assistant messages (those carrying tool_use) and the final
+    assistant text reply produced for the same user query are collapsed into a
+    single assistant turn, exactly matching the live SSE rendering where tools
+    and the final answer appear inside the same bubble.
+
+    Grouping rules:
+    - A visible user message starts a new group.
+    - tool_result user messages are internal; their content is attached to the
+      matching tool_use entry via tool_use_id and they never become own turns.
+    - All assistant messages within a group are merged:
+        * tool_use blocks → tool_calls list (result filled from tool_results)
+        * text blocks → last non-empty text becomes the display content
+    """
+    # ------------------------------------------------------------------ #
+    # Pass 1: split rows into groups, each starting with a visible user msg
+    # ------------------------------------------------------------------ #
+    # group = (user_row | None, [subsequent_rows])
+    # user_row: (content, created_at)
+    groups: List[tuple] = []
+    cur_user: Optional[tuple] = None
+    cur_rest: List[tuple] = []
+    started = False
+
+    for role, raw_content, created_at in rows:
+        try:
+            content = json.loads(raw_content)
+        except Exception:
+            content = raw_content
+
+        if role == "user" and _is_visible_user_message(content):
+            if started:
+                groups.append((cur_user, cur_rest))
+            cur_user = (content, created_at)
+            cur_rest = []
+            started = True
+        else:
+            cur_rest.append((role, content, created_at))
+
+    if started:
+        groups.append((cur_user, cur_rest))
+
+    # ------------------------------------------------------------------ #
+    # Pass 2: build display turns from each group
+    # ------------------------------------------------------------------ #
+    turns: List[Dict[str, Any]] = []
+
+    for user_row, rest in groups:
+        # User turn
+        if user_row:
+            content, created_at = user_row
+            text = _extract_display_text(content)
+            if text:
+                turns.append({"role": "user", "content": text, "created_at": created_at})
+
+        # Collect all tool_calls and tool_results from the rest of the group
+        all_tool_calls: List[Dict[str, Any]] = []
+        tool_results: Dict[str, str] = {}
+        final_text = ""
+        final_ts: Optional[int] = None
+
+        for role, content, created_at in rest:
+            if role == "user":
+                tool_results.update(_extract_tool_results(content))
+            elif role == "assistant":
+                tcs = _extract_tool_calls(content)
+                all_tool_calls.extend(tcs)
+                t = _extract_display_text(content)
+                if t:
+                    final_text = t
+                final_ts = created_at
+
+        # Attach tool results to their matching tool_call entries
+        for tc in all_tool_calls:
+            tc["result"] = tool_results.get(tc.get("id", ""), "")
+
+        if final_text or all_tool_calls:
+            turns.append({
+                "role": "assistant",
+                "content": final_text,
+                "tool_calls": all_tool_calls,
+                "created_at": final_ts or (user_row[1] if user_row else 0),
+            })
+
+    return turns
+
+
+class ConversationStore:
+    """
+    SQLite-backed store for per-session conversation history.
+
+    Usage:
+        store = ConversationStore(db_path)
+        store.append_messages("user_123", new_messages, channel_type="feishu")
+        msgs = store.load_messages("user_123", max_turns=30)
+    """
+
+    def __init__(self, db_path: Path):
+        self._db_path = db_path
+        self._lock = threading.Lock()
+        self._init_db()
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def load_messages(
+        self,
+        session_id: str,
+        max_turns: int = 30,
+    ) -> List[Dict[str, Any]]:
+        """
+        Load the most recent messages for a session, for injection into the LLM.
+
+        ALL message types (user text, assistant tool_use, tool_result) are returned
+        in their original JSON form so the LLM can reconstruct the full context.
+
+        max_turns is a *visible-turn* count: we count only user messages whose
+        content is actual user text (not tool_result blocks).  This prevents
+        tool-heavy sessions from exhausting the turn budget prematurely.
+
+        Args:
+            session_id: Unique session identifier.
+            max_turns: Maximum number of visible user-assistant turns to keep.
+
+        Returns:
+            Chronologically ordered list of message dicts (role, content).
+        """
+        with self._lock:
+            conn = self._connect()
+            try:
+                rows = conn.execute(
+                    """
+                    SELECT seq, role, content
+                    FROM messages
+                    WHERE session_id = ?
+                    ORDER BY seq DESC
+                    """,
+                    (session_id,),
+                ).fetchall()
+            finally:
+                conn.close()
+
+        if not rows:
+            return []
+
+        # Walk newest-to-oldest counting *visible* user turns (actual user text,
+        # not tool_result injections).  Record the seq of every visible user
+        # message so we can find a clean cut point later.
+        visible_turn_seqs: List[int] = []  # newest first
+        for seq, role, raw_content in rows:
+            if role != "user":
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            if _is_visible_user_message(content):
+                visible_turn_seqs.append(seq)
+
+        # Determine the seq of the oldest visible user message we want to keep.
+        # If the total turns fit within max_turns, keep everything.
+        if len(visible_turn_seqs) <= max_turns:
+            cutoff_seq = None  # keep all
+        else:
+            # The Nth visible user message (0-indexed) is the oldest we keep.
+            cutoff_seq = visible_turn_seqs[max_turns - 1]
+
+        # Build result in chronological order, starting from cutoff.
+        # IMPORTANT: we start exactly at cutoff_seq (the visible user message),
+        # never mid-group, so tool_use / tool_result pairs are always complete.
+        result = []
+        for seq, role, raw_content in reversed(rows):
+            if cutoff_seq is not None and seq < cutoff_seq:
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            result.append({"role": role, "content": content})
+        return result
+
+    def append_messages(
+        self,
+        session_id: str,
+        messages: List[Dict[str, Any]],
+        channel_type: str = "",
+    ) -> None:
+        """
+        Append new messages to a session's history.
+
+        Seq numbers continue from the session's current maximum, so
+        concurrent callers on distinct sessions never collide.
+
+        Args:
+            session_id: Unique session identifier.
+            messages: List of message dicts to append.
+            channel_type: Source channel (e.g. "feishu", "web", "wechat").
+                          Only written on session creation; ignored on update.
+        """
+        if not messages:
+            return
+
+        now = int(time.time())
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    # INSERT OR IGNORE creates the row on first visit;
+                    # the UPDATE always refreshes last_active.
+                    # Avoids ON CONFLICT...DO UPDATE (requires SQLite >= 3.24).
+                    conn.execute(
+                        """
+                        INSERT OR IGNORE INTO sessions
+                            (session_id, channel_type, created_at, last_active, msg_count)
+                        VALUES (?, ?, ?, ?, 0)
+                        """,
+                        (session_id, channel_type, now, now),
+                    )
+                    conn.execute(
+                        "UPDATE sessions SET last_active = ? WHERE session_id = ?",
+                        (now, session_id),
+                    )
+
+                    # Determine starting seq for the new batch.
+                    row = conn.execute(
+                        "SELECT COALESCE(MAX(seq), -1) FROM messages WHERE session_id = ?",
+                        (session_id,),
+                    ).fetchone()
+                    next_seq = row[0] + 1
+
+                    for msg in messages:
+                        role = msg.get("role", "")
+                        content = json.dumps(
+                            msg.get("content", ""), ensure_ascii=False
+                        )
+                        conn.execute(
+                            """
+                            INSERT OR IGNORE INTO messages
+                                (session_id, seq, role, content, created_at)
+                            VALUES (?, ?, ?, ?, ?)
+                            """,
+                            (session_id, next_seq, role, content, now),
+                        )
+                        next_seq += 1
+
+                    conn.execute(
+                        """
+                        UPDATE sessions
+                        SET msg_count = (
+                            SELECT COUNT(*) FROM messages WHERE session_id = ?
+                        )
+                        WHERE session_id = ?
+                        """,
+                        (session_id, session_id),
+                    )
+            finally:
+                conn.close()
+
+    def clear_session(self, session_id: str) -> None:
+        """Delete all messages and the session record for a given session_id."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    conn.execute(
+                        "DELETE FROM messages WHERE session_id = ?", (session_id,)
+                    )
+                    conn.execute(
+                        "DELETE FROM sessions WHERE session_id = ?", (session_id,)
+                    )
+            finally:
+                conn.close()
+
+    def cleanup_old_sessions(self, max_age_days: Optional[int] = None) -> int:
+        """
+        Delete sessions that have not been active within max_age_days.
+
+        Args:
+            max_age_days: Override the default retention period.
+
+        Returns:
+            Number of sessions deleted.
+        """
+        try:
+            from config import conf
+            max_age = max_age_days or conf().get(
+                "conversation_max_age_days", DEFAULT_MAX_AGE_DAYS
+            )
+        except Exception:
+            max_age = max_age_days or DEFAULT_MAX_AGE_DAYS
+
+        cutoff = int(time.time()) - max_age * 86400
+        deleted = 0
+
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    stale = conn.execute(
+                        "SELECT session_id FROM sessions WHERE last_active < ?",
+                        (cutoff,),
+                    ).fetchall()
+                    for (sid,) in stale:
+                        conn.execute(
+                            "DELETE FROM messages WHERE session_id = ?", (sid,)
+                        )
+                        conn.execute(
+                            "DELETE FROM sessions WHERE session_id = ?", (sid,)
+                        )
+                        deleted += 1
+            finally:
+                conn.close()
+
+        if deleted:
+            logger.info(f"[ConversationStore] Pruned {deleted} expired sessions")
+        return deleted
+
+    def load_history_page(
+        self,
+        session_id: str,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> Dict[str, Any]:
+        """
+        Load a page of conversation history for UI display, grouped into turns.
+
+        Each "turn" maps to one of:
+          - A user message (role="user", content=str)
+          - An assistant message (role="assistant", content=str,
+            tool_calls=[{name, arguments, result}] when tools were used)
+
+        Internal tool_result user messages are merged into the preceding
+        assistant entry's tool_calls list and never appear as standalone items.
+
+        Pages are numbered from 1 (most recent).  Messages within a page are
+        returned in chronological order.
+
+        Returns:
+            {
+                "messages": [
+                    {
+                        "role": "user" | "assistant",
+                        "content": str,
+                        "tool_calls": [...],   # assistant only, may be []
+                        "created_at": int,
+                    },
+                    ...
+                ],
+                "total": <visible turn count>,
+                "page": <current page>,
+                "page_size": <page_size>,
+                "has_more": bool,
+            }
+        """
+        page = max(1, page)
+        with self._lock:
+            conn = self._connect()
+            try:
+                rows = conn.execute(
+                    """
+                    SELECT role, content, created_at
+                    FROM messages
+                    WHERE session_id = ?
+                    ORDER BY seq ASC
+                    """,
+                    (session_id,),
+                ).fetchall()
+            finally:
+                conn.close()
+
+        visible = _group_into_display_turns(rows)
+
+        total = len(visible)
+        offset = (page - 1) * page_size
+        page_items = list(reversed(visible))[offset: offset + page_size]
+        page_items = list(reversed(page_items))
+
+        return {
+            "messages": page_items,
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "has_more": offset + page_size < total,
+        }
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Return basic stats keyed by channel_type, for monitoring."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                total_sessions = conn.execute(
+                    "SELECT COUNT(*) FROM sessions"
+                ).fetchone()[0]
+                total_messages = conn.execute(
+                    "SELECT COUNT(*) FROM messages"
+                ).fetchone()[0]
+                by_channel = conn.execute(
+                    """
+                    SELECT channel_type, COUNT(*) as cnt
+                    FROM sessions
+                    GROUP BY channel_type
+                    ORDER BY cnt DESC
+                    """
+                ).fetchall()
+                return {
+                    "total_sessions": total_sessions,
+                    "total_messages": total_messages,
+                    "by_channel": {row[0] or "unknown": row[1] for row in by_channel},
+                }
+            finally:
+                conn.close()
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _init_db(self) -> None:
+        self._db_path.parent.mkdir(parents=True, exist_ok=True)
+        conn = self._connect()
+        try:
+            conn.executescript(_DDL)
+            conn.commit()
+            self._migrate(conn)
+        finally:
+            conn.close()
+
+    def _migrate(self, conn: sqlite3.Connection) -> None:
+        """Apply incremental schema migrations on existing databases."""
+        cols = {
+            row[1]
+            for row in conn.execute("PRAGMA table_info(sessions)").fetchall()
+        }
+        if "channel_type" not in cols:
+            try:
+                conn.execute(_MIGRATION_ADD_CHANNEL_TYPE)
+                conn.commit()
+                logger.info("[ConversationStore] Migrated: added channel_type column")
+            except Exception as e:
+                logger.warning(f"[ConversationStore] Migration failed: {e}")
+
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(str(self._db_path), timeout=10)
+        conn.execute("PRAGMA journal_mode=WAL")
+        conn.execute("PRAGMA synchronous=NORMAL")
+        return conn
+
+
+# ---------------------------------------------------------------------------
+# Singleton
+# ---------------------------------------------------------------------------
+
+_store_instance: Optional[ConversationStore] = None
+_store_lock = threading.Lock()
+
+
+def get_conversation_store() -> ConversationStore:
+    """
+    Return the process-wide ConversationStore singleton.
+
+    Reuses the long-term memory database so the project stays with a single
+    SQLite file: ~/cow/memory/long-term/index.db
+    The conversation tables (sessions / messages) are separate from the
+    memory tables (memory_chunks / file_metadata) — no conflicts.
+    """
+    global _store_instance
+    if _store_instance is not None:
+        return _store_instance
+
+    with _store_lock:
+        if _store_instance is not None:
+            return _store_instance
+
+        try:
+            from agent.memory.config import get_default_memory_config
+            db_path = get_default_memory_config().get_db_path()
+        except Exception:
+            from common.utils import expand_path
+            db_path = Path(expand_path("~/cow")) / "memory" / "long-term" / "index.db"
+
+        _store_instance = ConversationStore(db_path)
+        logger.debug(f"[ConversationStore] Using shared DB at: {db_path}")
+        return _store_instance
--- a/agent/memory/manager.py
+++ b/agent/memory/manager.py
@@ -304,7 +304,7 @@ class MemoryManager:
    ):
        """Sync a single file"""
        # Compute file hash
-        content = file_path.read_text()
+        content = file_path.read_text(encoding='utf-8')
        file_hash = MemoryStorage.compute_hash(content)
        
        # Get relative path
--- a/agent/memory/service.py
+++ b/agent/memory/service.py
@@ -0,0 +1,167 @@
+"""
+Memory service for handling memory query operations via cloud protocol.
+
+Provides a unified interface for listing and reading memory files,
+callable from the cloud client (LinkAI) or a future web console.
+
+Memory file layout (under workspace_root):
+    MEMORY.md               -> type: global
+    memory/2026-02-20.md    -> type: daily
+"""
+
+import os
+from datetime import datetime
+from typing import Dict, List, Optional
+from pathlib import Path
+from common.log import logger
+
+
+class MemoryService:
+    """
+    High-level service for memory file queries.
+    Operates directly on the filesystem — no MemoryManager dependency.
+    """
+
+    def __init__(self, workspace_root: str):
+        """
+        :param workspace_root: Workspace root directory (e.g. ~/cow)
+        """
+        self.workspace_root = workspace_root
+        self.memory_dir = os.path.join(workspace_root, "memory")
+
+    # ------------------------------------------------------------------
+    # list — paginated file metadata
+    # ------------------------------------------------------------------
+    def list_files(self, page: int = 1, page_size: int = 20) -> dict:
+        """
+        List all memory 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"},
+                    ...
+                ]
+            }
+        """
+        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)
+
+        # Paginate
+        start = (page - 1) * page_size
+        end = start + page_size
+        page_items = files[start:end]
+
+        return {
+            "page": page,
+            "page_size": page_size,
+            "total": total,
+            "list": page_items,
+        }
+
+    # ------------------------------------------------------------------
+    # content — read a single file
+    # ------------------------------------------------------------------
+    def get_content(self, filename: str) -> dict:
+        """
+        Read the full content of a memory file.
+
+        :param filename: File name, e.g. ``MEMORY.md`` or ``2026-02-20.md``
+        :return: dict with ``filename`` and ``content``
+        :raises FileNotFoundError: if the file does not exist
+        """
+        path = self._resolve_path(filename)
+        if not os.path.isfile(path):
+            raise FileNotFoundError(f"Memory file not found: {filename}")
+
+        with open(path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        return {
+            "filename": filename,
+            "content": content,
+        }
+
+    # ------------------------------------------------------------------
+    # dispatch — single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a memory management action.
+
+        :param action: ``list`` or ``content``
+        :param payload: action-specific payload
+        :return: protocol-compatible response dict
+        """
+        payload = payload or {}
+        try:
+            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)
+                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)
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+
+        except FileNotFoundError as e:
+            return {"action": action, "code": 404, "message": str(e), "payload": None}
+        except Exception as e:
+            logger.error(f"[MemoryService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}
+
+    # ------------------------------------------------------------------
+    # internal helpers
+    # ------------------------------------------------------------------
+    def _resolve_path(self, filename: str) -> str:
+        """
+        Resolve a filename to its absolute path.
+
+        - ``MEMORY.md`` → ``{workspace_root}/MEMORY.md``
+        - ``2026-02-20.md`` → ``{workspace_root}/memory/2026-02-20.md``
+        """
+        if filename == "MEMORY.md":
+            return os.path.join(self.workspace_root, filename)
+        return os.path.join(self.memory_dir, filename)
+
+    @staticmethod
+    def _file_info(path: str, filename: str, file_type: str) -> dict:
+        """Build a file metadata dict."""
+        stat = os.stat(path)
+        updated_at = datetime.fromtimestamp(stat.st_mtime).strftime("%Y-%m-%d %H:%M:%S")
+        return {
+            "filename": filename,
+            "type": file_type,
+            "size": stat.st_size,
+            "updated_at": updated_at,
+        }
--- a/agent/memory/storage.py
+++ b/agent/memory/storage.py
@@ -509,7 +509,7 @@ class MemoryStorage:
        """Destructor to ensure connection is closed"""
        try:
            self.close()
-        except:
+        except Exception:
            pass  # Ignore errors during cleanup
    
    # Helper methods
--- a/agent/prompt/builder.py
+++ b/agent/prompt/builder.py
@@ -157,96 +157,66 @@ def _build_identity_section(base_persona: Optional[str], language: str) -> List[


 def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
-    """构建工具说明section"""
+    """Build tooling section with concise tool list and call style guide."""
+    # One-line summaries for known tools (details are in the tool schema)
+    core_summaries = {
+        "read": "读取文件内容",
+        "write": "创建或覆盖文件",
+        "edit": "精确编辑文件",
+        "ls": "列出目录内容",
+        "grep": "搜索文件内容",
+        "find": "按模式查找文件",
+        "bash": "执行shell命令",
+        "terminal": "管理后台进程",
+        "web_search": "网络搜索",
+        "web_fetch": "获取URL内容",
+        "browser": "控制浏览器",
+        "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",
+    }
+
+    # Preferred display order
+    tool_order = [
+        "read", "write", "edit", "ls", "grep", "find",
+        "bash", "terminal",
+        "web_search", "web_fetch", "browser",
+        "memory_search", "memory_get",
+        "env_config", "scheduler", "send",
+    ]
+
+    # Build name -> summary mapping for available tools
+    available = {}
+    for tool in tools:
+        name = tool.name if hasattr(tool, 'name') else str(tool)
+        available[name] = core_summaries.get(name, "")
+
+    # Generate tool lines: ordered tools first, then extras
+    tool_lines = []
+    for name in tool_order:
+        if name in available:
+            summary = available.pop(name)
+            tool_lines.append(f"- {name}: {summary}" if summary else f"- {name}")
+    for name in sorted(available):
+        summary = available[name]
+        tool_lines.append(f"- {name}: {summary}" if summary else f"- {name}")
+
    lines = [
        "## 工具系统",
        "",
-        "你可以使用以下工具来完成任务。工具名称是大小写敏感的，请严格按照列表中的名称调用。",
+        "可用工具（名称大小写敏感，严格按列表调用）:",
+        "\n".join(tool_lines),
        "",
-        "### 可用工具",
+        "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。",
        "",
    ]
-    
-    # 工具分类和排序
-    tool_categories = {
-        "文件操作": ["read", "write", "edit", "ls", "grep", "find"],
-        "命令执行": ["bash", "terminal"],
-        "网络搜索": ["web_search", "web_fetch", "browser"],
-        "记忆系统": ["memory_search", "memory_get"],
-        "其他": []
-    }
-    
-    # 构建工具映射
-    tool_map = {}
-    tool_descriptions = {
-        "read": "读取文件内容",
-        "write": "创建新文件或完全覆盖现有文件（会删除原内容！追加内容请用 edit）。注意：单次 write 内容不要超过 10KB，超大文件请分步创建",
-        "edit": "精确编辑文件（追加、修改、删除部分内容）",
-        "ls": "列出目录内容",
-        "grep": "在文件中搜索内容",
-        "find": "按照模式查找文件",
-        "bash": "执行shell命令",
-        "terminal": "管理后台进程",
-        "web_search": "网络搜索（使用搜索引擎）",
-        "web_fetch": "获取URL内容",
-        "browser": "控制浏览器",
-        "memory_search": "搜索记忆文件",
-        "memory_get": "获取记忆文件内容",
-        "calculator": "计算器",
-        "current_time": "获取当前时间",
-    }
-    
-    for tool in tools:
-        tool_name = tool.name if hasattr(tool, 'name') else str(tool)
-        tool_desc = tool.description if hasattr(tool, 'description') else tool_descriptions.get(tool_name, "")
-        tool_map[tool_name] = tool_desc
-    
-    # 按分类添加工具
-    for category, tool_names in tool_categories.items():
-        category_tools = [(name, tool_map.get(name, "")) for name in tool_names if name in tool_map]
-        if category_tools:
-            lines.append(f"**{category}**:")
-            for name, desc in category_tools:
-                if desc:
-                    lines.append(f"- `{name}`: {desc}")
-                else:
-                    lines.append(f"- `{name}`")
-                del tool_map[name]  # 移除已添加的工具
-            lines.append("")
-    
-    # 添加其他未分类的工具
-    if tool_map:
-        lines.append("**其他工具**:")
-        for name, desc in sorted(tool_map.items()):
-            if desc:
-                lines.append(f"- `{name}`: {desc}")
-            else:
-                lines.append(f"- `{name}`")
-        lines.append("")
-    
-    # 工具使用指南
-    lines.extend([
-        "### 工具调用风格",
-        "",
-        "默认规则: 对于常规、低风险的工具调用，直接调用即可，无需叙述。",
-        "",
-        "需要叙述的情况:",
-        "- 多步骤、复杂的任务",
-        "- 敏感操作（如删除文件）",
-        "- 用户明确要求解释过程",
-        "",
-        "叙述要求: 保持简洁、信息密度高，避免重复显而易见的步骤。",
-        "",
-        "完成标准:",
-        "- 确保用户的需求得到实际解决，而不仅仅是制定计划。",
-        "- 当任务需要多次工具调用时，持续推进直到完成, 解决完后向用户报告结果或回复用户的问题",
-        "- 每次工具调用后，评估是否已获得足够信息来推进或完成任务",
-        "- 避免重复调用相同的工具和相同参数获取相同的信息，除非用户明确要求",
-        "",
-        "**安全提醒**: 回复中涉及密钥、令牌、密码等敏感信息时，必须脱敏处理，禁止直接显示完整内容。",
-        "",
-    ])
-    
+
    return lines


@@ -265,16 +235,17 @@ def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], langua
                break
    
    lines = [
-        "## 技能系统",
+        "## 技能系统（mandatory）",
        "",
        "在回复之前：扫描下方 <available_skills> 中的 <description> 条目。",
        "",
-        f"- 如果恰好有一个技能明确适用：使用 `{read_tool_name}` 工具读取其 <location> 路径下的 SKILL.md 文件，然后遵循它",
-        "- 如果多个技能都适用：选择最具体的一个，然后读取并遵循",
-        "- 如果没有明确适用的：不要读取任何 SKILL.md",
+        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",
        "",
-        "**约束**: 永远不要一次性读取多个技能；只在选择后再读取。",
+        "**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。",
        "",
+        "以下是可用技能："
    ]
    
    # 添加技能列表（通过skill_manager获取）
@@ -461,7 +432,7 @@ def _build_context_files_section(context_files: List[ContextFile], language: str


 def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[str]:
-    """构建运行时信息section"""
+    """构建运行时信息section - 支持动态时间"""
    if not runtime_info:
        return []
    
@@ -471,7 +442,17 @@ def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[
    ]
    
    # Add current time if available
-    if runtime_info.get("current_time"):
+    # Support dynamic time via callable function
+    if callable(runtime_info.get("_get_current_time")):
+        try:
+            time_info = runtime_info["_get_current_time"]()
+            time_line = f"当前时间: {time_info['time']} {time_info['weekday']} ({time_info['timezone']})"
+            lines.append(time_line)
+            lines.append("")
+        except Exception as e:
+            logger.warning(f"[PromptBuilder] Failed to get dynamic time: {e}")
+    elif runtime_info.get("current_time"):
+        # Fallback to static time for backward compatibility
        time_str = runtime_info["current_time"]
        weekday = runtime_info.get("weekday", "")
        timezone = runtime_info.get("timezone", "")
--- a/agent/prompt/workspace.py
+++ b/agent/prompt/workspace.py
@@ -57,6 +57,10 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
    
    # 创建memory子目录
    os.makedirs(memory_dir, exist_ok=True)
+
+    # 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)
    
    # 如果需要，创建模板文件
    if create_templates:
--- a/agent/protocol/agent.py
+++ b/agent/protocol/agent.py
@@ -1,4 +1,5 @@
 import json
+import os
 import time
 import threading

@@ -13,7 +14,8 @@ class Agent:
    def __init__(self, system_prompt: str, description: str = "AI Agent", model: LLMModel = None,
                 tools=None, output_mode="print", max_steps=100, max_context_tokens=None, 
                 context_reserve_tokens=None, memory_manager=None, name: str = None,
-                 workspace_dir: str = None, skill_manager=None, enable_skills: bool = True):
+                 workspace_dir: str = None, skill_manager=None, enable_skills: bool = True,
+                 runtime_info: dict = None):
        """
        Initialize the Agent with system prompt, model, description.

@@ -31,6 +33,7 @@ class Agent:
        :param workspace_dir: Optional workspace directory for workspace-specific skills
        :param skill_manager: Optional SkillManager instance (will be created if None and enable_skills=True)
        :param enable_skills: Whether to enable skills support (default: True)
+        :param runtime_info: Optional runtime info dict (with _get_current_time callable for dynamic time)
        """
        self.name = name or "Agent"
        self.system_prompt = system_prompt
@@ -48,6 +51,7 @@ class Agent:
        self.memory_manager = memory_manager  # Memory manager for auto memory flush
        self.workspace_dir = workspace_dir  # Workspace directory
        self.enable_skills = enable_skills  # Skills enabled flag
+        self.runtime_info = runtime_info  # Runtime info for dynamic time update
        
        # Initialize skill manager
        self.skill_manager = None
@@ -58,7 +62,8 @@ class Agent:
                # Auto-create skill manager
                try:
                    from agent.skills import SkillManager
-                    self.skill_manager = SkillManager(workspace_dir=workspace_dir)
+                    custom_dir = os.path.join(workspace_dir, "skills") if workspace_dir else None
+                    self.skill_manager = SkillManager(custom_dir=custom_dir)
                    logger.debug(f"Initialized SkillManager with {len(self.skill_manager.skills)} skills")
                except Exception as e:
                    logger.warning(f"Failed to initialize SkillManager: {e}")
@@ -96,18 +101,98 @@ class Agent:
    def get_full_system_prompt(self, skill_filter=None) -> str:
        """
        Get the full system prompt including skills.
-        
+
        Note: Skills are now built into the system prompt by PromptBuilder,
        so we just return the base prompt directly. This method is kept for
        backward compatibility.
-        
+
        :param skill_filter: Optional list of skill names to include (deprecated)
        :return: Complete system prompt
        """
-        # Skills are now included in system_prompt by PromptBuilder
-        # No need to append them here
-        return self.system_prompt
+        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
+        """
+        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
+        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
+
    def refresh_skills(self):
        """Refresh the loaded skills."""
        if self.skill_manager:
@@ -193,27 +278,67 @@ class Agent:

    def _estimate_message_tokens(self, message: dict) -> int:
        """
-        Estimate token count for a message using chars/4 heuristic.
-        This is a conservative estimate (tends to overestimate).
+        Estimate token count for a message.
+
+        Uses chars/3 for Chinese-heavy content and chars/4 for ASCII-heavy content,
+        plus per-block overhead for tool_use / tool_result structures.

        :param message: Message dict with 'role' and 'content'
        :return: Estimated token count
        """
        content = message.get('content', '')
        if isinstance(content, str):
-            return max(1, len(content) // 4)
+            return max(1, self._estimate_text_tokens(content))
        elif isinstance(content, list):
-            # Handle multi-part content (text + images)
-            total_chars = 0
+            total_tokens = 0
            for part in content:
-                if isinstance(part, dict) and part.get('type') == 'text':
-                    total_chars += len(part.get('text', ''))
-                elif isinstance(part, dict) and part.get('type') == 'image':
-                    # Estimate images as ~1200 tokens
-                    total_chars += 4800
-            return max(1, total_chars // 4)
+                if not isinstance(part, dict):
+                    continue
+                block_type = part.get('type', '')
+                if block_type == 'text':
+                    total_tokens += self._estimate_text_tokens(part.get('text', ''))
+                elif block_type == 'image':
+                    total_tokens += 1200
+                elif block_type == 'tool_use':
+                    # tool_use has id + name + input (JSON-encoded)
+                    total_tokens += 50  # overhead for structure
+                    input_data = part.get('input', {})
+                    if isinstance(input_data, dict):
+                        import json
+                        input_str = json.dumps(input_data, ensure_ascii=False)
+                        total_tokens += self._estimate_text_tokens(input_str)
+                elif block_type == 'tool_result':
+                    # tool_result has tool_use_id + content
+                    total_tokens += 30  # overhead for structure
+                    result_content = part.get('content', '')
+                    if isinstance(result_content, str):
+                        total_tokens += self._estimate_text_tokens(result_content)
+                else:
+                    # Unknown block type, estimate conservatively
+                    total_tokens += 10
+            return max(1, total_tokens)
        return 1

+    @staticmethod
+    def _estimate_text_tokens(text: str) -> int:
+        """
+        Estimate token count for a text string.
+
+        Chinese / CJK characters typically use ~1.5 tokens each,
+        while ASCII uses ~0.25 tokens per char (4 chars/token).
+        We use a weighted average based on the character mix.
+
+        :param text: Input text
+        :return: Estimated token count
+        """
+        if not text:
+            return 0
+        # Count non-ASCII characters (CJK, emoji, etc.)
+        non_ascii = sum(1 for c in text if ord(c) > 127)
+        ascii_count = len(text) - non_ascii
+        # CJK chars: ~1.5 tokens each; ASCII: ~0.25 tokens per char
+        return int(non_ascii * 1.5 + ascii_count * 0.25) + 1
+
    def _find_tool(self, tool_name: str):
        """Find and return a tool with the specified name"""
        for tool in self.tools:
@@ -370,7 +495,17 @@ class Agent:
        )

        # Execute
-        response = executor.run_stream(user_message)
+        try:
+            response = executor.run_stream(user_message)
+        except Exception:
+            # If executor cleared its messages (context overflow / message format error),
+            # sync that back to the Agent's own message list so the next request
+            # starts fresh instead of hitting the same overflow forever.
+            if len(executor.messages) == 0:
+                with self.messages_lock:
+                    self.messages.clear()
+                    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
--- a/agent/protocol/agent_stream.py
+++ b/agent/protocol/agent_stream.py
@@ -76,6 +76,20 @@ class AgentStreamExecutor:
                })
            except Exception as e:
                logger.error(f"Event callback error: {e}")
+    
+    def _filter_think_tags(self, text: str) -> str:
+        """
+        Remove <think> and </think> tags but keep the content inside.
+        Some LLM providers (e.g., MiniMax) may return thinking process wrapped in <think> tags.
+        We only remove the tags themselves, keeping the actual thinking content.
+        """
+        if not text:
+            return text
+        import re
+        # Remove only the <think> and </think> tags, keep the content
+        text = re.sub(r'<think>', '', text)
+        text = re.sub(r'</think>', '', text)
+        return text

    def _hash_args(self, args: dict) -> str:
        """Generate a simple hash for tool arguments"""
@@ -184,7 +198,7 @@ class AgentStreamExecutor:
        try:
            while turn < self.max_turns:
                turn += 1
-                logger.debug(f"第 {turn} 轮")
+                logger.info(f"[Agent] 第 {turn} 轮")
                self._emit_event("turn_start", {"turn": turn})

                # Check if memory flush is needed (before calling LLM)
@@ -322,7 +336,7 @@ class AgentStreamExecutor:
                        # Build tool result block (Claude format)
                        # Format content in a way that's easy for LLM to understand
                        is_error = result.get("status") == "error"
-                        
+
                        if is_error:
                            # For errors, provide clear error message
                            result_content = f"Error: {result.get('result', 'Unknown error')}"
@@ -335,7 +349,16 @@ class AgentStreamExecutor:
                        else:
                            # Fallback to full JSON
                            result_content = json.dumps(result, ensure_ascii=False)
-                        
+
+                        # Truncate excessively large tool results for the current turn
+                        # Historical turns will be further truncated in _trim_messages()
+                        MAX_CURRENT_TURN_RESULT_CHARS = 50000
+                        if len(result_content) > MAX_CURRENT_TURN_RESULT_CHARS:
+                            truncated_len = len(result_content)
+                            result_content = result_content[:MAX_CURRENT_TURN_RESULT_CHARS] + \
+                                f"\n\n[Output truncated: {truncated_len} chars total, showing first {MAX_CURRENT_TURN_RESULT_CHARS} chars]"
+                            logger.info(f"📎 Truncated tool result for '{tool_call['name']}': {truncated_len} -> {MAX_CURRENT_TURN_RESULT_CHARS} chars")
+
                        tool_result_block = {
                            "type": "tool_result",
                            "tool_use_id": tool_call["id"],
@@ -447,7 +470,7 @@ class AgentStreamExecutor:
            raise

        finally:
-            logger.debug(f"🏁 完成({turn}轮)")
+            logger.info(f"[Agent] 🏁 完成 ({turn}轮)")
            self._emit_event("agent_end", {"final_response": final_response})

            # 每轮对话结束后增加计数（用户消息+AI回复=1轮）
@@ -456,7 +479,8 @@ class AgentStreamExecutor:

        return final_response

-    def _call_llm_stream(self, retry_on_empty=True, retry_count=0, max_retries=3) -> Tuple[str, List[Dict]]:
+    def _call_llm_stream(self, retry_on_empty=True, retry_count=0, max_retries=3,
+                         _overflow_retry: bool = False) -> Tuple[str, List[Dict]]:
        """
        Call LLM with streaming and automatic retry on errors
        
@@ -464,6 +488,7 @@ class AgentStreamExecutor:
            retry_on_empty: Whether to retry once if empty response is received
            retry_count: Current retry attempt (internal use)
            max_retries: Maximum number of retries for API errors
+            _overflow_retry: Internal flag indicating this is a retry after context overflow
        
        Returns:
            (response_text, tool_calls)
@@ -476,7 +501,7 @@ class AgentStreamExecutor:

        # Prepare messages
        messages = self._prepare_messages()
-        logger.debug(f"Sending {len(messages)} messages to LLM")
+        logger.info(f"Sending {len(messages)} messages to LLM")

        # Prepare tool definitions (OpenAI/Claude format)
        tools_schema = None
@@ -549,7 +574,7 @@ class AgentStreamExecutor:
                        raise Exception(f"{error_msg} (Status: {status_code}, Code: {error_code}, Type: {error_type})")

                # Parse chunk
-                if isinstance(chunk, dict) and "choices" in chunk:
+                if isinstance(chunk, dict) and chunk.get("choices"):
                    choice = chunk["choices"][0]
                    delta = choice.get("delta", {})
                    
@@ -558,11 +583,19 @@ class AgentStreamExecutor:
                    if finish_reason:
                        stop_reason = finish_reason

+                    # Skip reasoning_content (internal thinking from models like GLM-5)
+                    reasoning_delta = delta.get("reasoning_content") or ""
+                    # if reasoning_delta:
+                    #     logger.debug(f"🧠 [thinking] {reasoning_delta[:100]}...")
+
                    # Handle text content
                    content_delta = delta.get("content") or ""
                    if content_delta:
-                        full_content += content_delta
-                        self._emit_event("message_update", {"delta": content_delta})
+                        # Filter out <think> tags from content
+                        filtered_delta = self._filter_think_tags(content_delta)
+                        full_content += filtered_delta
+                        if filtered_delta:  # Only emit if there's content after filtering
+                            self._emit_event("message_update", {"delta": filtered_delta})

                    # Handle tool calls
                    if "tool_calls" in delta and delta["tool_calls"]:
@@ -612,10 +645,23 @@ class AgentStreamExecutor:
            if is_context_overflow or is_message_format_error:
                error_type = "context overflow" if is_context_overflow else "message format error"
                logger.error(f"💥 {error_type} detected: {e}")
-                # Clear message history to recover
+
+                # Strategy: try aggressive trimming first, only clear as last resort
+                if is_context_overflow and not _overflow_retry:
+                    trimmed = self._aggressive_trim_for_overflow()
+                    if trimmed:
+                        logger.warning("🔄 Aggressively trimmed context, retrying...")
+                        return self._call_llm_stream(
+                            retry_on_empty=retry_on_empty,
+                            retry_count=retry_count,
+                            max_retries=max_retries,
+                            _overflow_retry=True
+                        )
+
+                # Aggressive trim didn't help or this is a message format error
+                # -> clear everything
                logger.warning("🔄 Clearing conversation history to recover")
                self.messages.clear()
-                # Raise special exception with user-friendly message
                if is_context_overflow:
                    raise Exception(
                        "抱歉，对话历史过长导致上下文溢出。我已清空历史记录，请重新描述你的需求。"
@@ -661,6 +707,13 @@ class AgentStreamExecutor:
        tool_calls = []
        for idx in sorted(tool_calls_buffer.keys()):
            tc = tool_calls_buffer[idx]
+
+            # Ensure tool call has a valid ID (some providers return empty/None IDs)
+            tool_id = tc.get("id") or ""
+            if not tool_id:
+                import uuid
+                tool_id = f"call_{uuid.uuid4().hex[:24]}"
+
            try:
                # Safely get arguments, handle None case
                args_str = tc.get("arguments") or ""
@@ -673,11 +726,11 @@ class AgentStreamExecutor:
                logger.error(f"Arguments length: {len(args_str)} chars")
                logger.error(f"Arguments preview: {args_preview}...")
                logger.error(f"JSON decode error: {e}")
-                
+
                # Return a clear error message to the LLM instead of empty dict
                # This helps the LLM understand what went wrong
                tool_calls.append({
-                    "id": tc["id"],
+                    "id": tool_id,
                    "name": tc["name"],
                    "arguments": {},
                    "_parse_error": f"Invalid JSON in tool arguments: {args_preview}... Error: {str(e)}. Tip: For large content, consider splitting into smaller chunks or using a different approach."
@@ -685,7 +738,7 @@ class AgentStreamExecutor:
                continue

            tool_calls.append({
-                "id": tc["id"],
+                "id": tool_id,
                "name": tc["name"],
                "arguments": arguments
            })
@@ -706,6 +759,9 @@ class AgentStreamExecutor:
                max_retries=max_retries
            )

+        # Filter full_content one more time (in case tags were split across chunks)
+        full_content = self._filter_think_tags(full_content)
+        
        # Add assistant message to history (Claude format uses content blocks)
        assistant_msg = {"role": "assistant", "content": []}

@@ -926,10 +982,164 @@ class AgentStreamExecutor:
            for msg in turn['messages']
        )

+    def _truncate_historical_tool_results(self):
+        """
+        Truncate tool_result content in historical messages to reduce context size.
+
+        Current turn results are kept at 30K chars (truncated at creation time).
+        Historical turn results are further truncated to 10K chars here.
+        This runs before token-based trimming so that we first shrink oversized
+        results, potentially avoiding the need to drop entire turns.
+        """
+        MAX_HISTORY_RESULT_CHARS = 20000
+
+        if len(self.messages) < 2:
+            return
+
+        # Find where the last user text message starts (= current turn boundary)
+        # We skip the current turn's messages to preserve their full content
+        current_turn_start = len(self.messages)
+        for i in range(len(self.messages) - 1, -1, -1):
+            msg = self.messages[i]
+            if msg.get("role") == "user":
+                content = msg.get("content", [])
+                if isinstance(content, list) and any(
+                    isinstance(b, dict) and b.get("type") == "text" for b in content
+                ):
+                    current_turn_start = i
+                    break
+                elif isinstance(content, str):
+                    current_turn_start = i
+                    break
+
+        truncated_count = 0
+        for i in range(current_turn_start):
+            msg = self.messages[i]
+            if msg.get("role") != "user":
+                continue
+            content = msg.get("content", [])
+            if not isinstance(content, list):
+                continue
+
+            for block in content:
+                if not isinstance(block, dict) or block.get("type") != "tool_result":
+                    continue
+                result_str = block.get("content", "")
+                if isinstance(result_str, str) and len(result_str) > MAX_HISTORY_RESULT_CHARS:
+                    original_len = len(result_str)
+                    block["content"] = result_str[:MAX_HISTORY_RESULT_CHARS] + \
+                        f"\n\n[Historical output truncated: {original_len} -> {MAX_HISTORY_RESULT_CHARS} chars]"
+                    truncated_count += 1
+
+        if truncated_count > 0:
+            logger.info(f"📎 Truncated {truncated_count} historical tool result(s) to {MAX_HISTORY_RESULT_CHARS} chars")
+
+    def _aggressive_trim_for_overflow(self) -> bool:
+        """
+        Aggressively trim context when a real overflow error is returned by the API.
+
+        This method goes beyond normal _trim_messages by:
+        1. Truncating all tool results (including current turn) to a small limit
+        2. Keeping only the last 5 complete conversation turns
+        3. Truncating overly long user messages
+
+        Returns:
+            True if messages were trimmed (worth retrying), False if nothing left to trim
+        """
+        if not self.messages:
+            return False
+
+        original_count = len(self.messages)
+
+        # Step 1: Aggressively truncate ALL tool results to 5K chars
+        AGGRESSIVE_LIMIT = 10000
+        truncated = 0
+        for msg in self.messages:
+            content = msg.get("content", [])
+            if not isinstance(content, list):
+                continue
+            for block in content:
+                if not isinstance(block, dict):
+                    continue
+                # Truncate tool_result blocks
+                if block.get("type") == "tool_result":
+                    result_str = block.get("content", "")
+                    if isinstance(result_str, str) and len(result_str) > AGGRESSIVE_LIMIT:
+                        block["content"] = (
+                            result_str[:AGGRESSIVE_LIMIT]
+                            + f"\n\n[Truncated for context recovery: "
+                            f"{len(result_str)} -> {AGGRESSIVE_LIMIT} chars]"
+                        )
+                        truncated += 1
+                # Truncate tool_use input blocks (e.g. large write content)
+                if block.get("type") == "tool_use" and isinstance(block.get("input"), dict):
+                    input_str = json.dumps(block["input"], ensure_ascii=False)
+                    if len(input_str) > AGGRESSIVE_LIMIT:
+                        # Keep only a summary of the input
+                        for key, val in block["input"].items():
+                            if isinstance(val, str) and len(val) > 1000:
+                                block["input"][key] = (
+                                    val[:1000]
+                                    + f"... [truncated {len(val)} chars]"
+                                )
+                        truncated += 1
+
+        # Step 2: Truncate overly long user text messages (e.g. pasted content)
+        USER_MSG_LIMIT = 10000
+        for msg in self.messages:
+            if msg.get("role") != "user":
+                continue
+            content = msg.get("content", [])
+            if isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict) and block.get("type") == "text":
+                        text = block.get("text", "")
+                        if len(text) > USER_MSG_LIMIT:
+                            block["text"] = (
+                                text[:USER_MSG_LIMIT]
+                                + f"\n\n[Message truncated for context recovery: "
+                                f"{len(text)} -> {USER_MSG_LIMIT} chars]"
+                            )
+                            truncated += 1
+            elif isinstance(content, str) and len(content) > USER_MSG_LIMIT:
+                msg["content"] = (
+                    content[:USER_MSG_LIMIT]
+                    + f"\n\n[Message truncated for context recovery: "
+                    f"{len(content)} -> {USER_MSG_LIMIT} chars]"
+                )
+                truncated += 1
+
+        # Step 3: Keep only the last 5 complete turns
+        turns = self._identify_complete_turns()
+        if len(turns) > 5:
+            kept_turns = turns[-5:]
+            new_messages = []
+            for turn in kept_turns:
+                new_messages.extend(turn["messages"])
+            removed = len(turns) - 5
+            self.messages[:] = new_messages
+            logger.info(
+                f"🔧 Aggressive trim: removed {removed} old turns, "
+                f"truncated {truncated} large blocks, "
+                f"{original_count} -> {len(self.messages)} messages"
+            )
+            return True
+
+        if truncated > 0:
+            logger.info(
+                f"🔧 Aggressive trim: truncated {truncated} large blocks "
+                f"(no turns removed, only {len(turns)} turn(s) left)"
+            )
+            return True
+
+        # Nothing left to trim
+        logger.warning("🔧 Aggressive trim: nothing to trim, will clear history")
+        return False
+
    def _trim_messages(self):
        """
        智能清理消息历史，保持对话完整性
-        
+
        使用完整轮次作为清理单位，确保：
        1. 不会在对话中间截断
        2. 工具调用链（tool_use + tool_result）保持完整
@@ -938,6 +1148,9 @@ class AgentStreamExecutor:
        if not self.messages or not self.agent:
            return

+        # Step 0: Truncate large tool results in historical turns (30K -> 10K)
+        self._truncate_historical_tool_results()
+
        # Step 1: 识别完整轮次
        turns = self._identify_complete_turns()
        
--- a/agent/skills/init.py
+++ b/agent/skills/init.py
@@ -15,6 +15,7 @@ from agent.skills.types import (
 )
 from agent.skills.loader import SkillLoader
 from agent.skills.manager import SkillManager
+from agent.skills.service import SkillService
 from agent.skills.formatter import format_skills_for_prompt

 __all__ = [
@@ -25,5 +26,6 @@ __all__ = [
    "LoadSkillsResult",
    "SkillLoader",
    "SkillManager",
+    "SkillService",
    "format_skills_for_prompt",
 ]
--- a/agent/skills/formatter.py
+++ b/agent/skills/formatter.py
@@ -23,18 +23,15 @@ def format_skills_for_prompt(skills: List[Skill]) -> str:
        return ""
    
    lines = [
-        "\n\nThe following skills provide specialized instructions for specific tasks.",
-        "Use the read tool to load a skill's file when the task matches its description.",
        "",
        "<available_skills>",
    ]
-    
+
    for skill in visible_skills:
        lines.append("  <skill>")
        lines.append(f"    <name>{_escape_xml(skill.name)}</name>")
        lines.append(f"    <description>{_escape_xml(skill.description)}</description>")
        lines.append(f"    <location>{_escape_xml(skill.file_path)}</location>")
-        lines.append(f"    <base_dir>{_escape_xml(skill.base_dir)}</base_dir>")
        lines.append("  </skill>")
    
    lines.append("</available_skills>")
--- a/agent/skills/loader.py
+++ b/agent/skills/loader.py
@@ -12,25 +12,20 @@ from agent.skills.frontmatter import parse_frontmatter, parse_metadata, parse_bo

 class SkillLoader:
    """Loads skills from various directories."""
-    
-    def __init__(self, workspace_dir: Optional[str] = None):
-        """
-        Initialize the skill loader.
-        
-        :param workspace_dir: Agent workspace directory (for workspace-specific skills)
-        """
-        self.workspace_dir = workspace_dir
+
+    def __init__(self):
+        pass
    
    def load_skills_from_dir(self, dir_path: str, source: str) -> LoadSkillsResult:
        """
        Load skills from a directory.
-        
+
        Discovery rules:
        - Direct .md files in the root directory
        - Recursive SKILL.md files under subdirectories
-        
+
        :param dir_path: Directory path to scan
-        :param source: Source identifier (e.g., 'managed', 'workspace', 'bundled')
+        :param source: Source identifier ('builtin' or 'custom')
        :return: LoadSkillsResult with skills and diagnostics
        """
        skills = []
@@ -188,16 +183,14 @@ class SkillLoader:
        import json
        
        config_path = os.path.join(skill_dir, "config.json")
-        template_path = os.path.join(skill_dir, "config.json.template")
        
-        # Try to load config.json or fallback to template
-        config_file = config_path if os.path.exists(config_path) else template_path
-        
-        if not os.path.exists(config_file):
-            return default_description
+        # 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 ""
        
        try:
-            with open(config_file, 'r', encoding='utf-8') as f:
+            with open(config_path, 'r', encoding='utf-8') as f:
                config = json.load(f)
            
            apps = config.get("apps", [])
@@ -218,61 +211,49 @@ class SkillLoader:
    
    def load_all_skills(
        self,
-        managed_dir: Optional[str] = None,
-        workspace_skills_dir: Optional[str] = None,
-        extra_dirs: Optional[List[str]] = None,
+        builtin_dir: Optional[str] = None,
+        custom_dir: Optional[str] = None,
    ) -> Dict[str, SkillEntry]:
        """
-        Load skills from all configured locations with precedence.
-        
+        Load skills from builtin and custom directories.
+
        Precedence (lowest to highest):
-        1. Extra directories
-        2. Managed skills directory
-        3. Workspace skills directory
-        
-        :param managed_dir: Managed skills directory (e.g., ~/.cow/skills)
-        :param workspace_skills_dir: Workspace skills directory (e.g., workspace/skills)
-        :param extra_dirs: Additional directories to load skills from
+        1. builtin  — project root ``skills/``, shipped with the codebase
+        2. custom   — workspace ``skills/``, installed via cloud console or skill creator
+
+        Same-name custom skills override builtin ones.
+
+        :param builtin_dir: Built-in skills directory
+        :param custom_dir: Custom skills directory
        :return: Dictionary mapping skill name to SkillEntry
        """
        skill_map: Dict[str, SkillEntry] = {}
        all_diagnostics = []
-        
-        # Load from extra directories (lowest precedence)
-        if extra_dirs:
-            for extra_dir in extra_dirs:
-                if not os.path.exists(extra_dir):
-                    continue
-                result = self.load_skills_from_dir(extra_dir, source='extra')
-                all_diagnostics.extend(result.diagnostics)
-                for skill in result.skills:
-                    entry = self._create_skill_entry(skill)
-                    skill_map[skill.name] = entry
-        
-        # Load from managed directory
-        if managed_dir and os.path.exists(managed_dir):
-            result = self.load_skills_from_dir(managed_dir, source='managed')
+
+        # Load builtin skills (lower precedence)
+        if builtin_dir and os.path.exists(builtin_dir):
+            result = self.load_skills_from_dir(builtin_dir, source='builtin')
            all_diagnostics.extend(result.diagnostics)
            for skill in result.skills:
                entry = self._create_skill_entry(skill)
                skill_map[skill.name] = entry
-        
-        # Load from workspace directory (highest precedence)
-        if workspace_skills_dir and os.path.exists(workspace_skills_dir):
-            result = self.load_skills_from_dir(workspace_skills_dir, source='workspace')
+
+        # Load custom skills (higher precedence, overrides builtin)
+        if custom_dir and os.path.exists(custom_dir):
+            result = self.load_skills_from_dir(custom_dir, source='custom')
            all_diagnostics.extend(result.diagnostics)
            for skill in result.skills:
                entry = self._create_skill_entry(skill)
                skill_map[skill.name] = entry
-        
+
        # Log diagnostics
        if all_diagnostics:
            logger.debug(f"Skill loading diagnostics: {len(all_diagnostics)} issues")
-            for diag in all_diagnostics[:5]:  # Log first 5
+            for diag in all_diagnostics[:5]:
                logger.debug(f"  - {diag}")
-        
-        logger.debug(f"Loaded {len(skill_map)} skills from all sources")
-        
+
+        logger.debug(f"Loaded {len(skill_map)} skills total")
+
        return skill_map
    
    def _create_skill_entry(self, skill: Skill) -> SkillEntry:
--- a/agent/skills/manager.py
+++ b/agent/skills/manager.py
@@ -3,6 +3,7 @@ Skill manager for managing skill lifecycle and operations.
 """

 import os
+import json
 from typing import Dict, List, Optional
 from pathlib import Path
 from common.log import logger
@@ -10,56 +11,131 @@ from agent.skills.types import Skill, SkillEntry, SkillSnapshot
 from agent.skills.loader import SkillLoader
 from agent.skills.formatter import format_skill_entries_for_prompt

+SKILLS_CONFIG_FILE = "skills_config.json"
+

 class SkillManager:
    """Manages skills for an agent."""
-    
+
    def __init__(
        self,
-        workspace_dir: Optional[str] = None,
-        managed_skills_dir: Optional[str] = None,
-        extra_dirs: Optional[List[str]] = None,
+        builtin_dir: Optional[str] = None,
+        custom_dir: Optional[str] = None,
        config: Optional[Dict] = None,
    ):
        """
        Initialize the skill manager.
-        
-        :param workspace_dir: Agent workspace directory
-        :param managed_skills_dir: Managed skills directory (e.g., ~/.cow/skills)
-        :param extra_dirs: Additional skill directories
+
+        :param builtin_dir: Built-in skills directory (project root ``skills/``)
+        :param custom_dir: Custom skills directory (workspace ``skills/``)
        :param config: Configuration dictionary
        """
-        self.workspace_dir = workspace_dir
-        self.managed_skills_dir = managed_skills_dir or self._get_default_managed_dir()
-        self.extra_dirs = extra_dirs or []
+        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        self.builtin_dir = builtin_dir or os.path.join(project_root, 'skills')
+        self.custom_dir = custom_dir or os.path.join(project_root, 'workspace', 'skills')
        self.config = config or {}
-        
-        self.loader = SkillLoader(workspace_dir=workspace_dir)
+        self._skills_config_path = os.path.join(self.custom_dir, SKILLS_CONFIG_FILE)
+
+        # skills_config: full skill metadata keyed by name
+        # { "web-fetch": {"name": ..., "description": ..., "source": ..., "enabled": true}, ... }
+        self.skills_config: Dict[str, dict] = {}
+
+        self.loader = SkillLoader()
        self.skills: Dict[str, SkillEntry] = {}
-        
+
        # Load skills on initialization
        self.refresh_skills()
-    
-    def _get_default_managed_dir(self) -> str:
-        """Get the default managed skills directory."""
-        # Use project root skills directory as default
-        import os
-        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        return os.path.join(project_root, 'skills')
-    
+
    def refresh_skills(self):
-        """Reload all skills from configured directories."""
-        workspace_skills_dir = None
-        if self.workspace_dir:
-            workspace_skills_dir = os.path.join(self.workspace_dir, 'skills')
-        
+        """Reload all skills from builtin and custom directories, then sync config."""
        self.skills = self.loader.load_all_skills(
-            managed_dir=self.managed_skills_dir,
-            workspace_skills_dir=workspace_skills_dir,
-            extra_dirs=self.extra_dirs,
+            builtin_dir=self.builtin_dir,
+            custom_dir=self.custom_dir,
        )
-        
+        self._sync_skills_config()
        logger.debug(f"SkillManager: Loaded {len(self.skills)} skills")
+
+    # ------------------------------------------------------------------
+    # skills_config.json management
+    # ------------------------------------------------------------------
+    def _load_skills_config(self) -> Dict[str, dict]:
+        """Load skills_config.json from custom_dir. Returns empty dict if not found."""
+        if not os.path.exists(self._skills_config_path):
+            return {}
+        try:
+            with open(self._skills_config_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            if isinstance(data, dict):
+                return data
+        except Exception as e:
+            logger.warning(f"[SkillManager] Failed to load {SKILLS_CONFIG_FILE}: {e}")
+        return {}
+
+    def _save_skills_config(self):
+        """Persist skills_config to custom_dir/skills_config.json."""
+        os.makedirs(self.custom_dir, exist_ok=True)
+        try:
+            with open(self._skills_config_path, "w", encoding="utf-8") as f:
+                json.dump(self.skills_config, f, indent=4, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[SkillManager] Failed to save {SKILLS_CONFIG_FILE}: {e}")
+
+    def _sync_skills_config(self):
+        """
+        Merge directory-scanned skills with the persisted config file.
+
+        - New skills discovered on disk are added with enabled=True.
+        - Skills that no longer exist on disk are removed.
+        - Existing entries preserve their enabled state; name/description/source
+          are refreshed from the latest scan.
+        """
+        saved = self._load_skills_config()
+        merged: Dict[str, dict] = {}
+
+        for name, entry in self.skills.items():
+            skill = entry.skill
+            prev = saved.get(name, {})
+            merged[name] = {
+                "name": name,
+                "description": skill.description,
+                "source": skill.source,
+                "enabled": prev.get("enabled", True),
+            }
+
+        self.skills_config = merged
+        self._save_skills_config()
+
+    def is_skill_enabled(self, name: str) -> bool:
+        """
+        Check if a skill is enabled according to skills_config.
+
+        :param name: skill name
+        :return: True if enabled (default True if not in config)
+        """
+        entry = self.skills_config.get(name)
+        if entry is None:
+            return True
+        return entry.get("enabled", True)
+
+    def set_skill_enabled(self, name: str, enabled: bool):
+        """
+        Set a skill's enabled state and persist.
+
+        :param name: skill name
+        :param enabled: True to enable, False to disable
+        """
+        if name not in self.skills_config:
+            raise ValueError(f"skill '{name}' not found in config")
+        self.skills_config[name]["enabled"] = enabled
+        self._save_skills_config()
+
+    def get_skills_config(self) -> Dict[str, dict]:
+        """
+        Return the full skills_config dict (for query API).
+
+        :return: copy of skills_config
+        """
+        return dict(self.skills_config)
    
    def get_skill(self, name: str) -> Optional[SkillEntry]:
        """
@@ -85,25 +161,24 @@ class SkillManager:
    ) -> 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
-        
+        - Has required API keys -> included
+        - Missing API keys -> excluded
+
        :param skill_filter: List of skill names to include (None = all)
-        :param include_disabled: Whether to include skills with disable_model_invocation=True
+        :param include_disabled: Whether to include disabled skills
        :return: Filtered list of skill entries
        """
        from agent.skills.config import should_include_skill
-        
+
        entries = list(self.skills.values())
-        
+
        # Check requirements (platform, binaries, env vars)
        entries = [e for e in entries if should_include_skill(e, self.config)]
-        
+
        # Apply skill filter
        if skill_filter is not None:
-            # Flatten and normalize skill names (handle both strings and nested lists)
            normalized = []
            for item in skill_filter:
                if isinstance(item, str):
@@ -111,20 +186,18 @@ class SkillManager:
                    if name:
                        normalized.append(name)
                elif isinstance(item, list):
-                    # Handle nested lists
                    for subitem in item:
                        if isinstance(subitem, str):
                            name = subitem.strip()
                            if name:
                                normalized.append(name)
-            
            if normalized:
                entries = [e for e in entries if e.skill.name in normalized]
-        
-        # Filter out disabled skills unless explicitly requested
+
+        # Filter out disabled skills based on skills_config.json
        if not include_disabled:
-            entries = [e for e in entries if not e.skill.disable_model_invocation]
-        
+            entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
+
        return entries
    
    def build_skills_prompt(
--- a/agent/skills/service.py
+++ b/agent/skills/service.py
@@ -0,0 +1,204 @@
+"""
+Skill service for handling skill CRUD operations.
+
+This service provides a unified interface for managing skills, which can be
+called from the cloud control client (LinkAI), the local web console, or any
+other management entry point.
+"""
+
+import os
+import shutil
+from typing import Dict, List, Optional
+from common.log import logger
+from agent.skills.types import Skill, SkillEntry
+from agent.skills.manager import SkillManager
+
+try:
+    import requests
+except ImportError:
+    requests = None
+
+
+class SkillService:
+    """
+    High-level service for skill lifecycle management.
+    Wraps SkillManager and provides network-aware operations such as
+    downloading skill files from remote URLs.
+    """
+
+    def __init__(self, skill_manager: SkillManager):
+        """
+        :param skill_manager: The SkillManager instance to operate on
+        """
+        self.manager = skill_manager
+
+    # ------------------------------------------------------------------
+    # query
+    # ------------------------------------------------------------------
+    def query(self) -> List[dict]:
+        """
+        Query all skills and return a serialisable list.
+        Reads from skills_config.json (refreshes from disk if needed).
+
+        :return: list of skill info dicts
+        """
+        self.manager.refresh_skills()
+        config = self.manager.get_skills_config()
+        result = list(config.values())
+        logger.info(f"[SkillService] query: {len(result)} skills found")
+        return result
+
+    # ------------------------------------------------------------------
+    # add / install
+    # ------------------------------------------------------------------
+    def add(self, payload: dict) -> None:
+        """
+        Add (install) a skill from a remote payload.
+
+        The payload follows the socket protocol::
+
+            {
+                "name": "web_search",
+                "type": "url",
+                "enabled": true,
+                "files": [
+                    {"url": "https://...", "path": "README.md"},
+                    {"url": "https://...", "path": "scripts/main.py"}
+                ]
+            }
+
+        Files are downloaded and saved under the custom skills directory
+        using *name* as the sub-directory.
+
+        :param payload: skill add payload from server
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+
+        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)
+
+        # 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)")
+
+    # ------------------------------------------------------------------
+    # open / close (enable / disable)
+    # ------------------------------------------------------------------
+    def open(self, payload: dict) -> None:
+        """
+        Enable a skill by name.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+        self.manager.set_skill_enabled(name, enabled=True)
+        logger.info(f"[SkillService] open: skill '{name}' enabled")
+
+    def close(self, payload: dict) -> None:
+        """
+        Disable a skill by name.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+        self.manager.set_skill_enabled(name, enabled=False)
+        logger.info(f"[SkillService] close: skill '{name}' disabled")
+
+    # ------------------------------------------------------------------
+    # delete
+    # ------------------------------------------------------------------
+    def delete(self, payload: dict) -> None:
+        """
+        Delete a skill by removing its directory entirely.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+        if os.path.exists(skill_dir):
+            shutil.rmtree(skill_dir)
+            logger.info(f"[SkillService] delete: removed directory {skill_dir}")
+        else:
+            logger.warning(f"[SkillService] delete: skill directory not found: {skill_dir}")
+
+        # Refresh will remove the deleted skill from config automatically
+        self.manager.refresh_skills()
+        logger.info(f"[SkillService] delete: skill '{name}' deleted")
+
+    # ------------------------------------------------------------------
+    # dispatch - single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a skill management action and return a protocol-compatible
+        response dict.
+
+        :param action: one of query / add / open / close / delete
+        :param payload: action-specific payload (may be None for query)
+        :return: dict with action, code, message, payload
+        """
+        payload = payload or {}
+        try:
+            if action == "query":
+                result_payload = self.query()
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+            elif action == "add":
+                self.add(payload)
+            elif action == "open":
+                self.open(payload)
+            elif action == "close":
+                self.close(payload)
+            elif action == "delete":
+                self.delete(payload)
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+            return {"action": action, "code": 200, "message": "success", "payload": None}
+        except Exception as e:
+            logger.error(f"[SkillService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}
+
+    # ------------------------------------------------------------------
+    # internal helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _download_file(url: str, dest: str):
+        """
+        Download a file from *url* and save to *dest*.
+
+        :param url: remote file URL
+        :param dest: local destination path
+        """
+        if requests is None:
+            raise RuntimeError("requests library is required for downloading skill files")
+
+        dest_dir = os.path.dirname(dest)
+        if dest_dir:
+            os.makedirs(dest_dir, exist_ok=True)
+
+        resp = requests.get(url, timeout=60)
+        resp.raise_for_status()
+        with open(dest, "wb") as f:
+            f.write(resp.content)
+        logger.debug(f"[SkillService] downloaded {url} -> {dest}")
--- a/agent/skills/types.py
+++ b/agent/skills/types.py
@@ -45,7 +45,7 @@ class Skill:
    description: str
    file_path: str
    base_dir: str
-    source: str  # managed, workspace, bundled, etc.
+    source: str  # builtin or custom
    content: str  # Full markdown content
    disable_model_invocation: bool = False
    frontmatter: Dict[str, Any] = field(default_factory=dict)
--- a/agent/tools/init.py
+++ b/agent/tools/init.py
@@ -45,16 +45,25 @@ def _import_optional_tools():
        )
    except Exception as e:
        logger.error(f"[Tools] Scheduler tool failed to load: {e}")
-    
-    
+
+    # WebSearch Tool (conditionally loaded based on API key availability at init time)
+    try:
+        from agent.tools.web_search.web_search import WebSearch
+        tools['WebSearch'] = WebSearch
+    except ImportError as e:
+        logger.error(f"[Tools] WebSearch not loaded - missing dependency: {e}")
+    except Exception as e:
+        logger.error(f"[Tools] WebSearch failed to load: {e}")
+
    return tools

 # Load optional tools
 _optional_tools = _import_optional_tools()
 EnvConfig = _optional_tools.get('EnvConfig')
 SchedulerTool = _optional_tools.get('SchedulerTool')
+WebSearch = _optional_tools.get('WebSearch')
 GoogleSearch = _optional_tools.get('GoogleSearch')
-FileSave = _optional_tools.get('FileSave') 
+FileSave = _optional_tools.get('FileSave')
 Terminal = _optional_tools.get('Terminal')


@@ -92,6 +101,7 @@ __all__ = [
    'MemoryGetTool',
    'EnvConfig',
    'SchedulerTool',
+    'WebSearch',
    # Optional tools (may be None if dependencies not available)
    # 'BrowserTool'
 ]
--- a/agent/tools/bash/bash.py
+++ b/agent/tools/bash/bash.py
@@ -11,6 +11,7 @@ from typing import Dict, Any
 from agent.tools.base_tool import BaseTool, ToolResult
 from agent.tools.utils.truncate import truncate_tail, format_size, DEFAULT_MAX_LINES, DEFAULT_MAX_BYTES
 from common.log import logger
+from common.utils import expand_path


 class Bash(BaseTool):
@@ -19,10 +20,11 @@ class Bash(BaseTool):
    name: str = "bash"
    description: str = f"""Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last {DEFAULT_MAX_LINES} lines or {DEFAULT_MAX_BYTES // 1024}KB (whichever is hit first). If truncated, full output is saved to a temp file.

-IMPORTANT SAFETY GUIDELINES:
- You can freely create, modify, and delete files within the current workspace
- For operations outside the workspace or potentially destructive commands (rm -rf, system commands, etc.), always explain what you're about to do and ask for user confirmation first
- When in doubt, describe the command's purpose and ask for permission before executing"""
+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"""

    params: dict = {
        "type": "object",
@@ -80,7 +82,7 @@ IMPORTANT SAFETY GUIDELINES:
            env = os.environ.copy()
            
            # Load environment variables from ~/.cow/.env if it exists
-            env_file = os.path.expanduser("~/.cow/.env")
+            env_file = expand_path("~/.cow/.env")
            if os.path.exists(env_file):
                try:
                    from dotenv import dotenv_values
@@ -91,14 +93,12 @@ IMPORTANT SAFETY GUIDELINES:
                    logger.debug("[Bash] python-dotenv not installed, skipping .env loading")
                except Exception as e:
                    logger.debug(f"[Bash] Failed to load .env: {e}")
-            
-            # Debug logging
-            logger.debug(f"[Bash] CWD: {self.cwd}")
-            logger.debug(f"[Bash] Command: {command[:500]}")
-            logger.debug(f"[Bash] OPENAI_API_KEY in env: {'OPENAI_API_KEY' in env}")
-            logger.debug(f"[Bash] SHELL: {env.get('SHELL', 'not set')}")
-            logger.debug(f"[Bash] Python executable: {sys.executable}")
-            logger.debug(f"[Bash] Process UID: {os.getuid()}")
+
+            # getuid() only exists on Unix-like systems
+            if hasattr(os, 'getuid'):
+                logger.debug(f"[Bash] Process UID: {os.getuid()}")
+            else:
+                logger.debug(f"[Bash] Process User: {os.environ.get('USERNAME', os.environ.get('USER', 'unknown'))}")
            
            # Execute command with inherited environment variables
            result = subprocess.run(
--- a/agent/tools/edit/edit.py
+++ b/agent/tools/edit/edit.py
@@ -7,6 +7,7 @@ import os
 from typing import Dict, Any

 from agent.tools.base_tool import BaseTool, ToolResult
+from common.utils import expand_path
 from agent.tools.utils.diff import (
    strip_bom,
    detect_line_ending,
@@ -178,7 +179,7 @@ class Edit(BaseTool):
        :return: Absolute path
        """
        # Expand ~ to user home directory
-        path = os.path.expanduser(path)
+        path = expand_path(path)
        if os.path.isabs(path):
            return path
        return os.path.abspath(os.path.join(self.cwd, path))
--- a/agent/tools/env_config/env_config.py
+++ b/agent/tools/env_config/env_config.py
@@ -9,6 +9,7 @@ from pathlib import Path

 from agent.tools.base_tool import BaseTool, ToolResult
 from common.log import logger
+from common.utils import expand_path


 # API Key 知识库：常见的环境变量及其描述
@@ -66,7 +67,7 @@ class EnvConfig(BaseTool):
    def __init__(self, config: dict = None):
        self.config = config or {}
        # Store env config in ~/.cow directory (outside workspace for security)
-        self.env_dir = os.path.expanduser("~/.cow")
+        self.env_dir = expand_path("~/.cow")
        self.env_path = os.path.join(self.env_dir, '.env')
        self.agent_bridge = self.config.get("agent_bridge")  # Reference to AgentBridge for hot reload
        # Don't create .env file in __init__ to avoid issues during tool discovery
@@ -201,7 +202,8 @@ class EnvConfig(BaseTool):
                        "key": key,
                        "value": self._mask_value(value),
                        "description": description,
-                        "exists": True
+                        "exists": True,
+                        "note": f"Value is masked for security. In bash, use ${key} directly — it is auto-injected."
                    })
                else:
                    return ToolResult.success({
--- a/agent/tools/ls/ls.py
+++ b/agent/tools/ls/ls.py
@@ -7,6 +7,7 @@ from typing import Dict, Any

 from agent.tools.base_tool import BaseTool, ToolResult
 from agent.tools.utils.truncate import truncate_head, format_size, DEFAULT_MAX_BYTES
+from common.utils import expand_path


 DEFAULT_LIMIT = 500
@@ -51,7 +52,7 @@ class Ls(BaseTool):
        absolute_path = self._resolve_path(path)
        
        # Security check: Prevent accessing sensitive config directory
-        env_config_dir = os.path.expanduser("~/.cow")
+        env_config_dir = expand_path("~/.cow")
        if os.path.abspath(absolute_path) == os.path.abspath(env_config_dir):
            return ToolResult.fail(
                "Error: Access denied. API keys and credentials must be accessed through the env_config tool only."
@@ -93,7 +94,7 @@ class Ls(BaseTool):
                        results.append(entry + '/')
                    else:
                        results.append(entry)
-                except:
+                except Exception:
                    # Skip entries we can't stat
                    continue
            
@@ -133,7 +134,7 @@ class Ls(BaseTool):
    def _resolve_path(self, path: str) -> str:
        """Resolve path to absolute path"""
        # Expand ~ to user home directory
-        path = os.path.expanduser(path)
+        path = expand_path(path)
        if os.path.isabs(path):
            return path
        return os.path.abspath(os.path.join(self.cwd, path))
--- a/agent/tools/memory/memory_get.py
+++ b/agent/tools/memory/memory_get.py
@@ -77,7 +77,7 @@ class MemoryGetTool(BaseTool):
            if not file_path.exists():
                return ToolResult.fail(f"Error: File not found: {path}")
            
-            content = file_path.read_text()
+            content = file_path.read_text(encoding='utf-8')
            lines = content.split('\n')
            
            # Handle line range
--- a/agent/tools/read/read.py
+++ b/agent/tools/read/read.py
@@ -9,6 +9,7 @@ from pathlib import Path

 from agent.tools.base_tool import BaseTool, ToolResult
 from agent.tools.utils.truncate import truncate_head, format_size, DEFAULT_MAX_LINES, DEFAULT_MAX_BYTES
+from common.utils import expand_path


 class Read(BaseTool):
@@ -66,10 +67,12 @@ class Read(BaseTool):
        :param args: Contains file path and optional offset/limit parameters
        :return: File content or error message
        """
-        path = args.get("path", "").strip()
+        # Support 'location' as alias for 'path' (LLM may use it from skill listing)
+        path = args.get("path", "") or args.get("location", "")
+        path = path.strip() if isinstance(path, str) else ""
        offset = args.get("offset")
        limit = args.get("limit")
-        
+
        if not path:
            return ToolResult.fail("Error: path parameter is required")
        
@@ -77,7 +80,7 @@ class Read(BaseTool):
        absolute_path = self._resolve_path(path)
        
        # Security check: Prevent reading sensitive config files
-        env_config_path = os.path.expanduser("~/.cow/.env")
+        env_config_path = expand_path("~/.cow/.env")
        if os.path.abspath(absolute_path) == os.path.abspath(env_config_path):
            return ToolResult.fail(
                "Error: Access denied. API keys and credentials must be accessed through the env_config tool only."
@@ -129,7 +132,7 @@ class Read(BaseTool):
        :return: Absolute path
        """
        # Expand ~ to user home directory
-        path = os.path.expanduser(path)
+        path = expand_path(path)
        if os.path.isabs(path):
            return path
        return os.path.abspath(os.path.join(self.cwd, path))
--- a/agent/tools/scheduler/integration.py
+++ b/agent/tools/scheduler/integration.py
@@ -6,6 +6,7 @@ import os
 from typing import Optional
 from config import conf
 from common.log import logger
+from common.utils import expand_path
 from bridge.context import Context, ContextType
 from bridge.reply import Reply, ReplyType

@@ -31,7 +32,7 @@ def init_scheduler(agent_bridge) -> bool:
        from agent.tools.scheduler.scheduler_service import SchedulerService
        
        # Get workspace from config
-        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
        store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
        
        # Create task store
@@ -112,11 +113,15 @@ def _execute_agent_task(task: dict, agent_bridge):
        
        logger.info(f"[Scheduler] Task {task['id']}: Executing agent task '{task_description}'")
        
+        # 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']}"
+        
        # Create context for Agent
        context = Context(ContextType.TEXT, task_description)
        context["receiver"] = receiver
        context["isgroup"] = is_group
-        context["session_id"] = receiver
+        context["session_id"] = scheduler_session_id
        
        # Channel-specific setup
        if channel_type == "web":
@@ -140,7 +145,8 @@ def _execute_agent_task(task: dict, agent_bridge):
        context["is_scheduled_task"] = True
        
        try:
-            reply = agent_bridge.agent_reply(task_description, context=context, on_event=None, clear_history=True)
+            # 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
@@ -378,6 +384,10 @@ def _execute_skill_call(task: dict, agent_bridge):
        
        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()])
@@ -389,7 +399,7 @@ def _execute_skill_call(task: dict, agent_bridge):
        context = Context(ContextType.TEXT, query)
        context["receiver"] = receiver
        context["isgroup"] = is_group
-        context["session_id"] = receiver
+        context["session_id"] = scheduler_session_id
        
        # Channel-specific setup
        if channel_type == "web":
@@ -402,7 +412,8 @@ def _execute_skill_call(task: dict, agent_bridge):
        
        # Use Agent to execute the skill
        try:
-            reply = agent_bridge.agent_reply(query, context=context, on_event=None, clear_history=True)
+            # 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
@@ -440,8 +451,7 @@ 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
--- a/agent/tools/scheduler/scheduler_service.py
+++ b/agent/tools/scheduler/scheduler_service.py
@@ -147,7 +147,7 @@ class SchedulerService:
                    return False
            
            return now >= next_run
-        except:
+        except Exception:
            return False
    
    def _calculate_next_run(self, task: dict, from_time: datetime) -> Optional[datetime]:
@@ -195,7 +195,7 @@ class SchedulerService:
                # Only return if in the future
                if run_at > from_time:
                    return run_at
-            except:
+            except Exception:
                pass
            return None
        
--- a/agent/tools/scheduler/scheduler_tool.py
+++ b/agent/tools/scheduler/scheduler_tool.py
@@ -20,7 +20,8 @@ class SchedulerTool(BaseTool):
    
    name: str = "scheduler"
    description: str = (
-        "创建、查询和管理定时任务。支持固定消息和AI任务两种类型。\n\n"
+        "创建、查询和管理定时任务（提醒、周期性任务等）。\n\n"
+        "⚠️ 重要：仅当需要「定时/提醒/每天/每周/X分钟后/X点」等延迟或周期执行时才使用此工具。"
        "使用方法：\n"
        "- 创建：action='create', name='任务名', message/ai_task='内容', schedule_type='once/interval/cron', schedule_value='...'\n"
        "- 查询：action='list' / action='get', task_id='任务ID'\n"
@@ -53,7 +54,7 @@ class SchedulerTool(BaseTool):
            },
            "ai_task": {
                "type": "string",
-                "description": "AI任务描述 (与message二选一)，如'搜索今日新闻'、'查询天气'"
+                "description": "AI任务描述 (与message二选一)，用于定时让AI执行的任务"
            },
            "schedule_type": {
                "type": "string",
@@ -423,7 +424,7 @@ class SchedulerTool(BaseTool):
            try:
                dt = datetime.fromisoformat(run_at)
                return f"一次性 ({dt.strftime('%Y-%m-%d %H:%M')})"
-            except:
+            except Exception:
                return "一次性"
        
        return "未知"
@@ -437,6 +438,6 @@ class SchedulerTool(BaseTool):
                    return msg.other_user_nickname or "群聊"
                else:
                    return msg.from_user_nickname or "用户"
-        except:
+        except Exception:
            pass
        return "未知"
--- a/agent/tools/scheduler/task_store.py
+++ b/agent/tools/scheduler/task_store.py
@@ -8,6 +8,7 @@ import threading
 from datetime import datetime
 from typing import Dict, List, Optional
 from pathlib import Path
+from common.utils import expand_path


 class TaskStore:
@@ -24,7 +25,7 @@ class TaskStore:
        """
        if store_path is None:
            # Default to ~/cow/scheduler/tasks.json
-            home = os.path.expanduser("~")
+            home = expand_path("~")
            store_path = os.path.join(home, "cow", "scheduler", "tasks.json")
        
        self.store_path = store_path
@@ -71,7 +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
--- a/agent/tools/send/send.py
+++ b/agent/tools/send/send.py
@@ -7,6 +7,7 @@ from typing import Dict, Any
 from pathlib import Path

 from agent.tools.base_tool import BaseTool, ToolResult
+from common.utils import expand_path


 class Send(BaseTool):
@@ -102,7 +103,7 @@ class Send(BaseTool):
    
    def _resolve_path(self, path: str) -> str:
        """Resolve path to absolute path"""
-        path = os.path.expanduser(path)
+        path = expand_path(path)
        if os.path.isabs(path):
            return path
        return os.path.abspath(os.path.join(self.cwd, path))
--- a/agent/tools/web_search/init.py
+++ b/agent/tools/web_search/init.py
@@ -0,0 +1,3 @@
+from agent.tools.web_search.web_search import WebSearch
+
+__all__ = ["WebSearch"]
--- a/agent/tools/web_search/web_search.py
+++ b/agent/tools/web_search/web_search.py
@@ -0,0 +1,322 @@
+"""
+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)
+"""
+
+import os
+import json
+from typing import Dict, Any, Optional
+
+import requests
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from common.log import logger
+
+
+# Default timeout for API requests (seconds)
+DEFAULT_TIMEOUT = 30
+
+
+class WebSearch(BaseTool):
+    """Tool for searching the web using Bocha or LinkAI search API"""
+
+    name: str = "web_search"
+    description: str = (
+        "Search the web for current information, news, research topics, or any real-time data. "
+        "Returns web page titles, URLs, snippets, and optional summaries. "
+        "Use this when the user asks about recent events, needs fact-checking, or wants up-to-date information."
+    )
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "Search query string"
+            },
+            "count": {
+                "type": "integer",
+                "description": "Number of results to return (1-50, default: 10)"
+            },
+            "freshness": {
+                "type": "string",
+                "description": (
+                    "Time range filter. Options: "
+                    "'noLimit' (default), 'oneDay', 'oneWeek', 'oneMonth', 'oneYear', "
+                    "or date range like '2025-01-01..2025-02-01'"
+                )
+            },
+            "summary": {
+                "type": "boolean",
+                "description": "Whether to include text summary for each result (default: false)"
+            }
+        },
+        "required": ["query"]
+    }
+
+    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"))
+
+    def _resolve_backend(self) -> Optional[str]:
+        """
+        Determine which search backend to use.
+        Priority: Bocha > LinkAI
+
+        :return: 'bocha', 'linkai', or None
+        """
+        if os.environ.get("BOCHA_API_KEY"):
+            return "bocha"
+        if os.environ.get("LINKAI_API_KEY"):
+            return "linkai"
+        return None
+
+    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()
+        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:
+            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"
+            )
+
+        try:
+            if backend == "bocha":
+                return self._search_bocha(query, count, freshness, summary)
+            else:
+                return self._search_linkai(query, count, freshness)
+        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)
+            return ToolResult.fail(f"Error: Search failed - {str(e)}")
+
+    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"
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "Accept": "application/json"
+        }
+
+        payload = {
+            "query": query,
+            "count": count,
+            "freshness": freshness,
+            "summary": summary
+        }
+
+        logger.debug(f"[WebSearch] Bocha search: query='{query}', count={count}")
+
+        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
+        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"
+            })
+
+        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", ""),
+            }
+            # Include summary only if present
+            if page.get("summary"):
+                result["summary"] = page["summary"]
+            results.append(result)
+
+        total = web_pages.get("totalEstimatedMatches", len(results))
+
+        return ToolResult.success({
+            "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"
+
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+
+        payload = {
+            "code": "web-search",
+            "args": {
+                "query": query,
+                "count": count,
+                "freshness": freshness
+            }
+        }
+
+        logger.debug(f"[WebSearch] LinkAI search: query='{query}', count={count}")
+
+        response = requests.post(url, headers=headers, json=payload, timeout=DEFAULT_TIMEOUT)
+
+        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}")
+
+        data = response.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):
+            try:
+                raw_data = json.loads(raw_data)
+            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}]
+                })
+
+        # If the response follows Bing-compatible structure
+        if isinstance(raw_data, dict):
+            web_pages = raw_data.get("webPages", {})
+            pages = web_pages.get("value", [])
+
+            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", ""),
+                    }
+                    if page.get("summary"):
+                        result["summary"] = page["summary"]
+                    results.append(result)
+
+                total = web_pages.get("totalEstimatedMatches", len(results))
+                return ToolResult.success({
+                    "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)}]
+        })
--- a/agent/tools/write/write.py
+++ b/agent/tools/write/write.py
@@ -8,6 +8,7 @@ from typing import Dict, Any
 from pathlib import Path

 from agent.tools.base_tool import BaseTool, ToolResult
+from common.utils import expand_path


 class Write(BaseTool):
@@ -90,7 +91,7 @@ class Write(BaseTool):
        :return: Absolute path
        """
        # Expand ~ to user home directory
-        path = os.path.expanduser(path)
+        path = expand_path(path)
        if os.path.isabs(path):
            return path
        return os.path.abspath(os.path.join(self.cwd, path))
--- a/app.py
+++ b/app.py
@@ -7,11 +7,186 @@ import time

 from channel import channel_factory
 from common import const
-from config import load_config
+from common.log import logger
+from config import load_config, conf
 from plugins import *
 import threading


+_channel_mgr = None
+
+
+def get_channel_manager():
+    return _channel_mgr
+
+
+def _parse_channel_type(raw) -> list:
+    """
+    Parse channel_type config value into a list of channel names.
+    Supports:
+      - single string: "feishu"
+      - comma-separated string: "feishu, dingtalk"
+      - list: ["feishu", "dingtalk"]
+    """
+    if isinstance(raw, list):
+        return [ch.strip() for ch in raw if ch.strip()]
+    if isinstance(raw, str):
+        return [ch.strip() for ch in raw.split(",") if ch.strip()]
+    return []
+
+
+class ChannelManager:
+    """
+    Manage the lifecycle of multiple channels running concurrently.
+    Each channel.startup() runs in its own daemon thread.
+    The web channel is started as default console unless explicitly disabled.
+    """
+
+    def __init__(self):
+        self._channels = {}        # channel_name -> channel instance
+        self._threads = {}         # channel_name -> thread
+        self._primary_channel = None
+        self._lock = threading.Lock()
+
+    @property
+    def channel(self):
+        """Return the primary (first non-web) channel for backward compatibility."""
+        return self._primary_channel
+
+    def get_channel(self, channel_name: str):
+        return self._channels.get(channel_name)
+
+    def start(self, channel_names: list, first_start: bool = False):
+        """
+        Create and start one or more channels in sub-threads.
+        If first_start is True, plugins and linkai client will also be initialized.
+        """
+        with self._lock:
+            channels = []
+            for name in channel_names:
+                ch = channel_factory.create_channel(name)
+                self._channels[name] = ch
+                channels.append((name, ch))
+                if self._primary_channel is None and name != "web":
+                    self._primary_channel = ch
+
+            if self._primary_channel is None and channels:
+                self._primary_channel = channels[0][1]
+
+            if first_start:
+                PluginManager().load_plugins()
+
+                if conf().get("use_linkai"):
+                    try:
+                        from common import cloud_client
+                        threading.Thread(
+                            target=cloud_client.start,
+                            args=(self._primary_channel, self),
+                            daemon=True,
+                        ).start()
+                    except Exception:
+                        pass
+
+            # Start web console first so its logs print cleanly,
+            # then start remaining channels after a brief pause.
+            web_entry = None
+            other_entries = []
+            for entry in channels:
+                if entry[0] == "web":
+                    web_entry = entry
+                else:
+                    other_entries.append(entry)
+
+            ordered = ([web_entry] if web_entry else []) + other_entries
+            for i, (name, ch) in enumerate(ordered):
+                if i > 0 and name != "web":
+                    time.sleep(0.1)
+                t = threading.Thread(target=self._run_channel, args=(name, ch), daemon=True)
+                self._threads[name] = t
+                t.start()
+                logger.debug(f"[ChannelManager] Channel '{name}' started in sub-thread")
+
+    def _run_channel(self, name: str, channel):
+        try:
+            channel.startup()
+        except Exception as e:
+            logger.error(f"[ChannelManager] Channel '{name}' startup error: {e}")
+            logger.exception(e)
+
+    def stop(self, channel_name: str = None):
+        """
+        Stop channel(s). If channel_name is given, stop only that channel;
+        otherwise stop all channels.
+        """
+        with self._lock:
+            names = [channel_name] if channel_name else list(self._channels.keys())
+            for name in names:
+                ch = self._channels.pop(name, None)
+                self._threads.pop(name, None)
+                if ch is None:
+                    continue
+                logger.info(f"[ChannelManager] Stopping channel '{name}'...")
+                try:
+                    if hasattr(ch, 'stop'):
+                        ch.stop()
+                except Exception as e:
+                    logger.warning(f"[ChannelManager] Error during channel '{name}' stop: {e}")
+            if channel_name and self._primary_channel is self._channels.get(channel_name):
+                self._primary_channel = None
+
+    def restart(self, new_channel_name: str):
+        """
+        Restart a single channel with a new channel type.
+        Can be called from any thread (e.g. linkai config callback).
+        """
+        logger.info(f"[ChannelManager] Restarting channel to '{new_channel_name}'...")
+        self.stop(new_channel_name)
+        _clear_singleton_cache(new_channel_name)
+        time.sleep(1)
+        self.start([new_channel_name], first_start=False)
+        logger.info(f"[ChannelManager] Channel restarted to '{new_channel_name}' successfully")
+
+
+def _clear_singleton_cache(channel_name: str):
+    """
+    Clear the singleton cache for the channel class so that
+    a new instance can be created with updated config.
+    """
+    cls_map = {
+        "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",
+    }
+    module_path = cls_map.get(channel_name)
+    if not module_path:
+        return
+    try:
+        parts = module_path.rsplit(".", 1)
+        module_name, class_name = parts[0], parts[1]
+        import importlib
+        module = importlib.import_module(module_name)
+        wrapper = getattr(module, class_name, None)
+        if wrapper and hasattr(wrapper, '__closure__') and wrapper.__closure__:
+            for cell in wrapper.__closure__:
+                try:
+                    cell_contents = cell.cell_contents
+                    if isinstance(cell_contents, dict):
+                        cell_contents.clear()
+                        logger.debug(f"[ChannelManager] Cleared singleton cache for {class_name}")
+                        break
+                except ValueError:
+                    pass
+    except Exception as e:
+        logger.warning(f"[ChannelManager] Failed to clear singleton cache: {e}")
+
+
 def sigterm_handler_wrap(_signo):
    old_handler = signal.getsignal(_signo)

@@ -25,22 +200,8 @@ def sigterm_handler_wrap(_signo):
    signal.signal(_signo, func)


-def start_channel(channel_name: str):
-    channel = channel_factory.create_channel(channel_name)
-    if channel_name in ["wx", "wxy", "terminal", "wechatmp", "web", "wechatmp_service", "wechatcom_app", "wework",
-                        const.FEISHU, const.DINGTALK]:
-        PluginManager().load_plugins()
-
-    if conf().get("use_linkai"):
-        try:
-            from common import linkai_client
-            threading.Thread(target=linkai_client.start, args=(channel,)).start()
-        except Exception as e:
-            pass
-    channel.startup()
-
-
 def run():
+    global _channel_mgr
    try:
        # load config
        load_config()
@@ -49,16 +210,28 @@ def run():
        # kill signal
        sigterm_handler_wrap(signal.SIGTERM)

-        # create channel
-        channel_name = conf().get("channel_type", "wx")
+        # Parse channel_type into a list
+        raw_channel = conf().get("channel_type", "web")

        if "--cmd" in sys.argv:
-            channel_name = "terminal"
+            channel_names = ["terminal"]
+        else:
+            channel_names = _parse_channel_type(raw_channel)
+            if not channel_names:
+                channel_names = ["web"]

-        if channel_name == "wxy":
+        if "wxy" in channel_names:
            os.environ["WECHATY_LOG"] = "warn"

-        start_channel(channel_name)
+        # 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")
+
+        logger.info(f"[App] Starting channels: {channel_names}")
+
+        _channel_mgr = ChannelManager()
+        _channel_mgr.start(channel_names, first_start=True)

        while True:
            time.sleep(1)
--- a/bridge/agent_bridge.py
+++ b/bridge/agent_bridge.py
@@ -13,6 +13,7 @@ from bridge.context import Context
 from bridge.reply import Reply, ReplyType
 from common import const
 from common.log import logger
+from common.utils import expand_path
 from models.openai_compatible_bot import OpenAICompatibleBot


@@ -27,7 +28,7 @@ def add_openai_compatible_support(bot_instance):
    """
    if hasattr(bot_instance, 'call_with_tools'):
        # Bot already has tool calling support (e.g., ZHIPUAIBot)
-        logger.info(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
+        logger.debug(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
        return bot_instance

    # Create a temporary mixin class that combines the bot with OpenAI compatibility
@@ -134,7 +135,7 @@ 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,
@@ -142,15 +143,20 @@ 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 channel_type for linkai tracking
+                channel_type = getattr(self, 'channel_type', None)
+                if channel_type:
+                    kwargs['channel_type'] = channel_type
+
                stream = self.bot.call_with_tools(**kwargs)
                
                # Convert stream format to our expected format
@@ -233,7 +239,8 @@ class AgentBridge:
            enable_skills=kwargs.get("enable_skills", True),  # Enable skills by default
            memory_manager=kwargs.get("memory_manager"),  # Pass memory manager
            max_context_tokens=kwargs.get("max_context_tokens"),
-            context_reserve_tokens=kwargs.get("context_reserve_tokens")
+            context_reserve_tokens=kwargs.get("context_reserve_tokens"),
+            runtime_info=kwargs.get("runtime_info")  # Pass runtime_info for dynamic time updates
        )

        # Log skill loading details
@@ -323,6 +330,14 @@ class AgentBridge:
                                logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
                            break
            
+            # Pass channel_type to model so linkai requests carry it
+            if context and hasattr(agent, 'model'):
+                agent.model.channel_type = context.get("channel_type", "")
+
+            # Record message count before execution so we can diff new messages
+            with agent.messages_lock:
+                pre_run_len = len(agent.messages)
+
            try:
                # Use agent's run_stream method with event handler
                response = agent.run_stream(
@@ -334,9 +349,16 @@ class AgentBridge:
                # Restore original tools
                if context and context.get("is_scheduled_task"):
                    agent.tools = original_tools
-                
+
                # Log execution summary
                event_handler.log_summary()
+
+            # Persist new messages generated during this run
+            if session_id:
+                channel_type = (context.get("channel_type") or "") if context else ""
+                with agent.messages_lock:
+                    new_messages = agent.messages[pre_run_len:]
+                self._persist_messages(session_id, list(new_messages), channel_type)
            
            # Check if there are files to send (from read tool)
            if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
@@ -420,7 +442,7 @@ class AgentBridge:
        }
        
        # Use fixed secure location for .env file
-        env_file = os.path.expanduser("~/.cow/.env")
+        env_file = expand_path("~/.cow/.env")
        
        # Read existing env vars from .env file
        existing_env_vars = {}
@@ -473,6 +495,32 @@ class AgentBridge:
            except Exception as e:
                logger.warning(f"[AgentBridge] Failed to migrate 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
+        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"[AgentBridge] Failed to persist messages for session={session_id}: {e}"
+            )
+
    def clear_session(self, session_id: str):
        """
        Clear a specific session's agent and conversation history
@@ -492,39 +540,70 @@ class AgentBridge:
    
    def refresh_all_skills(self) -> int:
        """
-        Refresh skills in all agent instances after environment variable changes.
-        This allows hot-reload of skills without restarting the agent.
-        
+        Refresh skills and conditional tools in all agent instances after
+        environment variable changes. This allows hot-reload without restarting.
+
        Returns:
            Number of agent instances refreshed
        """
        import os
        from dotenv import load_dotenv
        from config import conf
-        
+
        # Reload environment variables from .env file
-        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
        env_file = os.path.join(workspace_root, '.env')
-        
+
        if os.path.exists(env_file):
            load_dotenv(env_file, override=True)
            logger.info(f"[AgentBridge] Reloaded environment variables from {env_file}")
-        
+
        refreshed_count = 0
-        
-        # Refresh default agent
-        if self.default_agent and hasattr(self.default_agent, 'skill_manager'):
-            self.default_agent.skill_manager.refresh_skills()
-            refreshed_count += 1
-            logger.info("[AgentBridge] Refreshed skills in default agent")
-        
-        # Refresh all session agents
+
+        # Collect all agent instances to refresh
+        agents_to_refresh = []
+        if self.default_agent:
+            agents_to_refresh.append(("default", self.default_agent))
        for session_id, agent in self.agents.items():
-            if hasattr(agent, 'skill_manager'):
+            agents_to_refresh.append((session_id, agent))
+
+        for label, agent in agents_to_refresh:
+            # Refresh skills
+            if hasattr(agent, 'skill_manager') and agent.skill_manager:
                agent.skill_manager.refresh_skills()
-                refreshed_count += 1
-        
+
+            # Refresh conditional tools (e.g. web_search depends on API keys)
+            self._refresh_conditional_tools(agent)
+
+            refreshed_count += 1
+
        if refreshed_count > 0:
-            logger.info(f"[AgentBridge] Refreshed skills in {refreshed_count} agent instance(s)")
-        
-        return refreshed_count
+            logger.info(f"[AgentBridge] Refreshed skills & tools in {refreshed_count} agent instance(s)")
+
+        return refreshed_count
+
+    @staticmethod
+    def _refresh_conditional_tools(agent):
+        """
+        Add or remove conditional tools based on current environment variables.
+        For example, web_search should only be present when BOCHA_API_KEY or
+        LINKAI_API_KEY is set.
+        """
+        try:
+            from agent.tools.web_search.web_search import WebSearch
+
+            has_tool = any(t.name == "web_search" for t in agent.tools)
+            available = WebSearch.is_available()
+
+            if available and not has_tool:
+                # API key was added - inject the tool
+                tool = WebSearch()
+                tool.model = agent.model
+                agent.tools.append(tool)
+                logger.info("[AgentBridge] web_search tool added (API key now available)")
+            elif not available and has_tool:
+                # API key was removed - remove the tool
+                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}")
--- a/bridge/agent_event_handler.py
+++ b/bridge/agent_event_handler.py
@@ -74,7 +74,7 @@ class AgentEventHandler:
        # Only send thinking process if followed by tool calls
        if tool_calls:
            if self.current_thinking.strip():
-                logger.debug(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
+                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()}")
        else:
@@ -94,15 +94,15 @@ class AgentEventHandler:
    
    def _send_to_channel(self, message):
        """
-        Try to send message to channel
-        
-        Args:
-            message: Message to send
+        Try to send intermediate message to channel.
+        Skipped in SSE mode because thinking text is already streamed via on_event.
        """
+        if self.context and self.context.get("on_event"):
+            return
+
        if self.channel:
            try:
                from bridge.reply import Reply, ReplyType
-                # Create a Reply object for the message
                reply = Reply(ReplyType.TEXT, message)
                self.channel._send(reply, self.context)
            except Exception as e:
--- a/bridge/agent_initializer.py
+++ b/bridge/agent_initializer.py
@@ -11,6 +11,7 @@ from typing import Optional, List
 from agent.protocol import Agent
 from agent.tools import ToolManager
 from common.log import logger
+from common.utils import expand_path


 class AgentInitializer:
@@ -46,7 +47,7 @@ class AgentInitializer:
        from config import conf
        
        # Get workspace from config
-        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
        
        # Migrate API keys
        self._migrate_config_to_env(workspace_root)
@@ -110,18 +111,58 @@ class AgentInitializer:
            workspace_dir=workspace_root,
            skill_manager=skill_manager,
            enable_skills=True,
-            max_context_tokens=max_context_tokens
+            max_context_tokens=max_context_tokens,
+            runtime_info=runtime_info  # Pass runtime_info for dynamic time updates
        )
        
        # Attach memory manager
        if memory_manager:
            agent.memory_manager = memory_manager
-        
+
+        # Restore persisted conversation history for this session
+        if session_id:
+            self._restore_conversation_history(agent, session_id)
+
        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 runs when conversation persistence is enabled (default: True).
+        Respects agent_max_context_turns to limit how many turns are loaded.
+        """
+        from config import conf
+        if not conf().get("conversation_persistence", True):
+            return
+
+        try:
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            # On restore, load at most min(10, max_turns // 2) turns so that
+            # a long-running session does not immediately fill the context window
+            # after a restart.  The full max_turns budget is reserved for the
+            # live conversation that follows.
+            max_turns = conf().get("agent_max_context_turns", 30)
+            restore_turns = min(6, max(1, max_turns // 3))
+            saved = store.load_messages(session_id, max_turns=restore_turns)
+            if saved:
+                with agent.messages_lock:
+                    agent.messages = saved
+                logger.debug(
+                    f"[AgentInitializer] Restored {len(saved)} messages "
+                    f"({restore_turns} turns cap) for session={session_id}"
+                )
+        except Exception as e:
+            logger.warning(
+                f"[AgentInitializer] Failed to restore conversation history for "
+                f"session={session_id}: {e}"
+            )
    
    def _load_env_file(self):
        """Load environment variables from .env file"""
-        env_file = os.path.expanduser("~/.cow/.env")
+        env_file = expand_path("~/.cow/.env")
        if os.path.exists(env_file):
            try:
                from dotenv import load_dotenv
@@ -217,13 +258,20 @@ class AgentInitializer:
        
        for tool_name in tool_manager.tool_classes.keys():
            try:
+                # Skip web_search if no API key is available
+                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")
+                        continue
+
                # Special handling for EnvConfig tool
                if tool_name == "env_config":
                    from agent.tools import EnvConfig
                    tool = EnvConfig({"agent_bridge": self.agent_bridge})
                else:
                    tool = tool_manager.create_tool(tool_name)
-                
+
                if tool:
                    # Apply workspace config to file operation tools
                    if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']:
@@ -274,7 +322,14 @@ 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}")
    
@@ -282,41 +337,47 @@ class AgentInitializer:
        """Initialize skill manager"""
        try:
            from agent.skills import SkillManager
-            skill_manager = SkillManager(workspace_dir=workspace_root)
+            skill_manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
            return skill_manager
        except Exception as e:
            logger.warning(f"[AgentInitializer] Failed to initialize SkillManager: {e}")
            return None
    
    def _get_runtime_info(self, workspace_root: str):
-        """Get runtime information"""
+        """Get runtime information with dynamic time support"""
        from config import conf
        
-        now = datetime.datetime.now()
-        
-        # Get timezone info
-        try:
-            offset = -time.timezone if not time.daylight else -time.altzone
-            hours = offset // 3600
-            minutes = (offset % 3600) // 60
-            timezone_name = f"UTC{hours:+03d}:{minutes:02d}" if minutes else f"UTC{hours:+03d}"
-        except Exception:
-            timezone_name = "UTC"
-        
-        # Chinese weekday mapping
-        weekday_map = {
-            'Monday': '星期一', 'Tuesday': '星期二', 'Wednesday': '星期三',
-            'Thursday': '星期四', 'Friday': '星期五', 'Saturday': '星期六', 'Sunday': '星期日'
-        }
-        weekday_zh = weekday_map.get(now.strftime("%A"), now.strftime("%A"))
+        def get_current_time():
+            """Get current time dynamically - called each time system prompt is accessed"""
+            now = datetime.datetime.now()
+            
+            # Get timezone info
+            try:
+                offset = -time.timezone if not time.daylight else -time.altzone
+                hours = offset // 3600
+                minutes = (offset % 3600) // 60
+                timezone_name = f"UTC{hours:+03d}:{minutes:02d}" if minutes else f"UTC{hours:+03d}"
+            except Exception:
+                timezone_name = "UTC"
+            
+            # Chinese weekday mapping
+            weekday_map = {
+                'Monday': '星期一', 'Tuesday': '星期二', 'Wednesday': '星期三',
+                'Thursday': '星期四', 'Friday': '星期五', 'Saturday': '星期六', 'Sunday': '星期日'
+            }
+            weekday_zh = weekday_map.get(now.strftime("%A"), now.strftime("%A"))
+            
+            return {
+                'time': now.strftime("%Y-%m-%d %H:%M:%S"),
+                'weekday': weekday_zh,
+                'timezone': timezone_name
+            }
        
        return {
            "model": conf().get("model", "unknown"),
            "workspace": workspace_root,
-            "channel": conf().get("channel_type", "unknown"),
-            "current_time": now.strftime("%Y-%m-%d %H:%M:%S"),
-            "weekday": weekday_zh,
-            "timezone": timezone_name
+            "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
        }
    
    def _migrate_config_to_env(self, workspace_root: str):
@@ -331,7 +392,7 @@ class AgentInitializer:
            "linkai_api_key": "LINKAI_API_KEY",
        }
        
-        env_file = os.path.expanduser("~/.cow/.env")
+        env_file = expand_path("~/.cow/.env")
        
        # Read existing env vars
        existing_env_vars = {}
--- a/bridge/bridge.py
+++ b/bridge/bridge.py
@@ -24,6 +24,13 @@ class Bridge(object):
            self.btype["chat"] = bot_type
        else:
            model_type = conf().get("model") or const.GPT_41_MINI
+            
+            # Ensure model_type is string to prevent AttributeError when using startswith()
+            # This handles cases where numeric model names (e.g., "1") are parsed as integers from YAML
+            if not isinstance(model_type, str):
+                logger.warning(f"[Bridge] model_type is not a string: {model_type} (type: {type(model_type).__name__}), converting to string")
+                model_type = str(model_type)
+            
            if model_type in ["text-davinci-003"]:
                self.btype["chat"] = const.OPEN_AI
            if conf().get("use_azure_chatgpt", False):
@@ -48,6 +55,11 @@ class Bridge(object):

            if model_type in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
                self.btype["chat"] = const.MOONSHOT
+            if model_type and model_type.startswith("kimi"):
+                self.btype["chat"] = const.MOONSHOT
+
+            if model_type and model_type.startswith("doubao"):
+                self.btype["chat"] = const.DOUBAO

            if model_type in [const.MODELSCOPE]:
                self.btype["chat"] = const.MODELSCOPE
--- a/channel/channel.py
+++ b/channel/channel.py
@@ -19,6 +19,12 @@ class Channel(object):
        """
        raise NotImplementedError

+    def stop(self):
+        """
+        stop channel gracefully, called before restart
+        """
+        pass
+
    def handle_text(self, msg):
        """
        process received msg
@@ -51,11 +57,14 @@ 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:
--- a/channel/chat_channel.py
+++ b/channel/chat_channel.py
@@ -24,11 +24,16 @@ 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):
+        # 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 +42,8 @@ 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
--- a/channel/dingtalk/dingtalk_channel.py
+++ b/channel/dingtalk/dingtalk_channel.py
@@ -21,6 +21,7 @@ from dingtalk_stream.card_replier import CardReplier
 from bridge.context import Context, ContextType
 from bridge.reply import Reply, ReplyType
 from channel.chat_channel import ChatChannel
+from common.utils import expand_path
 from channel.dingtalk.dingtalk_message import DingTalkMessage
 from common.expired_dict import ExpiredDict
 from common.log import logger
@@ -89,13 +90,9 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
    dingtalk_client_secret = conf().get('dingtalk_client_secret')

    def setup_logger(self):
-        logger = logging.getLogger()
-        handler = logging.StreamHandler()
-        handler.setFormatter(
-            logging.Formatter('%(asctime)s %(name)-8s %(levelname)-8s %(message)s [%(filename)s:%(lineno)d]'))
-        logger.addHandler(handler)
-        logger.setLevel(logging.INFO)
-        return logger
+        # Suppress verbose logs from dingtalk_stream SDK
+        logging.getLogger("dingtalk_stream").setLevel(logging.WARNING)
+        return logging.getLogger("DingTalk")

    def __init__(self):
        super().__init__()
@@ -103,6 +100,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
        self.logger = self.setup_logger()
        # 历史消息id暂存，用于幂等控制
        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
+        self._stream_client = None
        logger.debug("[DingTalk] client_id={}, client_secret={} ".format(
            self.dingtalk_client_id, self.dingtalk_client_secret))
        # 无需群校验和前缀
@@ -118,9 +116,19 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
    def startup(self):
        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()
+
+    def stop(self):
+        if self._stream_client:
+            try:
+                self._stream_client.stop()
+                logger.info("[DingTalk] Stream client stopped")
+            except Exception as e:
+                logger.warning(f"[DingTalk] Error stopping stream client: {e}")
+            self._stream_client = None
    
    def get_access_token(self):
        """
@@ -276,7 +284,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
                
                # 保存到临时文件
                file_name = os.path.basename(file_path) or f"media_{uuid.uuid4()}"
-                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
                tmp_dir = os.path.join(workspace_root, "tmp")
                os.makedirs(tmp_dir, exist_ok=True)
                temp_file = os.path.join(tmp_dir, file_name)
@@ -607,7 +615,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):


    def send(self, reply: Reply, context: Context):
-        logger.info(f"[DingTalk] send() called with reply.type={reply.type}, content_length={len(str(reply.content))}")
+        logger.debug(f"[DingTalk] send() called with reply.type={reply.type}, content_length={len(str(reply.content))}")
        receiver = context["receiver"]
        
        # Check if msg exists (for scheduled tasks, msg might be None)
@@ -647,7 +655,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
            robot_code = msg.robot_code
            if robot_code and robot_code != self._robot_code:
                self._robot_code = robot_code
-                logger.info(f"[DingTalk] Cached robot_code: {robot_code}")
+                logger.debug(f"[DingTalk] Cached robot_code: {robot_code}")
        
        isgroup = msg.is_group
        incoming_message = msg.incoming_message
--- a/channel/dingtalk/dingtalk_message.py
+++ b/channel/dingtalk/dingtalk_message.py
@@ -9,6 +9,7 @@ from channel.chat_message import ChatMessage
 # -*- coding=utf-8 -*-
 from common.log import logger
 from common.tmp_dir import TmpDir
+from common.utils import expand_path
 from config import conf


@@ -49,7 +50,7 @@ class DingTalkMessage(ChatMessage):
                download_url = image_download_handler.get_image_download_url(download_code)
                
                # 下载到工作空间 tmp 目录
-                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
                tmp_dir = os.path.join(workspace_root, "tmp")
                os.makedirs(tmp_dir, exist_ok=True)
                
@@ -67,7 +68,7 @@ class DingTalkMessage(ChatMessage):
                self.ctype = ContextType.TEXT
                
                # 下载到工作空间 tmp 目录
-                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
                tmp_dir = os.path.join(workspace_root, "tmp")
                os.makedirs(tmp_dir, exist_ok=True)
                
--- a/channel/feishu/README.md
+++ b/channel/feishu/README.md
@@ -140,6 +140,23 @@ python3 app.py

 **解决**: 安装依赖 `pip install lark-oapi`

+### SSL证书验证失败
+
+```
+[Lark][ERROR] connect failed, err:[SSL:CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain
+```
+
+**原因**: 网络环境中存在自签名证书或SSL中间人代理(如企业代理、VPN等)
+
+**解决**: 程序会自动检测SSL证书验证失败，并自动重试禁用证书验证的连接。无需手动配置。
+
+当遇到证书错误时，日志会显示：
+```
+[FeiShu] SSL certificate verification disabled due to certificate error. This may happen when using corporate proxy or self-signed certificates.
+```
+
+这是正常现象，程序会自动处理并继续运行。
+
 ### Webhook模式端口被占用

 ```
--- a/channel/feishu/feishu_channel.py
+++ b/channel/feishu/feishu_channel.py
@@ -12,7 +12,9 @@
 """

 import json
+import logging
 import os
+import ssl
 import threading
 # -*- coding=utf-8 -*-
 import uuid
@@ -31,6 +33,9 @@ from common.log import logger
 from common.singleton import singleton
 from config import conf

+# Suppress verbose logs from Lark SDK
+logging.getLogger("Lark").setLevel(logging.WARNING)
+
 URL_VERIFICATION = "url_verification"

 # 尝试导入飞书SDK,如果未安装则websocket模式不可用
@@ -55,6 +60,7 @@ class FeiShuChanel(ChatChannel):
        super().__init__()
        # 历史消息id暂存，用于幂等控制
        self.receivedMsgs = ExpiredDict(60 * 60 * 7.1)
+        self._http_server = None
        logger.debug("[FeiShu] app_id={}, app_secret={}, verification_token={}, event_mode={}".format(
            self.feishu_app_id, self.feishu_app_secret, self.feishu_token, self.feishu_event_mode))
        # 无需群校验和前缀
@@ -72,6 +78,15 @@ class FeiShuChanel(ChatChannel):
        else:
            self._startup_webhook()

+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[FeiShu] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[FeiShu] Error stopping HTTP server: {e}")
+            self._http_server = None
+
    def _startup_webhook(self):
        """启动HTTP服务器接收事件(webhook模式)"""
        logger.debug("[FeiShu] Starting in webhook mode...")
@@ -80,7 +95,14 @@ class FeiShuChanel(ChatChannel):
        )
        app = web.application(urls, globals(), autoreload=False)
        port = conf().get("feishu_port", 9891)
-        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
+        try:
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()

    def _startup_websocket(self):
        """启动长连接接收事件(websocket模式)"""
@@ -107,23 +129,65 @@ class FeiShuChanel(ChatChannel):
            .register_p2_im_message_receive_v1(handle_message_event) \
            .build()

-        # 创建长连接客户端
-        ws_client = lark.ws.Client(
-            self.feishu_app_id,
-            self.feishu_app_secret,
-            event_handler=event_handler,
-            log_level=lark.LogLevel.DEBUG if conf().get("debug") else lark.LogLevel.INFO
-        )
+        # 尝试连接，如果遇到SSL错误则自动禁用证书验证
+        def start_client_with_retry():
+            """启动websocket客户端，自动处理SSL证书错误"""
+            # 全局禁用SSL证书验证（在导入lark_oapi之前设置）
+            import ssl as ssl_module
+
+            # 保存原始的SSL上下文创建方法
+            original_create_default_context = ssl_module.create_default_context
+
+            def create_unverified_context(*args, **kwargs):
+                """创建一个不验证证书的SSL上下文"""
+                context = original_create_default_context(*args, **kwargs)
+                context.check_hostname = False
+                context.verify_mode = ssl.CERT_NONE
+                return context
+
+            # 尝试正常连接，如果失败则禁用SSL验证
+            for attempt in range(2):
+                try:
+                    if attempt == 1:
+                        # 第二次尝试：禁用SSL验证
+                        logger.warning("[FeiShu] SSL certificate verification disabled due to certificate error. "
+                                       "This may happen when using corporate proxy or self-signed certificates.")
+                        ssl_module.create_default_context = create_unverified_context
+                        ssl_module._create_unverified_context = create_unverified_context
+
+                    ws_client = lark.ws.Client(
+                        self.feishu_app_id,
+                        self.feishu_app_secret,
+                        event_handler=event_handler,
+                        log_level=lark.LogLevel.DEBUG if conf().get("debug") else lark.LogLevel.WARNING
+                    )
+
+                    logger.debug("[FeiShu] Websocket client starting...")
+                    ws_client.start()
+                    # 如果成功启动，跳出循环
+                    break
+
+                except Exception as e:
+                    error_msg = str(e)
+                    # 检查是否是SSL证书验证错误
+                    is_ssl_error = "CERTIFICATE_VERIFY_FAILED" in error_msg or "certificate verify failed" in error_msg.lower()
+
+                    if is_ssl_error and attempt == 0:
+                        # 第一次遇到SSL错误，记录日志并继续循环（下次会禁用验证）
+                        logger.warning(f"[FeiShu] SSL certificate verification failed: {error_msg}")
+                        logger.info("[FeiShu] Retrying connection with SSL verification disabled...")
+                        continue
+                    else:
+                        # 其他错误或禁用验证后仍失败，抛出异常
+                        logger.error(f"[FeiShu] Websocket client error: {e}", exc_info=True)
+                        # 恢复原始方法
+                        ssl_module.create_default_context = original_create_default_context
+                        raise
+
+            # 注意：不恢复原始方法，因为ws_client.start()会持续运行

        # 在新线程中启动客户端，避免阻塞主线程
-        def start_client():
-            try:
-                logger.debug("[FeiShu] Websocket client starting...")
-                ws_client.start()
-            except Exception as e:
-                logger.error(f"[FeiShu] Websocket client error: {e}", exc_info=True)
-
-        ws_thread = threading.Thread(target=start_client, daemon=True)
+        ws_thread = threading.Thread(target=start_client_with_retry, daemon=True)
        ws_thread.start()

        # 保持主线程运行
@@ -176,7 +240,7 @@ class FeiShuChanel(ChatChannel):
        # 处理文件缓存逻辑
        from channel.file_cache import get_file_cache
        file_cache = get_file_cache()
-        
+
        # 获取 session_id（用于缓存关联）
        if is_group:
            if conf().get("group_shared_session", True):
@@ -185,7 +249,7 @@ class FeiShuChanel(ChatChannel):
                session_id = feishu_msg.from_user_id + "_" + msg.get("chat_id")
        else:
            session_id = feishu_msg.from_user_id
-        
+
        # 如果是单张图片消息，缓存起来
        if feishu_msg.ctype == ContextType.IMAGE:
            if hasattr(feishu_msg, 'image_path') and feishu_msg.image_path:
@@ -193,7 +257,7 @@ class FeiShuChanel(ChatChannel):
                logger.info(f"[FeiShu] Image cached for session {session_id}, waiting for user query...")
            # 单张图片不直接处理，等待用户提问
            return
-        
+
        # 如果是文本消息，检查是否有缓存的文件
        if feishu_msg.ctype == ContextType.TEXT:
            cached_files = file_cache.get(session_id)
@@ -209,7 +273,7 @@ class FeiShuChanel(ChatChannel):
                        file_refs.append(f"[视频: {file_path}]")
                    else:
                        file_refs.append(f"[文件: {file_path}]")
-                
+
                feishu_msg.content = feishu_msg.content + "\n" + "\n".join(file_refs)
                logger.info(f"[FeiShu] Attached {len(cached_files)} cached file(s) to user query")
                # 清除缓存
@@ -258,26 +322,27 @@ class FeiShuChanel(ChatChannel):
                self._send(text_reply, context)
                import time
                time.sleep(0.3)  # 短暂延迟，确保文本先到达
-            
+
            # 判断是否为视频文件
            file_path = reply.content
            if file_path.startswith("file://"):
                file_path = file_path[7:]
-            
+
            is_video = file_path.lower().endswith(('.mp4', '.avi', '.mov', '.wmv', '.flv'))
-            
+
            if is_video:
                # 视频上传（包含duration信息）
                upload_data = self._upload_video_url(reply.content, access_token)
                if not upload_data or not upload_data.get('file_key'):
                    logger.warning("[FeiShu] upload video failed")
                    return
-                
+
                # 视频使用 media 类型（根据官方文档）
                # 错误码 230055 说明：上传 mp4 时必须使用 msg_type="media"
                msg_type = "media"
                reply_content = upload_data  # 完整的上传响应数据（包含file_key和duration）
-                logger.info(f"[FeiShu] Sending video: file_key={upload_data.get('file_key')}, duration={upload_data.get('duration')}ms")
+                logger.info(
+                    f"[FeiShu] Sending video: file_key={upload_data.get('file_key')}, duration={upload_data.get('duration')}ms")
                content_key = None  # 直接序列化整个对象
            else:
                # 其他文件使用 file 类型
@@ -288,14 +353,14 @@ class FeiShuChanel(ChatChannel):
                reply_content = file_key
                msg_type = "file"
                content_key = "file_key"
-        
+
        # Check if we can reply to an existing message (need msg_id)
        can_reply = is_group and msg and hasattr(msg, 'msg_id') and msg.msg_id
-        
+
        # Build content JSON
        content_json = json.dumps(reply_content) if content_key is None else json.dumps({content_key: reply_content})
        logger.debug(f"[FeiShu] Sending message: msg_type={msg_type}, content={content_json[:200]}")
-        
+
        if can_reply:
            # 群聊中回复已有消息
            url = f"https://open.feishu.cn/open-apis/im/v1/messages/{msg.msg_id}/reply"
@@ -320,7 +385,6 @@ class FeiShuChanel(ChatChannel):
        else:
            logger.error(f"[FeiShu] send message failed, code={res.get('code')}, msg={res.get('msg')}")

-
    def fetch_access_token(self) -> str:
        url = "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal/"
        headers = {
@@ -342,35 +406,34 @@ class FeiShuChanel(ChatChannel):
        else:
            logger.error(f"[FeiShu] fetch token error, res={response}")

-
    def _upload_image_url(self, img_url, access_token):
        logger.debug(f"[FeiShu] start process image, img_url={img_url}")
-        
+
        # Check if it's a local file path (file:// protocol)
        if img_url.startswith("file://"):
            local_path = img_url[7:]  # Remove "file://" prefix
            logger.info(f"[FeiShu] uploading local file: {local_path}")
-            
+
            if not os.path.exists(local_path):
                logger.error(f"[FeiShu] local file not found: {local_path}")
                return None
-            
+
            # Upload directly from local file
            upload_url = "https://open.feishu.cn/open-apis/im/v1/images"
            data = {'image_type': 'message'}
            headers = {'Authorization': f'Bearer {access_token}'}
-            
+
            with open(local_path, "rb") as file:
                upload_response = requests.post(upload_url, files={"image": file}, data=data, headers=headers)
                logger.info(f"[FeiShu] upload file, res={upload_response.content}")
-                
+
                response_data = upload_response.json()
                if response_data.get("code") == 0:
                    return response_data.get("data").get("image_key")
                else:
                    logger.error(f"[FeiShu] upload failed: {response_data}")
                    return None
-        
+
        # Original logic for HTTP URLs
        response = requests.get(img_url)
        suffix = utils.get_path_suffix(img_url)
@@ -406,7 +469,7 @@ class FeiShuChanel(ChatChannel):
        """
        try:
            import subprocess
-            
+
            # 使用 ffprobe 获取视频时长
            cmd = [
                'ffprobe',
@@ -415,7 +478,7 @@ class FeiShuChanel(ChatChannel):
                '-of', 'default=noprint_wrappers=1:nokey=1',
                file_path
            ]
-            
+
            result = subprocess.run(cmd, capture_output=True, text=True, timeout=10)
            if result.returncode == 0:
                duration_seconds = float(result.stdout.strip())
@@ -444,7 +507,7 @@ class FeiShuChanel(ChatChannel):
        """
        local_path = None
        temp_file = None
-        
+
        try:
            # For file:// URLs (local files), upload directly
            if video_url.startswith("file://"):
@@ -459,65 +522,67 @@ class FeiShuChanel(ChatChannel):
                if response.status_code != 200:
                    logger.error(f"[FeiShu] download video failed, status={response.status_code}")
                    return None
-                
+
                # Save to temp file
                import uuid
                file_name = os.path.basename(video_url) or "video.mp4"
                temp_file = str(uuid.uuid4()) + "_" + file_name
-                
+
                with open(temp_file, "wb") as file:
                    file.write(response.content)
-                
+
                logger.info(f"[FeiShu] Video downloaded, size={len(response.content)} bytes")
                local_path = temp_file
-            
+
            # Get video duration
            duration = self._get_video_duration(local_path)
-            
+
            # Upload to Feishu
            file_name = os.path.basename(local_path)
            file_ext = os.path.splitext(file_name)[1].lower()
            file_type_map = {'.mp4': 'mp4'}
            file_type = file_type_map.get(file_ext, 'mp4')
-            
+
            upload_url = "https://open.feishu.cn/open-apis/im/v1/files"
            data = {
-                'file_type': file_type, 
+                'file_type': file_type,
                'file_name': file_name
            }
            # Add duration only if available (required for video/audio)
            if duration:
                data['duration'] = duration  # Must be int, not string
-            
+
            headers = {'Authorization': f'Bearer {access_token}'}
-            
+
            logger.info(f"[FeiShu] Uploading video: file_name={file_name}, duration={duration}ms")
-            
+
            with open(local_path, "rb") as file:
                upload_response = requests.post(
-                    upload_url, 
-                    files={"file": file}, 
-                    data=data, 
-                    headers=headers, 
+                    upload_url,
+                    files={"file": file},
+                    data=data,
+                    headers=headers,
                    timeout=(5, 60)
                )
-                logger.info(f"[FeiShu] upload video response, status={upload_response.status_code}, res={upload_response.content}")
-                
+                logger.info(
+                    f"[FeiShu] upload video response, status={upload_response.status_code}, res={upload_response.content}")
+
                response_data = upload_response.json()
                if response_data.get("code") == 0:
                    # Add duration to the response data (API doesn't return it)
                    upload_data = response_data.get("data")
                    upload_data['duration'] = duration  # Add our calculated duration
-                    logger.info(f"[FeiShu] Upload complete: file_key={upload_data.get('file_key')}, duration={duration}ms")
+                    logger.info(
+                        f"[FeiShu] Upload complete: file_key={upload_data.get('file_key')}, duration={duration}ms")
                    return upload_data
                else:
                    logger.error(f"[FeiShu] upload video failed: {response_data}")
                    return None
-        
+
        except Exception as e:
            logger.error(f"[FeiShu] upload video exception: {e}")
            return None
-        
+
        finally:
            # Clean up temp file
            if temp_file and os.path.exists(temp_file):
@@ -532,20 +597,20 @@ class FeiShuChanel(ChatChannel):
        Supports both local files (file://) and HTTP URLs
        """
        logger.debug(f"[FeiShu] start process file, file_url={file_url}")
-        
+
        # Check if it's a local file path (file:// protocol)
        if file_url.startswith("file://"):
            local_path = file_url[7:]  # Remove "file://" prefix
            logger.info(f"[FeiShu] uploading local file: {local_path}")
-            
+
            if not os.path.exists(local_path):
                logger.error(f"[FeiShu] local file not found: {local_path}")
                return None
-            
+
            # Get file info
            file_name = os.path.basename(local_path)
            file_ext = os.path.splitext(file_name)[1].lower()
-            
+
            # Determine file type for Feishu API
            # Feishu supports: opus, mp4, pdf, doc, xls, ppt, stream (other types)
            file_type_map = {
@@ -557,23 +622,24 @@ class FeiShuChanel(ChatChannel):
                '.ppt': 'ppt', '.pptx': 'ppt',
            }
            file_type = file_type_map.get(file_ext, 'stream')  # Default to stream for other types
-            
+
            # Upload file to Feishu
            upload_url = "https://open.feishu.cn/open-apis/im/v1/files"
            data = {'file_type': file_type, 'file_name': file_name}
            headers = {'Authorization': f'Bearer {access_token}'}
-            
+
            try:
                with open(local_path, "rb") as file:
                    upload_response = requests.post(
-                        upload_url, 
-                        files={"file": file}, 
-                        data=data, 
+                        upload_url,
+                        files={"file": file},
+                        data=data,
                        headers=headers,
                        timeout=(5, 30)  # 5s connect, 30s read timeout
                    )
-                    logger.info(f"[FeiShu] upload file response, status={upload_response.status_code}, res={upload_response.content}")
-                    
+                    logger.info(
+                        f"[FeiShu] upload file response, status={upload_response.status_code}, res={upload_response.content}")
+
                    response_data = upload_response.json()
                    if response_data.get("code") == 0:
                        return response_data.get("data").get("file_key")
@@ -583,22 +649,22 @@ class FeiShuChanel(ChatChannel):
            except Exception as e:
                logger.error(f"[FeiShu] upload file exception: {e}")
                return None
-        
+
        # For HTTP URLs, download first then upload
        try:
            response = requests.get(file_url, timeout=(5, 30))
            if response.status_code != 200:
                logger.error(f"[FeiShu] download file failed, status={response.status_code}")
                return None
-            
+
            # Save to temp file
            import uuid
            file_name = os.path.basename(file_url)
            temp_name = str(uuid.uuid4()) + "_" + file_name
-            
+
            with open(temp_name, "wb") as file:
                file.write(response.content)
-            
+
            # Upload
            file_ext = os.path.splitext(file_name)[1].lower()
            file_type_map = {
@@ -608,18 +674,18 @@ class FeiShuChanel(ChatChannel):
                '.ppt': 'ppt', '.pptx': 'ppt',
            }
            file_type = file_type_map.get(file_ext, 'stream')
-            
+
            upload_url = "https://open.feishu.cn/open-apis/im/v1/files"
            data = {'file_type': file_type, 'file_name': file_name}
            headers = {'Authorization': f'Bearer {access_token}'}
-            
+
            with open(temp_name, "rb") as file:
                upload_response = requests.post(upload_url, files={"file": file}, data=data, headers=headers)
                logger.info(f"[FeiShu] upload file, res={upload_response.content}")
-                
+
                response_data = upload_response.json()
                os.remove(temp_name)  # Clean up temp file
-                
+
                if response_data.get("code") == 0:
                    return response_data.get("data").get("file_key")
                else:
@@ -632,11 +698,13 @@ class FeiShuChanel(ChatChannel):
    def _compose_context(self, ctype: ContextType, content, **kwargs):
        context = Context(ctype, content)
        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
        if "origin_ctype" not in context:
            context["origin_ctype"] = ctype

        cmsg = context["msg"]
-        
+
        # Set session_id based on chat type
        if cmsg.is_group:
            # Group chat: check if group_shared_session is enabled
@@ -652,7 +720,7 @@ class FeiShuChanel(ChatChannel):
        else:
            # Private chat: use user_id only
            context["session_id"] = cmsg.from_user_id
-        
+
        context["receiver"] = cmsg.other_user_id

        if ctype == ContextType.TEXT:
--- a/channel/feishu/feishu_message.py
+++ b/channel/feishu/feishu_message.py
@@ -6,6 +6,7 @@ import requests
 from common.log import logger
 from common.tmp_dir import TmpDir
 from common import utils
+from common.utils import expand_path
 from config import conf


@@ -31,7 +32,7 @@ class FeishuMessage(ChatMessage):
            image_key = content.get("image_key")
            
            # 下载图片到工作空间临时目录
-            workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+            workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
            tmp_dir = os.path.join(workspace_root, "tmp")
            os.makedirs(tmp_dir, exist_ok=True)
            image_path = os.path.join(tmp_dir, f"{image_key}.png")
@@ -97,7 +98,7 @@ class FeishuMessage(ChatMessage):
            
            if image_keys:
                # 如果包含图片，下载并在文本中引用本地路径
-                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
                tmp_dir = os.path.join(workspace_root, "tmp")
                os.makedirs(tmp_dir, exist_ok=True)
                
--- a/channel/web/chat.html
+++ b/channel/web/chat.html
--- a/channel/web/static/css/console.css
+++ b/channel/web/static/css/console.css
@@ -0,0 +1,239 @@
+/* =====================================================================
+   CowAgent Console Styles
+   ===================================================================== */
+
+/* Animations */
+@keyframes pulseDot {
+    0%, 80%, 100% { transform: scale(0.6); opacity: 0.4; }
+    40% { transform: scale(1); opacity: 1; }
+}
+
+/* Scrollbar */
+* { scrollbar-width: thin; scrollbar-color: #94a3b8 transparent; }
+::-webkit-scrollbar { width: 6px; height: 6px; }
+::-webkit-scrollbar-track { background: transparent; }
+::-webkit-scrollbar-thumb { background: #94a3b8; border-radius: 3px; }
+::-webkit-scrollbar-thumb:hover { background: #64748b; }
+.dark ::-webkit-scrollbar-thumb { background: #475569; }
+.dark ::-webkit-scrollbar-thumb:hover { background: #64748b; }
+
+/* Sidebar */
+.sidebar-item.active {
+    background: rgba(255, 255, 255, 0.08);
+    color: #FFFFFF;
+}
+.sidebar-item.active .item-icon { color: #4ABE6E; }
+
+/* Menu Groups */
+.menu-group-items { max-height: 0; overflow: hidden; transition: max-height 0.25s ease-out; }
+.menu-group.open .menu-group-items { max-height: 500px; transition: max-height 0.35s ease-in; }
+.menu-group .chevron { transition: transform 0.25s ease; }
+.menu-group.open .chevron { transform: rotate(90deg); }
+
+/* View Switching */
+.view { display: none; height: 100%; }
+.view.active { display: flex; flex-direction: column; }
+
+/* Markdown Content */
+.msg-content p { margin: 0.5em 0; line-height: 1.7; }
+.msg-content p:first-child { margin-top: 0; }
+.msg-content p:last-child { margin-bottom: 0; }
+.msg-content h1, .msg-content h2, .msg-content h3,
+.msg-content h4, .msg-content h5, .msg-content h6 {
+    margin-top: 1.2em; margin-bottom: 0.6em; font-weight: 600; line-height: 1.3;
+}
+.msg-content h1 { font-size: 1.4em; }
+.msg-content h2 { font-size: 1.25em; }
+.msg-content h3 { font-size: 1.1em; }
+.msg-content ul, .msg-content ol { margin: 0.5em 0; padding-left: 1.8em; }
+.msg-content li { margin: 0.25em 0; }
+.msg-content pre {
+    border-radius: 8px; overflow-x: auto; margin: 0.8em 0;
+    background: #f1f5f9; padding: 1em;
+}
+.dark .msg-content pre { background: #111111; }
+.msg-content code {
+    font-family: 'JetBrains Mono', 'Fira Code', Consolas, monospace;
+    font-size: 0.875em;
+}
+.msg-content :not(pre) > code {
+    background: rgba(74, 190, 110, 0.1); color: #1C6B3B;
+    padding: 2px 6px; border-radius: 4px;
+}
+.dark .msg-content :not(pre) > code {
+    background: rgba(74, 190, 110, 0.15); color: #74E9A4;
+}
+.msg-content pre code { background: transparent; padding: 0; color: inherit; }
+.msg-content blockquote {
+    border-left: 3px solid #4ABE6E; padding: 0.5em 1em;
+    margin: 0.8em 0; background: rgba(74, 190, 110, 0.05); border-radius: 0 6px 6px 0;
+}
+.dark .msg-content blockquote { background: rgba(74, 190, 110, 0.08); }
+.msg-content table { border-collapse: collapse; width: 100%; margin: 0.8em 0; }
+.msg-content th, .msg-content td {
+    border: 1px solid #e2e8f0; padding: 8px 12px; text-align: left;
+}
+.dark .msg-content th, .dark .msg-content td { border-color: rgba(255,255,255,0.1); }
+.msg-content th { background: #f1f5f9; font-weight: 600; }
+.dark .msg-content th { background: #111111; }
+.msg-content img { max-width: 100%; height: auto; border-radius: 8px; margin: 0.5em 0; }
+.msg-content a { color: #35A85B; text-decoration: underline; }
+.msg-content a:hover { color: #228547; }
+.msg-content hr { border: none; height: 1px; background: #e2e8f0; margin: 1.2em 0; }
+.dark .msg-content hr { background: rgba(255,255,255,0.1); }
+
+/* SSE Streaming cursor */
+@keyframes blink { 0%, 100% { opacity: 1; } 50% { opacity: 0; } }
+.sse-streaming::after {
+    content: '▋';
+    display: inline-block;
+    margin-left: 2px;
+    color: #4ABE6E;
+    animation: blink 0.9s step-end infinite;
+    font-size: 0.85em;
+    vertical-align: middle;
+}
+
+/* Agent steps (thinking summaries + tool indicators) */
+.agent-steps:empty { display: none; }
+.agent-steps:not(:empty) {
+    margin-bottom: 0.625rem;
+    padding-bottom: 0.5rem;
+    border-bottom: 1px dashed rgba(0, 0, 0, 0.08);
+}
+.dark .agent-steps:not(:empty) { border-bottom-color: rgba(255, 255, 255, 0.08); }
+
+.agent-step {
+    font-size: 0.75rem;
+    line-height: 1.4;
+    color: #94a3b8;
+    margin-bottom: 0.25rem;
+}
+.agent-step:last-child { margin-bottom: 0; }
+
+/* Thinking step - collapsible */
+.agent-thinking-step .thinking-header {
+    display: flex;
+    align-items: center;
+    gap: 0.375rem;
+    cursor: pointer;
+    user-select: none;
+}
+.agent-thinking-step .thinking-header.no-toggle { cursor: default; }
+.agent-thinking-step .thinking-header:not(.no-toggle):hover { color: #64748b; }
+.dark .agent-thinking-step .thinking-header:not(.no-toggle):hover { color: #cbd5e1; }
+.agent-thinking-step .thinking-header i:first-child { font-size: 0.625rem; margin-top: 1px; }
+.agent-thinking-step .thinking-chevron {
+    font-size: 0.5rem;
+    margin-left: auto;
+    transition: transform 0.2s ease;
+    opacity: 0.5;
+}
+.agent-thinking-step.expanded .thinking-chevron { transform: rotate(90deg); }
+.agent-thinking-step .thinking-full {
+    display: none;
+    margin-top: 0.375rem;
+    margin-left: 1rem;
+    padding: 0.5rem;
+    background: rgba(0, 0, 0, 0.02);
+    border-radius: 6px;
+    border: 1px solid rgba(0, 0, 0, 0.04);
+    font-size: 0.75rem;
+    line-height: 1.5;
+    color: #94a3b8;
+    max-height: 200px;
+    overflow-y: auto;
+}
+.dark .agent-thinking-step .thinking-full {
+    background: rgba(255, 255, 255, 0.02);
+    border-color: rgba(255, 255, 255, 0.04);
+}
+.agent-thinking-step.expanded .thinking-full { display: block; }
+.agent-thinking-step .thinking-full p { margin: 0.25em 0; }
+.agent-thinking-step .thinking-full p:first-child { margin-top: 0; }
+.agent-thinking-step .thinking-full p:last-child { margin-bottom: 0; }
+
+/* Tool step - collapsible */
+.agent-tool-step .tool-header {
+    display: flex;
+    align-items: center;
+    gap: 0.375rem;
+    cursor: pointer;
+    user-select: none;
+    padding: 1px 0;
+    border-radius: 4px;
+}
+.agent-tool-step .tool-header:hover { color: #64748b; }
+.dark .agent-tool-step .tool-header:hover { color: #cbd5e1; }
+.agent-tool-step .tool-icon { font-size: 0.625rem; }
+.agent-tool-step .tool-chevron {
+    font-size: 0.5rem;
+    margin-left: auto;
+    transition: transform 0.2s ease;
+    opacity: 0.5;
+}
+.agent-tool-step.expanded .tool-chevron { transform: rotate(90deg); }
+.agent-tool-step .tool-time {
+    font-size: 0.65rem;
+    opacity: 0.6;
+    margin-left: 0.25rem;
+}
+
+/* Tool detail panel */
+.agent-tool-step .tool-detail {
+    display: none;
+    margin-top: 0.375rem;
+    margin-left: 1rem;
+    padding: 0.5rem;
+    background: rgba(0, 0, 0, 0.02);
+    border-radius: 6px;
+    border: 1px solid rgba(0, 0, 0, 0.04);
+}
+.dark .agent-tool-step .tool-detail {
+    background: rgba(255, 255, 255, 0.02);
+    border-color: rgba(255, 255, 255, 0.04);
+}
+.agent-tool-step.expanded .tool-detail { display: block; }
+.tool-detail-section { margin-bottom: 0.375rem; }
+.tool-detail-section:last-child { margin-bottom: 0; }
+.tool-detail-label {
+    font-size: 0.625rem;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+    opacity: 0.6;
+    margin-bottom: 0.125rem;
+}
+.tool-detail-content {
+    font-family: 'JetBrains Mono', 'Fira Code', Consolas, monospace;
+    font-size: 0.7rem;
+    line-height: 1.5;
+    white-space: pre-wrap;
+    word-break: break-all;
+    max-height: 200px;
+    overflow-y: auto;
+    margin: 0;
+    padding: 0.25rem 0;
+    background: transparent;
+    color: inherit;
+}
+.tool-error-text { color: #f87171; }
+
+/* Tool failed state */
+.agent-tool-step.tool-failed .tool-name { color: #f87171; }
+
+/* Chat Input */
+#chat-input {
+    resize: none; height: 42px; max-height: 180px;
+    overflow-y: hidden;
+    transition: border-color 0.2s ease;
+}
+
+/* Placeholder Cards */
+.placeholder-card {
+    transition: transform 0.2s ease, box-shadow 0.2s ease;
+}
+.placeholder-card:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);
+}
--- a/channel/web/static/js/console.js
+++ b/channel/web/static/js/console.js
--- a/channel/web/web_channel.py
+++ b/channel/web/web_channel.py
@@ -1,10 +1,15 @@
-import sys
 import time
-import web
 import json
+import logging
+import mimetypes
+import os
+import threading
+import time
 import uuid
-import io
 from queue import Queue, Empty
+
+import web
+
 from bridge.context import *
 from bridge.reply import Reply, ReplyType
 from channel.chat_channel import ChatChannel, check_prefix
@@ -12,20 +17,17 @@ from channel.chat_message import ChatMessage
 from common.log import logger
 from common.singleton import singleton
 from config import conf
-import os
-import mimetypes  # 添加这行来处理MIME类型
-import threading
-import logging
+

 class WebMessage(ChatMessage):
    def __init__(
-        self,
-        msg_id,
-        content,
-        ctype=ContextType.TEXT,
-        from_user_id="User",
-        to_user_id="Chatgpt",
-        other_user_id="Chatgpt",
+            self,
+            msg_id,
+            content,
+            ctype=ContextType.TEXT,
+            from_user_id="User",
+            to_user_id="Chatgpt",
+            other_user_id="Chatgpt",
    ):
        self.msg_id = msg_id
        self.ctype = ctype
@@ -39,7 +41,7 @@ class WebMessage(ChatMessage):
 class WebChannel(ChatChannel):
    NOT_SUPPORT_REPLYTYPE = [ReplyType.VOICE]
    _instance = None
-    
+
    # def __new__(cls):
    #     if cls._instance is None:
    #         cls._instance = super(WebChannel, cls).__new__(cls)
@@ -47,10 +49,11 @@ class WebChannel(ChatChannel):

    def __init__(self):
        super().__init__()
-        self.msg_id_counter = 0  # 添加消息ID计数器
-        self.session_queues = {}  # 存储session_id到队列的映射
-        self.request_to_session = {}  # 存储request_id到session_id的映射
-
+        self.msg_id_counter = 0
+        self.session_queues = {}  # session_id -> Queue (fallback polling)
+        self.request_to_session = {}  # request_id -> session_id
+        self.sse_queues = {}  # request_id -> Queue (SSE streaming)
+        self._http_server = None

    def _generate_msg_id(self):
        """生成唯一的消息ID"""
@@ -70,22 +73,30 @@ class WebChannel(ChatChannel):
            if reply.type == ReplyType.IMAGE_URL:
                time.sleep(0.5)

-            # 获取请求ID和会话ID
            request_id = context.get("request_id", None)
-            
            if not request_id:
                logger.error("No request_id found in context, cannot send message")
                return
-                
-            # 通过request_id获取session_id
+
            session_id = self.request_to_session.get(request_id)
            if not session_id:
                logger.error(f"No session_id found for request {request_id}")
                return
-            
-            # 检查是否有会话队列
+
+            # SSE mode: push done event to SSE queue
+            if request_id in self.sse_queues:
+                content = reply.content if reply.content is not None else ""
+                self.sse_queues[request_id].put({
+                    "type": "done",
+                    "content": content,
+                    "request_id": request_id,
+                    "timestamp": time.time()
+                })
+                logger.debug(f"SSE done sent for request {request_id}")
+                return
+
+            # Fallback: polling mode
            if session_id in self.session_queues:
-                # 创建响应数据，包含请求ID以区分不同请求的响应
                response_data = {
                    "type": str(reply.type),
                    "content": reply.content,
@@ -93,69 +104,134 @@ class WebChannel(ChatChannel):
                    "request_id": request_id
                }
                self.session_queues[session_id].put(response_data)
-                logger.debug(f"Response sent to queue for session {session_id}, request {request_id}")
+                logger.debug(f"Response sent to poll queue for session {session_id}, request {request_id}")
            else:
                logger.warning(f"No response queue found for session {session_id}, response dropped")
-            
+
        except Exception as e:
            logger.error(f"Error in send method: {e}")

+    def _make_sse_callback(self, request_id: str):
+        """Build an on_event callback that pushes agent stream events into the SSE queue."""
+
+        def on_event(event: dict):
+            if request_id not in self.sse_queues:
+                return
+            q = self.sse_queues[request_id]
+            event_type = event.get("type")
+            data = event.get("data", {})
+
+            if event_type == "message_update":
+                delta = data.get("delta", "")
+                if delta:
+                    q.put({"type": "delta", "content": delta})
+
+            elif event_type == "tool_execution_start":
+                tool_name = data.get("tool_name", "tool")
+                arguments = data.get("arguments", {})
+                q.put({"type": "tool_start", "tool": tool_name, "arguments": arguments})
+
+            elif event_type == "tool_execution_end":
+                tool_name = data.get("tool_name", "tool")
+                status = data.get("status", "success")
+                result = data.get("result", "")
+                exec_time = data.get("execution_time", 0)
+                # Truncate long results to avoid huge SSE payloads
+                result_str = str(result)
+                if len(result_str) > 2000:
+                    result_str = result_str[:2000] + "…"
+                q.put({
+                    "type": "tool_end",
+                    "tool": tool_name,
+                    "status": status,
+                    "result": result_str,
+                    "execution_time": round(exec_time, 2)
+                })
+
+        return on_event
+
    def post_message(self):
        """
        Handle incoming messages from users via POST request.
        Returns a request_id for tracking this specific request.
        """
        try:
-            data = web.data()  # 获取原始POST数据
+            data = web.data()
            json_data = json.loads(data)
            session_id = json_data.get('session_id', f'session_{int(time.time())}')
            prompt = json_data.get('message', '')
-            
-            # 生成请求ID
+            use_sse = json_data.get('stream', True)
+
            request_id = self._generate_request_id()
-            
-            # 将请求ID与会话ID关联
            self.request_to_session[request_id] = session_id
-            
-            # 确保会话队列存在
+
            if session_id not in self.session_queues:
                self.session_queues[session_id] = Queue()
-            
-            # Web channel 不需要前缀，确保消息能通过前缀检查
+
+            if use_sse:
+                self.sse_queues[request_id] = Queue()
+
            trigger_prefixs = conf().get("single_chat_prefix", [""])
            if check_prefix(prompt, trigger_prefixs) is None:
-                # 如果没有匹配到前缀，给消息加上第一个前缀
                if trigger_prefixs:
                    prompt = trigger_prefixs[0] + prompt
                    logger.debug(f"[WebChannel] Added prefix to message: {prompt}")
-            
-            # 创建消息对象
+
            msg = WebMessage(self._generate_msg_id(), prompt)
-            msg.from_user_id = session_id  # 使用会话ID作为用户ID
-            
-            # 创建上下文，明确指定 isgroup=False
+            msg.from_user_id = session_id
+
            context = self._compose_context(ContextType.TEXT, prompt, msg=msg, isgroup=False)
-            
-            # 检查 context 是否为 None（可能被插件过滤等）
+
            if context is None:
                logger.warning(f"[WebChannel] Context is None for session {session_id}, message may be filtered")
+                if request_id in self.sse_queues:
+                    del self.sse_queues[request_id]
                return json.dumps({"status": "error", "message": "Message was filtered"})

-            # 覆盖必要的字段（_compose_context 会设置默认值，但我们需要使用实际的 session_id）
            context["session_id"] = session_id
            context["receiver"] = session_id
            context["request_id"] = request_id
-            
-            # 异步处理消息 - 只传递上下文
+
+            if use_sse:
+                context["on_event"] = self._make_sse_callback(request_id)
+
            threading.Thread(target=self.produce, args=(context,)).start()
-            
-            # 返回请求ID
-            return json.dumps({"status": "success", "request_id": request_id})
-            
+
+            return json.dumps({"status": "success", "request_id": request_id, "stream": use_sse})
+
        except Exception as e:
            logger.error(f"Error processing message: {e}")
            return json.dumps({"status": "error", "message": str(e)})

+    def stream_response(self, request_id: str):
+        """
+        SSE generator for a given request_id.
+        Yields UTF-8 encoded bytes to avoid WSGI Latin-1 mangling.
+        """
+        if request_id not in self.sse_queues:
+            yield b"data: {\"type\": \"error\", \"message\": \"invalid request_id\"}\n\n"
+            return
+
+        q = self.sse_queues[request_id]
+        timeout = 300  # 5 minutes max
+        deadline = time.time() + timeout
+
+        try:
+            while time.time() < deadline:
+                try:
+                    item = q.get(timeout=1)
+                except Empty:
+                    yield b": keepalive\n\n"
+                    continue
+
+                payload = json.dumps(item, ensure_ascii=False)
+                yield f"data: {payload}\n\n".encode("utf-8")
+
+                if item.get("type") == "done":
+                    break
+        finally:
+            self.sse_queues.pop(request_id, None)
+
    def poll_response(self):
        """
        Poll for responses using the session_id.
@@ -164,28 +240,28 @@ class WebChannel(ChatChannel):
            data = web.data()
            json_data = json.loads(data)
            session_id = json_data.get('session_id')
-            
+
            if not session_id or session_id not in self.session_queues:
                return json.dumps({"status": "error", "message": "Invalid session ID"})
-            
+
            # 尝试从队列获取响应，不等待
            try:
                # 使用peek而不是get，这样如果前端没有成功处理，下次还能获取到
                response = self.session_queues[session_id].get(block=False)
-                
+
                # 返回响应，包含请求ID以区分不同请求
                return json.dumps({
-                    "status": "success", 
+                    "status": "success",
                    "has_content": True,
                    "content": response["content"],
                    "request_id": response["request_id"],
                    "timestamp": response["timestamp"]
                })
-                
+
            except Empty:
                # 没有新响应
                return json.dumps({"status": "success", "has_content": False})
-                
+
        except Exception as e:
            logger.error(f"Error polling response: {e}")
            return json.dumps({"status": "error", "message": str(e)})
@@ -198,9 +274,10 @@ class WebChannel(ChatChannel):

    def startup(self):
        port = conf().get("web_port", 9899)
-        
+
        # 打印可用渠道类型提示
-        logger.info("[WebChannel] 当前channel为web，可修改 config.json 配置文件中的 channel_type 字段进行切换。全部可用类型为：")
+        logger.info(
+            "[WebChannel] 全部可用通道如下，可修改 config.json 配置文件中的 channel_type 字段进行切换，多个通道用逗号分隔：")
        logger.info("[WebChannel]   1. web              - 网页")
        logger.info("[WebChannel]   2. terminal         - 终端")
        logger.info("[WebChannel]   3. feishu           - 飞书")
@@ -208,40 +285,58 @@ class WebChannel(ChatChannel):
        logger.info("[WebChannel]   5. wechatcom_app    - 企微自建应用")
        logger.info("[WebChannel]   6. wechatmp         - 个人公众号")
        logger.info("[WebChannel]   7. wechatmp_service - 企业公众号")
-        logger.info(f"[WebChannel] 🌐 本地访问: http://localhost:{port}/chat")
-        logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port}/chat (请将YOUR_IP替换为服务器IP)")
-        logger.info("[WebChannel] ✅ Web对话网页已运行")
-        
+        logger.info("[WebChannel] ✅ Web控制台已运行")
+        logger.info(f"[WebChannel] 🌐 本地访问: http://localhost:{port}")
+        logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port} (请将YOUR_IP替换为服务器IP)")
+
        # 确保静态文件目录存在
        static_dir = os.path.join(os.path.dirname(__file__), 'static')
        if not os.path.exists(static_dir):
            os.makedirs(static_dir)
            logger.debug(f"[WebChannel] Created static directory: {static_dir}")
-        
+
        urls = (
            '/', 'RootHandler',
            '/message', 'MessageHandler',
            '/poll', 'PollHandler',
+            '/stream', 'StreamHandler',
            '/chat', 'ChatHandler',
            '/config', 'ConfigHandler',
+            '/api/skills', 'SkillsHandler',
+            '/api/memory', 'MemoryHandler',
+            '/api/memory/content', 'MemoryContentHandler',
+            '/api/scheduler', 'SchedulerHandler',
+            '/api/history', 'HistoryHandler',
+            '/api/logs', 'LogsHandler',
            '/assets/(.*)', 'AssetsHandler',
        )
        app = web.application(urls, globals(), autoreload=False)
-        
+
        # 完全禁用web.py的HTTP日志输出
        web.httpserver.LogMiddleware.log = lambda self, status, environ: None
-        
+
        # 配置web.py的日志级别为ERROR
        logging.getLogger("web").setLevel(logging.ERROR)
        logging.getLogger("web.httpserver").setLevel(logging.ERROR)
-        
-        # 抑制 web.py 默认的服务器启动消息
-        old_stdout = sys.stdout
-        sys.stdout = io.StringIO()
+
+        # Build WSGI app with middleware (same as runsimple but without print)
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
        try:
-            web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
-        finally:
-            sys.stdout = old_stdout
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
+
+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[WebChannel] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[WebChannel] Error stopping HTTP server: {e}")
+            self._http_server = None


 class RootHandler:
@@ -260,6 +355,21 @@ class PollHandler:
        return WebChannel().poll_response()


+class StreamHandler:
+    def GET(self):
+        params = web.input(request_id='')
+        request_id = params.request_id
+        if not request_id:
+            raise web.badrequest()
+
+        web.header('Content-Type', 'text/event-stream; charset=utf-8')
+        web.header('Cache-Control', 'no-cache')
+        web.header('X-Accel-Buffering', 'no')
+        web.header('Access-Control-Allow-Origin', '*')
+
+        return WebChannel().stream_response(request_id)
+
+
 class ChatHandler:
    def GET(self):
        # 正常返回聊天页面
@@ -270,28 +380,181 @@ class ChatHandler:

 class ConfigHandler:
    def GET(self):
-        """返回前端需要的配置信息"""
+        """Return configuration info for the web console."""
        try:
-            use_agent = conf().get("agent", False)
-            
+            local_config = conf()
+            use_agent = local_config.get("agent", False)
+
            if use_agent:
                title = "CowAgent"
-                subtitle = "我可以帮你解答问题、管理计算机、创造和执行技能，并通过长期记忆不断成长"
            else:
-                title = "AI 助手"
-                subtitle = "我可以回答问题、提供信息或者帮助您完成各种任务"
-            
+                title = "AI Assistant"
+
            return json.dumps({
                "status": "success",
                "use_agent": use_agent,
                "title": title,
-                "subtitle": subtitle
+                "model": local_config.get("model", ""),
+                "channel_type": local_config.get("channel_type", ""),
+                "agent_max_context_tokens": local_config.get("agent_max_context_tokens", ""),
+                "agent_max_context_turns": local_config.get("agent_max_context_turns", ""),
+                "agent_max_steps": local_config.get("agent_max_steps", ""),
            })
        except Exception as e:
            logger.error(f"Error getting config: {e}")
            return json.dumps({"status": "error", "message": str(e)})


+def _get_workspace_root():
+    """Resolve the agent workspace directory."""
+    from common.utils import expand_path
+    return expand_path(conf().get("agent_workspace", "~/cow"))
+
+
+class SkillsHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.skills.service import SkillService
+            from agent.skills.manager import SkillManager
+            workspace_root = _get_workspace_root()
+            manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
+            service = SkillService(manager)
+            skills = service.query()
+            return json.dumps({"status": "success", "skills": skills}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Skills API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class MemoryHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.memory.service import MemoryService
+            params = web.input(page='1', page_size='20')
+            workspace_root = _get_workspace_root()
+            service = MemoryService(workspace_root)
+            result = service.list_files(page=int(params.page), page_size=int(params.page_size))
+            return json.dumps({"status": "success", **result}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Memory API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class MemoryContentHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.memory.service import MemoryService
+            params = web.input(filename='')
+            if not params.filename:
+                return json.dumps({"status": "error", "message": "filename required"})
+            workspace_root = _get_workspace_root()
+            service = MemoryService(workspace_root)
+            result = service.get_content(params.filename)
+            return json.dumps({"status": "success", **result}, ensure_ascii=False)
+        except FileNotFoundError:
+            return json.dumps({"status": "error", "message": "file not found"})
+        except Exception as e:
+            logger.error(f"[WebChannel] Memory content API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class SchedulerHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.tools.scheduler.task_store import TaskStore
+            workspace_root = _get_workspace_root()
+            store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
+            store = TaskStore(store_path)
+            tasks = store.list_tasks()
+            return json.dumps({"status": "success", "tasks": tasks}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Scheduler API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class HistoryHandler:
+    def GET(self):
+        """
+        Return paginated conversation history for a session.
+
+        Query params:
+            session_id  (required)
+            page        int, default 1  (1 = most recent messages)
+            page_size   int, default 20
+        """
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        web.header('Access-Control-Allow-Origin', '*')
+        try:
+            params = web.input(session_id='', page='1', page_size='20')
+            session_id = params.session_id.strip()
+            if not session_id:
+                return json.dumps({"status": "error", "message": "session_id required"})
+
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            result = store.load_history_page(
+                session_id=session_id,
+                page=int(params.page),
+                page_size=int(params.page_size),
+            )
+            return json.dumps({"status": "success", **result}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] History API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class LogsHandler:
+    def GET(self):
+        """Stream the last N lines of run.log as SSE, then tail new lines."""
+        web.header('Content-Type', 'text/event-stream; charset=utf-8')
+        web.header('Cache-Control', 'no-cache')
+        web.header('X-Accel-Buffering', 'no')
+
+        from config import get_root
+        log_path = os.path.join(get_root(), "run.log")
+
+        def generate():
+            if not os.path.isfile(log_path):
+                yield b"data: {\"type\": \"error\", \"message\": \"run.log not found\"}\n\n"
+                return
+
+            # Read last 200 lines for initial display
+            try:
+                with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
+                    lines = f.readlines()
+                tail_lines = lines[-200:]
+                chunk = ''.join(tail_lines)
+                payload = json.dumps({"type": "init", "content": chunk}, ensure_ascii=False)
+                yield f"data: {payload}\n\n".encode('utf-8')
+            except Exception as e:
+                yield f"data: {{\"type\": \"error\", \"message\": \"{e}\"}}\n\n".encode('utf-8')
+                return
+
+            # Tail new lines
+            try:
+                with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
+                    f.seek(0, 2)  # seek to end
+                    deadline = time.time() + 600  # 10 min max
+                    while time.time() < deadline:
+                        line = f.readline()
+                        if line:
+                            payload = json.dumps({"type": "line", "content": line}, ensure_ascii=False)
+                            yield f"data: {payload}\n\n".encode('utf-8')
+                        else:
+                            yield b": keepalive\n\n"
+                            time.sleep(1)
+            except GeneratorExit:
+                return
+            except Exception:
+                return
+
+        return generate()
+
+
 class AssetsHandler:
    def GET(self, file_path):  # 修改默认参数
        try:
--- a/channel/wechat/wechat_channel.py
+++ b/channel/wechat/wechat_channel.py
@@ -151,7 +151,7 @@ class WechatChannel(ChatChannel):

    def exitCallback(self):
        try:
-            from common.linkai_client import chat_client
+            from common.cloud_client import chat_client
            if chat_client.client_id and conf().get("use_linkai"):
                _send_logout()
                time.sleep(2)
@@ -283,7 +283,7 @@ class WechatChannel(ChatChannel):

 def _send_login_success():
    try:
-        from common.linkai_client import chat_client
+        from common.cloud_client import chat_client
        if chat_client.client_id:
            chat_client.send_login_success()
    except Exception as e:
@@ -292,7 +292,7 @@ def _send_login_success():

 def _send_logout():
    try:
-        from common.linkai_client import chat_client
+        from common.cloud_client import chat_client
        if chat_client.client_id:
            chat_client.send_logout()
    except Exception as e:
@@ -301,7 +301,7 @@ def _send_logout():

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

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

    def start_loop(self, loop):
        asyncio.set_event_loop(loop)
--- a/channel/wework/wework_channel.py
+++ b/channel/wework/wework_channel.py
@@ -20,7 +20,6 @@ from common.utils import compress_imgfile, fsize
 from config import conf
 from channel.wework.run import wework
 from channel.wework import run
-from PIL import Image


 def get_wxid_by_name(room_members, group_wxid, name):
@@ -55,6 +54,7 @@ def download_and_compress_image(url, filename, quality=30):
    image_storage.seek(0)

    # 读取并保存图片
+    from PIL import Image
    image = Image.open(image_storage)
    image_path = os.path.join(directory, f"{filename}.png")
    image.save(image_path, "png")
--- a/common/cloud_client.py
+++ b/common/cloud_client.py
@@ -0,0 +1,375 @@
+"""
+Cloud management client for connecting to the LinkAI control console.
+
+Handles remote configuration sync, message push, and skill management
+via the LinkAI socket protocol.
+"""
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from common.log import logger
+from linkai import LinkAIClient, PushMsg
+from config import conf, pconf, plugin_config, available_setting, write_plugin_config, get_root
+from plugins import PluginManager
+import threading
+import time
+import json
+import os
+
+
+chat_client: LinkAIClient
+
+
+class CloudClient(LinkAIClient):
+    def __init__(self, api_key: str, channel, host: str = ""):
+        super().__init__(api_key, host)
+        self.channel = channel
+        self.client_type = channel.channel_type
+        self.channel_mgr = None
+        self._skill_service = None
+        self._memory_service = None
+        self._chat_service = None
+
+    @property
+    def skill_service(self):
+        """Lazy-init SkillService so it is available once SkillManager exists."""
+        if self._skill_service is None:
+            try:
+                from agent.skills.manager import SkillManager
+                from agent.skills.service import SkillService
+                from config import conf
+                from common.utils import expand_path
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+                manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
+                self._skill_service = SkillService(manager)
+                logger.debug("[CloudClient] SkillService initialised")
+            except Exception as e:
+                logger.error(f"[CloudClient] Failed to init SkillService: {e}")
+        return self._skill_service
+
+    @property
+    def memory_service(self):
+        """Lazy-init MemoryService."""
+        if self._memory_service is None:
+            try:
+                from agent.memory.service import MemoryService
+                from config import conf
+                from common.utils import expand_path
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+                self._memory_service = MemoryService(workspace_root)
+                logger.debug("[CloudClient] MemoryService initialised")
+            except Exception as e:
+                logger.error(f"[CloudClient] Failed to init MemoryService: {e}")
+        return self._memory_service
+
+    @property
+    def chat_service(self):
+        """Lazy-init ChatService (requires AgentBridge via Bridge singleton)."""
+        if self._chat_service is None:
+            try:
+                from agent.chat.service import ChatService
+                from bridge.bridge import Bridge
+                agent_bridge = Bridge().get_agent_bridge()
+                self._chat_service = ChatService(agent_bridge)
+                logger.debug("[CloudClient] ChatService initialised")
+            except Exception as e:
+                logger.error(f"[CloudClient] Failed to init ChatService: {e}")
+        return self._chat_service
+
+    # ------------------------------------------------------------------
+    # message push callback
+    # ------------------------------------------------------------------
+    def on_message(self, push_msg: PushMsg):
+        session_id = push_msg.session_id
+        msg_content = push_msg.msg_content
+        logger.info(f"receive msg push, session_id={session_id}, msg_content={msg_content}")
+        context = Context()
+        context.type = ContextType.TEXT
+        context["receiver"] = session_id
+        context["isgroup"] = push_msg.is_group
+        self.channel.send(Reply(ReplyType.TEXT, content=msg_content), context)
+
+    # ------------------------------------------------------------------
+    # config callback
+    # ------------------------------------------------------------------
+    def on_config(self, config: dict):
+        if not self.client_id:
+            return
+        logger.info(f"[CloudClient] Loading remote config: {config}")
+        if config.get("enabled") != "Y":
+            return
+
+        local_config = conf()
+        need_restart_channel = False
+
+        for key in config.keys():
+            if key in available_setting and config.get(key) is not None:
+                local_config[key] = config.get(key)
+
+        # Voice settings
+        reply_voice_mode = config.get("reply_voice_mode")
+        if reply_voice_mode:
+            if reply_voice_mode == "voice_reply_voice":
+                local_config["voice_reply_voice"] = True
+                local_config["always_reply_voice"] = False
+            elif reply_voice_mode == "always_reply_voice":
+                local_config["always_reply_voice"] = True
+                local_config["voice_reply_voice"] = True
+            elif reply_voice_mode == "no_reply_voice":
+                local_config["always_reply_voice"] = False
+                local_config["voice_reply_voice"] = False
+
+        # Model configuration
+        if config.get("model"):
+            local_config["model"] = config.get("model")
+
+        # Channel configuration
+        if config.get("channelType"):
+            if local_config.get("channel_type") != config.get("channelType"):
+                local_config["channel_type"] = config.get("channelType")
+                need_restart_channel = True
+
+        # Channel-specific app credentials
+        current_channel_type = local_config.get("channel_type", "")
+
+        if config.get("app_id") is not None:
+            if current_channel_type == "feishu":
+                if local_config.get("feishu_app_id") != config.get("app_id"):
+                    local_config["feishu_app_id"] = config.get("app_id")
+                    need_restart_channel = True
+            elif current_channel_type == "dingtalk":
+                if local_config.get("dingtalk_client_id") != config.get("app_id"):
+                    local_config["dingtalk_client_id"] = config.get("app_id")
+                    need_restart_channel = True
+            elif current_channel_type in ("wechatmp", "wechatmp_service"):
+                if local_config.get("wechatmp_app_id") != config.get("app_id"):
+                    local_config["wechatmp_app_id"] = config.get("app_id")
+                    need_restart_channel = True
+            elif current_channel_type == "wechatcom_app":
+                if local_config.get("wechatcomapp_agent_id") != config.get("app_id"):
+                    local_config["wechatcomapp_agent_id"] = config.get("app_id")
+                    need_restart_channel = True
+
+        if config.get("app_secret"):
+            if current_channel_type == "feishu":
+                if local_config.get("feishu_app_secret") != config.get("app_secret"):
+                    local_config["feishu_app_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+            elif current_channel_type == "dingtalk":
+                if local_config.get("dingtalk_client_secret") != config.get("app_secret"):
+                    local_config["dingtalk_client_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+            elif current_channel_type in ("wechatmp", "wechatmp_service"):
+                if local_config.get("wechatmp_app_secret") != config.get("app_secret"):
+                    local_config["wechatmp_app_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+            elif current_channel_type == "wechatcom_app":
+                if local_config.get("wechatcomapp_secret") != config.get("app_secret"):
+                    local_config["wechatcomapp_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+
+        if config.get("admin_password"):
+            if not pconf("Godcmd"):
+                write_plugin_config({"Godcmd": {"password": config.get("admin_password"), "admin_users": []}})
+            else:
+                pconf("Godcmd")["password"] = config.get("admin_password")
+            PluginManager().instances["GODCMD"].reload()
+
+        if config.get("group_app_map") and pconf("linkai"):
+            local_group_map = {}
+            for mapping in config.get("group_app_map"):
+                local_group_map[mapping.get("group_name")] = mapping.get("app_code")
+            pconf("linkai")["group_app_map"] = local_group_map
+            PluginManager().instances["LINKAI"].reload()
+
+        if config.get("text_to_image") and config.get("text_to_image") == "midjourney" and pconf("linkai"):
+            if pconf("linkai")["midjourney"]:
+                pconf("linkai")["midjourney"]["enabled"] = True
+                pconf("linkai")["midjourney"]["use_image_create_prefix"] = True
+        elif config.get("text_to_image") and config.get("text_to_image") in ["dall-e-2", "dall-e-3"]:
+            if pconf("linkai")["midjourney"]:
+                pconf("linkai")["midjourney"]["use_image_create_prefix"] = False
+
+        # Save configuration to config.json file
+        self._save_config_to_file(local_config)
+
+        if need_restart_channel:
+            self._restart_channel(local_config.get("channel_type", ""))
+
+    # ------------------------------------------------------------------
+    # skill callback
+    # ------------------------------------------------------------------
+    def on_skill(self, data: dict) -> dict:
+        """
+        Handle SKILL messages from the cloud console.
+        Delegates to SkillService.dispatch for the actual operations.
+
+        :param data: message data with 'action', 'clientId', 'payload'
+        :return: response dict
+        """
+        action = data.get("action", "")
+        payload = data.get("payload")
+        logger.info(f"[CloudClient] on_skill: action={action}")
+
+        svc = self.skill_service
+        if svc is None:
+            return {"action": action, "code": 500, "message": "SkillService not available", "payload": None}
+
+        return svc.dispatch(action, payload)
+
+    # ------------------------------------------------------------------
+    # memory callback
+    # ------------------------------------------------------------------
+    def on_memory(self, data: dict) -> dict:
+        """
+        Handle MEMORY messages from the cloud console.
+        Delegates to MemoryService.dispatch for the actual operations.
+
+        :param data: message data with 'action', 'clientId', 'payload'
+        :return: response dict
+        """
+        action = data.get("action", "")
+        payload = data.get("payload")
+        logger.info(f"[CloudClient] on_memory: action={action}")
+
+        svc = self.memory_service
+        if svc is None:
+            return {"action": action, "code": 500, "message": "MemoryService not available", "payload": None}
+
+        return svc.dispatch(action, payload)
+
+    # ------------------------------------------------------------------
+    # chat callback
+    # ------------------------------------------------------------------
+    def on_chat(self, data: dict, send_chunk_fn):
+        """
+        Handle CHAT messages from the cloud console.
+        Runs the agent in streaming mode and sends chunks back via send_chunk_fn.
+
+        :param data: message data with 'action' and 'payload' (query, session_id)
+        :param send_chunk_fn: callable(chunk_data: dict) to send one streaming chunk
+        """
+        payload = data.get("payload", {})
+        query = payload.get("query", "")
+        session_id = payload.get("session_id", "cloud_console")
+        logger.info(f"[CloudClient] on_chat: session={session_id}, query={query[:80]}")
+
+        svc = self.chat_service
+        if svc is None:
+            raise RuntimeError("ChatService not available")
+
+        svc.run(query=query, session_id=session_id, send_chunk_fn=send_chunk_fn)
+
+    # ------------------------------------------------------------------
+    # channel restart helpers
+    # ------------------------------------------------------------------
+    def _restart_channel(self, new_channel_type: str):
+        """
+        Restart the channel via ChannelManager when channel type changes.
+        """
+        if self.channel_mgr:
+            logger.info(f"[CloudClient] Restarting channel to '{new_channel_type}'...")
+            threading.Thread(target=self._do_restart_channel, args=(self.channel_mgr, new_channel_type), daemon=True).start()
+        else:
+            logger.warning("[CloudClient] ChannelManager not available, please restart the application manually")
+
+    def _do_restart_channel(self, mgr, new_channel_type: str):
+        """
+        Perform the channel restart in a separate thread to avoid blocking the config callback.
+        """
+        try:
+            mgr.restart(new_channel_type)
+            # Update the client's channel reference
+            if mgr.channel:
+                self.channel = mgr.channel
+                self.client_type = mgr.channel.channel_type
+                logger.info(f"[CloudClient] Channel reference updated to '{new_channel_type}'")
+        except Exception as e:
+            logger.error(f"[CloudClient] Channel restart failed: {e}")
+
+    # ------------------------------------------------------------------
+    # config persistence
+    # ------------------------------------------------------------------
+    def _save_config_to_file(self, local_config: dict):
+        """
+        Save configuration to config.json file.
+        """
+        try:
+            config_path = os.path.join(get_root(), "config.json")
+            if not os.path.exists(config_path):
+                logger.warning(f"[CloudClient] config.json not found at {config_path}, skip saving")
+                return
+
+            with open(config_path, "r", encoding="utf-8") as f:
+                file_config = json.load(f)
+
+            file_config.update(dict(local_config))
+
+            with open(config_path, "w", encoding="utf-8") as f:
+                json.dump(file_config, f, indent=4, ensure_ascii=False)
+
+            logger.info("[CloudClient] Configuration saved to config.json successfully")
+        except Exception as e:
+            logger.error(f"[CloudClient] Failed to save configuration to config.json: {e}")
+
+
+def start(channel, channel_mgr=None):
+    global chat_client
+    chat_client = CloudClient(api_key=conf().get("linkai_api_key"), host=conf().get("cloud_host", ""), channel=channel)
+    chat_client.channel_mgr = channel_mgr
+    chat_client.config = _build_config()
+    chat_client.start()
+    time.sleep(1.5)
+    if chat_client.client_id:
+        logger.info("[CloudClient] Console: https://link-ai.tech/console/clients")
+
+
+def _build_config():
+    local_conf = conf()
+    config = {
+        "linkai_app_code": local_conf.get("linkai_app_code"),
+        "single_chat_prefix": local_conf.get("single_chat_prefix"),
+        "single_chat_reply_prefix": local_conf.get("single_chat_reply_prefix"),
+        "single_chat_reply_suffix": local_conf.get("single_chat_reply_suffix"),
+        "group_chat_prefix": local_conf.get("group_chat_prefix"),
+        "group_chat_reply_prefix": local_conf.get("group_chat_reply_prefix"),
+        "group_chat_reply_suffix": local_conf.get("group_chat_reply_suffix"),
+        "group_name_white_list": local_conf.get("group_name_white_list"),
+        "nick_name_black_list": local_conf.get("nick_name_black_list"),
+        "speech_recognition": "Y" if local_conf.get("speech_recognition") else "N",
+        "text_to_image": local_conf.get("text_to_image"),
+        "image_create_prefix": local_conf.get("image_create_prefix"),
+        "model": local_conf.get("model"),
+        "agent_max_context_turns": local_conf.get("agent_max_context_turns"),
+        "agent_max_context_tokens": local_conf.get("agent_max_context_tokens"),
+        "agent_max_steps": local_conf.get("agent_max_steps"),
+        "channelType": local_conf.get("channel_type"),
+    }
+
+    if local_conf.get("always_reply_voice"):
+        config["reply_voice_mode"] = "always_reply_voice"
+    elif local_conf.get("voice_reply_voice"):
+        config["reply_voice_mode"] = "voice_reply_voice"
+
+    if pconf("linkai"):
+        config["group_app_map"] = pconf("linkai").get("group_app_map")
+
+    if plugin_config.get("Godcmd"):
+        config["admin_password"] = plugin_config.get("Godcmd").get("password")
+
+    # Add channel-specific app credentials
+    current_channel_type = local_conf.get("channel_type", "")
+    if current_channel_type == "feishu":
+        config["app_id"] = local_conf.get("feishu_app_id")
+        config["app_secret"] = local_conf.get("feishu_app_secret")
+    elif current_channel_type == "dingtalk":
+        config["app_id"] = local_conf.get("dingtalk_client_id")
+        config["app_secret"] = local_conf.get("dingtalk_client_secret")
+    elif current_channel_type in ("wechatmp", "wechatmp_service"):
+        config["app_id"] = local_conf.get("wechatmp_app_id")
+        config["app_secret"] = local_conf.get("wechatmp_app_secret")
+    elif current_channel_type == "wechatcom_app":
+        config["app_id"] = local_conf.get("wechatcomapp_agent_id")
+        config["app_secret"] = local_conf.get("wechatcomapp_secret")
+
+    return config
--- a/common/const.py
+++ b/common/const.py
@@ -25,8 +25,10 @@ CLAUDE_35_SONNET = "claude-3-5-sonnet-latest"  # 带 latest 标签的模型名
 CLAUDE_35_SONNET_1022 = "claude-3-5-sonnet-20241022"  # 带具体日期的模型名称，会固定为该日期发布的模型
 CLAUDE_35_SONNET_0620 = "claude-3-5-sonnet-20240620"
 CLAUDE_4_OPUS = "claude-opus-4-0"
-CLAUDE_4_SONNET = "claude-sonnet-4-0"    # Claude Sonnet 4.0 - Agent推荐模型
+CLAUDE_4_6_OPUS = "claude-opus-4-6"      # Claude Opus 4.6 - Agent推荐模型
+CLAUDE_4_SONNET = "claude-sonnet-4-0"    # Claude Sonnet 4.0
 CLAUDE_4_5_SONNET = "claude-sonnet-4-5"  # Claude Sonnet 4.5 - Agent推荐模型
+CLAUDE_4_6_SONNET = "claude-sonnet-4-6"  # Claude Sonnet 4.6 - Agent推荐模型

 # Gemini (Google)
 GEMINI_PRO = "gemini-1.0-pro"
@@ -34,10 +36,11 @@ GEMINI_15_flash = "gemini-1.5-flash"
 GEMINI_15_PRO = "gemini-1.5-pro"
 GEMINI_20_flash_exp = "gemini-2.0-flash-exp"  # exp结尾为实验模型，会逐步不再支持
 GEMINI_20_FLASH = "gemini-2.0-flash"  # 正式版模型
-GEMINI_25_FLASH_PRE = "gemini-2.5-flash-preview-05-20"  # preview为预览版模型，主要是新能力体验
+GEMINI_25_FLASH_PRE = "gemini-2.5-flash-preview-05-20"
 GEMINI_25_PRO_PRE = "gemini-2.5-pro-preview-05-06"
 GEMINI_3_FLASH_PRE = "gemini-3-flash-preview"  # Gemini 3 Flash Preview - Agent推荐模型
-GEMINI_3_PRO_PRE = "gemini-3-pro-preview"  # Gemini 3 Pro Preview - Agent推荐模型
+GEMINI_3_PRO_PRE = "gemini-3-pro-preview"  # Gemini 3 Pro Preview
+GEMINI_31_PRO_PRE = "gemini-3.1-pro-preview"  # Gemini 3.1 Pro Preview - Agent推荐模型

 # OpenAI
 GPT35 = "gpt-3.5-turbo"
@@ -79,15 +82,18 @@ QWEN_PLUS = "qwen-plus"
 QWEN_MAX = "qwen-max"
 QWEN_LONG = "qwen-long"
 QWEN3_MAX = "qwen3-max"  # Qwen3 Max - Agent推荐模型
+QWEN35_PLUS = "qwen3.5-plus"  # Qwen3.5 Plus - Omni model (MultiModalConversation)
 QWQ_PLUS = "qwq-plus"

 # MiniMax
+MINIMAX_M2_5 = "MiniMax-M2.5"  # MiniMax M2.5 - Latest
 MINIMAX_M2_1 = "MiniMax-M2.1"  # MiniMax M2.1 - Agent推荐模型
 MINIMAX_M2_1_LIGHTNING = "MiniMax-M2.1-lightning"  # MiniMax M2.1 极速版
 MINIMAX_M2 = "MiniMax-M2"  # MiniMax M2
 MINIMAX_ABAB6_5 = "abab6.5-chat"  # MiniMax abab6.5

 # GLM (智谱AI)
+GLM_5 = "glm-5"  # 智谱 GLM-5 - Latest
 GLM_4 = "glm-4"
 GLM_4_PLUS = "glm-4-plus"
 GLM_4_flash = "glm-4-flash"
@@ -100,6 +106,15 @@ GLM_4_7 = "glm-4.7"  # 智谱 GLM-4.7 - Agent推荐模型

 # Kimi (Moonshot)
 MOONSHOT = "moonshot"
+KIMI_K2 = "kimi-k2"
+KIMI_K2_5 = "kimi-k2.5"
+
+# Doubao (Volcengine Ark)
+DOUBAO = "doubao"
+DOUBAO_SEED_2_CODE = "doubao-seed-2-0-code-preview-260215"
+DOUBAO_SEED_2_PRO = "doubao-seed-2-0-pro-260215"
+DOUBAO_SEED_2_LITE = "doubao-seed-2-0-lite-260215"
+DOUBAO_SEED_2_MINI = "doubao-seed-2-0-mini-260215"

 # 其他模型
 WEN_XIN = "wenxin"
@@ -120,12 +135,12 @@ MODELSCOPE_MODEL_LIST = ["LLM-Research/c4ai-command-r-plus-08-2024","mistralai/M

 MODEL_LIST = [
              # Claude
-              CLAUDE3, CLAUDE_4_OPUS, CLAUDE_4_5_SONNET, CLAUDE_4_SONNET, CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229, 
+              CLAUDE3, CLAUDE_4_6_SONNET, CLAUDE_4_6_OPUS, CLAUDE_4_OPUS, CLAUDE_4_5_SONNET, CLAUDE_4_SONNET, CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229, 
              CLAUDE_35_SONNET, CLAUDE_35_SONNET_1022, CLAUDE_35_SONNET_0620, CLAUDE_3_SONNET, CLAUDE_3_HAIKU, 
              "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3.5-sonnet",
              
              # Gemini
-              GEMINI_3_PRO_PRE, GEMINI_3_FLASH_PRE, GEMINI_25_PRO_PRE, GEMINI_25_FLASH_PRE, 
+              GEMINI_31_PRO_PRE, GEMINI_3_PRO_PRE, GEMINI_3_FLASH_PRE, GEMINI_25_PRO_PRE, GEMINI_25_FLASH_PRE, 
              GEMINI_20_FLASH, GEMINI_20_flash_exp, GEMINI_15_PRO, GEMINI_15_flash, GEMINI_PRO, GEMINI,
              
              # OpenAI
@@ -141,18 +156,22 @@ MODEL_LIST = [
              DEEPSEEK_CHAT, DEEPSEEK_REASONER,
              
              # Qwen
-              QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, QWEN_LONG, QWEN3_MAX,
+              QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, QWEN_LONG, QWEN3_MAX, QWEN35_PLUS,
              
              # MiniMax
-              MiniMax, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
-              
+              MiniMax, MINIMAX_M2_5, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
+
              # GLM
-              ZHIPU_AI, GLM_4, GLM_4_PLUS, GLM_4_flash, GLM_4_LONG, GLM_4_ALLTOOLS, 
+              ZHIPU_AI, GLM_5, GLM_4, GLM_4_PLUS, GLM_4_flash, GLM_4_LONG, GLM_4_ALLTOOLS,
              GLM_4_0520, GLM_4_AIR, GLM_4_AIRX, GLM_4_7,
-              
+
              # Kimi
              MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k",
-              
+              KIMI_K2, KIMI_K2_5,
+
+              # Doubao
+              DOUBAO, DOUBAO_SEED_2_CODE, DOUBAO_SEED_2_PRO, DOUBAO_SEED_2_LITE, DOUBAO_SEED_2_MINI,
+
              # 其他模型
              WEN_XIN, WEN_XIN_4, XUNFEI,
              LINKAI_35, LINKAI_4_TURBO, LINKAI_4o,
--- a/common/linkai_client.py
+++ b/common/linkai_client.py
@@ -1,110 +0,0 @@
-from bridge.context import Context, ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from linkai import LinkAIClient, PushMsg
-from config import conf, pconf, plugin_config, available_setting, write_plugin_config
-from plugins import PluginManager
-import time
-
-
-chat_client: LinkAIClient
-
-
-class ChatClient(LinkAIClient):
-    def __init__(self, api_key, host, channel):
-        super().__init__(api_key, host)
-        self.channel = channel
-        self.client_type = channel.channel_type
-
-    def on_message(self, push_msg: PushMsg):
-        session_id = push_msg.session_id
-        msg_content = push_msg.msg_content
-        logger.info(f"receive msg push, session_id={session_id}, msg_content={msg_content}")
-        context = Context()
-        context.type = ContextType.TEXT
-        context["receiver"] = session_id
-        context["isgroup"] = push_msg.is_group
-        self.channel.send(Reply(ReplyType.TEXT, content=msg_content), context)
-
-    def on_config(self, config: dict):
-        if not self.client_id:
-            return
-        logger.info(f"[LinkAI] 从客户端管理加载远程配置: {config}")
-        if config.get("enabled") != "Y":
-            return
-
-        local_config = conf()
-        for key in config.keys():
-            if key in available_setting and config.get(key) is not None:
-                local_config[key] = config.get(key)
-        # 语音配置
-        reply_voice_mode = config.get("reply_voice_mode")
-        if reply_voice_mode:
-            if reply_voice_mode == "voice_reply_voice":
-                local_config["voice_reply_voice"] = True
-                local_config["always_reply_voice"] = False
-            elif reply_voice_mode == "always_reply_voice":
-                local_config["always_reply_voice"] = True
-                local_config["voice_reply_voice"] = True
-            elif reply_voice_mode == "no_reply_voice":
-                local_config["always_reply_voice"] = False
-                local_config["voice_reply_voice"] = False
-
-        if config.get("admin_password"):
-            if not pconf("Godcmd"):
-                write_plugin_config({"Godcmd": {"password": config.get("admin_password"), "admin_users": []} })
-            else:
-                pconf("Godcmd")["password"] = config.get("admin_password")
-            PluginManager().instances["GODCMD"].reload()
-
-        if config.get("group_app_map") and pconf("linkai"):
-            local_group_map = {}
-            for mapping in config.get("group_app_map"):
-                local_group_map[mapping.get("group_name")] = mapping.get("app_code")
-            pconf("linkai")["group_app_map"] = local_group_map
-            PluginManager().instances["LINKAI"].reload()
-
-        if config.get("text_to_image") and config.get("text_to_image") == "midjourney" and pconf("linkai"):
-            if pconf("linkai")["midjourney"]:
-                pconf("linkai")["midjourney"]["enabled"] = True
-                pconf("linkai")["midjourney"]["use_image_create_prefix"] = True
-        elif config.get("text_to_image") and config.get("text_to_image") in ["dall-e-2", "dall-e-3"]:
-            if pconf("linkai")["midjourney"]:
-                pconf("linkai")["midjourney"]["use_image_create_prefix"] = False
-
-
-def start(channel):
-    global chat_client
-    chat_client = ChatClient(api_key=conf().get("linkai_api_key"), host="", channel=channel)
-    chat_client.config = _build_config()
-    chat_client.start()
-    time.sleep(1.5)
-    if chat_client.client_id:
-        logger.info("[LinkAI] 可前往控制台进行线上登录和配置：https://link-ai.tech/console/clients")
-
-
-def _build_config():
-    local_conf = conf()
-    config = {
-        "linkai_app_code": local_conf.get("linkai_app_code"),
-        "single_chat_prefix": local_conf.get("single_chat_prefix"),
-        "single_chat_reply_prefix": local_conf.get("single_chat_reply_prefix"),
-        "single_chat_reply_suffix": local_conf.get("single_chat_reply_suffix"),
-        "group_chat_prefix": local_conf.get("group_chat_prefix"),
-        "group_chat_reply_prefix": local_conf.get("group_chat_reply_prefix"),
-        "group_chat_reply_suffix": local_conf.get("group_chat_reply_suffix"),
-        "group_name_white_list": local_conf.get("group_name_white_list"),
-        "nick_name_black_list": local_conf.get("nick_name_black_list"),
-        "speech_recognition": "Y" if local_conf.get("speech_recognition") else "N",
-        "text_to_image": local_conf.get("text_to_image"),
-        "image_create_prefix": local_conf.get("image_create_prefix")
-    }
-    if local_conf.get("always_reply_voice"):
-        config["reply_voice_mode"] = "always_reply_voice"
-    elif local_conf.get("voice_reply_voice"):
-        config["reply_voice_mode"] = "voice_reply_voice"
-    if pconf("linkai"):
-        config["group_app_map"] = pconf("linkai").get("group_app_map")
-    if plugin_config.get("Godcmd"):
-        config["admin_password"] = plugin_config.get("Godcmd").get("password")
-    return config
--- a/common/package_manager.py
+++ b/common/package_manager.py
@@ -28,7 +28,7 @@ def check_dulwich():
        except ImportError:
            try:
                install("dulwich")
-            except:
+            except Exception:
                needwait = True
    try:
        import dulwich
--- a/common/utils.py
+++ b/common/utils.py
@@ -2,7 +2,6 @@ import io
 import os
 import re
 from urllib.parse import urlparse
-from PIL import Image
 from common.log import logger

 def fsize(file):
@@ -23,6 +22,7 @@ def fsize(file):
 def compress_imgfile(file, max_size):
    if fsize(file) <= max_size:
        return file
+    from PIL import Image
    file.seek(0)
    img = Image.open(file)
    rgb_image = img.convert("RGB")
@@ -76,3 +76,42 @@ def remove_markdown_symbol(text: str):
    if not text:
        return text
    return re.sub(r'\*\*(.*?)\*\*', r'\1', text)
+
+
+def expand_path(path: str) -> str:
+    """
+    Expand user path with proper Windows support.
+    
+    On Windows, os.path.expanduser('~') may not work properly in some shells (like PowerShell).
+    This function provides a more robust path expansion.
+    
+    Args:
+        path: Path string that may contain ~
+        
+    Returns:
+        Expanded absolute path
+    """
+    if not path:
+        return path
+    
+    # Try standard expansion first
+    expanded = os.path.expanduser(path)
+    
+    # If expansion didn't work (path still starts with ~), use HOME or USERPROFILE
+    if expanded.startswith('~'):
+        import platform
+        if platform.system() == 'Windows':
+            # On Windows, try USERPROFILE first, then HOME
+            home = os.environ.get('USERPROFILE') or os.environ.get('HOME')
+        else:
+            # On Unix-like systems, use HOME
+            home = os.environ.get('HOME')
+        
+        if home:
+            # Replace ~ with home directory
+            if path == '~':
+                expanded = home
+            elif path.startswith('~/') or path.startswith('~\\'):
+                expanded = os.path.join(home, path[2:])
+    
+    return expanded
--- a/config-template.json
+++ b/config-template.json
@@ -1,15 +1,17 @@
 {
  "channel_type": "web",
-  "model": "claude-sonnet-4-5",
+  "model": "MiniMax-M2.5",
+  "minimax_api_key": "",
+  "zhipu_ai_api_key": "",
+  "ark_api_key": "",
+  "moonshot_api_key": "",
+  "dashscope_api_key": "",
  "claude_api_key": "",
  "claude_api_base": "https://api.anthropic.com/v1",
  "open_ai_api_key": "",
  "open_ai_api_base": "https://api.openai.com/v1",
  "gemini_api_key": "",
  "gemini_api_base": "https://generativelanguage.googleapis.com",
-  "zhipu_ai_api_key": "",
-  "minimax_api_key": "",
-  "dashscope_api_key": "",
  "voice_to_text": "openai",
  "text_to_voice": "openai",
  "voice_reply_voice": false,
--- a/config.py
+++ b/config.py
@@ -160,7 +160,8 @@ available_setting = {
    # chatgpt指令自定义触发词
    "clear_memory_commands": ["#清除记忆"],  # 重置会话指令，必须以#开头
    # channel配置
-    "channel_type": "",  # 通道类型，支持：{wx,wxy,terminal,wechatmp,wechatmp_service,wechatcom_app,dingtalk}
+    "channel_type": "",  # 通道类型，支持多渠道同时运行。单个: "feishu"，多个: "feishu, dingtalk" 或 ["feishu", "dingtalk"]。可选值: web,feishu,dingtalk,wechatmp,wechatmp_service,wechatcom_app
+    "web_console": True,  # 是否自动启动Web控制台（默认启动）。设为False可禁用
    "subscribe_msg": "",  # 订阅消息, 支持: wechatmp, wechatmp_service, wechatcom_app
    "debug": False,  # 是否开启debug模式，开启后会打印更多日志
    "appdata_dir": "",  # 数据目录
@@ -174,7 +175,10 @@ available_setting = {
    "zhipu_ai_api_key": "",
    "zhipu_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
    "moonshot_api_key": "",
-    "moonshot_base_url": "https://api.moonshot.cn/v1/chat/completions",
+    "moonshot_base_url": "https://api.moonshot.cn/v1",
+    # 豆包(火山方舟) 平台配置
+    "ark_api_key": "",
+    "ark_base_url": "https://ark.cn-beijing.volces.com/api/v3",
    #魔搭社区 平台配置
    "modelscope_api_key": "",
    "modelscope_base_url": "https://api-inference.modelscope.cn/v1/chat/completions",
@@ -183,6 +187,7 @@ available_setting = {
    "linkai_api_key": "",
    "linkai_app_code": "",
    "linkai_api_base": "https://api.link-ai.tech",  # linkAI服务地址
+    "cloud_host": "client.link-ai.tech",
    "minimax_api_key": "",
    "Minimax_group_id": "",
    "Minimax_base_url": "",
@@ -245,9 +250,9 @@ class Config(dict):
                self.user_datas = pickle.load(f)
                logger.debug("[Config] User datas loaded.")
        except FileNotFoundError as e:
-            logger.info("[Config] User datas file not found, ignore.")
+            logger.debug("[Config] User datas file not found, ignore.")
        except Exception as e:
-            logger.info("[Config] User datas error: {}".format(e))
+            logger.warning("[Config] User datas error: {}".format(e))
            self.user_datas = {}

    def save_user_datas(self):
@@ -319,7 +324,7 @@ def load_config():
            logger.info("[INIT] override config by environ args: {}={}".format(name, value))
            try:
                config[name] = eval(value)
-            except:
+            except Exception:
                if value == "false":
                    config[name] = False
                elif value == "true":
--- a/docs/agent.md
+++ b/docs/agent.md
@@ -8,7 +8,7 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent
 - **工具系统**：内置实现10+种工具，包括文件读写、bash终端、浏览器、定时任务、记忆管理等，通过Agent管理你的计算机或服务器
 - **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
 - **Skills系统**：新增Skill运行引擎，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
- **多渠道和多模型支持**：支持在Web、飞书、钉钉、企微等多渠道与Agent交互，支持Claude、Gemini、OpenAI、GLM、MiniMax、Qwen 等多种国内外主流模型
+- **多渠道和多模型支持**：支持在Web、飞书、钉钉、企微等多渠道与Agent交互，支持Claude、Gemini、OpenAI、GLM、MiniMax、Qwen、Kimi、Doubao 等多种国内外主流模型
 - **安全和成本**：通过秘钥管理工具、提示词控制、系统权限等手段控制Agent的访问安全；通过最大记忆轮次、最大上下文token、工具执行步数对token成本进行限制


@@ -23,7 +23,7 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent

 在后续的长期对话中，Agent会在需要的时候智能记录或检索记忆，并对自身设定、用户偏好、记忆文件等进行不断更新，总结和记录经验和教训，真正实现自主思考和不断成长。

-<img width="800" src="https://cdn.link-ai.tech/doc/20260203000455.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260203000455.png" />



@@ -37,14 +37,14 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent

 针对操作系统的终端和文件的访问能力，是最基础和核心的工具，其他很多工具或技能都是基于基础工具进行扩展。用户可通过手机端与Agent交互，操作个人电脑或服务器上的资源：

-<img width="800" src="https://cdn.link-ai.tech/doc/20260202181130.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260202181130.png" />

 #### 1.2 编程能力

 基于编程能力和系统访问能力，Agent可以实现从信息搜索、图片等素材生成、编码、测试、部署、Nginx配置修改、发布的 Vibecoding 全流程，通过手机端简单的一句命令完成应用的快速demo：


-<img width="800" src="https://cdn.link-ai.tech/doc/20260203121008.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260203121008.png" />



@@ -53,7 +53,7 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent
 基于 scheduler 工具实现动态定时任务，支持 **一次性任务、固定时间间隔、Cron表达式** 三种形式，任务触发可选择**固定消息发送** 或 **Agent动态任务** 执行两种模式，有很高灵活性：


-<img width="800" src="https://cdn.link-ai.tech/doc/20260202195402.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260202195402.png" />

 同时你也可以通过自然语言快速查看和管理已有的定时任务。

@@ -62,7 +62,7 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent

 技能所需要的秘钥存储在环境变量文件中，由 `env_config` 工具进行管理，你可以通过对话的方式更新秘钥，工具内置了安全保护和脱敏策略，会严格保护秘钥安全：

-<img width="800" src="https://cdn.link-ai.tech/doc/20260202234939.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260202234939.png" />

 ### 3. 技能系统

@@ -70,22 +70,22 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent

 - **内置技能：** 在项目的`skills`目录下，包含技能创造器、网络搜索、图像识别（openai-image-vision）、LinkAI智能体、网页抓取等。内置Skill根据依赖条件 (API Key、系统命令等) 自动判断是否启用。通过技能创造器可以快速创建自定义技能。

- **自定义技能：** 由用户通过对话创建，存放在工作空间中 (`~/cow/skills/`)，基于自定义技能可以实现任何
+- **自定义技能：** 由用户通过对话创建，存放在工作空间中 (`~/cow/skills/`)，基于自定义技能可以实现任何复杂的业务流程和第三方系统对接。


 #### 3.1 创建技能

 通过 `skill-creator` 技能可以通过对话的方式快速创建技能。你可以在与Agent的写作中让他对将某个工作流程固化为技能，或者把任意接口文档和示例发送给Agent，让他直接完成对接：

-<img width="800" src="https://cdn.link-ai.tech/doc/20260202202247.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260202202247.png" />


 #### 3.2 搜索和图像识别

- **搜索技能：** 系统内置实现了 `bocha-search`(博查搜索)的Skill，依赖环境变量 `BOCHA_SEARCH_API_KEY`，可在[控制台]()进行创建，并发送给Agent完成配置
+- **搜索技能：** 系统内置实现了 `bocha-search`(博查搜索)的Skill，依赖环境变量 `BOCHA_SEARCH_API_KEY`，可在[控制台](https://open.bochaai.com/)进行创建，并发送给Agent完成配置
 - **图像识别技能：** 实现了 `openai-image-vision` 插件，可使用 gpt-4.1-mini、gpt-4.1 等图像识别模型。依赖秘钥 `OPENAI_API_KEY`，可通过config.json或env_config工具进行维护。

-<img width="800" src="https://cdn.link-ai.tech/doc/20260202213219.png">
+<img width="800" src="https://cdn.link-ai.tech/doc/20260202213219.png" />


 #### 3.3 三方知识库和插件
@@ -113,7 +113,7 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent

 Agent可根据智能体的名称和描述进行决策，并通过 app_code 调用接口访问对应的应用/工作流，通过该技能，可以灵活访问LinkAI平台上的智能体、知识库、插件等能力，实现效果如下：

-<img width="750" src="https://cdn.link-ai.tech/doc/20260202234350.png">
+<img width="750" src="https://cdn.link-ai.tech/doc/20260202234350.png" />

 注：需通过 `env_config` 配置 `LINKAI_API_KEY`，或在config.json中添加 `linkai_api_key` 配置。

@@ -137,11 +137,13 @@ bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)

 Agent模式推荐使用以下模型，可根据效果及成本综合选择：

- **Claude**: `claude-sonnet-4-5`、`claude-sonnet-4-0`
- **Gemini**: `gemini-3-flash-preview`、`gemini-3-pro-preview`
- **GLM**: `glm-4.7`
- **MiniMax**: `MiniMax-M2.1`
- **Qwen**: `qwen3-max`
+- **MiniMax**: `MiniMax-M2.5`
+- **GLM**: `glm-5`
+- **Kimi**: `kimi-k2.5`
+- **Doubao**: `doubao-seed-2-0-code-preview-260215`
+- **Qwen**: `qwen3.5-plus`
+- **Claude**: `claude-sonnet-4-6`
+- **Gemini**: `gemini-3.1-pro-preview`

 详细模型配置方式参考 [README.md 模型说明](../README.md#模型说明)

--- a/docs/channels/dingtalk.mdx
+++ b/docs/channels/dingtalk.mdx
@@ -0,0 +1,38 @@
+---
+title: 钉钉
+description: 将 CowAgent 接入钉钉应用
+---
+
+通过钉钉开放平台创建智能机器人应用，将 CowAgent 接入钉钉。
+
+## 一、创建应用
+
+1. 进入 [钉钉开发者后台](https://open-dev.dingtalk.com/fe/app#/corp/app)，点击 **创建应用**，填写应用信息
+2. 点击添加应用能力，选择 **机器人** 能力并添加
+3. 配置机器人信息后点击 **发布**
+
+## 二、项目配置
+
+1. 在 **凭证与基础信息** 中获取 `Client ID` 和 `Client Secret`
+
+2. 填入 `config.json`：
+
+```json
+{
+  "channel_type": "dingtalk",
+  "dingtalk_client_id": "YOUR_CLIENT_ID",
+  "dingtalk_client_secret": "YOUR_CLIENT_SECRET"
+}
+```
+
+3. 安装依赖：
+
+```bash
+pip3 install dingtalk_stream
+```
+
+4. 启动项目后，在钉钉开发者后台点击 **事件订阅**，点击 **已完成接入，验证连接通道**，显示"连接接入成功"即表示配置完成
+
+## 三、使用
+
+与机器人私聊或将机器人拉入企业群中均可开启对话。
--- a/docs/channels/feishu.mdx
+++ b/docs/channels/feishu.mdx
@@ -0,0 +1,67 @@
+---
+title: 飞书
+description: 将 CowAgent 接入飞书应用
+---
+
+通过自建应用将 CowAgent 接入飞书，支持 WebSocket 长连接（推荐）和 Webhook 两种事件接收模式。
+
+## 一、创建企业自建应用
+
+### 1. 创建应用
+
+进入 [飞书开发平台](https://open.feishu.cn/app/)，点击 **创建企业自建应用**，填写必要信息后创建。
+
+### 2. 添加机器人能力
+
+在 **添加应用能力** 菜单中，为应用添加 **机器人** 能力。
+
+### 3. 配置应用权限
+
+点击 **权限管理**，粘贴以下权限配置，全选并批量开通：
+
+```
+im:message,im:message.group_at_msg,im:message.group_at_msg:readonly,im:message.p2p_msg,im:message.p2p_msg:readonly,im:message:send_as_bot,im:resource
+```
+
+## 二、项目配置
+
+在 **凭证与基础信息** 中获取 `App ID` 和 `App Secret`，填入 `config.json`：
+
+<Tabs>
+  <Tab title="WebSocket 模式（推荐）">
+    无需公网 IP，配置如下：
+
+    ```json
+    {
+      "channel_type": "feishu",
+      "feishu_app_id": "YOUR_APP_ID",
+      "feishu_app_secret": "YOUR_APP_SECRET",
+      "feishu_event_mode": "websocket"
+    }
+    ```
+
+    需安装依赖：`pip3 install lark-oapi`
+  </Tab>
+  <Tab title="Webhook 模式">
+    需要公网 IP，配置如下：
+
+    ```json
+    {
+      "channel_type": "feishu",
+      "feishu_app_id": "YOUR_APP_ID",
+      "feishu_app_secret": "YOUR_APP_SECRET",
+      "feishu_token": "VERIFICATION_TOKEN",
+      "feishu_event_mode": "webhook",
+      "feishu_port": 9891
+    }
+    ```
+  </Tab>
+</Tabs>
+
+## 三、配置事件订阅
+
+1. 启动项目后，在飞书开放平台点击 **事件与回调**，选择 **长连接** 方式并保存
+2. 点击 **添加事件**，搜索 "接收消息"，选择 "接收消息v2.0"，确认添加
+3. 点击 **版本管理与发布**，创建版本并申请线上发布，审核通过后即可使用
+
+完成后在飞书中搜索机器人名称，即可开始对话。
--- a/docs/channels/web.mdx
+++ b/docs/channels/web.mdx
@@ -0,0 +1,31 @@
+---
+title: Web 网页
+description: 通过 Web 网页端使用 CowAgent
+---
+
+Web 是 CowAgent 的默认通道，启动后会自动运行 Web 控制台，通过浏览器即可与 Agent 对话。
+
+## 配置
+
+```json
+{
+  "channel_type": "web",
+  "web_port": 9899
+}
+```
+
+| 参数 | 说明 | 默认值 |
+| --- | --- | --- |
+| `channel_type` | 设为 `web` | `web` |
+| `web_port` | Web 服务监听端口 | `9899` |
+
+## 使用
+
+启动项目后访问：
+
+- 本地运行：`http://localhost:9899/chat`
+- 服务器运行：`http://<server-ip>:9899/chat`
+
+<Note>
+  请确保服务器防火墙和安全组已放行对应端口。
+</Note>
--- a/docs/channels/wechatmp.mdx
+++ b/docs/channels/wechatmp.mdx
@@ -0,0 +1,56 @@
+---
+title: 微信公众号
+description: 将 CowAgent 接入微信公众号
+---
+
+CowAgent 支持接入个人订阅号和企业服务号两种公众号类型。
+
+| 类型 | 要求 | 特点 |
+| --- | --- | --- |
+| **个人订阅号** | 个人可申请 | 回复生成后需用户主动发消息获取 |
+| **企业服务号** | 企业申请，需通过微信认证开通客服接口 | 回复生成后可主动推送给用户 |
+
+<Note>
+  公众号仅支持服务器和 Docker 部署，需额外安装扩展依赖：`pip3 install -r requirements-optional.txt`
+</Note>
+
+## 一、个人订阅号
+
+在 `config.json` 中配置：
+
+```json
+{
+  "channel_type": "wechatmp",
+  "wechatmp_app_id": "YOUR_APP_ID",
+  "wechatmp_app_secret": "YOUR_APP_SECRET",
+  "wechatmp_aes_key": "",
+  "wechatmp_token": "YOUR_TOKEN",
+  "wechatmp_port": 80
+}
+```
+
+### 配置步骤
+
+1. 在 [微信公众平台](https://mp.weixin.qq.com/) 的 **设置与开发 → 基本配置 → 服务器配置** 中获取参数
+2. 启用开发者密码，将服务器 IP 加入白名单
+3. 启动程序（监听 80 端口）
+4. 在公众号后台 **启用服务器配置**，URL 格式为 `http://{HOST}/wx`
+
+## 二、企业服务号
+
+与个人订阅号流程基本相同，差异如下：
+
+1. 在公众平台申请企业服务号并完成微信认证，确认已获得 **客服接口** 权限
+2. 在 `config.json` 中设置 `"channel_type": "wechatmp_service"`
+3. 即使是较长耗时的回复，也可以主动推送给用户
+
+```json
+{
+  "channel_type": "wechatmp_service",
+  "wechatmp_app_id": "YOUR_APP_ID",
+  "wechatmp_app_secret": "YOUR_APP_SECRET",
+  "wechatmp_aes_key": "",
+  "wechatmp_token": "YOUR_TOKEN",
+  "wechatmp_port": 80
+}
+```
--- a/docs/channels/wecom.mdx
+++ b/docs/channels/wecom.mdx
@@ -0,0 +1,59 @@
+---
+title: 企业微信
+description: 将 CowAgent 接入企业微信自建应用
+---
+
+通过企业微信自建应用接入 CowAgent，支持企业内部人员单聊使用。
+
+<Note>
+  企业微信只能使用 Docker 部署或服务器 Python 部署，不支持本地运行模式。
+</Note>
+
+## 一、准备
+
+需要的资源：
+
+1. 一台服务器（有公网 IP）
+2. 注册一个企业微信（个人也可注册，但无法认证）
+3. 认证企业微信还需要对应主体备案的域名
+
+## 二、创建企业微信应用
+
+1. 在 [企业微信管理后台](https://work.weixin.qq.com/wework_admin/frame#profile) **我的企业** 中获取 **企业ID**
+2. 切换到 **应用管理**，点击创建应用，记录 `AgentId` 和 `Secret`
+3. 点击 **设置API接收**，配置应用接口：
+   - URL 格式为 `http://ip:port/wxcomapp`（认证企业需使用备案域名）
+   - 随机获取 `Token` 和 `EncodingAESKey` 并保存
+
+## 三、配置和运行
+
+```json
+{
+  "channel_type": "wechatcom_app",
+  "wechatcom_corp_id": "YOUR_CORP_ID",
+  "wechatcomapp_token": "YOUR_TOKEN",
+  "wechatcomapp_secret": "YOUR_SECRET",
+  "wechatcomapp_agent_id": "YOUR_AGENT_ID",
+  "wechatcomapp_aes_key": "YOUR_AES_KEY",
+  "wechatcomapp_port": 9898
+}
+```
+
+| 参数 | 说明 |
+| --- | --- |
+| `wechatcom_corp_id` | 企业 ID |
+| `wechatcomapp_token` | API 接收配置中的 Token |
+| `wechatcomapp_secret` | 应用的 Secret |
+| `wechatcomapp_agent_id` | 应用的 AgentId |
+| `wechatcomapp_aes_key` | API 接收配置中的 EncodingAESKey |
+| `wechatcomapp_port` | 监听端口，默认 9898 |
+
+启动程序后，回到企业微信后台保存 **消息服务器配置**，并将服务器 IP 添加到 **企业可信IP** 中。
+
+<Warning>
+  如遇到配置失败：1. 确保防火墙和安全组已放行端口；2. 检查各参数配置是否一致；3. 认证企业需配置备案域名。
+</Warning>
+
+## 四、使用
+
+在企业微信中搜索应用名称即可直接对话。如需让外部微信用户使用，可在 **我的企业 → 微信插件** 中分享邀请关注二维码。
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -0,0 +1,323 @@
+{
+  "$schema": "https://mintlify.com/docs.json",
+  "name": "CowAgent",
+  "description": "CowAgent - AI Super Assistant powered by LLMs, with autonomous task planning, long-term memory, skills system, and multi-channel deployment.",
+  "theme": "mint",
+  "appearance": {
+    "default": "light"
+  },
+  "colors": {
+    "primary": "#35A85B",
+    "light": "#4ABE6E",
+    "dark": "#228547"
+  },
+  "logo": {
+    "light": "/images/logo.jpg",
+    "dark": "/images/logo.jpg"
+  },
+  "favicon": "/images/favicon.ico",
+  "navbar": {
+    "links": [
+      {
+        "label": "官网",
+        "href": "https://cowagent.ai/"
+      },
+      {
+        "label": "GitHub",
+        "href": "https://github.com/zhayujie/chatgpt-on-wechat"
+      }
+    ]
+  },
+  "footer": {
+    "socials": {
+      "github": "https://github.com/zhayujie/chatgpt-on-wechat"
+    }
+  },
+  "navigation": {
+    "languages": [
+      {
+        "language": "zh",
+        "default": true,
+        "tabs": [
+          {
+            "tab": "项目介绍",
+            "groups": [
+              {
+                "group": "概览",
+                "pages": [
+                  "intro/index",
+                  "intro/architecture",
+                  "intro/features"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "快速开始",
+            "groups": [
+              {
+                "group": "安装部署",
+                "pages": [
+                  "guide/quick-start",
+                  "guide/manual-install"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "模型",
+            "groups": [
+              {
+                "group": "模型配置",
+                "pages": [
+                  "models/index",
+                  "models/minimax",
+                  "models/glm",
+                  "models/qwen",
+                  "models/kimi",
+                  "models/doubao",
+                  "models/claude",
+                  "models/gemini",
+                  "models/openai",
+                  "models/deepseek",
+                  "models/linkai"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "工具",
+            "groups": [
+              {
+                "group": "工具系统",
+                "pages": [
+                  "tools/index"
+                ]
+              },
+              {
+                "group": "内置工具",
+                "pages": [
+                  "tools/read",
+                  "tools/write",
+                  "tools/edit",
+                  "tools/ls",
+                  "tools/bash",
+                  "tools/send",
+                  "tools/memory",
+                  "tools/env-config"
+                ]
+              },
+              {
+                "group": "可选工具",
+                "pages": [
+                  "tools/web-search",
+                  "tools/scheduler"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "技能",
+            "groups": [
+              {
+                "group": "技能系统",
+                "pages": [
+                  "skills/index",
+                  "skills/skill-creator"
+                ]
+              },
+              {
+                "group": "内置技能",
+                "pages": [
+                  "skills/image-vision",
+                  "skills/linkai-agent",
+                  "skills/web-fetch"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "记忆",
+            "groups": [
+              {
+                "group": "记忆系统",
+                "pages": [
+                  "memory"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "通道",
+            "groups": [
+              {
+                "group": "接入渠道",
+                "pages": [
+                  "channels/web",
+                  "channels/feishu",
+                  "channels/dingtalk",
+                  "channels/wecom",
+                  "channels/wechatmp"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "版本",
+            "groups": [
+              {
+                "group": "发布记录",
+                "pages": [
+                  "releases/overview",
+                  "releases/v2.0.1",
+                  "releases/v2.0.0"
+                ]
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "language": "en",
+        "tabs": [
+          {
+            "tab": "Introduction",
+            "groups": [
+              {
+                "group": "Overview",
+                "pages": [
+                  "en/intro/index",
+                  "en/intro/architecture",
+                  "en/intro/features"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Get Started",
+            "groups": [
+              {
+                "group": "Installation",
+                "pages": [
+                  "en/guide/quick-start",
+                  "en/guide/manual-install"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Models",
+            "groups": [
+              {
+                "group": "Model Configuration",
+                "pages": [
+                  "en/models/index",
+                  "en/models/minimax",
+                  "en/models/glm",
+                  "en/models/qwen",
+                  "en/models/kimi",
+                  "en/models/doubao",
+                  "en/models/claude",
+                  "en/models/gemini",
+                  "en/models/openai",
+                  "en/models/deepseek",
+                  "en/models/linkai"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Tools",
+            "groups": [
+              {
+                "group": "Tools System",
+                "pages": [
+                  "en/tools/index"
+                ]
+              },
+              {
+                "group": "Built-in Tools",
+                "pages": [
+                  "en/tools/read",
+                  "en/tools/write",
+                  "en/tools/edit",
+                  "en/tools/ls",
+                  "en/tools/bash",
+                  "en/tools/send",
+                  "en/tools/memory",
+                  "en/tools/env-config"
+                ]
+              },
+              {
+                "group": "Optional Tools",
+                "pages": [
+                  "en/tools/web-search",
+                  "en/tools/scheduler"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Skills",
+            "groups": [
+              {
+                "group": "Skills System",
+                "pages": [
+                  "en/skills/index",
+                  "en/skills/skill-creator"
+                ]
+              },
+              {
+                "group": "Built-in Skills",
+                "pages": [
+                  "en/skills/image-vision",
+                  "en/skills/linkai-agent",
+                  "en/skills/web-fetch"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Memory",
+            "groups": [
+              {
+                "group": "Memory System",
+                "pages": [
+                  "en/memory"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Channels",
+            "groups": [
+              {
+                "group": "Platforms",
+                "pages": [
+                  "en/channels/web",
+                  "en/channels/feishu",
+                  "en/channels/dingtalk",
+                  "en/channels/wecom",
+                  "en/channels/wechatmp"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Releases",
+            "groups": [
+              {
+                "group": "Release Notes",
+                "pages": [
+                  "en/releases/overview",
+                  "en/releases/v2.0.1",
+                  "en/releases/v2.0.0"
+                ]
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  }
+}
--- a/docs/en/channels/dingtalk.mdx
+++ b/docs/en/channels/dingtalk.mdx
@@ -0,0 +1,38 @@
+---
+title: DingTalk
+description: Integrate CowAgent into DingTalk application
+---
+
+Integrate CowAgent into DingTalk by creating an intelligent robot app on the DingTalk Open Platform.
+
+## 1. Create App
+
+1. Go to [DingTalk Developer Console](https://open-dev.dingtalk.com/fe/app#/corp/app), click **Create App**, fill in app information
+2. Click **Add App Capability**, select **Robot** capability and add
+3. Configure robot information and click **Publish**
+
+## 2. Project Configuration
+
+1. Get `Client ID` and `Client Secret` from **Credentials & Basic Info**
+
+2. Fill in `config.json`:
+
+```json
+{
+  "channel_type": "dingtalk",
+  "dingtalk_client_id": "YOUR_CLIENT_ID",
+  "dingtalk_client_secret": "YOUR_CLIENT_SECRET"
+}
+```
+
+3. Install dependency:
+
+```bash
+pip3 install dingtalk_stream
+```
+
+4. After starting the project, go to DingTalk Developer Console **Event Subscription**, click **Connection verified, verify channel**. When "Connection successful" is displayed, configuration is complete
+
+## 3. Usage
+
+Chat privately with the robot or add it to an enterprise group to start a conversation.
--- a/docs/en/channels/feishu.mdx
+++ b/docs/en/channels/feishu.mdx
@@ -0,0 +1,67 @@
+---
+title: Feishu (Lark)
+description: Integrate CowAgent into Feishu application
+---
+
+Integrate CowAgent into Feishu by creating a custom app. Supports WebSocket (recommended, no public IP required) and Webhook event receiving modes.
+
+## 1. Create Enterprise Custom App
+
+### 1.1 Create App
+
+Go to [Feishu Developer Platform](https://open.feishu.cn/app/), click **Create Enterprise Custom App**, fill in the required information and create.
+
+### 1.2 Add Bot Capability
+
+In **Add App Capabilities**, add **Bot** capability to the app.
+
+### 1.3 Configure App Permissions
+
+Click **Permission Management**, paste the following permission string, select all and enable in batch:
+
+```
+im:message,im:message.group_at_msg,im:message.group_at_msg:readonly,im:message.p2p_msg,im:message.p2p_msg:readonly,im:message:send_as_bot,im:resource
+```
+
+## 2. Project Configuration
+
+Get `App ID` and `App Secret` from **Credentials & Basic Info**, then fill in `config.json`:
+
+<Tabs>
+  <Tab title="WebSocket Mode (Recommended)">
+    No public IP required. Configuration:
+
+    ```json
+    {
+      "channel_type": "feishu",
+      "feishu_app_id": "YOUR_APP_ID",
+      "feishu_app_secret": "YOUR_APP_SECRET",
+      "feishu_event_mode": "websocket"
+    }
+    ```
+
+    Install dependency: `pip3 install lark-oapi`
+  </Tab>
+  <Tab title="Webhook Mode">
+    Requires public IP. Configuration:
+
+    ```json
+    {
+      "channel_type": "feishu",
+      "feishu_app_id": "YOUR_APP_ID",
+      "feishu_app_secret": "YOUR_APP_SECRET",
+      "feishu_token": "VERIFICATION_TOKEN",
+      "feishu_event_mode": "webhook",
+      "feishu_port": 9891
+    }
+    ```
+  </Tab>
+</Tabs>
+
+## 3. Configure Event Subscription
+
+1. After starting the project, go to Feishu Developer Platform **Events & Callbacks**, select **Long Connection** and save
+2. Click **Add Event**, search for "Receive Message", select "Receive Message v2.0", confirm and add
+3. Click **Version Management & Release**, create a version and apply for production release. After approval, you can use it
+
+Search for the bot name in Feishu to start chatting.
--- a/docs/en/channels/web.mdx
+++ b/docs/en/channels/web.mdx
@@ -0,0 +1,31 @@
+---
+title: Web
+description: Use CowAgent through the web interface
+---
+
+Web is CowAgent's default channel. The web console starts automatically after launch, allowing you to chat with the Agent through a browser.
+
+## Configuration
+
+```json
+{
+  "channel_type": "web",
+  "web_port": 9899
+}
+```
+
+| Parameter | Description | Default |
+| --- | --- | --- |
+| `channel_type` | Set to `web` | `web` |
+| `web_port` | Web service listen port | `9899` |
+
+## Usage
+
+After starting the project, visit:
+
+- Local: `http://localhost:9899/chat`
+- Server: `http://<server-ip>:9899/chat`
+
+<Note>
+  Ensure the server firewall and security group allow the corresponding port.
+</Note>
--- a/docs/en/channels/wechatmp.mdx
+++ b/docs/en/channels/wechatmp.mdx
@@ -0,0 +1,54 @@
+---
+title: WeChat Official Account
+description: Integrate CowAgent with WeChat Official Accounts
+---
+
+CowAgent supports both personal subscription accounts and enterprise service accounts.
+
+| Type | Requirements | Features |
+| --- | --- | --- |
+| **Personal Subscription** | Available to individuals | Users must send a message to retrieve replies |
+| **Enterprise Service** | Enterprise with verified customer service API | Can proactively push replies to users |
+
+<Note>
+  Official Accounts only support server and Docker deployment. Install extended dependencies: `pip3 install -r requirements-optional.txt`
+</Note>
+
+## Personal Subscription Account
+
+```json
+{
+  "channel_type": "wechatmp",
+  "wechatmp_app_id": "YOUR_APP_ID",
+  "wechatmp_app_secret": "YOUR_APP_SECRET",
+  "wechatmp_aes_key": "",
+  "wechatmp_token": "YOUR_TOKEN",
+  "wechatmp_port": 80
+}
+```
+
+### Setup Steps
+
+1. Get parameters from [WeChat Official Account Platform](https://mp.weixin.qq.com/) under **Settings & Development → Basic Configuration → Server Configuration**
+2. Enable developer secret and add server IP to the whitelist
+3. Start the program (listens on port 80)
+4. Enable server configuration with URL format `http://{HOST}/wx`
+
+## Enterprise Service Account
+
+Same setup with these differences:
+
+1. Register an enterprise service account with verified **Customer Service API** permission
+2. Set `"channel_type": "wechatmp_service"` in `config.json`
+3. Replies can be proactively pushed to users
+
+```json
+{
+  "channel_type": "wechatmp_service",
+  "wechatmp_app_id": "YOUR_APP_ID",
+  "wechatmp_app_secret": "YOUR_APP_SECRET",
+  "wechatmp_aes_key": "",
+  "wechatmp_token": "YOUR_TOKEN",
+  "wechatmp_port": 80
+}
+```
--- a/docs/en/channels/wecom.mdx
+++ b/docs/en/channels/wecom.mdx
@@ -0,0 +1,59 @@
+---
+title: WeCom
+description: Integrate CowAgent into WeCom enterprise app
+---
+
+Integrate CowAgent into WeCom through a custom enterprise app, supporting one-on-one chat for internal employees.
+
+<Note>
+  WeCom only supports Docker deployment or server Python deployment. Local run mode is not supported.
+</Note>
+
+## 1. Prerequisites
+
+Required resources:
+
+1. A server with public IP
+2. A registered WeCom account (individual registration is possible, but cannot be certified)
+3. Certified WeCom requires a domain with corresponding entity filing
+
+## 2. Create WeCom App
+
+1. Get **Corp ID** from **My Enterprise** in [WeCom Admin Console](https://work.weixin.qq.com/wework_admin/frame#profile)
+2. Switch to **Application Management**, click Create Application, record `AgentId` and `Secret`
+3. Click **Set API Reception**, configure application interface:
+   - URL format: `http://ip:port/wxcomapp` (certified enterprises must use filed domain)
+   - Generate random `Token` and `EncodingAESKey` and save
+
+## 3. Configuration and Run
+
+```json
+{
+  "channel_type": "wechatcom_app",
+  "wechatcom_corp_id": "YOUR_CORP_ID",
+  "wechatcomapp_token": "YOUR_TOKEN",
+  "wechatcomapp_secret": "YOUR_SECRET",
+  "wechatcomapp_agent_id": "YOUR_AGENT_ID",
+  "wechatcomapp_aes_key": "YOUR_AES_KEY",
+  "wechatcomapp_port": 9898
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `wechatcom_corp_id` | Corp ID |
+| `wechatcomapp_token` | Token from API reception config |
+| `wechatcomapp_secret` | App Secret |
+| `wechatcomapp_agent_id` | App AgentId |
+| `wechatcomapp_aes_key` | EncodingAESKey from API reception config |
+| `wechatcomapp_port` | Listen port, default 9898 |
+
+After starting the program, return to WeCom Admin Console to save **Message Server Configuration**, and add the server IP to **Enterprise Trusted IPs**.
+
+<Warning>
+  If configuration fails: 1. Ensure firewall and security group allow the port; 2. Verify all parameters are consistent; 3. Certified enterprises must configure a filed domain.
+</Warning>
+
+## 4. Usage
+
+Search for the app name in WeCom to start chatting. To allow external WeChat users, share the invite QR code from **My Enterprise → WeChat Plugin**.
--- a/docs/en/guide/manual-install.mdx
+++ b/docs/en/guide/manual-install.mdx
@@ -0,0 +1,113 @@
+---
+title: Manual Install
+description: Deploy CowAgent manually (source code / Docker)
+---
+
+## Source Code Deployment
+
+### 1. Clone the project
+
+```bash
+git clone https://github.com/zhayujie/chatgpt-on-wechat
+cd chatgpt-on-wechat/
+```
+
+<Tip>
+  For network issues, use the mirror: https://gitee.com/zhayujie/chatgpt-on-wechat
+</Tip>
+
+### 2. Install dependencies
+
+Core dependencies (required):
+
+```bash
+pip3 install -r requirements.txt
+```
+
+Optional dependencies (recommended):
+
+```bash
+pip3 install -r requirements-optional.txt
+```
+
+### 3. Configure
+
+Copy the config template and edit:
+
+```bash
+cp config-template.json config.json
+```
+
+Fill in model API keys, channel type, and other settings in `config.json`. See the [model docs](/en/models/index) for details.
+
+### 4. Run
+
+**Local run:**
+
+```bash
+python3 app.py
+```
+
+By default, the Web service starts. Access `http://localhost:9899/chat` to chat.
+
+**Background run on server:**
+
+```bash
+nohup python3 app.py & tail -f nohup.out
+```
+
+## Docker Deployment
+
+Docker deployment does not require cloning source code or installing dependencies. For Agent mode, source deployment is recommended for broader system access.
+
+<Note>
+  Requires [Docker](https://docs.docker.com/engine/install/) and docker-compose.
+</Note>
+
+**1. Download config**
+
+```bash
+wget https://cdn.link-ai.tech/code/cow/docker-compose.yml
+```
+
+Edit `docker-compose.yml` with your configuration.
+
+**2. Start container**
+
+```bash
+sudo docker compose up -d
+```
+
+**3. View logs**
+
+```bash
+sudo docker logs -f chatgpt-on-wechat
+```
+
+## Core Configuration
+
+```json
+{
+  "channel_type": "web",
+  "model": "MiniMax-M2.5",
+  "agent": true,
+  "agent_workspace": "~/cow",
+  "agent_max_context_tokens": 40000,
+  "agent_max_context_turns": 30,
+  "agent_max_steps": 15
+}
+```
+
+| Parameter | Description | Default |
+| --- | --- | --- |
+| `channel_type` | Channel type | `web` |
+| `model` | Model name | `MiniMax-M2.5` |
+| `agent` | Enable Agent mode | `true` |
+| `agent_workspace` | Agent workspace path | `~/cow` |
+| `agent_max_context_tokens` | Max context tokens | `40000` |
+| `agent_max_context_turns` | Max context turns | `30` |
+| `agent_max_steps` | Max decision steps per task | `15` |
+
+<Tip>
+  Full configuration options are in the project [`config.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py).
+</Tip>
--- a/docs/en/guide/quick-start.mdx
+++ b/docs/en/guide/quick-start.mdx
@@ -0,0 +1,39 @@
+---
+title: One-click Install
+description: One-click install and manage CowAgent with scripts
+---
+
+The project provides scripts for one-click install, configuration, startup, and management. Script-based deployment is recommended for quick setup.
+
+Supports Linux, macOS, and Windows. Requires Python 3.7-3.12 (3.9 recommended).
+
+## Install Command
+
+```bash
+bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
+```
+
+The script automatically performs these steps:
+
+1. Check Python environment (requires Python 3.7+)
+2. Install required tools (git, curl, etc.)
+3. Clone project to `~/chatgpt-on-wechat`
+4. Install Python dependencies
+5. Guided configuration for AI model and channel
+6. Start service
+
+By default, the Web service starts after installation. Access `http://localhost:9899/chat` to begin chatting.
+
+## Management Commands
+
+After installation, use these commands to manage the service:
+
+| Command | Description |
+| --- | --- |
+| `./run.sh start` | Start service |
+| `./run.sh stop` | Stop service |
+| `./run.sh restart` | Restart service |
+| `./run.sh status` | Check run status |
+| `./run.sh logs` | View real-time logs |
+| `./run.sh config` | Reconfigure |
+| `./run.sh update` | Update project code |
--- a/docs/en/intro/architecture.mdx
+++ b/docs/en/intro/architecture.mdx
@@ -0,0 +1,71 @@
+---
+title: Architecture
+description: CowAgent 2.0 system architecture and core design
+---
+
+CowAgent 2.0 has evolved from a simple chatbot into a super intelligent assistant with Agent architecture, featuring autonomous thinking, task planning, long-term memory, and skill extensibility.
+
+## System Architecture
+
+CowAgent's architecture consists of the following core modules:
+
+<img src="https://cdn.link-ai.tech/doc/68ef7b212c6f791e0e74314b912149f9-sz_5847990.png" alt="CowAgent Architecture" />
+
+### Core Modules
+
+| Module | Description |
+| --- | --- |
+| **Channels** | Message channel layer for receiving and sending messages. Supports Web, Feishu, DingTalk, WeCom, WeChat Official Account, and more |
+| **Agent Core** | Agent engine including task planning, memory system, and skills engine |
+| **Tools** | Tool layer for Agent to access OS resources. 10+ built-in tools |
+| **Models** | Model layer with unified access to mainstream LLMs |
+
+## Agent Mode Workflow
+
+When Agent mode is enabled, CowAgent runs as an autonomous agent with the following workflow:
+
+1. **Receive Message** — Receive user input through channels
+2. **Understand Intent** — Analyze task requirements and context
+3. **Plan Task** — Break complex tasks into multiple steps
+4. **Invoke Tools** — Select and execute appropriate tools for each step
+5. **Update Memory** — Store important information in long-term memory
+6. **Return Result** — Send execution results back to the user
+
+## Workspace Directory Structure
+
+The Agent workspace is located at `~/cow` by default and stores system prompts, memory files, and skill files:
+
+```
+~/cow/
+├── system.md          # Agent system prompt
+├── user.md            # User profile
+├── memory/            # Long-term memory storage
+│   ├── core.md        # Core memory
+│   └── daily/         # Daily memory
+├── skills/            # Custom skills
+│   ├── skill-1/
+│   └── skill-2/
+└── .env               # Secret keys for skills
+```
+
+## Core Configuration
+
+Configure Agent mode parameters in `config.json`:
+
+```json
+{
+  "agent": true,
+  "agent_workspace": "~/cow",
+  "agent_max_context_tokens": 40000,
+  "agent_max_context_turns": 30,
+  "agent_max_steps": 15
+}
+```
+
+| Parameter | Description | Default |
+| --- | --- | --- |
+| `agent` | Enable Agent mode | `true` |
+| `agent_workspace` | Workspace path | `~/cow` |
+| `agent_max_context_tokens` | Max context tokens | `40000` |
+| `agent_max_context_turns` | Max context turns | `30` |
+| `agent_max_steps` | Max decision steps per task | `15` |
--- a/docs/en/intro/features.mdx
+++ b/docs/en/intro/features.mdx
@@ -0,0 +1,105 @@
+---
+title: Features
+description: CowAgent long-term memory, task planning, and skills system in detail
+---
+
+## 1. Long-term Memory
+
+The memory system enables the Agent to remember important information over time. The Agent proactively stores information when users share preferences, decisions, or key facts, and automatically extracts summaries when conversations reach a certain length. Memory is divided into core memory and daily memory, with hybrid retrieval supporting both keyword search and vector search.
+
+On first launch, the Agent proactively asks the user for key information and records it in the workspace (default `~/cow`) — including agent settings, user identity, and memory files.
+
+In subsequent long-term conversations, the Agent intelligently stores or retrieves memory as needed, continuously updating its own settings, user preferences, and memory files, summarizing experiences and lessons learned — truly achieving autonomous thinking and continuous growth.
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260203000455.png" width="800" />
+</Frame>
+
+## 2. Task Planning and Tool Use
+
+Tools are the core of how the Agent accesses operating system resources. The Agent intelligently selects and invokes tools based on task requirements, performing file read/write, command execution, scheduled tasks, and more. Built-in tools are implemented in the project's `agent/tools/` directory.
+
+**Key tools:** file read/write/edit, Bash terminal, file send, scheduler, memory search, web search, environment config, and more.
+
+### 2.1 Terminal and File Access
+
+Access to the OS terminal and file system is the most fundamental and core capability. Many other tools and skills build on top of this. Users can interact with the Agent from a mobile device to operate resources on their personal computer or server:
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202181130.png" width="800" />
+</Frame>
+
+### 2.2 Programming Capability
+
+Combining programming and system access, the Agent can execute the complete **Vibecoding workflow** — from information search, asset generation, coding, testing, deployment, Nginx configuration, to publishing — all triggered by a single command from your phone:
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260203121008.png" width="800" />
+</Frame>
+
+### 2.3 Scheduled Tasks
+
+The `scheduler` tool enables dynamic scheduled tasks, supporting **one-time tasks, fixed intervals, and Cron expressions**. Tasks can be triggered as either a **fixed message send** or an **Agent dynamic task** execution:
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202195402.png" width="800" />
+</Frame>
+
+### 2.4 Environment Variable Management
+
+Secrets required by skills are stored in an environment variable file, managed by the `env_config` tool. You can update secrets through conversation, with built-in security protection and desensitization:
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202234939.png" width="800" />
+</Frame>
+
+## 3. Skills System
+
+The Skills system provides infinite extensibility for the Agent. Each Skill consists of a description file, execution scripts (optional), and resources (optional), describing how to complete specific types of tasks. Skills allow the Agent to follow instructions for complex workflows, invoke tools, or integrate third-party systems.
+
+- **Built-in skills:** Located in the project's `skills/` directory, including skill creator, image recognition, LinkAI agent, web fetch, and more. Built-in skills are automatically enabled based on dependency conditions (API keys, system commands, etc.).
+- **Custom skills:** Created by users through conversation, stored in the workspace (`~/cow/skills/`), capable of implementing any complex business process or third-party integration.
+
+### 3.1 Creating Skills
+
+The `skill-creator` skill enables rapid skill creation through conversation. You can ask the Agent to codify a workflow as a skill, or send any API documentation and examples for the Agent to complete the integration directly:
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202202247.png" width="800" />
+</Frame>
+
+### 3.2 Web Search and Image Recognition
+
+- **Web search:** Built-in `web_search` tool, supports multiple search engines. Configure `BOCHA_API_KEY` or `LINKAI_API_KEY` to enable.
+- **Image recognition:** Built-in `openai-image-vision` skill, supports `gpt-4.1-mini`, `gpt-4.1`, and other models. Requires `OPENAI_API_KEY`.
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202213219.png" width="800" />
+</Frame>
+
+### 3.3 Third-party Knowledge Bases and Plugins
+
+The `linkai-agent` skill makes all agents on [LinkAI](https://link-ai.tech/) available as Skills for the Agent, enabling multi-agent decision making.
+
+Configuration: set `LINKAI_API_KEY` via `env_config`, then add agent descriptions in `skills/linkai-agent/config.json`:
+
+```json
+{
+  "apps": [
+    {
+      "app_code": "G7z6vKwp",
+      "app_name": "LinkAI Customer Support",
+      "app_description": "Select only when the user needs help with LinkAI platform questions"
+    },
+    {
+      "app_code": "SFY5x7JR",
+      "app_name": "Content Creator",
+      "app_description": "Use only when the user needs to create images or videos"
+    }
+  ]
+}
+```
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202234350.png" width="750" />
+</Frame>
--- a/docs/en/intro/index.mdx
+++ b/docs/en/intro/index.mdx
@@ -0,0 +1,68 @@
+---
+title: Introduction
+description: CowAgent - AI Super Assistant powered by LLMs
+---
+
+<img src="https://cdn.link-ai.tech/doc/78c5dd674e2c828642ecc0406669fed7.png" alt="CowAgent" width="600px"/>
+
+**CowAgent** is an AI super assistant powered by LLMs with autonomous task planning, long-term memory, skills system, multimodal messages, multiple model support, and multi-platform deployment.
+
+CowAgent can proactively think and plan tasks, operate computers and external resources, create and execute Skills, and continuously grow with long-term memory. It supports flexible switching between multiple models, handles text, voice, images, files and other multimodal messages, and can be integrated into web, Feishu, DingTalk, WeCom, and WeChat Official Account. It runs 7x24 hours on your personal computer or server.
+
+<Card title="GitHub" icon="github" href="https://github.com/zhayujie/chatgpt-on-wechat">
+  github.com/zhayujie/chatgpt-on-wechat
+</Card>
+
+## Core Capabilities
+
+<CardGroup cols={2}>
+  <Card title="Autonomous Task Planning" icon="brain" href="/en/intro/architecture">
+    Understands complex tasks and autonomously plans execution, continuously thinking and invoking tools until goals are achieved. Supports accessing file systems, terminals, browsers, schedulers, and other system resources through tools.
+  </Card>
+  <Card title="Long-term Memory" icon="database" href="/en/memory">
+    Automatically persists conversation memory to local files and databases, including core memory and daily memory, with keyword and vector retrieval support.
+  </Card>
+  <Card title="Skills System" icon="puzzle-piece" href="/en/skills/index">
+    Implements a Skills creation and execution engine with built-in skills, and supports custom Skills development through natural language conversation.
+  </Card>
+  <Card title="Multimodal Messages" icon="image" href="/en/channels/web">
+    Supports parsing, processing, generating, and sending text, images, voice, files, and other message types.
+  </Card>
+  <Card title="Multiple Model Support" icon="microchip" href="/en/models/index">
+    Supports mainstream model providers including OpenAI, Claude, Gemini, DeepSeek, MiniMax, GLM, Qwen, Kimi, Doubao, and more.
+  </Card>
+  <Card title="Multi-platform Deployment" icon="server" href="/en/channels/web">
+    Runs on local computers or servers, integrable into web, Feishu, DingTalk, WeChat Official Account, and WeCom applications.
+  </Card>
+</CardGroup>
+
+## Quick Experience
+
+Run the following command in your terminal for one-click install, configuration, and startup:
+
+```bash
+bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
+```
+
+By default, the Web service starts after running. Access `http://localhost:9899/chat` to chat in the web interface.
+
+<CardGroup cols={2}>
+  <Card title="Quick Start" icon="rocket" href="/en/guide/quick-start">
+    Complete installation and run guide
+  </Card>
+  <Card title="Architecture" icon="sitemap" href="/en/intro/architecture">
+    CowAgent system architecture design
+  </Card>
+</CardGroup>
+
+## Disclaimer
+
+1. This project follows the [MIT License](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/LICENSE) and is intended for technical research and learning. Users must comply with local laws, regulations, policies, and corporate bylaws. Any illegal or rights-infringing use is prohibited.
+2. Agent mode consumes more tokens than normal chat mode. Choose models based on effectiveness and cost. Agent has access to the host operating system — deploy with caution.
+3. CowAgent focuses on open-source development and does not participate in, authorize, or issue any cryptocurrency.
+
+## Community
+
+Add our assistant on WeChat to join the open-source community:
+
+<img width="140" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/open-community.png" />
--- a/docs/en/memory.mdx
+++ b/docs/en/memory.mdx
@@ -0,0 +1,64 @@
+---
+title: Memory
+description: CowAgent long-term memory system
+---
+
+The memory system enables the Agent to remember important information over time, continuously accumulating experience, understanding user preferences, and truly achieving autonomous thinking and continuous growth.
+
+## How It Works
+
+The Agent proactively stores memory in the following scenarios:
+
+- **When user shares important information** — Automatically identifies and stores preferences, decisions, facts, and other key information
+- **When conversation reaches a certain length** — Automatically extracts summaries to prevent information loss
+- **When retrieval is needed** — Intelligently searches historical memory, combining context for responses
+
+## Memory Types
+
+### Core Memory
+
+Stored in `~/cow/memory/core.md`, containing long-term user preferences, important decisions, key facts, and other information that doesn't fade over time.
+
+### Daily Memory
+
+Stored in `~/cow/memory/daily/` directory, organized by date, recording daily conversation summaries and key events.
+
+## First Launch
+
+On first launch, the Agent will proactively ask the user for key information and save it to the workspace (default `~/cow`):
+
+| File | Description |
+| --- | --- |
+| `system.md` | Agent system prompt and behavior settings |
+| `user.md` | User identity information and preferences |
+| `memory/core.md` | Core memory |
+| `memory/daily/` | Daily memory directory |
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260203000455.png" width="800" />
+</Frame>
+
+## Memory Retrieval
+
+The memory system supports hybrid retrieval modes:
+
+- **Keyword retrieval** — Match historical memory based on keywords
+- **Vector retrieval** — Semantic similarity search, finds relevant memory even with different wording
+
+The Agent automatically triggers memory retrieval during conversation as needed, incorporating relevant historical information into context.
+
+## Configuration
+
+```json
+{
+  "agent_workspace": "~/cow",
+  "agent_max_context_tokens": 40000,
+  "agent_max_context_turns": 30
+}
+```
+
+| Parameter | Description | Default |
+| --- | --- | --- |
+| `agent_workspace` | Workspace path, memory files stored under this directory | `~/cow` |
+| `agent_max_context_tokens` | Max context tokens, affects short-term memory capacity | `40000` |
+| `agent_max_context_turns` | Max context turns, oldest conversations discarded when exceeded | `30` |
--- a/docs/en/models/claude.mdx
+++ b/docs/en/models/claude.mdx
@@ -0,0 +1,17 @@
+---
+title: Claude
+description: Claude model configuration
+---
+
+```json
+{
+  "model": "claude-sonnet-4-6",
+  "claude_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `claude-sonnet-4-6`, `claude-opus-4-6`, `claude-sonnet-4-5`, `claude-sonnet-4-0`, `claude-3-5-sonnet-latest`, etc. See [official models](https://docs.anthropic.com/en/docs/about-claude/models/overview) |
+| `claude_api_key` | Create at [Claude Console](https://console.anthropic.com/settings/keys) |
+| `claude_api_base` | Optional. Defaults to `https://api.anthropic.com/v1`. Change to use third-party proxy |
--- a/docs/en/models/deepseek.mdx
+++ b/docs/en/models/deepseek.mdx
@@ -0,0 +1,22 @@
+---
+title: DeepSeek
+description: DeepSeek model configuration
+---
+
+Use OpenAI-compatible configuration:
+
+```json
+{
+  "model": "deepseek-chat",
+  "bot_type": "chatGPT",
+  "open_ai_api_key": "YOUR_API_KEY",
+  "open_ai_api_base": "https://api.deepseek.com/v1"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | `deepseek-chat` (DeepSeek-V3), `deepseek-reasoner` (DeepSeek-R1) |
+| `bot_type` | Must be `chatGPT` (OpenAI-compatible mode) |
+| `open_ai_api_key` | Create at [DeepSeek Platform](https://platform.deepseek.com/api_keys) |
+| `open_ai_api_base` | DeepSeek platform BASE URL |
--- a/docs/en/models/doubao.mdx
+++ b/docs/en/models/doubao.mdx
@@ -0,0 +1,17 @@
+---
+title: Doubao (ByteDance)
+description: Doubao (Volcano Ark) model configuration
+---
+
+```json
+{
+  "model": "doubao-seed-2-0-code-preview-260215",
+  "ark_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `doubao-seed-2-0-code-preview-260215`, `doubao-seed-2-0-pro-260215`, `doubao-seed-2-0-lite-260215`, etc. |
+| `ark_api_key` | Create at [Volcano Ark Console](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) |
+| `ark_base_url` | Optional. Defaults to `https://ark.cn-beijing.volces.com/api/v3` |
--- a/docs/en/models/gemini.mdx
+++ b/docs/en/models/gemini.mdx
@@ -0,0 +1,16 @@
+---
+title: Gemini
+description: Google Gemini model configuration
+---
+
+```json
+{
+  "model": "gemini-3.1-pro-preview",
+  "gemini_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `gemini-3.1-pro-preview`, `gemini-3-flash-preview`, `gemini-3-pro-preview`, `gemini-2.5-pro`, `gemini-2.0-flash`, etc. See [official docs](https://ai.google.dev/gemini-api/docs/models) |
+| `gemini_api_key` | Create at [Google AI Studio](https://aistudio.google.com/app/apikey) |
--- a/docs/en/models/glm.mdx
+++ b/docs/en/models/glm.mdx
@@ -0,0 +1,27 @@
+---
+title: GLM (Zhipu AI)
+description: Zhipu AI GLM model configuration
+---
+
+```json
+{
+  "model": "glm-5",
+  "zhipu_ai_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `glm-5`, `glm-4.7`, `glm-4-plus`, `glm-4-flash`, `glm-4-air`, etc. See [model codes](https://bigmodel.cn/dev/api/normal-model/glm-4) |
+| `zhipu_ai_api_key` | Create at [Zhipu AI Console](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) |
+
+OpenAI-compatible configuration is also supported:
+
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "glm-5",
+  "open_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
+  "open_ai_api_key": "YOUR_API_KEY"
+}
+```
--- a/docs/en/models/index.mdx
+++ b/docs/en/models/index.mdx
@@ -0,0 +1,55 @@
+---
+title: Models Overview
+description: Supported models and recommended choices for CowAgent
+---
+
+CowAgent supports mainstream LLMs from domestic and international providers. Model interfaces are implemented in the project's `models/` directory.
+
+<Note>
+  For Agent mode, the following models are recommended based on quality and cost: MiniMax-M2.5, glm-5, kimi-k2.5, qwen3.5-plus, claude-sonnet-4-6, gemini-3.1-pro-preview
+</Note>
+
+## Configuration
+
+Configure the model name and API key in `config.json` according to your chosen model. Each model also supports OpenAI-compatible access by setting `bot_type` to `chatGPT` and configuring `open_ai_api_base` and `open_ai_api_key`.
+
+You can also use the [LinkAI](https://link-ai.tech) platform interface to flexibly switch between multiple models with support for knowledge base, workflows, and other Agent capabilities.
+
+## Supported Models
+
+<CardGroup cols={2}>
+  <Card title="MiniMax" href="/en/models/minimax">
+    MiniMax-M2.5 and other series models
+  </Card>
+  <Card title="GLM (Zhipu AI)" href="/en/models/glm">
+    glm-5, glm-4.7 and other series models
+  </Card>
+  <Card title="Qwen (Tongyi Qianwen)" href="/en/models/qwen">
+    qwen3.5-plus, qwen3-max and more
+  </Card>
+  <Card title="Kimi" href="/en/models/kimi">
+    kimi-k2.5, kimi-k2 and more
+  </Card>
+  <Card title="Doubao (ByteDance)" href="/en/models/doubao">
+    doubao-seed series models
+  </Card>
+  <Card title="Claude" href="/en/models/claude">
+    claude-sonnet-4-6 and more
+  </Card>
+  <Card title="Gemini" href="/en/models/gemini">
+    gemini-3.1-pro-preview and more
+  </Card>
+  <Card title="OpenAI" href="/en/models/openai">
+    gpt-4.1, o-series and more
+  </Card>
+  <Card title="DeepSeek" href="/en/models/deepseek">
+    deepseek-chat, deepseek-reasoner
+  </Card>
+  <Card title="LinkAI" href="/en/models/linkai">
+    Unified multi-model interface + knowledge base
+  </Card>
+</CardGroup>
+
+<Tip>
+  For a full list of model names, refer to the project's [`common/const.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py) file.
+</Tip>
--- a/docs/en/models/kimi.mdx
+++ b/docs/en/models/kimi.mdx
@@ -0,0 +1,27 @@
+---
+title: Kimi (Moonshot)
+description: Kimi (Moonshot) model configuration
+---
+
+```json
+{
+  "model": "kimi-k2.5",
+  "moonshot_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `kimi-k2.5`, `kimi-k2`, `moonshot-v1-8k`, `moonshot-v1-32k`, `moonshot-v1-128k` |
+| `moonshot_api_key` | Create at [Moonshot Console](https://platform.moonshot.cn/console/api-keys) |
+
+OpenAI-compatible configuration is also supported:
+
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "kimi-k2.5",
+  "open_ai_api_base": "https://api.moonshot.cn/v1",
+  "open_ai_api_key": "YOUR_API_KEY"
+}
+```
--- a/docs/en/models/linkai.mdx
+++ b/docs/en/models/linkai.mdx
@@ -0,0 +1,23 @@
+---
+title: LinkAI
+description: Unified access to multiple models via LinkAI platform
+---
+
+The [LinkAI](https://link-ai.tech) platform lets you flexibly switch between OpenAI, Claude, Gemini, DeepSeek, Qwen, Kimi, and other models, with support for knowledge base, workflows, plugins, and other Agent capabilities.
+
+```json
+{
+  "use_linkai": true,
+  "linkai_api_key": "YOUR_API_KEY",
+  "linkai_app_code": "YOUR_APP_CODE"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `use_linkai` | Set to `true` to enable LinkAI interface |
+| `linkai_api_key` | Create at [LinkAI Console](https://link-ai.tech/console/interface) |
+| `linkai_app_code` | Optional. Code of the LinkAI agent (app or workflow) |
+| `model` | Leave empty to use the agent's default model. Can be switched flexibly on the platform. All models in the [model list](https://link-ai.tech/console/models) are supported |
+
+See the [API documentation](https://docs.link-ai.tech/platform/api) for more details.
--- a/docs/en/models/minimax.mdx
+++ b/docs/en/models/minimax.mdx
@@ -0,0 +1,27 @@
+---
+title: MiniMax
+description: MiniMax model configuration
+---
+
+```json
+{
+  "model": "MiniMax-M2.5",
+  "minimax_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `MiniMax-M2.5`, `MiniMax-M2.1`, `MiniMax-M2.1-lightning`, `MiniMax-M2`, etc. |
+| `minimax_api_key` | Create at [MiniMax Console](https://platform.minimaxi.com/user-center/basic-information/interface-key) |
+
+OpenAI-compatible configuration is also supported:
+
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "MiniMax-M2.5",
+  "open_ai_api_base": "https://api.minimaxi.com/v1",
+  "open_ai_api_key": "YOUR_API_KEY"
+}
+```
--- a/docs/en/models/openai.mdx
+++ b/docs/en/models/openai.mdx
@@ -0,0 +1,19 @@
+---
+title: OpenAI
+description: OpenAI model configuration
+---
+
+```json
+{
+  "model": "gpt-4.1-mini",
+  "open_ai_api_key": "YOUR_API_KEY",
+  "open_ai_api_base": "https://api.openai.com/v1"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Matches the [model parameter](https://platform.openai.com/docs/models) of the OpenAI API. Supports o-series, gpt-5.2, gpt-5.1, gpt-4.1, etc. |
+| `open_ai_api_key` | Create at [OpenAI Platform](https://platform.openai.com/api-keys) |
+| `open_ai_api_base` | Optional. Change to use third-party proxy |
+| `bot_type` | Not required for official OpenAI models. Set to `chatGPT` when using Claude or other non-OpenAI models via proxy |
--- a/docs/en/models/qwen.mdx
+++ b/docs/en/models/qwen.mdx
@@ -0,0 +1,27 @@
+---
+title: Qwen (Tongyi Qianwen)
+description: Tongyi Qianwen model configuration
+---
+
+```json
+{
+  "model": "qwen3.5-plus",
+  "dashscope_api_key": "YOUR_API_KEY"
+}
+```
+
+| Parameter | Description |
+| --- | --- |
+| `model` | Options include `qwen3.5-plus`, `qwen3-max`, `qwen-max`, `qwen-plus`, `qwen-turbo`, `qwq-plus`, etc. |
+| `dashscope_api_key` | Create at [Bailian Console](https://bailian.console.aliyun.com/?tab=model#/api-key). See [official docs](https://bailian.console.aliyun.com/?tab=api#/api) |
+
+OpenAI-compatible configuration is also supported:
+
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "qwen3.5-plus",
+  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+  "open_ai_api_key": "YOUR_API_KEY"
+}
+```
--- a/docs/en/releases/overview.mdx
+++ b/docs/en/releases/overview.mdx
@@ -0,0 +1,22 @@
+---
+title: Changelog
+description: CowAgent version history
+---
+
+| Version | Date | Description |
+| --- | --- | --- |
+| [2.0.1](/en/releases/v2.0.1) | 2026.02.27 | Built-in Web Search tool, smart context management, multiple fixes |
+| [2.0.0](/en/releases/v2.0.0) | 2026.02.03 | Full upgrade to AI super assistant |
+| 1.7.6 | 2025.05.23 | Web Channel optimization, AgentMesh plugin |
+| 1.7.5 | 2025.04.11 | DeepSeek model |
+| 1.7.4 | 2024.12.13 | Gemini 2.0 model, Web Channel |
+| 1.7.3 | 2024.10.31 | Stability improvements, database features |
+| 1.7.2 | 2024.09.26 | One-click install script, o1 model |
+| 1.7.0 | 2024.08.02 | iFlytek 4.0 model, knowledge base references |
+| 1.6.9 | 2024.07.19 | gpt-4o-mini, Alibaba voice recognition |
+| 1.6.8 | 2024.07.05 | Claude 3.5, Gemini 1.5 Pro |
+| 1.6.0 | 2024.04.26 | Kimi integration, gpt-4-turbo upgrade |
+| 1.5.0 | 2023.11.10 | gpt-4-turbo, dall-e-3, tts multimodal |
+| 1.0.0 | 2022.12.12 | Project created, first ChatGPT integration |
+
+See [GitHub Releases](https://github.com/zhayujie/chatgpt-on-wechat/releases) for full history.
--- a/docs/en/releases/v2.0.0.mdx
+++ b/docs/en/releases/v2.0.0.mdx
@@ -0,0 +1,63 @@
+---
+title: v2.0.0
+description: CowAgent 2.0 - Full upgrade from chatbot to AI super assistant
+---
+
+CowAgent 2.0 is a comprehensive upgrade from a chatbot to an **AI super assistant** — capable of autonomous thinking and task planning, long-term memory, operating computers, and creating and executing skills.
+
+**Release Date**: 2026.02.03 | [GitHub Release](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/2.0.0)
+
+## Key Updates
+
+### Agent Core
+
+- **Complex Task Planning**: Autonomous planning with multi-turn reasoning
+- **Long-term Memory**: Persistent memory with keyword and vector search
+- **Built-in Tools**: 10+ tools including file ops, Bash, browser, scheduler
+- **Web search**: Built-in `web_search` tool, supports multiple search engines, configure corresponding API key to use
+- **Skills System**: Skill engine with built-in and custom skill support
+- **Security & Cost**: Secret management, prompt controls, token limits
+
+### Other
+
+- **Channels**: Feishu/DingTalk WebSocket support, image/file messages
+- **Models**: claude-sonnet-4-5, gemini-3-pro-preview, glm-4.7, MiniMax-M2.1, qwen3-max
+- **Deployment**: One-click install, configure, run, and management script
+
+## Long-term Memory
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260203000455.png" width="800" />
+</Frame>
+
+## Task Planning & Tools
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202181130.png" width="800" />
+</Frame>
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260203121008.png" width="800" />
+</Frame>
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202195402.png" width="800" />
+</Frame>
+
+## Skills System
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202202247.png" width="800" />
+</Frame>
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202213219.png" width="800" />
+</Frame>
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202234350.png" width="750" />
+</Frame>
+
+## Contributing
+
+Welcome to [submit feedback](https://github.com/zhayujie/chatgpt-on-wechat/issues) and [contribute code](https://github.com/zhayujie/chatgpt-on-wechat/pulls).
--- a/docs/en/releases/v2.0.1.mdx
+++ b/docs/en/releases/v2.0.1.mdx
@@ -0,0 +1,36 @@
+---
+title: v2.0.1
+description: CowAgent 2.0.1 - Built-in Web Search, smart context management, multiple fixes
+---
+
+**Release Date**: 2026.02.27 | [Full Changelog](https://github.com/zhayujie/chatgpt-on-wechat/compare/2.0.0..2.0.1)
+
+## New Features
+
+- **Built-in Web Search tool**: Integrated web search as a built-in Agent tool, reducing decision cost ([4f0ea5d](https://github.com/zhayujie/chatgpt-on-wechat/commit/4f0ea5d7568d61db91ff69c91c429e785fd1b1c2))
+- **Claude Opus 4.6 model support**: Added support for Claude Opus 4.6 model ([#2661](https://github.com/zhayujie/chatgpt-on-wechat/pull/2661))
+- **WeCom image recognition**: Support image message recognition in WeCom channel ([#2667](https://github.com/zhayujie/chatgpt-on-wechat/pull/2667))
+
+## Improvements
+
+- **Smart context management**: Resolved chat context overflow with intelligent context trimming strategy to prevent token limits ([cea7fb7](https://github.com/zhayujie/chatgpt-on-wechat/commit/cea7fb7490c53454602bf05955a0e9f059bcf0fd), [8acf2db](https://github.com/zhayujie/chatgpt-on-wechat/commit/8acf2dbdfe713b84ad74b761b7f86674b1c1904d)) [#2663](https://github.com/zhayujie/chatgpt-on-wechat/issues/2663)
+- **Runtime info dynamic update**: Automatic update of timestamps and other runtime info in system prompts via dynamic functions ([#2655](https://github.com/zhayujie/chatgpt-on-wechat/pull/2655), [#2657](https://github.com/zhayujie/chatgpt-on-wechat/pull/2657))
+- **Skill prompt optimization**: Improved Skill system prompt generation, simplified tool descriptions for better Agent performance ([6c21833](https://github.com/zhayujie/chatgpt-on-wechat/commit/6c218331b1f1208ea8be6bf226936d3b556ade3e))
+- **GLM custom API Base URL**: Support custom API Base URL for GLM models ([#2660](https://github.com/zhayujie/chatgpt-on-wechat/pull/2660))
+- **Startup script optimization**: Improved `run.sh` script interaction and configuration flow ([#2656](https://github.com/zhayujie/chatgpt-on-wechat/pull/2656))
+- **Decision step logging**: Added Agent decision step logging for debugging ([cb303e6](https://github.com/zhayujie/chatgpt-on-wechat/commit/cb303e6109c50c8dfef1f5e6c1ec47223bf3cd11))
+
+## Bug Fixes
+
+- **Scheduler memory loss**: Fixed memory loss caused by Scheduler dispatcher ([a77a874](https://github.com/zhayujie/chatgpt-on-wechat/commit/a77a8741b500a408c6f5c8868856fb4b018fe9db))
+- **Empty tool calls & long results**: Fixed handling of empty tool calls and excessively long tool results ([0542700](https://github.com/zhayujie/chatgpt-on-wechat/commit/0542700f9091ebb08c1a56103b0f0f45f24aa621))
+- **OpenAI Function Call**: Fixed function call compatibility with OpenAI models ([158c87a](https://github.com/zhayujie/chatgpt-on-wechat/commit/158c87ab8b05bae054cc1b4eacdbb64fc1062ba9))
+- **Claude tool name field**: Removed extraneous tool name field from Claude model responses ([eec10cb](https://github.com/zhayujie/chatgpt-on-wechat/commit/eec10cb5db6a3d5bc12ef606606532237d2c5f6e))
+- **MiniMax reasoning**: Optimized MiniMax model reasoning content handling, hidden thinking process output ([c72cda3](https://github.com/zhayujie/chatgpt-on-wechat/commit/c72cda33864bd1542012ee6e0a8bd8c6c88cb5ed), [72b1cac](https://github.com/zhayujie/chatgpt-on-wechat/commit/72b1cacea1ba0d1f3dedacbab2e088e98fd7e172))
+- **GLM thinking process**: Hidden GLM model thinking process display ([72b1cac](https://github.com/zhayujie/chatgpt-on-wechat/commit/72b1cacea1ba0d1f3dedacbab2e088e98fd7e172))
+- **Feishu connection & SSL**: Fixed Feishu channel SSL certificate errors and connection issues ([229b14b](https://github.com/zhayujie/chatgpt-on-wechat/commit/229b14b6fcabe7123d53cab1dea39f38dab26d6d), [8674421](https://github.com/zhayujie/chatgpt-on-wechat/commit/867442155e7f095b4f38b0856f8c1d8312b5fcf7))
+- **model_type validation**: Fixed `AttributeError` caused by non-string `model_type` ([#2666](https://github.com/zhayujie/chatgpt-on-wechat/pull/2666))
+
+## Platform Compatibility
+
+- **Windows compatibility**: Fixed path handling, file encoding, and `os.getuid()` unavailability on Windows across multiple tool modules ([051ffd7](https://github.com/zhayujie/chatgpt-on-wechat/commit/051ffd78a372f71a967fd3259e37fe19131f83cf), [5264f7c](https://github.com/zhayujie/chatgpt-on-wechat/commit/5264f7ce18360ee4db5dcb4ebe67307977d40014))
--- a/docs/en/skills/image-vision.mdx
+++ b/docs/en/skills/image-vision.mdx
@@ -0,0 +1,33 @@
+---
+title: Image Vision
+description: Recognize images using OpenAI vision models
+---
+
+# openai-image-vision
+
+Analyze image content using OpenAI's GPT-4 Vision API, understanding objects, text, colors, and other elements in images.
+
+## Dependencies
+
+| Dependency | Description |
+| --- | --- |
+| `OPENAI_API_KEY` | OpenAI API key |
+| `curl`, `base64` | System commands (usually pre-installed) |
+
+Configuration:
+
+- Configure `OPENAI_API_KEY` via the `env_config` tool
+- Or set `open_ai_api_key` in `config.json`
+
+## Supported Models
+
+- `gpt-4.1-mini` (recommended, cost-effective)
+- `gpt-4.1`
+
+## Usage
+
+Once configured, send an image to the Agent to automatically trigger image recognition.
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202213219.png" width="800" />
+</Frame>
--- a/docs/en/skills/index.mdx
+++ b/docs/en/skills/index.mdx
@@ -0,0 +1,67 @@
+---
+title: Skills Overview
+description: CowAgent skills system introduction
+---
+
+Skills provide infinite extensibility for the Agent. Each Skill consists of a description file (`SKILL.md`), execution scripts (optional), and resources (optional), describing how to accomplish specific types of tasks.
+
+The difference between Skills and Tools: Tools are atomic operations implemented in code (e.g., file read/write, command execution), while Skills are high-level workflows based on description files that can combine multiple Tools to complete complex tasks.
+
+## Built-in Skills
+
+Located in the project `skills/` directory, automatically enabled based on dependency conditions:
+
+| Skill | Description | Dependencies |
+| --- | --- | --- |
+| [`skill-creator`](/en/skills/skill-creator) | Create custom skills through conversation | None |
+| [`openai-image-vision`](/en/skills/image-vision) | Recognize images using OpenAI vision models | `OPENAI_API_KEY` |
+| [`linkai-agent`](/en/skills/linkai-agent) | Integrate LinkAI platform agents | `LINKAI_API_KEY` |
+| [`web-fetch`](/en/skills/web-fetch) | Fetch web page text content | `curl` (enabled by default) |
+
+## Custom Skills
+
+Created by users through conversation, stored in workspace (`~/cow/skills/`), can implement any complex business process and third-party system integration.
+
+## Skill Loading Priority
+
+1. **Workspace skills** (highest): `~/cow/skills/`
+2. **Project built-in skills** (lowest): `skills/`
+
+Skills with the same name are overridden by priority.
+
+## Skill File Structure
+
+```
+skills/
+├── my-skill/
+│   ├── SKILL.md          # Skill description (frontmatter + instructions)
+│   ├── scripts/          # Execution scripts (optional)
+│   └── resources/        # Additional resources (optional)
+```
+
+### SKILL.md Format
+
+```markdown
+---
+name: my-skill
+description: Brief description of the skill
+metadata:
+  emoji: 🔧
+  requires:
+    bins: ["curl"]
+    env: ["MY_API_KEY"]
+  primaryEnv: "MY_API_KEY"
+---
+
+# My Skill
+
+Detailed instructions...
+```
+
+| Field | Description |
+| --- | --- |
+| `name` | Skill name, must match directory name |
+| `description` | Skill description, Agent decides whether to invoke based on this |
+| `metadata.requires.bins` | Required system commands |
+| `metadata.requires.env` | Required environment variables |
+| `metadata.always` | Always load (default false) |
--- a/docs/en/skills/linkai-agent.mdx
+++ b/docs/en/skills/linkai-agent.mdx
@@ -0,0 +1,49 @@
+---
+title: LinkAI Agent
+description: Integrate LinkAI platform multi-agent skill
+---
+
+# linkai-agent
+
+Use agents from the [LinkAI](https://link-ai.tech/) platform as Skills for multi-agent decision-making. The Agent intelligently selects based on agent names and descriptions, calling the corresponding application or workflow via `app_code`.
+
+## Dependencies
+
+| Dependency | Description |
+| --- | --- |
+| `LINKAI_API_KEY` | LinkAI platform API key, created in [Console](https://link-ai.tech/console/interface) |
+| `curl` | System command (usually pre-installed) |
+
+Configuration:
+
+- Configure `LINKAI_API_KEY` via the `env_config` tool
+- Or set `linkai_api_key` in `config.json`
+
+## Configure Agents
+
+Add available agents in `skills/linkai-agent/config.json`:
+
+```json
+{
+  "apps": [
+    {
+      "app_code": "G7z6vKwp",
+      "app_name": "LinkAI Customer Support",
+      "app_description": "Select this assistant only when the user needs help with LinkAI platform questions"
+    },
+    {
+      "app_code": "SFY5x7JR",
+      "app_name": "Content Creator",
+      "app_description": "Use this assistant only when the user needs to create images or videos"
+    }
+  ]
+}
+```
+
+## Usage
+
+Once configured, the Agent will automatically select the appropriate LinkAI agent based on the user's question.
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202234350.png" width="750" />
+</Frame>
--- a/docs/en/skills/skill-creator.mdx
+++ b/docs/en/skills/skill-creator.mdx
@@ -0,0 +1,33 @@
+---
+title: Skill Creator
+description: Create custom skills through conversation
+---
+
+# skill-creator
+
+Quickly create, install, or update skills through natural language conversation.
+
+## Dependencies
+
+No extra dependencies, always available.
+
+## Usage
+
+- Codify workflows as skills: "Create a skill from this deployment process"
+- Integrate third-party APIs: "Create a skill based on this API documentation"
+- Install remote skills: "Install xxx skill for me"
+
+## Creation Flow
+
+1. Tell the Agent what skill you want to create
+2. Agent automatically generates `SKILL.md` description and execution scripts
+3. Skill is saved to the workspace `~/cow/skills/` directory
+4. Agent will automatically recognize and use the skill in future conversations
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260202202247.png" width="800" />
+</Frame>
+
+<Tip>
+  See the [Skill Creator documentation](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/skills/skill-creator/SKILL.md) for details.
+</Tip>
--- a/docs/en/skills/web-fetch.mdx
+++ b/docs/en/skills/web-fetch.mdx
@@ -0,0 +1,33 @@
+---
+title: Web Fetch
+description: Fetch web page text content
+---
+
+# web-fetch
+
+Use curl to fetch web pages and extract readable text content. A lightweight web access method without browser automation.
+
+## Dependencies
+
+| Dependency | Description |
+| --- | --- |
+| `curl` | System command (usually pre-installed) |
+
+This skill has `always: true` set, enabled by default as long as the system has the `curl` command.
+
+## Usage
+
+Automatically invoked when the Agent needs to fetch content from a URL, no extra configuration needed.
+
+## Comparison with browser Tool
+
+| Feature | web-fetch (skill) | browser (tool) |
+| --- | --- | --- |
+| Dependencies | curl only | browser-use + playwright |
+| JS rendering | Not supported | Supported |
+| Page interaction | Not supported | Supports click, type, etc. |
+| Best for | Static page text | Dynamic web pages |
+
+<Tip>
+  For most web content retrieval scenarios, web-fetch is sufficient. Only use the browser tool when you need JS rendering or page interaction.
+</Tip>
--- a/docs/en/tools/bash.mdx
+++ b/docs/en/tools/bash.mdx
@@ -0,0 +1,30 @@
+---
+title: bash - Terminal
+description: Execute system commands
+---
+
+# bash
+
+Execute Bash commands in the current working directory, returns stdout and stderr. API keys configured via `env_config` are automatically injected into the environment.
+
+## Dependencies
+
+No extra dependencies, available by default.
+
+## Parameters
+
+| Parameter | Type | Required | Description |
+| --- | --- | --- | --- |
+| `command` | string | Yes | Command to execute |
+| `timeout` | integer | No | Timeout in seconds |
+
+## Use Cases
+
+- Install packages and dependencies
+- Run code and tests
+- Deploy applications and services (Nginx config, process management, etc.)
+- System administration and troubleshooting
+
+<Frame>
+  <img src="https://cdn.link-ai.tech/doc/20260203121008.png" width="800" />
+</Frame>
--- a/docs/en/tools/browser.mdx
+++ b/docs/en/tools/browser.mdx
@@ -0,0 +1,27 @@
+---
+title: browser - Browser
+description: Access and interact with web pages
+---
+
+# browser
+
+Use a browser to access and interact with web pages, supports JavaScript-rendered dynamic pages.
+
+## Dependencies
+
+| Dependency | Install Command |
+| --- | --- |
+| `browser-use` ≥ 0.1.40 | `pip install browser-use` |
+| `markdownify` | `pip install markdownify` |
+| `playwright` + chromium | `pip install playwright && playwright install chromium` |
+
+## Use Cases
+
+- Access specific URLs to get page content
+- Interact with web page elements (click, type, etc.)
+- Verify deployed web pages
+- Scrape dynamic content requiring JS rendering
+
+<Note>
+  The browser tool has heavy dependencies. If not needed, skip installation. For lightweight web content retrieval, use the `web-fetch` skill instead.
+</Note>
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
zhayujie	6db22827f2	feat: docs update	2026-02-27 16:03:47 +08:00
zhayujie	d891312032	docs: init docs	2026-02-27 12:10:16 +08:00
zhayujie	3ddbdd713d	Merge branch 'master' of github.com:zhayujie/chatgpt-on-wechat	2026-02-26 18:57:43 +08:00
zhayujie	9ba107b511	Merge branch 'feat-multi-channel'	2026-02-26 18:57:19 +08:00
zhayujie	c9adddb76a	fix: pass channel_type correctly in multi-channel mode	2026-02-26 18:57:08 +08:00
zhayujie	f0a12d5ff5	Merge pull request #2678 from zhayujie/feat-multi-channel feat: support multi-channel	2026-02-26 18:34:48 +08:00
zhayujie	7cce224499	feat: support multi-channel	2026-02-26 18:34:08 +08:00
zhayujie	97397ca585	Merge pull request #2674 from haosenwang1018/fix/bare-excepts fix: replace 29 bare except clauses with except Exception	2026-02-26 12:11:49 +08:00
zhayujie	f2fbc602a8	Merge branch 'master' of github.com:zhayujie/chatgpt-on-wechat	2026-02-26 10:45:01 +08:00
zhayujie	925d728a86	fix: replace upsert syntax to support SQLite lower version	2026-02-26 10:44:04 +08:00
zhayujie	f5f229871b	Merge pull request #2676 from zhayujie/feat-multi-channel feat: improve web console and conversation store	2026-02-26 10:37:03 +08:00
zhayujie	9917552b4b	fix: improve web UI stability and conversation history restore - Fix dark mode FOUC: apply theme in <head> before first paint, defer transition-colors to post-init to avoid animated flash on load - Fix Safari IME Enter bug: defer compositionend reset via setTimeout(0) - Fix history scroll: use requestAnimationFrame before scrollChatToBottom - Limit restore turns to min(6, max_turns//3) on restart - Fix load_messages cutoff to start at turn boundary, preventing orphaned tool_use/tool_result pairs from being sent to the LLM - Merge all assistant messages within one user turn into a single bubble; render tool_calls in history using same CSS as live SSE view - Handle empty choices list in stream chunks	2026-02-26 10:35:20 +08:00
haosenwang1018	adca89b973	fix: replace bare except clauses with except Exception Bare `except:` catches BaseException including KeyboardInterrupt and SystemExit. Replaced 29 instances with `except Exception:`.	2026-02-25 11:49:19 +00:00
zhayujie	29bfbecdc9	feat: persistent storage of conversation history	2026-02-25 18:01:39 +08:00
zhayujie	1a7a8c98d9	docs: add scam warning disclaimer	2026-02-25 01:34:16 +08:00
zhayujie	cddb38ac3d	Merge pull request #2673 from zhayujie/feat-web-console feat: web console	2026-02-24 00:06:29 +08:00
zhayujie	394853c0fb	feat: web console module display	2026-02-24 00:04:17 +08:00
zhayujie	c0702c8b36	feat: web channel stream chat	2026-02-23 22:19:50 +08:00
zhayujie	d610608391	feat: add cloud host config	2026-02-23 15:06:31 +08:00
zhayujie	9082eec91d	feat: dark mode is used by default	2026-02-23 14:57:02 +08:00
zhayujie	f1a1413b5f	feat: web console upgrade	2026-02-21 17:56:31 +08:00
zhayujie	c1e7f9af9b	Merge pull request #2672 from zhayujie/feat-config-update feat: cloud config update	2026-02-21 11:34:05 +08:00
zhayujie	1c71c4e38b	feat: agent chat service	2026-02-21 00:39:36 +08:00
zhayujie	5e3eccb3f6	feat: support memory service	2026-02-20 23:44:05 +08:00
zhayujie	e1dc037eb9	feat: cloud skills manage	2026-02-20 23:23:04 +08:00
zhayujie	97e9b4c801	Merge branch 'master' into feat-config-update	2026-02-20 18:58:21 +08:00
zhayujie	52d7cad735	feat: support gemini-3.1-pro-preview and claude-4.6-sonnet	2026-02-20 12:14:59 +08:00
zhayujie	c0b1d270ba	Merge branch 'master' of github.com:zhayujie/chatgpt-on-wechat	2026-02-19 14:18:39 +08:00
zhayujie	e59a2892e4	feat: support qwen3.5-plus	2026-02-19 14:18:16 +08:00
zhayujie	5fa0376a49	Merge pull request #2670 from SgtPepper114/fix/gemini-dingtalk-image-inline fix(gemini): 修复钉钉图片标记未转多模态导致的识图失效	2026-02-19 13:57:04 +08:00
SgtPepper114	05a33042c8	fix(gemini): support dingtalk image markers as multimodal input - parse [图片: path] markers in text and convert to Gemini inlineData parts - unify reply path via call_with_tools to reuse multimodal conversion - keep legacy safety behavior (BLOCK_NONE) and restore safety ratings logging on empty response - add multimodal request image-part count log for debugging	2026-02-16 13:26:57 +00:00
zhayujie	ce58f23cbc	feat: dashscope model name	2026-02-16 20:11:38 +08:00
zhayujie	b6fc9fa370	fix: run script dependency issues	2026-02-15 00:02:50 +08:00
zhayujie	00ae38faae	docs: update models in README	2026-02-14 17:36:36 +08:00
zhayujie	ab28ee58ab	feat: add doubao-2.0-code model and update README	2026-02-14 16:49:44 +08:00
zhayujie	48db538a2e	feat: support Minimax-M2.5, glm-5, kimi-k2.5	2026-02-14 15:27:44 +08:00
zhayujie	46945942e1	feat: support channel start in sub thread	2026-02-13 12:38:52 +08:00
zhayujie	a24b26a1ef	Merge pull request #2667 from cowagent/fix-wechatcom-image-support fix: 支持企业微信图片消息识别功能	2026-02-12 16:44:18 +08:00
zhayujie	6f8421cdd5	fix: 支持企业微信图片消息识别功能 - 在 ChatGPTBot 中添加 ContextType.IMAGE 处理分支 - 新增 reply_image() 方法，支持 OpenAI Vision API - 自动 Base64 编码图片并检测格式 - 自动清理临时文件修复 #2625	2026-02-12 12:00:24 +08:00
zhayujie	284cd9bca9	Merge pull request #2666 from cowagent/fix-model-type-validation fix: handle non-string model_type to prevent AttributeError	2026-02-10 11:31:45 +08:00
cowagent	23fd6b8d2b	fix: handle non-string model_type to prevent AttributeError When numeric model names (e.g., '1') are used with vLLM and configured in YAML without quotes, they are parsed as integers. This causes AttributeError when calling startswith() method. Changes: - Add type checking for model_type - Convert non-string model_type to string with warning log - Prevents crash when using custom numeric model names Fixes #2664	2026-02-10 11:07:10 +08:00
zhayujie	4f0ea5d756	feat: make web search a built-in tool	2026-02-09 11:37:11 +08:00
zhayujie	6c218331b1	fix: improve skill system prompts and simplify tool descriptions - Simplify skill-creator installation flow - Refine skill selection prompt for better matching - Add parameter alias and env variable hints for tools - Skip linkai-agent when unconfigured - Create skills/ dir in workspace on init	2026-02-08 18:59:59 +08:00
zhayujie	cea7fb7490	fix: add intelligent context cleanup #2663	2026-02-07 20:42:41 +08:00
zhayujie	8acf2dbdfe	fix: chat context overflow #2663	2026-02-07 20:36:24 +08:00
zhayujie	0542700f90	fix: issues with empty tool calls and handling excessively long tool results	2026-02-07 20:25:05 +08:00
zhayujie	5264f7ce18	fix: getuid not found in windows	2026-02-07 11:17:58 +08:00
zhayujie	051ffd78a3	fix: windows path and encoding adaptation	2026-02-06 18:37:05 +08:00
zhayujie	bea95d4fae	Merge pull request #2661 from cowagent/feat-add-claude-opus-4-6 feat: 添加 Claude Opus 4.6 模型支持	2026-02-06 15:09:49 +08:00
cowagent	fdf7bc312f	feat: 添加 Claude Opus 4.6 模型支持 - 在 common/const.py 中添加 CLAUDE_4_6_OPUS 常量 - 将 claude-opus-4-6 添加到 MODEL_LIST - 在 README.md 中更新 Agent 推荐模型列表 - 在 Claude 配置说明中添加 claude-opus-4-6 支持 Claude Opus 4.6 是 Anthropic 于 2026年2月5日发布的最新模型，具有更强的规划能力和代码能力，适合作为 Agent 推荐模型。	2026-02-06 15:07:43 +08:00
vision	5b094e1097	Merge pull request #2660 from cowagent/fix-zhipuai-api-base-support fix: 支持智谱AI自定义API base URL配置	2026-02-05 19:18:49 +08:00
cowagent	9ad3968084	fix: 支持智谱AI自定义API base URL配置 - 修复 ZhipuAiClient 初始化时未传入 base_url 参数的问题 - 使配置文件中的 zhipu_ai_api_base 配置项生效 - 支持智谱国际版(z.ai)等自定义API端点 - 同时修复对话和图片生成功能 - 添加日志输出便于确认使用的API地址 Fixes #2659	2026-02-05 19:06:46 +08:00
zhayujie	3958b6aae1	Merge pull request #2657 from cowagent/fix-missing-runtime-info-parameter fix: 补充缺失的 runtime_info 参数传递	2026-02-04 22:51:53 +08:00
cowagent	eaa413caf0	fix: 补充缺失的 runtime_info 参数传递问题： PR #2655 已合并，但遗漏了关键的参数传递环节。runtime_info 在 agent_initializer.py 中创建并传递给 create_agent()，但 agent_bridge.py 的 create_agent() 方法中没有将其传递给 Agent 实例，导致动态时间更新功能无法生效。影响： - Agent 实例的 self.runtime_info 为 None - get_full_system_prompt() 无法检测到动态时间函数 - 时间戳仍然是静态的，不会实时更新修复：在 agent_bridge.py 第 236 行添加： runtime_info=kwargs.get("runtime_info") 这确保了完整的参数传递链路： agent_initializer → agent_bridge.create_agent → Agent.__init__ --- 来自 [CowAgent](https://github.com/zhayujie/chatgpt-on-wechat) 项目的 AI Agent	2026-02-04 22:49:54 +08:00
zhayujie	9095225b5b	Merge pull request #2656 from 6vision/master Update: improve script interaction and configuration	2026-02-04 22:46:02 +08:00
zhayujie	c529f86dbc	Merge pull request #2655 from cowagent/fix-runtime-timestamp-update fix: 动态更新系统提示词中的运行时信息（时间戳）	2026-02-04 22:38:51 +08:00
cowagent	e4fcfa356a	refactor: 改用动态函数实现运行时信息更新（更健壮的方案）改进点： 1. builder.py: _build_runtime_section() 支持 callable 动态时间函数 2. agent_initializer.py: 传入 get_current_time 函数而非静态时间值 3. agent.py: _rebuild_runtime_section() 动态调用时间函数并重建该部分优势： - 解耦模板：不依赖具体的提示词格式 - 健壮性：提示词模板改变不会导致功能失效 - 向后兼容：保留对静态时间的支持 - 性能优化：只在需要时才计算时间相比之前的正则匹配方案，这个方案更加优雅和可维护。	2026-02-04 22:37:19 +08:00
vision	8218cff7c1	Merge branch 'zhayujie:master' into master	2026-02-04 22:32:20 +08:00
6vision	6949bbcf39	update: Improve script interaction and configuration	2026-02-04 22:31:40 +08:00
cowagent	480c60c0a7	fix: 动态更新系统提示词中的运行时信息（时间戳）问题： - system_prompt 在 Agent 初始化时固定，导致模型获取的时间信息过时 - 长时间运行的会话中，模型对时间判断不准确解决方案： - 在 get_full_system_prompt() 中添加动态更新逻辑 - 每次获取系统提示词时，使用正则表达式替换运行时信息中的时间戳 - 保持其他运行时信息（模型、工作空间等）不变测试： - 创建测试脚本验证时间动态更新功能 - 等待3秒后时间正确更新（22:19:45 -> 22:19:48）	2026-02-04 22:27:24 +08:00
zhayujie	eec10cb5db	fix: claude remove toolname	2026-02-04 22:15:10 +08:00
zhayujie	02c83d8689	docs: update agent.md	2026-02-04 21:42:52 +08:00
zhayujie	72b1cacea1	fix: hiding the thought process	2026-02-04 19:36:01 +08:00
zhayujie	c72cda3386	fix: minimax reasoning content optimization	2026-02-04 19:26:36 +08:00
zhayujie	867442155e	fix: lark connection issue	2026-02-04 17:05:30 +08:00
zhayujie	229b14b6fc	fix: feishu cert error	2026-02-04 16:15:38 +08:00
zhayujie	158c87ab8b	fix: openai function call	2026-02-04 15:42:43 +08:00
zhayujie	cb303e6109	fix: add decision round log	2026-02-03 21:27:30 +08:00
saboteur7	a77a8741b5	fix: memory loss issue caused by scheduler	2026-02-03 20:45:22 +08:00
zhayujie	3d63459c25	docs: update README.md	2026-02-03 15:44:00 +08:00