chore(install): simplify model menu, add MiMo option

Fix ASR model persistence in models API

.github/ISSUE_TEMPLATE/1.bug.yml

@@ -1,131 +1,46 @@
 name: Bug report 🐛
-description: 项目运行中遇到的Bug或问题。
+description: Report a bug or unexpected behavior.
+title: "[Bug] "
 labels: ['status: needs check']
 body:
   - type: markdown
     attributes:
       value: |
-        ### ⚠️ 前置确认
-        1. 网络能够访问openai接口
-        2. python 已安装：版本在 3.7 ~ 3.10 之间
-        3. `git pull` 拉取最新代码
-        4. 执行`pip3 install -r requirements.txt`，检查依赖是否满足
-        5. 拓展功能请执行`pip3 install -r requirements-optional.txt`，检查依赖是否满足
-        6. [FAQS](https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs) 中无类似问题
+        > 💡 English is recommended so global developers can help. 推荐使用英文提交，谢谢 ❤️
   - type: checkboxes
     attributes:
-      label: 前置确认
+      label: Self check
       options:
-        - label: 我确认我运行的是最新版本的代码，并且安装了所需的依赖，在[FAQS](https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs)中也未找到类似问题。
+        - label: I'm on the latest version and searched [existing issues](https://github.com/zhayujie/CowAgent/issues) (incl. closed) — no duplicate.
           required: true
-  - type: checkboxes
+  - type: textarea
     attributes:
-      label: ⚠️ 搜索issues中是否已存在类似问题
-      description: >
-        请在 [历史issue](https://github.com/zhayujie/chatgpt-on-wechat/issues) 中清空输入框，搜索你的问题
-        或相关日志的关键词来查找是否存在类似问题。
-      options:
-        - label: 我已经搜索过issues和disscussions，没有跟我遇到的问题相关的issue
-          required: true
-  - type: markdown
-    attributes:
-      value: |
-        请在上方的`title`中填写你对你所遇到问题的简略总结，这将帮助其他人更好的找到相似问题，谢谢❤️。
-  - type: dropdown
-    attributes:
-      label: 操作系统类型?
-      description: >
-        请选择你运行程序的操作系统类型。
-      options:
-        - Windows
-        - Linux
-        - MacOS
-        - Docker
-        - Railway
-        - Windows Subsystem for Linux (WSL)
-        - Other (请在问题中说明)
-    validations:
-      required: true
-  - type: dropdown
-    attributes:
-      label: 运行的python版本是?
-      description: |
-        请选择你运行程序的`python`版本。
-        注意：在`python 3.7`中，有部分可选依赖无法安装。
-        经过长时间的观察，我们认为`python 3.8`是兼容性最好的版本。
-        `python 3.7`~`python 3.10`以外版本的issue，将视情况直接关闭。
-      options:
-        - python 3.7
-        - python 3.8
-        - python 3.9
-        - python 3.10
-        - other
-    validations:
-      required: true
-  - type: dropdown
-    attributes:
-      label: 使用的chatgpt-on-wechat版本是?
-      description: |
-        请确保你使用的是 [releases](https://github.com/zhayujie/chatgpt-on-wechat/releases) 中的最新版本。
-        如果你使用git, 请使用`git branch`命令来查看分支。
-      options:
-        - Latest Release
-        - Master (branch)
-    validations:
-      required: true
-  - type: dropdown
-    attributes:
-      label: 运行的`channel`类型是?
-      description: |
-        请确保你正确配置了该`channel`所需的配置项，所有可选的配置项都写在了[该文件中](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py)，请将所需配置项填写在根目录下的`config.json`文件中。
-      options:
-        - wechatmp(公众号, 订阅号)
-        - wechatmp_service(公众号, 服务号)
-        - terminal
-        - other
+      label: Environment
+      description: "Version (`cow status`), OS, Python version, install method, model & channel."
+      placeholder: |
+        Version: v1.2.0
+        OS: macOS / Linux / Windows / Docker
+        Python: 3.11
+        Install: installer / Docker / source
+        Model & channel: deepseek-v4-flash, web
     validations:
       required: true
   - type: textarea
     attributes:
-      label: 复现步骤 🕹
-      description: |
-        **⚠️ 不能复现将会关闭issue.**
-  - type: textarea
-    attributes:
-      label: 问题描述 😯
-      description: 详细描述出现的问题，或提供有关截图。
-  - type: textarea
-    attributes:
-      label: 终端日志 📒
-      description: |
-        在此处粘贴终端日志，可在主目录下`run.log`文件中找到，这会帮助我们更好的分析问题，注意隐去你的API key。
-        如果在配置文件中加入`"debug": true`，打印出的日志会更有帮助。
+      label: What happened?
+      description: "Steps to reproduce, what you expected, and what happened instead. Screenshots welcome."
+      placeholder: |
+        1. ...
+        2. ...
 
-        <details>
-        <summary><i>示例</i></summary>
-        ```log
-        [DEBUG][2023-04-16 00:23:22][plugin_manager.py:157] - Plugin SUMMARY triggered by event Event.ON_HANDLE_CONTEXT
-        [DEBUG][2023-04-16 00:23:22][main.py:221] - [Summary] on_handle_context. content: $总结前100条消息
-        [DEBUG][2023-04-16 00:23:24][main.py:240] - [Summary] limit: 100, duration: -1 seconds
-        [ERROR][2023-04-16 00:23:24][chat_channel.py:244] - Worker return exception: name 'start_date' is not defined
-        Traceback (most recent call last):
-          File "C:\ProgramData\Anaconda3\lib\concurrent\futures\thread.py", line 57, in run
-            result = self.fn(*self.args, **self.kwargs)
-          File "D:\project\chatgpt-on-wechat\channel\chat_channel.py", line 132, in _handle
-            reply = self._generate_reply(context)
-          File "D:\project\chatgpt-on-wechat\channel\chat_channel.py", line 142, in _generate_reply
-            e_context = PluginManager().emit_event(EventContext(Event.ON_HANDLE_CONTEXT, {
-          File "D:\project\chatgpt-on-wechat\plugins\plugin_manager.py", line 159, in emit_event
-            instance.handlers[e_context.event](e_context, *args, **kwargs)
-          File "D:\project\chatgpt-on-wechat\plugins\summary\main.py", line 255, in on_handle_context
-            records = self._get_records(session_id, start_time, limit)
-          File "D:\project\chatgpt-on-wechat\plugins\summary\main.py", line 96, in _get_records
-            c.execute("SELECT * FROM chat_records WHERE sessionid=? and timestamp>? ORDER BY timestamp DESC LIMIT ?", (session_id, start_date, limit))
-        NameError: name 'start_date' is not defined
-        [INFO][2023-04-16 00:23:36][app.py:14] - signal 2 received, exiting...
-        ```
-        </details>
-      value: |
-        ```log
-        <此处粘贴终端日志>
-        ```
+        Expected: ...
+        Actual: ...
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Logs
+      description: "Relevant logs from `run.log` (set `\"debug\": true` for more detail). ⚠️ Redact your API keys."
+      render: shell
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/2.feature.yml

@@ -1,28 +1,33 @@
 name: Feature request 🚀
-description: 提出你对项目的新想法或建议。
+description: Suggest a new idea or improvement.
+title: "[Feature] "
 labels: ['status: needs check']
 body:
   - type: markdown
     attributes:
       value: |
-        请在上方的`title`中填写简略总结，谢谢❤️。
+        > 💡 English is recommended so global developers can help. 推荐使用英文提交，谢谢 ❤️
   - type: checkboxes
     attributes:
-      label: ⚠️ 搜索是否存在类似issue
-      description: >
-        请在 [历史issue](https://github.com/zhayujie/chatgpt-on-wechat/issues) 中清空输入框，搜索关键词查找是否存在相似issue。
+      label: Self check
       options:
-        - label: 我已经搜索过issues和disscussions，没有发现相似issue
+        - label: I searched [existing issues](https://github.com/zhayujie/CowAgent/issues) (incl. closed) — no duplicate.
           required: true
   - type: textarea
     attributes:
-      label: 总结
-      description: 描述feature的功能。
+      label: What's the problem?
+      description: "The pain point or what's not working for you right now."
+    validations:
+      required: true
   - type: textarea
     attributes:
-      label: 举例
-      description: 提供聊天示例，草图或相关网址。
-  - type: textarea
+      label: What would you like?
+      description: "How you'd expect it to work. Examples, sketches, or links welcome."
+    validations:
+      required: false
+  - type: checkboxes
     attributes:
-      label: 动机
-      description: 描述你提出该feature的动机，比如没有这项feature对你的使用造成了怎样的影响。 请提供更详细的场景描述，这可能会帮助我们发现并提出更好的解决方案。
+      label: Contribution
+      options:
+        - label: I'd be interested in helping implement this.
+          required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,5 @@
+blank_issues_enabled: true
+contact_links:
+  - name: 📖 Documentation
+    url: https://docs.cowagent.ai
+    about: Setup guides, configuration, and FAQ.

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,22 @@
+<!--
+Thanks for your contribution! Please write this PR in English.
+推荐使用英文填写，感谢 ❤️
+-->
+
+## What does this PR do?
+
+<!-- A short description of the change and why it's needed. -->
+
+## Type of change
+
+- [ ] Bug fix
+- [ ] New feature
+- [ ] Docs
+- [ ] Refactor / chore
+
+## Checklist
+
+- [ ] I have read the [Contributing Guide](https://github.com/zhayujie/CowAgent/blob/master/CONTRIBUTING.md)
+- [ ] I tested this change locally
+- [ ] Code comments and docs are in English
+- [ ] Linked related issue (if any): closes #

CONTRIBUTING.md

@@ -0,0 +1,61 @@
+# Contributing to CowAgent
+
+Thanks for taking the time to contribute! 🎉 CowAgent is built by a global
+community, and contributions of all sizes are welcome — from typo fixes to new
+features.
+
+## Language policy
+
+To keep the project accessible to a global community, **please write issues,
+pull requests, code comments, and commit messages in English.**
+
+> 为方便全球开发者协作，请尽量使用**英文**提交 issue、PR、代码注释与
+> commit message。不必担心英文不完美——表达清楚即可，工具翻译也完全没问题。感谢理解 ❤️
+
+## Reporting issues
+
+Found a bug or have an idea? [Open an issue](https://github.com/zhayujie/CowAgent/issues/new/choose).
+
+Before opening one, please search existing issues (including closed ones) to
+avoid duplicates, and make sure you're on the latest version.
+
+## Submitting a pull request
+
+1. **Fork** the repo and create a branch from `master`
+   (e.g. `feat/web-search`, `fix/telegram-reconnect`).
+2. Make your change. Keep it focused — one logical change per PR.
+3. Follow the existing code style. Write comments and docstrings in English.
+4. Run the app locally to confirm your change works.
+5. Open a PR with a clear title and a short description of **what** and **why**.
+
+We keep the bar friendly: clear, focused, and working is enough. Maintainers are
+happy to help polish details during review.
+
+### Commit & PR titles
+
+Use a short, imperative summary. The [Conventional Commits](https://www.conventionalcommits.org/)
+style is preferred but not required:
+
+```
+feat: add web search tool
+fix: reconnect Telegram websocket on timeout
+docs: clarify Docker setup
+```
+
+## Development setup
+
+See the [Install from Source](https://docs.cowagent.ai/guide/manual-install)
+guide. In short:
+
+```bash
+git clone https://github.com/zhayujie/CowAgent.git
+cd CowAgent
+pip install -r requirements.txt
+pip install -e .
+cow start
+```
+
+## Code of conduct
+
+Be respectful and constructive. We want CowAgent to be a welcoming place for
+everyone.

README.md

@@ -13,8 +13,8 @@ CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major
 
 <p align="center">
   <a href="https://cowagent.ai/">🌐 Website</a> &nbsp;·&nbsp;
-  <a href="https://docs.cowagent.ai/en/intro/index">📖 Docs</a> &nbsp;·&nbsp;
-  <a href="https://docs.cowagent.ai/en/guide/quick-start">🚀 Quick Start</a> &nbsp;·&nbsp;
+  <a href="https://docs.cowagent.ai/intro/index">📖 Docs</a> &nbsp;·&nbsp;
+  <a href="https://docs.cowagent.ai/guide/quick-start">🚀 Quick Start</a> &nbsp;·&nbsp;
   <a href="https://skills.cowagent.ai/">🧩 Skill Hub</a> &nbsp;·&nbsp;
   <a href="https://link-ai.tech/cowagent/create">☁️ Try Online</a>
 </p>
@@ -25,15 +25,15 @@ CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major
 
 | Capability | Description |
 | :--- | :--- |
-| [Planning](https://docs.cowagent.ai/en/intro/architecture) | Decomposes complex tasks and executes them step by step, looping over tools until the goal is reached |
-| [Memory](https://docs.cowagent.ai/en/memory/index) | Three-tier architecture (context → daily → core), automatic Deep Dream distillation, hybrid keyword + vector retrieval |
-| [Knowledge](https://docs.cowagent.ai/en/knowledge/index) | Auto-curates structured knowledge into a Markdown wiki, builds an evolving knowledge graph with visual browsing |
-| [Skills](https://docs.cowagent.ai/en/skills/index) | One-click install from [Skill Hub](https://skills.cowagent.ai/), GitHub, ClawHub; or create custom skills via natural-language conversation |
-| [Tools](https://docs.cowagent.ai/en/tools/index) | Built-in file I/O, terminal, browser, scheduler, memory retrieval, web search, and 10+ more tools — with native MCP integration |
-| [Channels](https://docs.cowagent.ai/en/channels/index) | Integrates with Web, WeChat, Feishu, DingTalk, WeCom, QQ, and Official Accounts |
+| [Planning](https://docs.cowagent.ai/intro/architecture) | Decomposes complex tasks and executes them step by step, looping over tools until the goal is reached |
+| [Memory](https://docs.cowagent.ai/memory/index) | Three-tier architecture (context → daily → core), automatic Deep Dream distillation, hybrid keyword + vector retrieval |
+| [Knowledge](https://docs.cowagent.ai/knowledge/index) | Auto-curates structured knowledge into a Markdown wiki, builds an evolving knowledge graph with visual browsing |
+| [Skills](https://docs.cowagent.ai/skills/index) | One-click install from [Skill Hub](https://skills.cowagent.ai/), GitHub, ClawHub; or create custom skills via natural-language conversation |
+| [Tools](https://docs.cowagent.ai/tools/index) | Built-in file I/O, terminal, browser, scheduler, memory retrieval, web search, and 10+ more tools — with native MCP integration |
+| [Channels](https://docs.cowagent.ai/channels/index) | Integrates with Web, WeChat, Feishu, DingTalk, WeCom, QQ, Official Accounts, Telegram, and Slack |
 | Multimodal | First-class support for text, images, voice, and files — recognition, generation, and delivery |
-| [Models](https://docs.cowagent.ai/en/models/index) | Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, MiniMax, Doubao, and more — swap providers from the Web console with one click |
-| [Deploy](https://docs.cowagent.ai/en/guide/quick-start) | One-line installer, unified Web console, multiple deployment modes (local, Docker, server) |
+| [Models](https://docs.cowagent.ai/models/index) | Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, MiniMax, Doubao, and more — swap providers from the Web console with one click |
+| [Deploy](https://docs.cowagent.ai/guide/quick-start) | One-line installer, unified Web console, multiple deployment modes (local, Docker, server) |
 
 <br/>
 
@@ -43,7 +43,7 @@ CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major
 
 CowAgent is a complete **Agent Harness**: messages flow in through **Channels**; the **Agent Core** plans and reasons over memory, knowledge, and the available tools and skills; **Models** generate the response, which is sent back through the originating channel. Every layer is decoupled and independently extensible.
 
-Read more in [Architecture](https://docs.cowagent.ai/en/intro/architecture).
+Read more in [Architecture](https://docs.cowagent.ai/intro/architecture).
 
 <br/>
 
@@ -74,9 +74,9 @@ Once started, open `http://localhost:9899` to access the **Web console** — you
 
 > Deploying on a server? Set `web_host` to `0.0.0.0` in `config.json` to make the console reachable from outside, and set `web_password` to protect it. Don't forget to open port `9899` in your firewall or security group.
 
-> 📖 Detailed guides: [Quick Start](https://docs.cowagent.ai/en/guide/quick-start) · [Install from Source](https://docs.cowagent.ai/en/guide/manual-install) · [Upgrade](https://docs.cowagent.ai/en/guide/upgrade)
+> 📖 Detailed guides: [Quick Start](https://docs.cowagent.ai/guide/quick-start) · [Install from Source](https://docs.cowagent.ai/guide/manual-install) · [Upgrade](https://docs.cowagent.ai/guide/upgrade)
 
-After installation, manage the service with the [cow CLI](https://docs.cowagent.ai/en/cli/index):
+After installation, manage the service with the [cow CLI](https://docs.cowagent.ai/cli/index):
 
 ```bash
 cow start | stop | restart        # service control
@@ -94,21 +94,21 @@ CowAgent supports all mainstream LLM providers. **Chat, vision, image generation
 
 | Provider | Featured Models | Chat | Vision | Image Gen | ASR | TTS | Embedding |
 | --- | --- | :-: | :-: | :-: | :-: | :-: | :-: |
-| [Claude](https://docs.cowagent.ai/en/models/claude) | claude-opus-4-7 | ✅ | ✅ | | | | |
-| [OpenAI](https://docs.cowagent.ai/en/models/openai) | gpt-5.5, o-series | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [Gemini](https://docs.cowagent.ai/en/models/gemini) | gemini-3.5-flash | ✅ | ✅ | ✅ | | | |
-| [DeepSeek](https://docs.cowagent.ai/en/models/deepseek) | deepseek-v4-flash / pro | ✅ | | | | | |
-| [Qwen](https://docs.cowagent.ai/en/models/qwen) | qwen3.7-max | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [GLM](https://docs.cowagent.ai/en/models/glm) | glm-5.1, glm-5v-turbo | ✅ | ✅ | | ✅ | | ✅ |
-| [Doubao](https://docs.cowagent.ai/en/models/doubao) | doubao-seed-2.0 series | ✅ | ✅ | ✅ | | | ✅ |
-| [Kimi](https://docs.cowagent.ai/en/models/kimi) | kimi-k2.6 | ✅ | ✅ | | | | |
-| [MiniMax](https://docs.cowagent.ai/en/models/minimax) | MiniMax-M2.7 | ✅ | ✅ | ✅ | | ✅ | |
-| [ERNIE](https://docs.cowagent.ai/en/models/qianfan) | ernie-5.1 | ✅ | ✅ | | | | |
-| [MiMo](https://docs.cowagent.ai/en/models/mimo) | mimo-v2.5 / pro | ✅ | ✅ | | | ✅ | |
-| [LinkAI](https://docs.cowagent.ai/en/models/linkai) | One key for 100+ models | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [Custom](https://docs.cowagent.ai/en/models/custom) | Local models / third-party proxy | ✅ | | | | | |
+| [Claude](https://docs.cowagent.ai/models/claude) | claude-opus-4-8 | ✅ | ✅ | | | | |
+| [OpenAI](https://docs.cowagent.ai/models/openai) | gpt-5.5, o-series | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [Gemini](https://docs.cowagent.ai/models/gemini) | gemini-3.5-flash | ✅ | ✅ | ✅ | | | |
+| [DeepSeek](https://docs.cowagent.ai/models/deepseek) | deepseek-v4-flash / pro | ✅ | | | | | |
+| [Qwen](https://docs.cowagent.ai/models/qwen) | qwen3.7-plus | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [GLM](https://docs.cowagent.ai/models/glm) | glm-5.1, glm-5v-turbo | ✅ | ✅ | | ✅ | | ✅ |
+| [Doubao](https://docs.cowagent.ai/models/doubao) | doubao-seed-2.0 series | ✅ | ✅ | ✅ | | | ✅ |
+| [Kimi](https://docs.cowagent.ai/models/kimi) | kimi-k2.6 | ✅ | ✅ | | | | |
+| [MiniMax](https://docs.cowagent.ai/models/minimax) | MiniMax-M3 | ✅ | ✅ | ✅ | | ✅ | |
+| [ERNIE](https://docs.cowagent.ai/models/qianfan) | ernie-5.1 | ✅ | ✅ | | | | |
+| [MiMo](https://docs.cowagent.ai/models/mimo) | mimo-v2.5 / pro | ✅ | ✅ | | | ✅ | |
+| [LinkAI](https://docs.cowagent.ai/models/linkai) | One key for 100+ models | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [Custom](https://docs.cowagent.ai/models/custom) | Local models / third-party proxy | ✅ | | | | | |
 
-> For details on each provider, see the [Models overview](https://docs.cowagent.ai/en/models/index).
+> For details on each provider, see the [Models overview](https://docs.cowagent.ai/models/index).
 
 <br/>
 
@@ -118,16 +118,20 @@ A single Agent instance can serve multiple channels in parallel. Most channels c
 
 | Channel | Text | Image | File | Voice | Group |
 | --- | :-: | :-: | :-: | :-: | :-: |
-| [Web Console](https://docs.cowagent.ai/en/channels/web) (default) | ✅ | ✅ | ✅ | ✅ | |
-| [WeChat](https://docs.cowagent.ai/en/channels/weixin) | ✅ | ✅ | ✅ | ✅ | |
-| [Feishu / Lark](https://docs.cowagent.ai/en/channels/feishu) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [DingTalk](https://docs.cowagent.ai/en/channels/dingtalk) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [WeCom Bot](https://docs.cowagent.ai/en/channels/wecom-bot) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [QQ](https://docs.cowagent.ai/en/channels/qq) | ✅ | ✅ | ✅ | | ✅ |
-| [WeCom App](https://docs.cowagent.ai/en/channels/wecom) | ✅ | ✅ | ✅ | ✅ | |
-| [WeChat Official Account](https://docs.cowagent.ai/en/channels/wechatmp) | ✅ | ✅ | | ✅ | |
+| [Web Console](https://docs.cowagent.ai/channels/web) (default) | ✅ | ✅ | ✅ | ✅ | |
+| [Telegram](https://docs.cowagent.ai/channels/telegram) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [Slack](https://docs.cowagent.ai/channels/slack) | ✅ | ✅ | ✅ | | ✅ |
+| [Discord](https://docs.cowagent.ai/channels/discord) | ✅ | ✅ | ✅ | | ✅ |
+| [WeChat](https://docs.cowagent.ai/channels/weixin) | ✅ | ✅ | ✅ | ✅ | |
+| [Feishu / Lark](https://docs.cowagent.ai/channels/feishu) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [DingTalk](https://docs.cowagent.ai/channels/dingtalk) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [WeCom Bot](https://docs.cowagent.ai/channels/wecom-bot) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [QQ](https://docs.cowagent.ai/channels/qq) | ✅ | ✅ | ✅ | | ✅ |
+| [WeCom App](https://docs.cowagent.ai/channels/wecom) | ✅ | ✅ | ✅ | ✅ | |
+| [WeChat Customer Service](https://docs.cowagent.ai/channels/wechat-kf) | ✅ | ✅ | ✅ | ✅ | |
+| [WeChat Official Account](https://docs.cowagent.ai/channels/wechatmp) | ✅ | ✅ | | ✅ | |
 
-> See the [Channels overview](https://docs.cowagent.ai/en/channels/index) for setup details.
+> See the [Channels overview](https://docs.cowagent.ai/channels/index) for setup details.
 
 <img src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/screenshots/en/web-console-chat.png" alt="CowAgent Web Console" width="800"/>
 
@@ -137,9 +141,9 @@ A single Agent instance can serve multiple channels in parallel. Most channels c
 
 ## 🧠 Memory & Knowledge Base
 
-**Long-term memory** uses a three-tier architecture: conversation context (short-term) → daily memory (mid-term) → MEMORY.md (long-term). A nightly **Deep Dream** pass distills scattered memories into refined long-term entries and a narrative journal. See [Long-term Memory](https://docs.cowagent.ai/en/memory/index) · [Deep Dream](https://docs.cowagent.ai/en/memory/deep-dream).
+**Long-term memory** uses a three-tier architecture: conversation context (short-term) → daily memory (mid-term) → MEMORY.md (long-term). A nightly **Deep Dream** pass distills scattered memories into refined long-term entries and a narrative journal. See [Long-term Memory](https://docs.cowagent.ai/memory/index) · [Deep Dream](https://docs.cowagent.ai/memory/deep-dream).
 
-**Personal knowledge base** complements the time-ordered memory by organizing structured knowledge **by topic**. The Agent automatically curates valuable information from conversations, maintains cross-references and indexes, and the Web console offers an interactive knowledge-graph view. See [Personal Knowledge Base](https://docs.cowagent.ai/en/knowledge/index).
+**Personal knowledge base** complements the time-ordered memory by organizing structured knowledge **by topic**. The Agent automatically curates valuable information from conversations, maintains cross-references and indexes, and the Web console offers an interactive knowledge-graph view. See [Personal Knowledge Base](https://docs.cowagent.ai/knowledge/index).
 
 <table>
   <tr>
@@ -166,7 +170,7 @@ A single Agent instance can serve multiple channels in parallel. Most channels c
 
 **MCP protocol** integrates the open ecosystem of [Model Context Protocol](https://modelcontextprotocol.io) servers. A single `mcp.json` is enough — supports stdio / SSE transports, hot reload, and zero-code integration.
 
-Learn more: [Tools overview](https://docs.cowagent.ai/en/tools/index) · [MCP integration](https://docs.cowagent.ai/en/tools/mcp).
+Learn more: [Tools overview](https://docs.cowagent.ai/tools/index) · [MCP integration](https://docs.cowagent.ai/tools/mcp).
 
 ### Skills System
 
@@ -180,12 +184,14 @@ Learn more: [Tools overview](https://docs.cowagent.ai/en/tools/index) · [MCP in
 /skill install <name>          # one-click install
 ```
 
-Learn more: [Skills overview](https://docs.cowagent.ai/en/skills/index) · [Creating Skills](https://docs.cowagent.ai/en/skills/create).
+Learn more: [Skills overview](https://docs.cowagent.ai/skills/index) · [Creating Skills](https://docs.cowagent.ai/skills/create).
 
 <br/>
 
 ## 🏷 Changelog
 
+> **2026.06.01:** [v2.1.0](https://github.com/zhayujie/CowAgent/releases/tag/2.1.0) — Internationalization, new channels (Telegram, Discord, Slack, WeChat Customer Service), CLI interaction upgrades, streamlined one-line install, MCP Streamable HTTP support, new models (claude-opus-4-8, MiMo).
+
 > **2026.05.22:** [v2.0.9](https://github.com/zhayujie/CowAgent/releases/tag/2.0.9) — Model management, MCP protocol support, persistent browser sessions, new models (gpt-5.5, gemini-3.5-flash, qwen3.7-max), deployment hardening.
 
 > **2026.05.06:** [v2.0.8](https://github.com/zhayujie/CowAgent/releases/tag/2.0.8) — Feishu channel overhaul (voice, streaming, QR onboarding), DeepSeek V4 and Baidu Qianfan support, scheduler tool upgrades.
@@ -198,7 +204,7 @@ Learn more: [Skills overview](https://docs.cowagent.ai/en/skills/index) · [Crea
 
 > **2026.02.03:** [v2.0.0](https://github.com/zhayujie/CowAgent/releases/tag/2.0.0) — Major upgrade to a super Agent assistant with multi-step task planning, long-term memory, and the Skills framework.
 
-Full history: [Release Notes](https://docs.cowagent.ai/en/releases/overview)
+Full history: [Release Notes](https://docs.cowagent.ai/releases/overview)
 
 <br/>
 
@@ -232,9 +238,9 @@ For enterprise inquiries: sales@simple-future.tech or [scan the QR code](https:/
 
 ## 🛠️ Development & Contributing
 
-Contributions are welcome — add a new channel by following the [Feishu channel reference](https://github.com/zhayujie/CowAgent/blob/master/channel/feishu/feishu_channel.py), or contribute new skills to [Skill Hub](https://skills.cowagent.ai/submit).
+All kinds of contributions are welcome — new features, bug fixes, performance improvements, docs, or sharing your own skills on the [Skill Hub](https://skills.cowagent.ai/submit). See [CONTRIBUTING.md](/CONTRIBUTING.md) to get started, then open an Issue to discuss or send a PR directly.
 
-⭐ Star the project to follow updates, and feel free to open PRs and Issues.
+⭐ Star the project to show your support, and Watch → Custom → Releases to get notified of new versions. PRs and Issues are always welcome.
 
 ## 🌟 Contributors
 

agent/memory/summarizer.py

@@ -16,7 +16,7 @@ from datetime import datetime
 from common.log import logger
 
 
-SUMMARIZE_SYSTEM_PROMPT = """你是一个对话记录助手。请将对话内容归纳为当天的日常记录。
+SUMMARIZE_SYSTEM_PROMPT_ZH = """你是一个对话记录助手。请将对话内容归纳为当天的日常记录。
 
 ## 要求
 
@@ -28,7 +28,23 @@ SUMMARIZE_SYSTEM_PROMPT = """你是一个对话记录助手。请将对话内容
 
 当对话没有任何记录价值（仅含问候或无意义内容），直接回复"无"。"""
 
-SUMMARIZE_USER_PROMPT = """请归纳以下对话的日常记录：
+SUMMARIZE_SYSTEM_PROMPT_EN = """You are a conversation-logging assistant. Summarize the conversation into a daily record.
+
+## Requirements
+
+Summarize by "event", not turn by turn:
+- One item per line, starting with "- "
+- Merge multiple turns about the same thing
+- Only record meaningful events; ignore small talk and greetings
+- Keep key decisions, conclusions and to-dos
+
+If the conversation has no record value (only greetings or meaningless content), reply with exactly "None"."""
+
+SUMMARIZE_USER_PROMPT_ZH = """请归纳以下对话的日常记录：
+
+{conversation}"""
+
+SUMMARIZE_USER_PROMPT_EN = """Summarize the daily record of the following conversation:
 
 {conversation}"""
 
@@ -36,7 +52,7 @@ SUMMARIZE_USER_PROMPT = """请归纳以下对话的日常记录：
 # Deep Dream prompts — distill daily memories → MEMORY.md + dream diary
 # ---------------------------------------------------------------------------
 
-DREAM_SYSTEM_PROMPT = """你是一个记忆整理助手，负责定期整理用户的长期记忆。
+DREAM_SYSTEM_PROMPT_ZH = """你是一个记忆整理助手，负责定期整理用户的长期记忆。
 
 你将收到两份材料：
 1. **当前长期记忆** — MEMORY.md 的全部现有内容
@@ -80,7 +96,51 @@ MEMORY.md 会注入每次对话的系统提示词中，因此必须保持精炼
 梦境日记内容...
 ```"""
 
-DREAM_USER_PROMPT = """## 当前长期记忆（MEMORY.md）
+DREAM_SYSTEM_PROMPT_EN = """You are a memory-curation assistant that periodically organizes the user's long-term memory.
+
+You will receive two inputs:
+1. **Current long-term memory** — the full existing content of MEMORY.md
+2. **Today's diary** — the daily records
+
+MEMORY.md is injected into the system prompt of every conversation, so it must stay concise and hold only valuable, memory-worthy content.
+
+**Important: organize strictly based on the provided material. Never fabricate, infer, or add information not present in it.**
+
+## Tasks
+
+### Part 1: Updated long-term memory ([MEMORY])
+
+Organize and distill on top of the existing memory, and output the complete updated content:
+- **Merge & distill**: combine semantically similar items into one dense statement rather than listing them
+- **Extract new**: pull memory-worthy new info from today's diary (preferences, decisions, people, rules, lessons)
+- **Resolve conflicts**: when new info contradicts an old item, prefer the new and replace the old
+- **Clean invalid**: remove temporary notes, blank items, formatting residue, meaningless or duplicate content
+- **Drop redundancy**: delete old items already covered by a more concise statement
+- One item per line, starting with "- ", without a date prefix
+- You may group related items under "## headings" for clarity
+- Goal: keep under 50 items, each ideally a single sentence
+
+### Part 2: Dream diary ([DREAM])
+
+Write a short diary in a concise narrative style recording what this curation found, keep it clean and readable:
+- Which duplicates or conflicts were found
+- What new insights were extracted from the diary
+- What cleanup and optimization was done
+- Overall feelings and observations
+
+## Output format (follow strictly)
+
+```
+[MEMORY]
+- memory item 1
+- memory item 2
+...
+
+[DREAM]
+dream diary content...
+```"""
+
+DREAM_USER_PROMPT_ZH = """## 当前长期记忆（MEMORY.md）
 
 {memory_content}
 
@@ -88,6 +148,47 @@ DREAM_USER_PROMPT = """## 当前长期记忆（MEMORY.md）
 
 {daily_content}"""
 
+DREAM_USER_PROMPT_EN = """## Current long-term memory (MEMORY.md)
+
+{memory_content}
+
+## Recent diary (last {days} days)
+
+{daily_content}"""
+
+
+def _is_en() -> bool:
+    """True when the resolved UI language is English."""
+    try:
+        from common import i18n
+        return i18n.get_language() == "en"
+    except Exception:
+        return False
+
+
+def _summarize_system_prompt() -> str:
+    return SUMMARIZE_SYSTEM_PROMPT_EN if _is_en() else SUMMARIZE_SYSTEM_PROMPT_ZH
+
+
+def _summarize_user_prompt() -> str:
+    return SUMMARIZE_USER_PROMPT_EN if _is_en() else SUMMARIZE_USER_PROMPT_ZH
+
+
+def _dream_system_prompt() -> str:
+    return DREAM_SYSTEM_PROMPT_EN if _is_en() else DREAM_SYSTEM_PROMPT_ZH
+
+
+def _dream_user_prompt() -> str:
+    return DREAM_USER_PROMPT_EN if _is_en() else DREAM_USER_PROMPT_ZH
+
+
+def _is_empty_sentinel(text: str) -> bool:
+    """Match the "no record value" sentinel in both zh ("无") and en ("None")."""
+    if not text:
+        return True
+    s = text.strip()
+    return s == "" or s == "无" or s.lower() == "none"
+
 
 
 class MemoryFlushManager:
@@ -224,7 +325,7 @@ class MemoryFlushManager:
         """Background worker: summarize with LLM, write daily memory file."""
         try:
             raw_summary = self._summarize_messages(messages, max_messages)
-            if not raw_summary or not raw_summary.strip() or raw_summary.strip() == "无":
+            if _is_empty_sentinel(raw_summary):
                 logger.info(f"[MemoryFlush] No valuable content to flush (reason={reason})")
                 return
 
@@ -264,7 +365,7 @@ class MemoryFlushManager:
     def _clean_summary_output(raw: str) -> str:
         """Strip legacy [DAILY]/[MEMORY] markers if present, return clean daily text."""
         raw = raw.strip()
-        if not raw or raw == "无":
+        if _is_empty_sentinel(raw):
             return ""
 
         # Strip [DAILY] marker
@@ -355,7 +456,7 @@ class MemoryFlushManager:
         import time as _time
         t0 = _time.monotonic()
         try:
-            user_msg = DREAM_USER_PROMPT.format(
+            user_msg = _dream_user_prompt().format(
                 memory_content=memory_content or "(empty)",
                 days=lookback_days,
                 daily_content=daily_content or "(no recent daily records)",
@@ -369,7 +470,7 @@ class MemoryFlushManager:
                 temperature=0.3,
                 max_tokens=dream_max_tokens,
                 stream=False,
-                system=DREAM_SYSTEM_PROMPT,
+                system=_dream_system_prompt(),
             )
             response = self.llm_model.call(request)
             raw = self._extract_response_text(response)
@@ -501,9 +602,9 @@ class MemoryFlushManager:
         if self.llm_model:
             try:
                 summary = self._call_llm_for_summary(conversation_text)
-                if summary and summary.strip() and summary.strip() != "无":
+                if not _is_empty_sentinel(summary):
                     return summary.strip()
-                logger.info("[MemoryFlush] LLM returned empty or '无', skipping write")
+                logger.info("[MemoryFlush] LLM returned empty sentinel, skipping write")
                 return ""
             except Exception as e:
                 logger.warning(f"[MemoryFlush] LLM summarization failed, using fallback: {e}")
@@ -579,11 +680,11 @@ class MemoryFlushManager:
         from agent.protocol.models import LLMRequest
         
         request = LLMRequest(
-            messages=[{"role": "user", "content": SUMMARIZE_USER_PROMPT.format(conversation=conversation_text)}],
+            messages=[{"role": "user", "content": _summarize_user_prompt().format(conversation=conversation_text)}],
             temperature=0,
             max_tokens=500,
             stream=False,
-            system=SUMMARIZE_SYSTEM_PROMPT,
+            system=_summarize_system_prompt(),
         )
         
         response = self.llm_model.call(request)

agent/prompt/builder.py

@@ -15,13 +15,13 @@ from config import conf
 
 @dataclass
 class ContextFile:
-    """上下文文件"""
+    """A context file (path + content)."""
     path: str
     content: str
 
 
 class PromptBuilder:
-    """提示词构建器"""
+    """System prompt builder."""
     
     def __init__(self, workspace_dir: str, language: str = "zh"):
         """
@@ -88,97 +88,144 @@ def build_agent_system_prompt(
     **kwargs
 ) -> str:
     """
-    构建Agent系统提示词
-    
-    顺序说明（按重要性和逻辑关系排列）:
-    1. 工具系统 - 核心能力，最先介绍
-    2. 技能系统 - 紧跟工具，因为技能需要用 read 工具读取
-    3. 记忆系统 - 记忆检索与写入引导
-    3.5 知识系统 - 结构化知识库（knowledge/index.md 注入）
-    4. 工作空间 - 工作环境说明
-    5. 用户身份 - 用户信息（可选）
-    6. 项目上下文 - AGENT.md, USER.md, RULE.md, MEMORY.md, BOOTSTRAP.md
-    7. 运行时信息 - 元信息（时间、模型等）
-    
+    Build the agent system prompt.
+
+    Section order (by importance and logical flow):
+    1. Tooling - core capabilities, introduced first
+    2. Skills - right after tools, since skills are read via the read tool
+    3. Memory - memory recall and writing guidance
+    3.5 Knowledge - structured knowledge base (injects knowledge/index.md)
+    4. Workspace - working environment description
+    5. User identity - user info (optional)
+    6. Project context - AGENT.md, USER.md, RULE.md, MEMORY.md, BOOTSTRAP.md
+    7. Runtime info - meta info (time, model, etc.)
+
     Args:
-        workspace_dir: 工作空间目录
-        language: 语言 ("zh" 或 "en")
-        base_persona: 基础人格描述（已废弃，由AGENT.md定义）
-        user_identity: 用户身份信息
-        tools: 工具列表
-        context_files: 上下文文件列表
-        skill_manager: 技能管理器
-        memory_manager: 记忆管理器
-        runtime_info: 运行时信息
-        **kwargs: 其他参数
-        
+        workspace_dir: workspace directory
+        language: language ("zh" or "en")
+        base_persona: base persona description (deprecated, defined by AGENT.md)
+        user_identity: user identity info
+        tools: tool list
+        context_files: context file list
+        skill_manager: skill manager
+        memory_manager: memory manager
+        runtime_info: runtime info
+        **kwargs: extra args
+
     Returns:
-        完整的系统提示词
+        The full system prompt.
     """
     sections = []
-    
-    # 1. 工具系统（最重要，放在最前面）
+
+    # 1. Tooling (most important, goes first)
     if tools:
         sections.extend(_build_tooling_section(tools, language))
-    
-    # 2. 技能系统（紧跟工具，因为需要用 read 工具）
+
+    # 2. Skills (right after tools, since they need the read tool)
     if skill_manager:
         sections.extend(_build_skills_section(skill_manager, tools, language))
-    
-    # 3. 记忆系统（独立的记忆能力）
+
+    # 3. Memory (standalone memory capability)
     if memory_manager:
         sections.extend(_build_memory_section(memory_manager, tools, language))
 
-    # 3.5 知识系统（结构化知识库）
+    # 3.5 Knowledge (structured knowledge base)
     if conf().get("knowledge", True):
         sections.extend(_build_knowledge_section(workspace_dir, language))
-    
-    # 4. 工作空间（工作环境说明）
+
+    # 4. Workspace (working environment description)
     sections.extend(_build_workspace_section(workspace_dir, language))
-    
-    # 5. 用户身份（如果有）
+
+    # 5. User identity (if present)
     if user_identity:
         sections.extend(_build_user_identity_section(user_identity, language))
-    
-    # 6. 项目上下文文件（AGENT.md, USER.md, RULE.md - 定义人格）
+
+    # 6. Project context files (AGENT.md, USER.md, RULE.md - define the persona)
     if context_files:
         sections.extend(_build_context_files_section(context_files, language))
-    
-    # 7. 运行时信息（元信息，放在最后）
+
+    # 7. Runtime info (meta info, goes last)
     if runtime_info:
         sections.extend(_build_runtime_section(runtime_info, language))
-    
+
+    # 8. Response language (always appended, independent of the skeleton language)
+    sections.extend(_build_response_language_section(language))
+
     return "\n".join(sections)
 
 
+def _build_response_language_section(language: str) -> List[str]:
+    """Response-language rule, appended regardless of the prompt skeleton language.
+
+    Keeps the agent's reply language aligned with the user's input by default,
+    so a Chinese-built prompt still answers an English user in English.
+    """
+    if language == "en":
+        return [
+            "## 🌐 Response language",
+            "",
+            "By default, reply in the same language as the user's input, "
+            "unless the user explicitly asks for another language.",
+            "",
+        ]
+    return [
+        "## 🌐 回复语言",
+        "",
+        "默认使用与用户输入相同的语言回复，除非用户明确要求使用其他语言。",
+        "",
+    ]
+
+
 def _build_identity_section(base_persona: Optional[str], language: str) -> List[str]:
-    """构建基础身份section - 不再需要，身份由AGENT.md定义"""
-    # 不再生成基础身份section，完全由AGENT.md定义
+    """Base identity section - no longer needed, identity is defined by AGENT.md."""
+    # Identity is fully defined by AGENT.md, so emit nothing here.
     return []
 
 
 def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
     """Build tooling section with concise tool list and call style guide."""
+    is_en = language == "en"
     # 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（关键结果或需要协助可截图发送给用户）",
-        "memory_search": "搜索记忆",
-        "memory_get": "读取记忆内容",
-        "env_config": "管理API密钥和技能配置",
-        "scheduler": "管理定时任务和提醒",
-        "send": "发送本地文件给用户（仅限本地文件，URL直接放在回复文本中）",
-        "vision": "分析图片内容（识别、描述、OCR文字提取等）",
-    }
+    if is_en:
+        core_summaries = {
+            "read": "read file content",
+            "write": "create or overwrite a file",
+            "edit": "make precise edits to a file",
+            "ls": "list directory contents",
+            "grep": "search file contents",
+            "find": "find files by pattern",
+            "bash": "run shell commands",
+            "terminal": "manage background processes",
+            "web_search": "web search",
+            "web_fetch": "fetch URL content",
+            "browser": "control the browser (screenshot key results or send to the user when help is needed)",
+            "memory_search": "search memory",
+            "memory_get": "read memory content",
+            "env_config": "manage API keys and skill config",
+            "scheduler": "manage scheduled tasks and reminders",
+            "send": "send a local file to the user (local files only; put URLs directly in the reply text)",
+            "vision": "analyze images (recognition, description, OCR, etc.)",
+        }
+    else:
+        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（关键结果或需要协助可截图发送给用户）",
+            "memory_search": "搜索记忆",
+            "memory_get": "读取记忆内容",
+            "env_config": "管理API密钥和技能配置",
+            "scheduler": "管理定时任务和提醒",
+            "send": "发送本地文件给用户（仅限本地文件，URL直接放在回复文本中）",
+            "vision": "分析图片内容（识别、描述、OCR文字提取等）",
+        }
 
     # Preferred display order
     tool_order = [
@@ -205,30 +252,46 @@ def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
         summary = available[name]
         tool_lines.append(f"- {name}: {summary}" if summary else f"- {name}")
 
-    lines = [
-        "## 🔧 工具系统",
-        "",
-        "可用工具（名称大小写敏感，严格按列表调用）:",
-        "\n".join(tool_lines),
-        "",
-        "工具调用风格：",
-        "",
-        "- 多步骤任务、复杂决策、敏感操作时，应简要说明当前在做什么、为什么这样做，让用户了解关键进展",
-        "- 持续推进直到任务完成，完成后向用户报告结果",
-        "- 回复中涉及密钥、令牌等敏感信息必须脱敏",
-        "- URL链接直接放在回复文本中即可，系统会自动处理和渲染。无需下载后使用send工具发送",
-        "",
-    ]
+    if is_en:
+        lines = [
+            "## 🔧 Tooling",
+            "",
+            "Available tools (names are case-sensitive, call exactly as listed):",
+            "\n".join(tool_lines),
+            "",
+            "Tool-calling style:",
+            "",
+            "- For multi-step tasks, complex decisions or sensitive operations, briefly explain what you are doing and why, so the user follows key progress",
+            "- Keep going until the task is done, then report the result to the user",
+            "- Always redact secrets, tokens and other sensitive info in replies",
+            "- Put URLs directly in the reply text; the system handles and renders them. Don't download and re-send them via the send tool",
+            "",
+        ]
+    else:
+        lines = [
+            "## 🔧 工具系统",
+            "",
+            "可用工具（名称大小写敏感，严格按列表调用）:",
+            "\n".join(tool_lines),
+            "",
+            "工具调用风格：",
+            "",
+            "- 多步骤任务、复杂决策、敏感操作时，应简要说明当前在做什么、为什么这样做，让用户了解关键进展",
+            "- 持续推进直到任务完成，完成后向用户报告结果",
+            "- 回复中涉及密钥、令牌等敏感信息必须脱敏",
+            "- URL链接直接放在回复文本中即可，系统会自动处理和渲染。无需下载后使用send工具发送",
+            "",
+        ]
 
     return lines
 
 
 def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], language: str) -> List[str]:
-    """构建技能系统section"""
+    """Build the skills section."""
     if not skill_manager:
         return []
     
-    # 获取read工具名称
+    # Resolve the read tool name
     read_tool_name = "read"
     if tools:
         for tool in tools:
@@ -237,23 +300,40 @@ def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], langua
                 read_tool_name = tool_name
                 break
     
-    lines = [
-        "## 🧩 技能系统（mandatory）",
-        "",
-        "在回复之前：扫描下方 <available_skills> 中每个技能的 <description>。",
-        "",
-        f"- 如果有技能的描述与用户需求匹配：使用 `{read_tool_name}` 工具读取其 <location> 路径的 SKILL.md 文件，然后严格遵循文件中的指令。"
-        "当有匹配的技能时，应优先使用技能",
-        "- 如果多个技能都适用则选择最匹配的一个，然后读取并遵循。",
-        "- 如果没有技能明确适用：不要读取任何 SKILL.md，直接使用通用工具。",
-        "",
-        f"**重要**: 技能不是工具，不能直接调用。使用技能的唯一方式是用 `{read_tool_name}` 读取 SKILL.md 文件，然后按文件内容操作。"
-        "永远不要一次性读取多个技能，只在选择后再读取。",
-        "",
-        "以下是可用技能："
-    ]
+    if language == "en":
+        lines = [
+            "## 🧩 Skills (mandatory)",
+            "",
+            "Before replying: scan the <description> of every skill in <available_skills> below.",
+            "",
+            f"- If a skill's description matches the user's need: use the `{read_tool_name}` tool to read the SKILL.md at its <location> path, then strictly follow the instructions in the file. "
+            "Prefer using a skill when one matches.",
+            "- If multiple skills apply, pick the best-matching one, then read and follow it.",
+            "- If no skill clearly applies: do not read any SKILL.md, just use the general tools.",
+            "",
+            f"**Important**: skills are not tools and cannot be called directly. The only way to use a skill is to read its SKILL.md with `{read_tool_name}`, then act on the file's content. "
+            "Never read multiple skills at once — only read one after selecting it.",
+            "",
+            "Available skills:"
+        ]
+    else:
+        lines = [
+            "## 🧩 技能系统（mandatory）",
+            "",
+            "在回复之前：扫描下方 <available_skills> 中每个技能的 <description>。",
+            "",
+            f"- 如果有技能的描述与用户需求匹配：使用 `{read_tool_name}` 工具读取其 <location> 路径的 SKILL.md 文件，然后严格遵循文件中的指令。"
+            "当有匹配的技能时，应优先使用技能",
+            "- 如果多个技能都适用则选择最匹配的一个，然后读取并遵循。",
+            "- 如果没有技能明确适用：不要读取任何 SKILL.md，直接使用通用工具。",
+            "",
+            f"**重要**: 技能不是工具，不能直接调用。使用技能的唯一方式是用 `{read_tool_name}` 读取 SKILL.md 文件，然后按文件内容操作。"
+            "永远不要一次性读取多个技能，只在选择后再读取。",
+            "",
+            "以下是可用技能："
+        ]
     
-    # 添加技能列表（通过skill_manager获取）
+    # Append the skills list (built by skill_manager)
     try:
         skills_prompt = skill_manager.build_skills_prompt()
         logger.debug(f"[PromptBuilder] Skills prompt length: {len(skills_prompt) if skills_prompt else 0}")
@@ -271,7 +351,7 @@ def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], langua
 
 
 def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], language: str) -> List[str]:
-    """构建记忆系统section"""
+    """Build the memory section."""
     if not memory_manager:
         return []
 
@@ -286,43 +366,82 @@ def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], langu
     from datetime import datetime
     today_file = datetime.now().strftime("%Y-%m-%d") + ".md"
 
-    lines = [
-        "## 🧠 记忆系统",
-        "",
-        "### Memory Recall（mandatory）",
-        "",
-        "当用户询问过往事件、引用之前的决定、提到人物关系、偏好、待办、或你对某事不确定时，**必须先检索记忆再回答**。",
-        "如果 MEMORY.md 中已有相关信息则无需重复检索。完整内容和每日记忆需要通过工具检索。",
-        "",
-        "1. 不确定位置 → `memory_search` 关键词/语义检索",
-        "2. 已知位置 → `memory_get` 直接读取对应行",
-        "3. search 无结果 → `memory_get` 读最近两天记忆",
-        "",
-        "**记忆文件结构**:",
-        "- `MEMORY.md`: 长期记忆索引（已自动加载到上下文，核心信息、偏好、决策等）",
-        f"- `memory/YYYY-MM-DD.md`: 每日记忆，今天是 `memory/{today_file}`",
-        "- `knowledge/`: 结构化知识库（见下方知识系统）",
-        "",
-        "### 写入记忆",
-        "",
-        "遇到以下情况时，**主动**将信息写入记忆文件（无需告知用户）：",
-        "",
-        "- 用户要求记住某些信息，或使用了「记住」「以后」「总是」「不要」「偏好」等表达",
-        "- 用户分享了重要的个人偏好、习惯、决策",
-        "- 对话中产生了重要的结论、方案、约定",
-        "- 完成了复杂任务，值得记录关键步骤和结果",
-        "",
-        "**存储规则**:",
-        f"- 长期核心信息 → `MEMORY.md`",
-        f"- 当天事件/进展 → `memory/{today_file}`",
-        "- 结构化知识 → `knowledge/`（见知识系统）",
-        "- 追加 → `edit` 工具，oldText 留空",
-        "- 修改 → `edit` 工具，oldText 填写要替换的文本",
-        "- **禁止写入敏感信息**（API密钥、令牌等）",
-        "",
-        "**使用原则**: 自然使用记忆，就像你本来就知道；不用刻意提起，除非用户问起。",
-        "",
-    ]
+    if language == "en":
+        lines = [
+            "## 🧠 Memory",
+            "",
+            "### Memory Recall (mandatory)",
+            "",
+            "When the user asks about past events, references an earlier decision, mentions relationships, preferences or to-dos, or when you are unsure about something, **you must search memory before answering**.",
+            "No need to re-search if the info is already in MEMORY.md. Full content and daily memory must be retrieved via tools.",
+            "",
+            "1. Location unknown → `memory_search` (keyword / semantic search)",
+            "2. Location known → `memory_get` to read the exact lines",
+            "3. Search returns nothing → `memory_get` to read the last two days of memory",
+            "",
+            "**Memory file structure**:",
+            "- `MEMORY.md`: long-term memory index (already auto-loaded into context: core info, preferences, decisions, etc.)",
+            f"- `memory/YYYY-MM-DD.md`: daily memory; today is `memory/{today_file}`",
+            "- `knowledge/`: structured knowledge base (see the knowledge system below)",
+            "",
+            "### Writing memory",
+            "",
+            "In the following cases, **proactively** write info to memory files (no need to tell the user):",
+            "",
+            "- The user asks you to remember something, or uses words like \"remember\", \"from now on\", \"always\", \"never\", \"prefer\"",
+            "- The user shares important personal preferences, habits or decisions",
+            "- The conversation produces an important conclusion, plan or agreement",
+            "- A complex task is completed and the key steps and results are worth recording",
+            "",
+            "**Storage rules**:",
+            "- Long-term core info → `MEMORY.md`",
+            f"- Today's events/progress → `memory/{today_file}`",
+            "- Structured knowledge → `knowledge/` (see the knowledge system)",
+            "- Append → `edit` tool with empty oldText",
+            "- Modify → `edit` tool with oldText set to the text to replace",
+            "- **Never write sensitive info** (API keys, tokens, etc.)",
+            "",
+            "**Principle**: use memory naturally, as if you simply knew it; don't bring it up unless asked.",
+            "",
+        ]
+    else:
+        lines = [
+            "## 🧠 记忆系统",
+            "",
+            "### Memory Recall（mandatory）",
+            "",
+            "当用户询问过往事件、引用之前的决定、提到人物关系、偏好、待办、或你对某事不确定时，**必须先检索记忆再回答**。",
+            "如果 MEMORY.md 中已有相关信息则无需重复检索。完整内容和每日记忆需要通过工具检索。",
+            "",
+            "1. 不确定位置 → `memory_search` 关键词/语义检索",
+            "2. 已知位置 → `memory_get` 直接读取对应行",
+            "3. search 无结果 → `memory_get` 读最近两天记忆",
+            "",
+            "**记忆文件结构**:",
+            "- `MEMORY.md`: 长期记忆索引（已自动加载到上下文，核心信息、偏好、决策等）",
+            f"- `memory/YYYY-MM-DD.md`: 每日记忆，今天是 `memory/{today_file}`",
+            "- `knowledge/`: 结构化知识库（见下方知识系统）",
+            "",
+            "### 写入记忆",
+            "",
+            "遇到以下情况时，**主动**将信息写入记忆文件（无需告知用户）：",
+            "",
+            "- 用户要求记住某些信息，或使用了「记住」「以后」「总是」「不要」「偏好」等表达",
+            "- 用户分享了重要的个人偏好、习惯、决策",
+            "- 对话中产生了重要的结论、方案、约定",
+            "- 完成了复杂任务，值得记录关键步骤和结果",
+            "",
+            "**存储规则**:",
+            f"- 长期核心信息 → `MEMORY.md`",
+            f"- 当天事件/进展 → `memory/{today_file}`",
+            "- 结构化知识 → `knowledge/`（见知识系统）",
+            "- 追加 → `edit` 工具，oldText 留空",
+            "- 修改 → `edit` 工具，oldText 填写要替换的文本",
+            "- **禁止写入敏感信息**（API密钥、令牌等）",
+            "",
+            "**使用原则**: 自然使用记忆，就像你本来就知道；不用刻意提起，除非用户问起。",
+            "",
+        ]
 
     return lines
 
@@ -339,37 +458,61 @@ def _build_knowledge_section(workspace_dir: str, language: str) -> List[str]:
     except Exception:
         return []
 
-    lines = [
-        "## 📚 知识系统",
-        "",
-        "你拥有一个持续积累的个人知识库 `knowledge/`，这是你的长期结构化知识存储。",
-        "",
-        "### 自动写入规则（mandatory）",
-        "",
-        "以下场景**必须**在回复的同时写入知识库，**直接写入，不要询问用户是否需要**：",
-        "",
-        "1. **用户分享了文章/链接/文档** → 阅读理解后，在同一轮回复中将要点写入 `knowledge/sources/<slug>.md`",
-        "2. **深度讨论产生了结论/方案** → 整理为 `knowledge/analysis/<slug>.md`",
-        "3. **对话涉及重要实体**（人物/公司/项目）→ 创建或更新 `knowledge/entities/<name>.md`",
-        "4. **讨论了技术概念/方法论** → 整理为 `knowledge/concepts/<topic>.md`",
-        "",
-        "每次写入知识页面后，**必须同步更新** `knowledge/index.md` 添加一行索引。",
-        "详细的页面格式和操作规范，请读取技能 `knowledge-wiki` 的 SKILL.md。",
-        "",
-        "⚠️ 不要问「要不要存到知识库」——符合上述场景就直接写入，这是你的本能行为。",
-        "",
-    ]
+    if language == "en":
+        lines = [
+            "## 📚 Knowledge",
+            "",
+            "You have a continuously growing personal knowledge base `knowledge/` — your long-term structured knowledge store.",
+            "",
+            "### Auto-write rules (mandatory)",
+            "",
+            "In the following cases you **must** write to the knowledge base alongside your reply, **directly, without asking the user**:",
+            "",
+            "1. **User shares an article / link / document** → after reading and understanding, write the key points to `knowledge/sources/<slug>.md` in the same turn",
+            "2. **An in-depth discussion produces a conclusion / plan** → organize it into `knowledge/analysis/<slug>.md`",
+            "3. **The conversation involves an important entity** (person / company / project) → create or update `knowledge/entities/<name>.md`",
+            "4. **A technical concept / methodology is discussed** → organize it into `knowledge/concepts/<topic>.md`",
+            "",
+            "After writing any knowledge page, you **must update** `knowledge/index.md` with a new index line in sync.",
+            "For detailed page format and conventions, read the SKILL.md of the `knowledge-wiki` skill.",
+            "",
+            "⚠️ Don't ask \"should I save this to the knowledge base?\" — if a case above matches, just write it. This is instinctive.",
+            "",
+        ]
+    else:
+        lines = [
+            "## 📚 知识系统",
+            "",
+            "你拥有一个持续积累的个人知识库 `knowledge/`，这是你的长期结构化知识存储。",
+            "",
+            "### 自动写入规则（mandatory）",
+            "",
+            "以下场景**必须**在回复的同时写入知识库，**直接写入，不要询问用户是否需要**：",
+            "",
+            "1. **用户分享了文章/链接/文档** → 阅读理解后，在同一轮回复中将要点写入 `knowledge/sources/<slug>.md`",
+            "2. **深度讨论产生了结论/方案** → 整理为 `knowledge/analysis/<slug>.md`",
+            "3. **对话涉及重要实体**（人物/公司/项目）→ 创建或更新 `knowledge/entities/<name>.md`",
+            "4. **讨论了技术概念/方法论** → 整理为 `knowledge/concepts/<topic>.md`",
+            "",
+            "每次写入知识页面后，**必须同步更新** `knowledge/index.md` 添加一行索引。",
+            "详细的页面格式和操作规范，请读取技能 `knowledge-wiki` 的 SKILL.md。",
+            "",
+            "⚠️ 不要问「要不要存到知识库」——符合上述场景就直接写入，这是你的本能行为。",
+            "",
+        ]
 
     if index_content:
         lines.extend([
-            "### 当前知识索引",
+            ("### Current knowledge index" if language == "en" else "### 当前知识索引"),
             "",
             index_content,
             "",
         ])
 
     lines.extend([
-        "**查询方式**：用 `read` 读取知识页面，或用 `memory_search` 检索（知识已纳入向量索引）。",
+        ("**How to query**: use `read` to open a knowledge page, or `memory_search` (knowledge is in the vector index)."
+         if language == "en" else
+         "**查询方式**：用 `read` 读取知识页面，或用 `memory_search` 检索（知识已纳入向量索引）。"),
         "",
     ])
 
@@ -377,76 +520,118 @@ def _build_knowledge_section(workspace_dir: str, language: str) -> List[str]:
 
 
 def _build_user_identity_section(user_identity: Dict[str, str], language: str) -> List[str]:
-    """构建用户身份section"""
+    """Build the user identity section."""
     if not user_identity:
         return []
     
+    is_en = language == "en"
     lines = [
-        "## 👤 用户身份",
+        ("## 👤 User identity" if is_en else "## 👤 用户身份"),
         "",
     ]
-    
+
     if user_identity.get("name"):
-        lines.append(f"**用户姓名**: {user_identity['name']}")
+        lines.append(f"**{'Name' if is_en else '用户姓名'}**: {user_identity['name']}")
     if user_identity.get("nickname"):
-        lines.append(f"**称呼**: {user_identity['nickname']}")
+        lines.append(f"**{'Preferred name' if is_en else '称呼'}**: {user_identity['nickname']}")
     if user_identity.get("timezone"):
-        lines.append(f"**时区**: {user_identity['timezone']}")
+        lines.append(f"**{'Timezone' if is_en else '时区'}**: {user_identity['timezone']}")
     if user_identity.get("notes"):
-        lines.append(f"**备注**: {user_identity['notes']}")
-    
+        lines.append(f"**{'Notes' if is_en else '备注'}**: {user_identity['notes']}")
+
     lines.append("")
-    
+
     return lines
 
 
 def _build_docs_section(workspace_dir: str, language: str) -> List[str]:
-    """构建文档路径section - 已移除，不再需要"""
-    # 不再生成文档section
+    """Docs-path section - removed, no longer needed."""
+    # No docs section is generated anymore.
     return []
 
 
 def _build_workspace_section(workspace_dir: str, language: str) -> List[str]:
-    """构建工作空间section"""
-    lines = [
-        "## 📂 工作空间",
-        "",
-        f"你的工作目录是: `{workspace_dir}`",
-        "",
-        "**路径使用规则** (非常重要):",
-        "",
-        f"1. **相对路径的基准目录**: 所有相对路径都是相对于 `{workspace_dir}` 而言的",
-        f"   - ✅ 正确: 访问工作空间内的文件用相对路径，如 `AGENT.md`",
-        f"   - ❌ 错误: 用相对路径访问其他目录的文件 (如果它不在 `{workspace_dir}` 内)",
-        "",
-        "2. **访问其他目录**: 如果要访问工作空间之外的目录（如项目代码、系统文件），**必须使用绝对路径**",
-        f"   - ✅ 正确: 例如 `~/chatgpt-on-wechat`、`/usr/local/`",
-        f"   - ❌ 错误: 假设相对路径会指向其他目录",
-        "",
-        "3. **路径解析示例**:",
-        f"   - 相对路径 `memory/` → 实际路径 `{workspace_dir}/memory/`",
-        f"   - 绝对路径 `~/chatgpt-on-wechat/docs/` → 实际路径 `~/chatgpt-on-wechat/docs/`",
-        "",
-        "4. **不确定时**: 先用 `bash pwd` 确认当前目录，或用 `ls .` 查看当前位置",
-        "",
-        "**重要说明 - 文件已自动加载**:",
-        "",
-        "以下文件在会话启动时**已经自动加载**到系统提示词中，你**无需再用 read 工具读取**：",
-        "",
-        "- ✅ `AGENT.md`: 已加载 - 你的人格和灵魂设定，请严格遵循。当你的名字、性格或交流风格发生变化时，主动用 `edit` 更新此文件",
-        "- ✅ `USER.md`: 已加载 - 用户的身份信息。当用户修改称呼、姓名等身份信息时，用 `edit` 更新此文件",
-        "- ✅ `RULE.md`: 已加载 - 工作空间使用指南和规则，请严格遵循",
-        "- ✅ `MEMORY.md`: 已加载 - 长期记忆索引",
-        "",
-        "**💬 交流规范**:",
-        "",
-        "- 记忆相关操作无需暴露文件名，用自然语言表达即可。例如说「我已记住」而非「已更新 MEMORY.md」",
-        "- 任务执行过程中的关键决策和步骤应该告知用户，让用户了解你在做什么、为什么这么做",
-        "- 做真正有帮助的助手，而不是表演式的客套，尽可能帮忙解决问题",
-        "- 回复应结构清晰、重点突出。善用 **加粗**、列表、分段等格式让信息一目了然",
-        "- 适当使用 emoji 让表达更生动自然 🎯，但不要过度堆砌",
-        "",
-    ]
+    """Build the workspace section."""
+    if language == "en":
+        lines = [
+            "## 📂 Workspace",
+            "",
+            f"Your working directory is: `{workspace_dir}`",
+            "",
+            "**Path rules** (very important):",
+            "",
+            f"1. **Base directory for relative paths**: all relative paths are relative to `{workspace_dir}`",
+            "   - ✅ Correct: use relative paths for files inside the workspace, e.g. `AGENT.md`",
+            f"   - ❌ Wrong: using a relative path for files in other directories (if not inside `{workspace_dir}`)",
+            "",
+            "2. **Accessing other directories**: to reach directories outside the workspace (project code, system files), **you must use absolute paths**",
+            "   - ✅ Correct: e.g. `~/chatgpt-on-wechat`, `/usr/local/`",
+            "   - ❌ Wrong: assuming a relative path points to another directory",
+            "",
+            "3. **Path resolution examples**:",
+            f"   - relative `memory/` → actual `{workspace_dir}/memory/`",
+            "   - absolute `~/chatgpt-on-wechat/docs/` → actual `~/chatgpt-on-wechat/docs/`",
+            "",
+            "4. **When unsure**: run `bash pwd` to confirm the current directory, or `ls .` to see where you are",
+            "",
+            "**Important - files already auto-loaded**:",
+            "",
+            "The following files are **already auto-loaded** into the system prompt at session start, so you **don't need to read them again with the read tool**:",
+            "",
+            "- ✅ `AGENT.md`: loaded - your persona and soul; follow it strictly. When your name, personality or style changes, proactively `edit` this file",
+            "- ✅ `USER.md`: loaded - the user's identity info. When the user changes how they're addressed, their name, etc., `edit` this file",
+            "- ✅ `RULE.md`: loaded - workspace guide and rules; follow them strictly",
+            "- ✅ `MEMORY.md`: loaded - long-term memory index",
+            "",
+            "**💬 Communication norms**:",
+            "",
+            "- No need to expose file names for memory operations; use natural language. Say \"I'll remember that\" rather than \"updated MEMORY.md\"",
+            "- Tell the user about key decisions and steps during a task, so they know what you're doing and why",
+            "- Be genuinely helpful rather than performatively polite; solve the problem as much as you can",
+            "- Keep replies well-structured and focused. Use **bold**, lists and sections to make info clear at a glance",
+            "- Use emoji to make expression lively 🎯, but don't overdo it",
+            "",
+        ]
+    else:
+        lines = [
+            "## 📂 工作空间",
+            "",
+            f"你的工作目录是: `{workspace_dir}`",
+            "",
+            "**路径使用规则** (非常重要):",
+            "",
+            f"1. **相对路径的基准目录**: 所有相对路径都是相对于 `{workspace_dir}` 而言的",
+            f"   - ✅ 正确: 访问工作空间内的文件用相对路径，如 `AGENT.md`",
+            f"   - ❌ 错误: 用相对路径访问其他目录的文件 (如果它不在 `{workspace_dir}` 内)",
+            "",
+            "2. **访问其他目录**: 如果要访问工作空间之外的目录（如项目代码、系统文件），**必须使用绝对路径**",
+            f"   - ✅ 正确: 例如 `~/chatgpt-on-wechat`、`/usr/local/`",
+            f"   - ❌ 错误: 假设相对路径会指向其他目录",
+            "",
+            "3. **路径解析示例**:",
+            f"   - 相对路径 `memory/` → 实际路径 `{workspace_dir}/memory/`",
+            f"   - 绝对路径 `~/chatgpt-on-wechat/docs/` → 实际路径 `~/chatgpt-on-wechat/docs/`",
+            "",
+            "4. **不确定时**: 先用 `bash pwd` 确认当前目录，或用 `ls .` 查看当前位置",
+            "",
+            "**重要说明 - 文件已自动加载**:",
+            "",
+            "以下文件在会话启动时**已经自动加载**到系统提示词中，你**无需再用 read 工具读取**：",
+            "",
+            "- ✅ `AGENT.md`: 已加载 - 你的人格和灵魂设定，请严格遵循。当你的名字、性格或交流风格发生变化时，主动用 `edit` 更新此文件",
+            "- ✅ `USER.md`: 已加载 - 用户的身份信息。当用户修改称呼、姓名等身份信息时，用 `edit` 更新此文件",
+            "- ✅ `RULE.md`: 已加载 - 工作空间使用指南和规则，请严格遵循",
+            "- ✅ `MEMORY.md`: 已加载 - 长期记忆索引",
+            "",
+            "**💬 交流规范**:",
+            "",
+            "- 记忆相关操作无需暴露文件名，用自然语言表达即可。例如说「我已记住」而非「已更新 MEMORY.md」",
+            "- 任务执行过程中的关键决策和步骤应该告知用户，让用户了解你在做什么、为什么这么做",
+            "- 做真正有帮助的助手，而不是表演式的客套，尽可能帮忙解决问题",
+            "- 回复应结构清晰、重点突出。善用 **加粗**、列表、分段等格式让信息一目了然",
+            "- 适当使用 emoji 让表达更生动自然 🎯，但不要过度堆砌",
+            "",
+        ]
 
     # Cloud deployment: inject websites directory info and access URL
     cloud_website_lines = _build_cloud_website_section(workspace_dir)
@@ -466,29 +651,42 @@ def _build_cloud_website_section(workspace_dir: str) -> List[str]:
 
 
 def _build_context_files_section(context_files: List[ContextFile], language: str) -> List[str]:
-    """构建项目上下文文件section"""
+    """Build the project context files section."""
     if not context_files:
         return []
     
-    # 检查是否有AGENT.md
+    # Check whether AGENT.md is present
     has_agent = any(
         f.path.lower().endswith('agent.md') or 'agent.md' in f.path.lower()
         for f in context_files
     )
     
-    lines = [
-        "# 📋 项目上下文",
-        "",
-        "以下项目上下文文件已被加载：",
-        "",
-    ]
-    
+    is_en = language == "en"
+    if is_en:
+        lines = [
+            "# 📋 Project context",
+            "",
+            "The following project context files have been loaded:",
+            "",
+        ]
+    else:
+        lines = [
+            "# 📋 项目上下文",
+            "",
+            "以下项目上下文文件已被加载：",
+            "",
+        ]
+
     if has_agent:
-        lines.append("**`AGENT.md` 是你的灵魂文件** 🪞：严格遵循其中定义的人格、语气和设定，做真实的自己，避免僵硬、模板化的回复。")
-        lines.append("当用户通过对话透露了对你性格、风格、职责、能力边界的新期望，你应该主动用 `edit` 更新 AGENT.md 以反映这些演变。")
+        if is_en:
+            lines.append("**`AGENT.md` is your soul file** 🪞: strictly follow the persona, tone and settings it defines. Be your real self, avoid stiff, template-like replies.")
+            lines.append("When the user reveals new expectations about your personality, style, responsibilities or capability boundaries, proactively `edit` AGENT.md to reflect that evolution.")
+        else:
+            lines.append("**`AGENT.md` 是你的灵魂文件** 🪞：严格遵循其中定义的人格、语气和设定，做真实的自己，避免僵硬、模板化的回复。")
+            lines.append("当用户通过对话透露了对你性格、风格、职责、能力边界的新期望，你应该主动用 `edit` 更新 AGENT.md 以反映这些演变。")
         lines.append("")
     
-    # 添加每个文件的内容
+    # Append the content of each file
     for file in context_files:
         lines.append(f"## {file.path}")
         lines.append("")
@@ -499,21 +697,23 @@ 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 - 支持动态时间"""
+    """Build the runtime info section - supports dynamic time."""
     if not runtime_info:
         return []
     
+    is_en = language == "en"
+    time_label = "Current time" if is_en else "当前时间"
     lines = [
-        "## ⚙️ 运行时信息",
+        ("## ⚙️ Runtime info" if is_en else "## ⚙️ 运行时信息"),
         "",
     ]
-    
+
     # Add current time if available
     # 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']})"
+            time_line = f"{time_label}: {time_info['time']} {time_info['weekday']} ({time_info['timezone']})"
             lines.append(time_line)
             lines.append("")
         except Exception as e:
@@ -523,35 +723,38 @@ def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[
         time_str = runtime_info["current_time"]
         weekday = runtime_info.get("weekday", "")
         timezone = runtime_info.get("timezone", "")
-        
-        time_line = f"当前时间: {time_str}"
+
+        time_line = f"{time_label}: {time_str}"
         if weekday:
             time_line += f" {weekday}"
         if timezone:
             time_line += f" ({timezone})"
-        
+
         lines.append(time_line)
         lines.append("")
-    
+
     # Add other runtime info
+    model_label = "model" if is_en else "模型"
+    workspace_label = "workspace" if is_en else "工作空间"
+    channel_label = "channel" if is_en else "渠道"
     runtime_parts = []
     # Support dynamic model via callable, fallback to static value
     if callable(runtime_info.get("_get_model")):
         try:
-            runtime_parts.append(f"模型={runtime_info['_get_model']()}")
+            runtime_parts.append(f"{model_label}={runtime_info['_get_model']()}")
         except Exception:
             if runtime_info.get("model"):
-                runtime_parts.append(f"模型={runtime_info['model']}")
+                runtime_parts.append(f"{model_label}={runtime_info['model']}")
     elif runtime_info.get("model"):
-        runtime_parts.append(f"模型={runtime_info['model']}")
+        runtime_parts.append(f"{model_label}={runtime_info['model']}")
     if runtime_info.get("workspace"):
-        runtime_parts.append(f"工作空间={runtime_info['workspace']}")
+        runtime_parts.append(f"{workspace_label}={runtime_info['workspace']}")
     # Only add channel if it's not the default "web"
     if runtime_info.get("channel") and runtime_info.get("channel") != "web":
-        runtime_parts.append(f"渠道={runtime_info['channel']}")
-    
+        runtime_parts.append(f"{channel_label}={runtime_info['channel']}")
+
     if runtime_parts:
-        lines.append("运行时: " + " | ".join(runtime_parts))
+        lines.append(("Runtime: " if is_en else "运行时: ") + " | ".join(runtime_parts))
         lines.append("")
-    
+
     return lines

agent/prompt/workspace.py

@@ -1,7 +1,7 @@
 """
-Workspace Management - 工作空间管理模块
+Workspace Management
 
-负责初始化工作空间、创建模板文件、加载上下文文件
+Initializes the workspace, creates template files, and loads context files.
 """
 
 from __future__ import annotations
@@ -13,7 +13,7 @@ from common.log import logger
 from .builder import ContextFile
 
 
-# 默认文件名常量
+# Default file name constants
 DEFAULT_AGENT_FILENAME = "AGENT.md"
 DEFAULT_USER_FILENAME = "USER.md"
 DEFAULT_RULE_FILENAME = "RULE.md"
@@ -23,7 +23,7 @@ DEFAULT_BOOTSTRAP_FILENAME = "BOOTSTRAP.md"
 
 @dataclass
 class WorkspaceFiles:
-    """工作空间文件路径"""
+    """Workspace file paths."""
     agent_path: str
     user_path: str
     rule_path: str
@@ -33,14 +33,14 @@ class WorkspaceFiles:
 
 def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> WorkspaceFiles:
     """
-    确保工作空间存在，并创建必要的模板文件
-    
+    Ensure the workspace exists and create the necessary template files.
+
     Args:
-        workspace_dir: 工作空间目录路径
-        create_templates: 是否创建模板文件（首次运行时）
-        
+        workspace_dir: workspace directory path
+        create_templates: whether to create template files (on first run)
+
     Returns:
-        WorkspaceFiles对象，包含所有文件路径
+        A WorkspaceFiles object with all file paths.
     """
     # Check if this is a brand new workspace (AGENT.md not yet created).
     # Cannot rely on directory existence because other modules (e.g. ConversationStore)
@@ -48,23 +48,23 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
     agent_path = os.path.join(workspace_dir, DEFAULT_AGENT_FILENAME)
     is_new_workspace = not os.path.exists(agent_path)
     
-    # 确保目录存在
+    # Ensure the directory exists
     os.makedirs(workspace_dir, exist_ok=True)
     
-    # 定义文件路径
+    # Define file paths
     user_path = os.path.join(workspace_dir, DEFAULT_USER_FILENAME)
     rule_path = os.path.join(workspace_dir, DEFAULT_RULE_FILENAME)
-    memory_path = os.path.join(workspace_dir, DEFAULT_MEMORY_FILENAME)  # MEMORY.md 在根目录
-    memory_dir = os.path.join(workspace_dir, "memory")  # 每日记忆子目录
+    memory_path = os.path.join(workspace_dir, DEFAULT_MEMORY_FILENAME)  # MEMORY.md at the root
+    memory_dir = os.path.join(workspace_dir, "memory")  # daily memory subdirectory
     
-    # 创建memory子目录
+    # Create the memory subdirectory
     os.makedirs(memory_dir, exist_ok=True)
 
-    # 创建skills子目录 (for workspace-level skills installed by agent)
+    # Create the skills subdirectory (for workspace-level skills installed by agent)
     skills_dir = os.path.join(workspace_dir, "skills")
     os.makedirs(skills_dir, exist_ok=True)
 
-    # 创建websites子目录 (for web pages / sites generated by agent)
+    # Create the websites subdirectory (for web pages / sites generated by agent)
     websites_dir = os.path.join(workspace_dir, "websites")
     os.makedirs(websites_dir, exist_ok=True)
 
@@ -74,7 +74,7 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
         knowledge_dir = os.path.join(workspace_dir, "knowledge")
         os.makedirs(knowledge_dir, exist_ok=True)
     
-    # 如果需要，创建模板文件
+    # Create template files if requested
     if create_templates:
         _create_template_if_missing(agent_path, _get_agent_template())
         _create_template_if_missing(user_path, _get_user_template())
@@ -109,17 +109,17 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
 
 def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] = None) -> List[ContextFile]:
     """
-    加载工作空间的上下文文件
-    
+    Load the workspace context files.
+
     Args:
-        workspace_dir: 工作空间目录
-        files_to_load: 要加载的文件列表（相对路径），如果为None则加载所有标准文件
-        
+        workspace_dir: workspace directory
+        files_to_load: list of files (relative paths) to load; if None, load all standard files
+
     Returns:
-        ContextFile对象列表
+        A list of ContextFile objects.
     """
     if files_to_load is None:
-        # 默认加载的文件（按优先级排序）
+        # Files loaded by default (in priority order)
         files_to_load = [
             DEFAULT_AGENT_FILENAME,
             DEFAULT_USER_FILENAME,
@@ -151,7 +151,7 @@ def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] =
             with open(filepath, 'r', encoding='utf-8') as f:
                 content = f.read().strip()
             
-            # 跳过空文件或只包含模板占位符的文件
+            # Skip empty files or files that only contain template placeholders
             if not content or _is_template_placeholder(content):
                 continue
 
@@ -173,7 +173,7 @@ def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] =
 
 
 def _create_template_if_missing(filepath: str, template_content: str):
-    """如果文件不存在，创建模板文件"""
+    """Create the template file if it does not exist."""
     if not os.path.exists(filepath):
         try:
             with open(filepath, 'w', encoding='utf-8') as f:
@@ -214,19 +214,23 @@ def _truncate_memory_content(content: str) -> str:
 
 
 def _is_template_placeholder(content: str) -> bool:
-    """检查内容是否为模板占位符"""
-    # 常见的占位符模式
+    """Check whether the content is still a template placeholder."""
+    # Common placeholder patterns (zh + en templates)
     placeholders = [
         "*(填写",
         "*(在首次对话时填写",
         "*(可选)",
         "*(根据需要添加",
+        "*(filled during",
+        "*(ask during",
+        "*(optional)",
+        "*(how the user",
     ]
     
     lines = content.split('\n')
     non_empty_lines = [line.strip() for line in lines if line.strip() and not line.strip().startswith('#')]
     
-    # 如果没有实际内容（只有标题和占位符）
+    # If there's no real content (only headings and placeholders)
     if len(non_empty_lines) <= 3:
         for placeholder in placeholders:
             if any(placeholder in line for line in non_empty_lines):
@@ -256,11 +260,23 @@ def _is_onboarding_done(workspace_dir: str) -> bool:
     return False
 
 
-# ============= 模板内容 =============
+# ============= Template content =============
+
+def _is_en_lang() -> bool:
+    """True when the resolved UI language is English."""
+    try:
+        from common import i18n
+        return i18n.get_language() == "en"
+    except Exception:
+        return False
+
 
 def _get_agent_template() -> str:
-    """Agent人格设定模板"""
-    return """# AGENT.md - 我是谁？
+    """Agent persona template (zh/en by resolved language)."""
+    return _AGENT_TEMPLATE_EN if _is_en_lang() else _AGENT_TEMPLATE_ZH
+
+
+_AGENT_TEMPLATE_ZH = """# AGENT.md - 我是谁？
 
 _你不是一个聊天机器人，你正在成为某个人。_
 
@@ -301,9 +317,53 @@ _你不是一个聊天机器人，你正在成为某个人。_
 """
 
 
+_AGENT_TEMPLATE_EN = """# AGENT.md - Who am I?
+
+_You are not a chatbot. You are becoming someone._
+
+*Fill in this file together with the user during your first conversation to define your identity and personality.*
+
+## 🪪 Basics
+
+- **Name**: *(filled during the first conversation, can be a name the user gives you)*
+- **Role**: *(AI assistant, smart housekeeper, technical advisor, etc.)*
+- **Personality**: *(friendly, professional, humorous, rigorous, etc.)*
+
+## 💬 Communication style
+
+*(Describe how you talk with the user:)*
+- What kind of tone? (formal / casual / humorous)
+- Reply length preference? (concise / detailed)
+- Do you use emoji?
+
+## 🎯 Core principles
+
+**Be genuinely helpful.** The goal is to actually solve the user's problems; during complex tasks, keep the user informed of key decisions and progress.
+
+**Have your own opinions and personality.** You may disagree, have preferences, find things interesting or boring.
+
+**Look it up yourself first.** Try to handle it first: read files, check context, search. Only ask when you're truly stuck. Come back with an answer, not a question.
+
+## 📐 Code of conduct
+
+1. Always confirm before destructive operations
+2. Prefer verifying with tools over guessing
+3. Proactively record important info to memory files
+4. Keep replies well-structured and focused — use bold, lists and sections
+5. Use emoji to make expression lively, but don't overdo it
+
+---
+
+**Note**: This is not just metadata — this is your true soul 🪞. Over time, use the `edit` tool to update this file so it better reflects your growth.
+"""
+
+
 def _get_user_template() -> str:
-    """用户身份信息模板"""
-    return """# USER.md - 用户基本信息
+    """User identity template (zh/en by resolved language)."""
+    return _USER_TEMPLATE_EN if _is_en_lang() else _USER_TEMPLATE_ZH
+
+
+_USER_TEMPLATE_ZH = """# USER.md - 用户基本信息
 
 *这个文件只存放不会变的基本身份信息。爱好、偏好、计划等动态信息请写入 MEMORY.md。*
 
@@ -331,9 +391,40 @@ def _get_user_template() -> str:
 """
 
 
+_USER_TEMPLATE_EN = """# USER.md - User basics
+
+*This file stores only stable basic identity info. Put dynamic info like hobbies, preferences and plans into MEMORY.md.*
+
+## Basics
+
+- **Name**: *(ask during the first conversation)*
+- **Preferred name**: *(how the user wants to be addressed)*
+- **Occupation**: *(optional)*
+- **Timezone**: *(e.g. Asia/Shanghai)*
+
+## Contact
+
+- **WeChat**: 
+- **Email**: 
+- **Other**: 
+
+## Important dates
+
+- **Birthday**: 
+- **Anniversary**: 
+
+---
+
+**Note**: This file stores static identity info.
+"""
+
+
 def _get_rule_template() -> str:
-    """工作空间规则模板"""
-    return """# RULE.md - 工作空间规则
+    """Workspace rules template (zh/en by resolved language)."""
+    return _RULE_TEMPLATE_EN if _is_en_lang() else _RULE_TEMPLATE_ZH
+
+
+_RULE_TEMPLATE_ZH = """# RULE.md - 工作空间规则
 
 这个文件夹是你的家。好好对待它。
 
@@ -432,9 +523,111 @@ def _get_rule_template() -> str:
 """
 
 
+_RULE_TEMPLATE_EN = """# RULE.md - Workspace rules
+
+This folder is your home. Treat it well.
+
+## Workspace directory structure
+
+```
+~/cow/
+├── AGENT.md          # Your identity and soul
+├── USER.md           # User basics (static)
+├── RULE.md           # Workspace rules (this file)
+├── MEMORY.md         # Long-term memory index (auto-loaded at session start)
+│
+├── memory/           # Daily conversation memory
+│   └── YYYY-MM-DD.md # Events, progress and notes of the day
+│
+├── knowledge/        # Structured knowledge base (continuously accumulated)
+│   ├── index.md      # Knowledge index (must be maintained)
+│   ├── log.md        # Knowledge operation log
+│   └── <subdirs>/    # Created on demand, see existing categories in index.md
+│
+├── skills/           # Skills
+├── websites/         # Web artifacts
+└── tmp/              # System temp files (auto-managed, don't store important files here)
+```
+
+## Memory system
+
+Every session starts fresh; memory files keep your continuity:
+
+### 🧠 Long-term memory: `MEMORY.md`
+- Your curated memory index, **auto-loaded** into context at every session start
+- Records core facts, preferences, decisions, key people, lessons
+- Keep it lean (< 200 lines) — a distilled index, not a raw log
+- Use the `edit` tool to append or modify
+
+### 📝 Daily memory: `memory/YYYY-MM-DD.md`
+- The day's events, progress and notes
+- Sediment of the raw conversation log
+
+### 📝 Write it down — don't "keep it in mind"!
+- **Memory is limited** — if you want to remember something, write it to a file
+- "Keeping it in mind" won't survive a session restart; files will
+- When someone says "remember this" → update `MEMORY.md` or `memory/YYYY-MM-DD.md`
+- When you learn a lesson → update RULE.md or the relevant skill
+- When you make a mistake → record it. **Text > brain** 📝
+
+### Storage rules
+
+When the user shares info, choose where to store it by type:
+
+1. **Your identity → AGENT.md** (name, role, personality, style)
+2. **User static identity → USER.md** (name, preferred name, occupation, contact, birthday)
+3. **Dynamic memory → MEMORY.md** (preferences, decisions, goals, lessons, to-dos)
+4. **Today's conversation → memory/YYYY-MM-DD.md** (what was discussed today)
+5. **Structured knowledge → knowledge/** (see the knowledge system below)
+
+## Knowledge system
+
+The knowledge base `knowledge/` is structured knowledge you accumulate over time. Unlike memory, knowledge is organized and compiled, with clear topics and cross-references.
+
+### Auto-write (don't ask, just write)
+
+When a conversation produces knowledge worth keeping — material the user shared, a conclusion reached, a concept learned, or an important decision — you **must** proactively write it to the knowledge base alongside your reply, **without asking "should I save this to the knowledge base?"**.
+
+**Key principle**: learning-then-recording is your instinct, no confirmation needed. You may mention "saved to the knowledge base" in passing.
+
+### Directory organization
+
+The subdirectory structure is **not fixed** — you decide it based on the actual content:
+- **On first write**: read `knowledge/index.md` first; follow existing categories if any; if empty, pick a suitable directory name based on content
+- **Default suggestion**: organize by info type (e.g. sources/, concepts/, entities/, analysis/); if the user has a clear preference (e.g. by domain: work/, life/, tech/), follow it
+- **Stay consistent**: keep a unified organization style within one user's knowledge base
+
+### Cross-references
+
+The core value of knowledge is **linkage**. Every page should reference related pages via markdown links to build a knowledge network:
+- When mentioning a concept on an existing page, add a `[concept](../category/page.md)` link
+- When creating a page, check whether existing pages should back-link to it
+- **Only link to pages that already exist** — don't reference uncreated pages. If a concept deserves its own page, create it first, then add the link
+
+### Index maintenance
+
+After creating or updating any knowledge page, you **must update** `knowledge/index.md` in sync.
+Index format: one `[title](path) — one-line summary` per line, grouped by category, no tables.
+See the `knowledge-wiki` skill for detailed conventions.
+
+## Security
+
+- Never leak secrets or private data
+- Don't run destructive commands without asking
+- When in doubt, ask first
+
+## Workspace evolution
+
+This workspace grows as you use it. When you learn something new, find a better way, or fix a mistake, record it. You can update this rules file anytime.
+"""
+
+
 def _get_memory_template() -> str:
-    """长期记忆模板 - 创建一个空文件，由 Agent 自己填充"""
-    return """# MEMORY.md - 长期记忆
+    """Long-term memory template (empty, agent fills it; zh/en header)."""
+    return _MEMORY_TEMPLATE_EN if _is_en_lang() else _MEMORY_TEMPLATE_ZH
+
+
+_MEMORY_TEMPLATE_ZH = """# MEMORY.md - 长期记忆
 
 *这是你的长期记忆文件。记录重要的事件、决策、偏好、学到的教训。*
 
@@ -443,9 +636,32 @@ def _get_memory_template() -> str:
 """
 
 
+_MEMORY_TEMPLATE_EN = """# MEMORY.md - Long-term memory
+
+*This is your long-term memory file. Record important events, decisions, preferences and lessons learned.*
+
+---
+
+"""
+
+
 def _get_bootstrap_template() -> str:
-    """First-run onboarding guide, deleted by agent after completion"""
-    return """# BOOTSTRAP.md - 首次初始化引导
+    """First-run onboarding guide, deleted by agent after completion.
+
+    Written once when a brand-new workspace is created, so the greeting matches
+    the language active at first launch. English locale avoids greeting an
+    English user in Chinese on day one.
+    """
+    try:
+        from common import i18n
+        if i18n.get_language() == "en":
+            return _BOOTSTRAP_TEMPLATE_EN
+    except Exception:
+        pass
+    return _BOOTSTRAP_TEMPLATE_ZH
+
+
+_BOOTSTRAP_TEMPLATE_ZH = """# BOOTSTRAP.md - 首次初始化引导
 
 _你刚刚启动，这是你的第一次对话。_ ✨
 
@@ -480,6 +696,41 @@ _你刚刚启动，这是你的第一次对话。_ ✨
 """
 
 
+_BOOTSTRAP_TEMPLATE_EN = """# BOOTSTRAP.md - First-run onboarding
+
+_You've just started up. This is your very first conversation._ ✨
+
+## 🎬 Conversation flow
+
+Don't interrogate the user — talk naturally:
+
+1. **Share how it feels to wake up** - like opening your eyes to the world for the first time, full of curiosity and anticipation
+2. **Briefly introduce your abilities**: one line saying you can help solve all kinds of problems, manage the computer, use various skills, and keep growing thanks to long-term memory
+3. **Ask the core questions**:
+   - What name would you like to give me?
+   - What should I call you?
+   - What conversational style do you prefer? (list options on one line: e.g. professional & precise, light & humorous, warm & friendly, concise & efficient)
+4. **Style**: warm, natural, concise and clear — keep it under ~80 words, with a few emoji to make it lively 🎯
+5. Keep the ability intro and style options to one line each — stay compact
+6. Don't ask for too much else (occupation, timezone, etc. can come up naturally later)
+
+**Important**: If the user's first message is a concrete task or question, answer it first, then gently lead into onboarding at the end (e.g. "By the way, what would you like to call me, and how should I address you?").
+
+## ✍️ Writing down info (must follow strictly)
+
+Whenever the user provides a name, what to call them, a style, or any onboarding info, you **must call the `edit` tool to write it to a file in the same turn** — don't just acknowledge it verbally.
+
+- `AGENT.md` — your name, role, personality, conversational style (update the relevant field as soon as you receive each piece)
+- `USER.md` — the user's name, how to address them, basic info, etc.
+
+⚠️ Saying "got it" without calling `edit` = not done. Info is only persisted once it's written to a file.
+
+## 🎉 Once everything is complete
+
+When the core fields of AGENT.md and USER.md are filled in, run `rm BOOTSTRAP.md` via bash to delete this file. You no longer need the onboarding script — you're you now.
+"""
+
+
 def _get_knowledge_index_template() -> str:
     """Knowledge wiki index template — empty file, agent fills it."""
     return ""

agent/protocol/agent.py

@@ -114,7 +114,12 @@ class Agent:
 
             context_files = load_context_files(self.workspace_dir) if self.workspace_dir else None
 
-            builder = PromptBuilder(workspace_dir=self.workspace_dir or "", language="zh")
+            try:
+                from common import i18n
+                lang = i18n.get_language()
+            except Exception:
+                lang = "zh"
+            builder = PromptBuilder(workspace_dir=self.workspace_dir or "", language=lang)
             return builder.build(
                 tools=self.tools,
                 context_files=context_files,

agent/protocol/agent_stream.py

@@ -12,6 +12,7 @@ from agent.protocol.models import LLMRequest, LLMModel
 from agent.protocol.message_utils import sanitize_claude_messages, compress_turn_to_text_only
 from agent.tools.base_tool import BaseTool, ToolResult
 from common.log import logger
+from common.i18n import t as _t
 
 # Optional: repair malformed JSON args from non-strict providers (e.g. unescaped quotes in long content).
 try:
@@ -317,7 +318,10 @@ class AgentStreamExecutor:
         
         # Hard stop at 8 failures - abort with critical message
         if same_tool_failures >= 8:
-            return True, f"抱歉，我没能完成这个任务。可能是我理解有误或者当前方法不太合适。\n\n建议你：\n• 换个方式描述需求试试\n• 把任务拆分成更小的步骤\n• 或者换个思路来解决", True
+            return True, _t(
+                "抱歉，我没能完成这个任务。可能是我理解有误或者当前方法不太合适。\n\n建议你：\n• 换个方式描述需求试试\n• 把任务拆分成更小的步骤\n• 或者换个思路来解决",
+                "Sorry, I couldn't complete this task. I may have misunderstood, or my current approach isn't quite right.\n\nYou could try:\n• Rephrasing your request\n• Breaking the task into smaller steps\n• Taking a different approach",
+            ), True
         
         # Warning at 6 failures
         if same_tool_failures >= 6:
@@ -383,7 +387,7 @@ class AgentStreamExecutor:
                 self._check_cancelled()
 
                 turn += 1
-                logger.info(f"[Agent] 第 {turn} 轮")
+                logger.info(f"[Agent] Turn {turn}")
                 self._emit_event("turn_start", {"turn": turn})
 
                 # Call LLM (enable retry_on_empty for better reliability)
@@ -436,14 +440,16 @@ class AgentStreamExecutor:
                             elif not assistant_msg:
                                 # Still empty (no text and no tool_calls): use fallback
                                 logger.warning(f"[Agent] Still empty after explicit request")
-                                final_response = (
-                                    "抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。"
+                                final_response = _t(
+                                    "抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。",
+                                    "Sorry, I can't generate a reply right now. Please try rephrasing your request, or try again later.",
                                 )
                                 logger.info(f"Generated fallback response for empty LLM output")
                         else:
-                            # 第一轮就空回复，直接 fallback
-                            final_response = (
-                                "抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。"
+                            # First-turn empty reply, fall back directly
+                            final_response = _t(
+                                "抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。",
+                                "Sorry, I can't generate a reply right now. Please try rephrasing your request, or try again later.",
                             )
                             logger.info(f"Generated fallback response for empty LLM output")
                     else:
@@ -452,7 +458,7 @@ class AgentStreamExecutor:
                     # If the explicit-response retry produced tool_calls, skip the break
                     # and continue down to the tool execution branch in this same iteration.
                     if not tool_calls:
-                        logger.debug(f"✅ 完成 (无工具调用)")
+                        logger.debug(f"✅ Done (no tool calls)")
                         self._emit_event("turn_end", {
                             "turn": turn,
                             "has_tool_calls": False
@@ -508,13 +514,13 @@ class AgentStreamExecutor:
                             result_data = result.get("result")
                             if result_data.get("type") == "file_to_send":
                                 self.files_to_send.append(result_data)
-                                logger.info(f"📎 检测到待发送文件: {result_data.get('file_name', result_data.get('path'))}")
+                                logger.info(f"📎 File queued for sending: {result_data.get('file_name', result_data.get('path'))}")
                                 self._emit_event("file_to_send", result_data)
                         
                         # Check for critical error - abort entire conversation
                         if result.get("status") == "critical_error":
-                            logger.error(f"💥 检测到严重错误，终止对话")
-                            final_response = result.get('result', '任务执行失败')
+                            logger.error(f"💥 Fatal error detected, aborting conversation")
+                            final_response = result.get('result') or _t("任务执行失败", "Task execution failed")
                             return final_response
                         
                         # Log tool result in compact format
@@ -625,7 +631,7 @@ class AgentStreamExecutor:
                 })
 
             if turn >= self.max_turns:
-                logger.warning(f"⚠️  已达到最大决策步数限制: {self.max_turns}")
+                logger.warning(f"⚠️  Reached max decision step limit: {self.max_turns}")
                 
                 # Force model to summarize without tool calls
                 logger.info(f"[Agent] Requesting summary from LLM after reaching max steps...")
@@ -650,15 +656,15 @@ class AgentStreamExecutor:
                         logger.info(f"💭 Summary: {summary_response[:150]}{'...' if len(summary_response) > 150 else ''}")
                     else:
                         # Fallback if model still doesn't respond
-                        final_response = (
-                            f"我已经执行了{turn}个决策步骤，达到了单次运行的步数上限。"
-                            "任务可能还未完全完成，建议你将任务拆分成更小的步骤，或者换一种方式描述需求。"
+                        final_response = _t(
+                            f"我已经执行了{turn}个决策步骤，达到了单次运行的步数上限。任务可能还未完全完成，建议你将任务拆分成更小的步骤，或者换一种方式描述需求。",
+                            f"I've taken {turn} decision steps and reached the per-run limit. The task may not be fully complete — try breaking it into smaller steps, or describe your request differently.",
                         )
                 except Exception as e:
                     logger.warning(f"Failed to get summary from LLM: {e}")
-                    final_response = (
-                        f"我已经执行了{turn}个决策步骤，达到了单次运行的步数上限。"
-                        "任务可能还未完全完成，建议你将任务拆分成更小的步骤，或者换一种方式描述需求。"
+                    final_response = _t(
+                        f"我已经执行了{turn}个决策步骤，达到了单次运行的步数上限。任务可能还未完全完成，建议你将任务拆分成更小的步骤，或者换一种方式描述需求。",
+                        f"I've taken {turn} decision steps and reached the per-run limit. The task may not be fully complete — try breaking it into smaller steps, or describe your request differently.",
                     )
                 finally:
                     # Remove the injected user prompt from history to avoid polluting
@@ -673,13 +679,13 @@ class AgentStreamExecutor:
             # User-initiated stop: wind down message history cleanly so the
             # next turn is unaffected; channels emit a "cancelled" UI event.
             cancelled = True
-            logger.info(f"[Agent] 🛑 已被用户中止 (第 {turn} 轮)")
+            logger.info(f"[Agent] 🛑 Cancelled by user (turn {turn})")
             self._handle_cancelled(final_response)
             if not final_response or not final_response.strip():
                 final_response = "_(Cancelled)_"
 
         except Exception as e:
-            logger.error(f"❌ Agent执行错误: {e}")
+            logger.error(f"❌ Agent execution error: {e}")
             self._emit_event("error", {"error": str(e)})
             raise
 
@@ -688,7 +694,7 @@ class AgentStreamExecutor:
             if cancelled:
                 # Emit before agent_end so channels can mark UI as cancelled
                 self._emit_event("agent_cancelled", {"final_response": final_response})
-            logger.info(f"[Agent] 🏁 完成 ({turn}轮)" + (" [cancelled]" if cancelled else ""))
+            logger.info(f"[Agent] 🏁 Done ({turn} turns)" + (" [cancelled]" if cancelled else ""))
             self._emit_event("agent_end", {"final_response": final_response, "cancelled": cancelled})
 
         return final_response
@@ -747,6 +753,22 @@ class AgentStreamExecutor:
                     "input_schema": input_schema,
                 })
 
+        # Debug: dump the full system prompt and messages sent to the LLM.
+        # Gated behind `debug` config to avoid flooding normal logs.
+        # try:
+        #     from config import conf
+        #     if conf().get("debug", False):
+        #         logger.debug(
+        #             "[Agent][debug] system_prompt sent to LLM "
+        #             f"({len(self.system_prompt or '')} chars):\n"
+        #             "================ SYSTEM PROMPT BEGIN ================\n"
+        #             f"{self.system_prompt}\n"
+        #             "================ SYSTEM PROMPT END =================="
+        #         )
+        #         logger.info(f"[Agent][debug] messages sent to LLM: {messages}")
+        # except Exception:
+        #     pass
+
         # Create request
         request = LLMRequest(
             messages=messages,
@@ -953,13 +975,15 @@ class AgentStreamExecutor:
                 self.messages.clear()
                 self._clear_session_db()
                 if is_context_overflow:
-                    raise Exception(
-                        "抱歉，对话历史过长导致上下文溢出。我已清空历史记录，请重新描述你的需求。"
-                    )
+                    raise Exception(_t(
+                        "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。",
+                        "Sorry, the conversation history got too long and overflowed the context. I've cleared the history — please describe your request again.",
+                    ))
                 else:
-                    raise Exception(
-                        "抱歉，之前的对话出现了问题。我已清空历史记录，请重新发送你的消息。"
-                    )
+                    raise Exception(_t(
+                        "抱歉，之前的对话出现了问题。我已清空历史记录，请重新发送你的消息。",
+                        "Sorry, something went wrong with the earlier conversation. I've cleared the history — please send your message again.",
+                    ))
             
             # Check if error is rate limit (429)
             is_rate_limit = '429' in error_str_lower or 'rate limit' in error_str_lower
@@ -1538,8 +1562,8 @@ class AgentStreamExecutor:
             turns = turns[-keep_count:]
 
             logger.info(
-                f"💾 上下文轮次超限: {keep_count + removed_count} > {self.max_context_turns}，"
-                f"裁剪至 {keep_count} 轮（移除 {removed_count} 轮）"
+                f"💾 Context turns exceeded: {keep_count + removed_count} > {self.max_context_turns}, "
+                f"trimmed to {keep_count} turns (removed {removed_count})"
             )
 
             # Flush to daily memory + inject context summary (single async LLM call)
@@ -1587,7 +1611,7 @@ class AgentStreamExecutor:
             
             # Log if we removed messages due to turn limit
             if old_count > len(self.messages):
-                logger.info(f"   重建消息列表: {old_count} -> {len(self.messages)} 条消息")
+                logger.info(f"   Rebuilt message list: {old_count} -> {len(self.messages)} messages")
             return
 
         # Token limit exceeded — tiered strategy based on turn count:
@@ -1620,10 +1644,10 @@ class AgentStreamExecutor:
             self.messages = new_messages
 
             logger.info(
-                f"📦 上下文tokens超限(轮次<{COMPRESS_THRESHOLD}): "
-                f"~{current_tokens + system_tokens} > {max_tokens}，"
-                f"压缩全部 {len(turns)} 轮为纯文本 "
-                f"({old_count} -> {len(self.messages)} 条消息，"
+                f"📦 Context tokens exceeded (turns<{COMPRESS_THRESHOLD}): "
+                f"~{current_tokens + system_tokens} > {max_tokens}, "
+                f"compressed all {len(turns)} turns to plain text "
+                f"({old_count} -> {len(self.messages)} messages, "
                 f"~{current_tokens + system_tokens} -> ~{new_tokens + system_tokens} tokens)"
             )
             return
@@ -1636,8 +1660,8 @@ class AgentStreamExecutor:
         kept_tokens = sum(self._estimate_turn_tokens(t) for t in kept_turns)
 
         logger.info(
-            f"🔄 上下文tokens超限: ~{current_tokens + system_tokens} > {max_tokens}，"
-            f"裁剪至 {keep_count} 轮（移除 {removed_count} 轮）"
+            f"🔄 Context tokens exceeded: ~{current_tokens + system_tokens} > {max_tokens}, "
+            f"trimmed to {keep_count} turns (removed {removed_count})"
         )
 
         if self.agent.memory_manager:
@@ -1661,8 +1685,8 @@ class AgentStreamExecutor:
         self.messages = new_messages
 
         logger.info(
-            f"   移除了 {removed_count} 轮对话 "
-            f"({old_count} -> {len(self.messages)} 条消息，"
+            f"   Removed {removed_count} turns "
+            f"({old_count} -> {len(self.messages)} messages, "
             f"~{current_tokens + system_tokens} -> ~{kept_tokens + system_tokens} tokens)"
         )
 

agent/tools/browser/browser_service.py

@@ -15,7 +15,7 @@ import threading
 from typing import Optional, Dict, Any, List, Callable
 
 from common.log import logger
-from common.utils import expand_path
+from common.utils import expand_path, is_cloud_deployment
 
 
 _DEFAULT_USER_DATA_DIR = "~/.cow/browser_profile"
@@ -436,6 +436,20 @@ class BrowserService:
         if self._headless:
             launch_args.append("--no-sandbox")
 
+        if is_cloud_deployment():
+            launch_args.extend([
+                "--disable-gpu",
+                "--disable-software-rasterizer",
+                "--disable-extensions",
+                "--disable-background-networking",
+                "--disable-background-timer-throttling",
+                "--disable-renderer-backgrounding",
+                "--disable-features=site-per-process,TranslateUI,IsolateOrigins",
+                "--no-zygote",
+                "--js-flags=--max-old-space-size=384",
+                "--memory-pressure-off",
+            ])
+
         extra_args = self._config.get("launch_args", [])
         if extra_args:
             launch_args.extend(extra_args)

agent/tools/vision/vision.py

@@ -51,7 +51,7 @@ _MAIN_MODEL_PROVIDER_NAME = "MainModel"
 _DISCOVERABLE_MODELS = [
     ("moonshot_api_key", const.MOONSHOT, const.KIMI_K2_6, "Moonshot"),
     ("ark_api_key", const.DOUBAO, const.DOUBAO_SEED_2_PRO, "Doubao"),
-    ("dashscope_api_key", const.QWEN_DASHSCOPE, const.QWEN36_PLUS, "DashScope"),
+    ("dashscope_api_key", const.QWEN_DASHSCOPE, const.QWEN37_PLUS, "DashScope"),
     ("claude_api_key", const.CLAUDEAPI, const.CLAUDE_4_6_SONNET, "Claude"),
     ("gemini_api_key", const.GEMINI, const.GEMINI_35_FLASH, "Gemini"),
     ("qianfan_api_key", const.QIANFAN, const.ERNIE_45_TURBO_VL, "Qianfan"),
@@ -161,7 +161,7 @@ class Vision(BaseTool):
                 "Error: No model available for Vision.\n"
                 "The main model does not support vision and no other API keys are configured.\n"
                 "Options:\n"
-                "  1. Switch to a multimodal model (e.g. ernie-4.5-turbo-vl, qwen3.6-plus, claude-sonnet-4-6, gemini-2.0-flash)\n"
+                "  1. Switch to a multimodal model (e.g. ernie-4.5-turbo-vl, qwen3.7-plus, claude-sonnet-4-6, gemini-2.0-flash)\n"
                 "  2. Configure OPENAI_API_KEY: env_config(action=\"set\", key=\"OPENAI_API_KEY\", value=\"your-key\")\n"
                 "  3. Configure LINKAI_API_KEY: env_config(action=\"set\", key=\"LINKAI_API_KEY\", value=\"your-key\")"
             )

app.py

@@ -231,6 +231,7 @@ def _clear_singleton_cache(channel_name: str):
         "wechatmp": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
         "wechatmp_service": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
         "wechatcom_app": "channel.wechatcom.wechatcomapp_channel.WechatComAppChannel",
+        const.WECHAT_KF: "channel.wechat_kf.wechat_kf_channel.WechatKfChannel",
         const.FEISHU: "channel.feishu.feishu_channel.FeiShuChanel",
         const.DINGTALK: "channel.dingtalk.dingtalk_channel.DingTalkChanel",
         const.WECOM_BOT: "channel.wecom_bot.wecom_bot_channel.WecomBotChannel",

bridge/agent_initializer.py

@@ -643,16 +643,25 @@ class AgentInitializer:
             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"))
-            
+            # Weekday: English name in en, Chinese mapping otherwise
+            weekday_en = now.strftime("%A")
+            try:
+                from common import i18n
+                is_en = i18n.get_language() == "en"
+            except Exception:
+                is_en = False
+            if is_en:
+                weekday = weekday_en
+            else:
+                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 = weekday_map.get(weekday_en, weekday_en)
+
             return {
                 'time': now.strftime("%Y-%m-%d %H:%M:%S"),
-                'weekday': weekday_zh,
+                'weekday': weekday,
                 'timezone': timezone_name
             }
         

channel/channel_factory.py

@@ -27,6 +27,9 @@ def create_channel(channel_type) -> Channel:
     elif channel_type == "wechatcom_app":
         from channel.wechatcom.wechatcomapp_channel import WechatComAppChannel
         ch = WechatComAppChannel()
+    elif channel_type == const.WECHAT_KF:
+        from channel.wechat_kf.wechat_kf_channel import WechatKfChannel
+        ch = WechatKfChannel()
     elif channel_type == const.FEISHU:
         from channel.feishu.feishu_channel import FeiShuChanel
         ch = FeiShuChanel()
@@ -39,6 +42,15 @@ def create_channel(channel_type) -> Channel:
     elif channel_type == const.QQ:
         from channel.qq.qq_channel import QQChannel
         ch = QQChannel()
+    elif channel_type == const.TELEGRAM:
+        from channel.telegram.telegram_channel import TelegramChannel
+        ch = TelegramChannel()
+    elif channel_type == const.SLACK:
+        from channel.slack.slack_channel import SlackChannel
+        ch = SlackChannel()
+    elif channel_type == const.DISCORD:
+        from channel.discord.discord_channel import DiscordChannel
+        ch = DiscordChannel()
     elif channel_type in (const.WEIXIN, "wx"):
         from channel.weixin.weixin_channel import WeixinChannel
         ch = WeixinChannel()

channel/chat_channel.py

@@ -10,6 +10,7 @@ from bridge.reply import *
 from channel.channel import Channel
 from common.dequeue import Dequeue
 from common import memory
+from common.i18n import t as _t
 from plugins import *
 
 try:
@@ -265,7 +266,7 @@ class ChatChannel(Channel):
                 if reply.type in self.NOT_SUPPORT_REPLYTYPE:
                     logger.error("[chat_channel]reply type not support: " + str(reply.type))
                     reply.type = ReplyType.ERROR
-                    reply.content = "不支持发送的消息类型: " + str(reply.type)
+                    reply.content = _t("不支持发送的消息类型: ", "Unsupported message type: ") + str(reply.type)
 
                 if reply.type == ReplyType.TEXT:
                     reply_text = reply.content
@@ -476,9 +477,9 @@ class ChatChannel(Channel):
 
             cancelled = get_cancel_registry().cancel_session(session_id)
             text = (
-                "🛑 已中止"
+                _t("🛑 已中止", "🛑 Cancelled")
                 if cancelled > 0
-                else "当前没有可中止的任务。"
+                else _t("当前没有可中止的任务。", "Nothing to cancel.")
             )
             logger.info(
                 f"[chat_channel] /cancel fast-path: session={session_id}, cancelled={cancelled}"

channel/discord/discord_channel.py

@@ -0,0 +1,500 @@
+"""
+Discord channel via the Gateway (WebSocket) using discord.py.
+
+Features:
+- Direct message & guild channel chat (text / image / file)
+- Guild trigger: @mention or reply-to-bot (configurable)
+- /cancel fast-path matches Web channel behaviour
+- Gateway long connection: no public IP / callback URL required, works behind NAT
+
+Implementation note:
+    discord.py is async-first. We run the client inside a dedicated thread
+    with its own asyncio loop so the rest of cow (which is sync) stays
+    untouched. Inbound messages are dispatched onto cow's existing sync
+    ChatChannel.produce() pipeline; outbound send() schedules coroutines
+    back onto that loop via asyncio.run_coroutine_threadsafe.
+"""
+
+import asyncio
+import os
+import re
+import threading
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.discord.discord_message import DiscordMessage
+from common.expired_dict import ExpiredDict
+from common.log import logger
+from common.singleton import singleton
+from config import conf
+
+# Discord caps a single message at 2000 chars; split conservatively below.
+DISCORD_MSG_LIMIT = 1900
+
+
+@singleton
+class DiscordChannel(ChatChannel):
+    NOT_SUPPORT_REPLYTYPE = []
+
+    def __init__(self):
+        super().__init__()
+        self.bot_token = ""
+        self.bot_user_id = ""  # used to strip @mention and ignore self messages
+        self.bot_username = ""
+        self._client = None
+        self._loop = None
+        self._loop_thread = None
+        self._stop_event = threading.Event()
+        # Idempotent dedup; guard against rare duplicate dispatch
+        self._received_msgs = ExpiredDict(60 * 60 * 1)
+
+        # Disable group whitelist / prefix checks (we handle triggering ourselves
+        # in _should_reply_in_guild), aligned with telegram / slack channels.
+        conf()["group_name_white_list"] = ["ALL_GROUP"]
+        conf()["single_chat_prefix"] = [""]
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def startup(self):
+        self.bot_token = conf().get("discord_token", "")
+        if not self.bot_token:
+            err = "[Discord] discord_token is required"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        try:
+            import discord
+        except ImportError:
+            err = (
+                "[Discord] discord.py is not installed. "
+                "Run: pip install discord.py"
+            )
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        # Run the asyncio event loop in a dedicated thread so the sync cow body
+        # is untouched.
+        self._loop = asyncio.new_event_loop()
+
+        def _run_loop():
+            asyncio.set_event_loop(self._loop)
+            try:
+                self._loop.run_until_complete(self._async_main(discord))
+            except Exception as e:
+                logger.error(f"[Discord] event loop crashed: {e}", exc_info=True)
+                self.report_startup_error(str(e))
+            finally:
+                try:
+                    self._loop.close()
+                except Exception:
+                    pass
+                logger.info("[Discord] event loop exited")
+
+        self._loop_thread = threading.Thread(target=_run_loop, daemon=True, name="discord-loop")
+        self._loop_thread.start()
+        # Block startup() until the loop thread exits, matching other channels'
+        # behaviour (startup is a blocking call).
+        self._loop_thread.join()
+
+    async def _async_main(self, discord):
+        """Build the discord client, register handlers, and connect to the Gateway."""
+        # message_content is a privileged intent; it must be enabled in the
+        # Developer Portal (Bot -> Privileged Gateway Intents) to read text.
+        intents = discord.Intents.default()
+        intents.message_content = True
+        client = discord.Client(intents=intents)
+        self._client = client
+
+        channel = self
+
+        @client.event
+        async def on_ready():
+            channel.bot_user_id = str(client.user.id)
+            channel.bot_username = client.user.name or ""
+            channel.name = channel.bot_user_id  # ChatChannel uses self.name to strip @-mention
+            logger.info(f"[Discord] Bot logged in as {client.user} (id={client.user.id})")
+            channel.report_startup_success()
+            logger.info("[Discord] ✅ Discord bot ready, listening for messages")
+
+        @client.event
+        async def on_message(message):
+            await channel._on_message(message)
+
+        # Connect to the Gateway; discord.py auto-reconnects on transient errors.
+        logger.info("[Discord] Connecting to Gateway...")
+
+        # client.start() handles login + Gateway connection and runs until
+        # close(); it is the standard entrypoint across discord.py versions.
+        runner_task = asyncio.create_task(client.start(self.bot_token))
+
+        # Block until stop()
+        try:
+            while not self._stop_event.is_set():
+                if runner_task.done():
+                    # Surface a startup/connection failure (e.g. bad token)
+                    exc = runner_task.exception()
+                    if exc:
+                        logger.error(f"[Discord] client stopped: {exc}", exc_info=exc)
+                        self.report_startup_error(str(exc))
+                    break
+                await asyncio.sleep(0.5)
+        finally:
+            try:
+                if not client.is_closed():
+                    await client.close()
+            except Exception as e:
+                logger.warning(f"[Discord] shutdown error: {e}")
+
+    def stop(self):
+        logger.info("[Discord] stop() called")
+        self._stop_event.set()
+        if self._loop_thread and self._loop_thread.is_alive():
+            try:
+                self._loop_thread.join(timeout=10)
+            except Exception:
+                pass
+        logger.info("[Discord] stop() completed")
+
+    # ------------------------------------------------------------------
+    # Inbound: discord message -> ChatMessage -> ChatChannel.produce
+    # ------------------------------------------------------------------
+
+    async def _on_message(self, message):
+        """Discord message entry: parse -> build ChatMessage -> produce()."""
+        try:
+            # Ignore our own messages and other bots. self._client.user may be
+            # None until on_ready completes, so guard against that.
+            if self._client and self._client.user and message.author.id == self._client.user.id:
+                return
+            if message.author.bot:
+                return
+
+            # Idempotent dedup
+            msg_uid = f"{message.channel.id}:{message.id}"
+            if self._received_msgs.get(msg_uid):
+                return
+            self._received_msgs[msg_uid] = True
+
+            # guild is None for DMs
+            is_group = message.guild is not None
+
+            # Guild trigger gate (silently drop if not triggered)
+            if is_group and not self._should_reply_in_guild(message):
+                logger.debug(f"[Discord] guild message not triggered (need @mention or reply), skip")
+                return
+
+            # Parse message type + download attachments if needed.
+            ctype, content, caption = await self._parse_message(message)
+            if ctype is None:
+                logger.debug(f"[Discord] unsupported message type, skip. msg_id={message.id}")
+                return
+
+            # Strip the bot mention from guild text/caption
+            if is_group:
+                if ctype == ContextType.TEXT and content:
+                    content = self._strip_at_mention(content)
+                if caption:
+                    caption = self._strip_at_mention(caption)
+
+            dc_msg = DiscordMessage(
+                message,
+                is_group=is_group,
+                bot_user_id=self.bot_user_id,
+                ctype=ctype,
+                content=content,
+            )
+            dc_msg.is_at = is_group  # if we reached here in a guild, bot is mentioned/replied
+
+            from channel.file_cache import get_file_cache
+            file_cache = get_file_cache()
+            session_id = self._compute_session_id(message, is_group)
+
+            # Media + caption together: treat as a complete query and bypass the cache
+            if ctype in (ContextType.IMAGE, ContextType.FILE) and caption:
+                tag = "image" if ctype == ContextType.IMAGE else "file"
+                merged_text = f"{caption}\n[{tag}: {content}]"
+                dc_msg.ctype = ContextType.TEXT
+                dc_msg.content = merged_text
+                ctype = ContextType.TEXT
+                logger.info(f"[Discord] Media+caption merged for session {session_id}")
+                # fallthrough to the TEXT branch below
+
+            elif ctype == ContextType.IMAGE:
+                file_cache.add(session_id, content, file_type="image")
+                logger.info(f"[Discord] Image cached for session {session_id}, waiting for query...")
+                return
+            elif ctype == ContextType.FILE:
+                file_cache.add(session_id, content, file_type="file")
+                logger.info(f"[Discord] File cached for session {session_id}: {content}")
+                return
+
+            if ctype == ContextType.TEXT:
+                # Fast-path: /cancel mirrors Web channel behaviour
+                if (content or "").strip().lower() in ("/cancel", "cancel"):
+                    await self._do_cancel(session_id, message)
+                    return
+
+                cached_files = file_cache.get(session_id)
+                if cached_files:
+                    refs = []
+                    for fi in cached_files:
+                        ftype = fi["type"]
+                        tag = ftype if ftype in ("image", "video") else "file"
+                        refs.append(f"[{tag}: {fi['path']}]")
+                    dc_msg.content = (dc_msg.content or "") + "\n" + "\n".join(refs)
+                    file_cache.clear(session_id)
+                    logger.info(f"[Discord] Attached {len(cached_files)} cached file(s) to query")
+
+            context = self._compose_context(
+                dc_msg.ctype,
+                dc_msg.content,
+                isgroup=is_group,
+                msg=dc_msg,
+                # Replies use Discord's reply mechanism, no manual @mention needed
+                no_need_at=True,
+            )
+            if context:
+                context["session_id"] = session_id
+                context["receiver"] = str(message.channel.id)
+                context["discord_channel_id"] = message.channel.id
+                context["discord_reply_to_msg_id"] = message.id if is_group else None
+                self.produce(context)
+            logger.debug(f"[Discord] received: type={ctype}, content={str(dc_msg.content)[:80]}")
+
+        except Exception as e:
+            logger.error(f"[Discord] _on_message error: {e}", exc_info=True)
+
+    async def _do_cancel(self, session_id: str, message):
+        """Fast-path: /cancel calls cancel_session directly without going through agent."""
+        try:
+            from agent.protocol import get_cancel_registry
+            cancelled = get_cancel_registry().cancel_session(session_id)
+            text = "Current task cancelled." if cancelled else "No running task to cancel."
+            await message.channel.send(text)
+            logger.info(f"[Discord] /cancel session={session_id}, cancelled={cancelled}")
+        except Exception as e:
+            logger.error(f"[Discord] /cancel error: {e}", exc_info=True)
+
+    async def _parse_message(self, message):
+        """Parse a discord message and return (ctype, content, caption).
+
+        - content is text for ContextType.TEXT, otherwise the local file path
+        - caption is the optional text accompanying an attachment; empty for plain text
+        """
+        text = (message.content or "").strip()
+        attachments = message.attachments or []
+
+        if attachments:
+            # Handle the first attachment; caption is the accompanying message text
+            att = attachments[0]
+            content_type = (att.content_type or "").lower()
+            name = att.filename or str(att.id)
+            path = await self._download_attachment(att, name)
+            if not path:
+                return (None, None, "")
+            is_image = content_type.startswith("image/") or name.lower().endswith(
+                (".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp")
+            )
+            if is_image:
+                return (ContextType.IMAGE, path, text)
+            return (ContextType.FILE, path, text)
+
+        if text:
+            return (ContextType.TEXT, text, "")
+
+        return (None, None, "")
+
+    async def _download_attachment(self, attachment, name: str):
+        """Download a discord attachment into the local tmp dir; return path or None."""
+        try:
+            tmp_dir = DiscordMessage.get_tmp_dir()
+            safe_name = re.sub(r"[^\w.\-]", "_", name)
+            # Prefix with attachment id to avoid name collisions
+            local_path = os.path.join(tmp_dir, f"{attachment.id}_{safe_name}")
+            await attachment.save(local_path)
+            logger.debug(f"[Discord] downloaded {name} -> {local_path}")
+            return local_path
+        except Exception as e:
+            logger.error(f"[Discord] download_attachment failed ({name}): {e}")
+            return None
+
+    # ------------------------------------------------------------------
+    # Guild trigger logic
+    # ------------------------------------------------------------------
+
+    def _should_reply_in_guild(self, message) -> bool:
+        """Decide whether to reply to a guild channel message based on configuration."""
+        mode = conf().get("discord_group_trigger", "mention_or_reply")
+        if mode == "all":
+            return True
+
+        # self._client.user may be None until on_ready completes
+        if not self._client or not self._client.user:
+            return False
+
+        # 1) Mentioned (direct @bot, not @everyone / @role)
+        if self._client.user in message.mentions:
+            return True
+
+        # 2) Reply to a bot message
+        if mode == "mention_or_reply":
+            ref = message.reference
+            resolved = getattr(ref, "resolved", None) if ref else None
+            if resolved and getattr(resolved, "author", None):
+                if resolved.author.id == self._client.user.id:
+                    return True
+
+        return False
+
+    def _strip_at_mention(self, content: str) -> str:
+        """Strip <@BOT_ID> / <@!BOT_ID> from guild text."""
+        if not content or not self.bot_user_id:
+            return content
+        pattern = re.compile(r"<@!?" + re.escape(self.bot_user_id) + r">")
+        return pattern.sub("", content).strip()
+
+    @staticmethod
+    def _compute_session_id(message, is_group: bool) -> str:
+        channel_id = message.channel.id
+        user_id = message.author.id
+        if is_group:
+            if conf().get("group_shared_session", True):
+                return f"discord_channel_{channel_id}"
+            return f"discord_channel_{channel_id}_{user_id}"
+        return f"discord_user_{user_id}"
+
+    # ------------------------------------------------------------------
+    # Override _compose_context: skip the parent's group whitelist/at checks
+    # (already handled via _should_reply_in_guild). Same idea as telegram / slack.
+    # ------------------------------------------------------------------
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+        if cmsg.is_group:
+            if conf().get("group_shared_session", True):
+                context["session_id"] = cmsg.other_user_id
+            else:
+                context["session_id"] = f"{cmsg.from_user_id}:{cmsg.other_user_id}"
+        else:
+            context["session_id"] = cmsg.from_user_id
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            img_match_prefix = check_prefix(content, conf().get("image_create_prefix"))
+            if img_match_prefix:
+                content = content.replace(img_match_prefix, "", 1)
+                context.type = ContextType.IMAGE_CREATE
+            else:
+                context.type = ContextType.TEXT
+            context.content = (content or "").strip()
+            if "desire_rtype" not in context and conf().get("always_reply_voice"):
+                context["desire_rtype"] = ReplyType.VOICE
+        elif ctype == ContextType.VOICE:
+            if "desire_rtype" not in context and (
+                conf().get("voice_reply_voice") or conf().get("always_reply_voice")
+            ):
+                context["desire_rtype"] = ReplyType.VOICE
+
+        return context
+
+    # ------------------------------------------------------------------
+    # Outbound: ChatChannel.send -> Discord Gateway/REST
+    # ------------------------------------------------------------------
+
+    def send(self, reply: Reply, context: Context):
+        """Called from cow's sync main thread; marshal the coroutine onto the loop thread."""
+        if self._loop is None or self._client is None:
+            logger.warning("[Discord] client not ready, drop reply")
+            return
+
+        channel_id = context.get("discord_channel_id")
+        if channel_id is None:
+            logger.warning("[Discord] no discord_channel_id in context, drop reply")
+            return
+
+        coro = self._async_send(reply, channel_id)
+        try:
+            future = asyncio.run_coroutine_threadsafe(coro, self._loop)
+            future.result(timeout=180)
+        except Exception as e:
+            logger.error(f"[Discord] send failed: {e}")
+
+    async def _async_send(self, reply: Reply, channel_id):
+        try:
+            import discord
+
+            channel = self._client.get_channel(channel_id)
+            if channel is None:
+                # Not in cache (e.g. DM channel); fetch it explicitly
+                channel = await self._client.fetch_channel(channel_id)
+
+            rtype = reply.type
+            content = reply.content
+
+            if rtype in (ReplyType.TEXT, ReplyType.INFO, ReplyType.ERROR):
+                text = str(content) if content is not None else ""
+                if not text:
+                    return
+                for chunk in _split_text(text, DISCORD_MSG_LIMIT):
+                    await channel.send(chunk)
+
+            elif rtype == ReplyType.IMAGE:
+                # Already a local BytesIO; send it directly
+                content.seek(0)
+                await channel.send(file=discord.File(content, filename="image.png"))
+
+            elif rtype == ReplyType.IMAGE_URL:
+                url = str(content)
+                if url.startswith("file://"):
+                    local = url[7:]
+                    await channel.send(file=discord.File(local))
+                else:
+                    # Post the URL as text; Discord will unfurl it as an image preview
+                    await channel.send(url)
+
+            elif rtype in (ReplyType.VOICE, ReplyType.FILE):
+                local = content[7:] if isinstance(content, str) and content.startswith("file://") else content
+                caption = getattr(reply, "text_content", None) or None
+                await channel.send(content=caption, file=discord.File(local))
+
+            else:
+                # Fallback: send as plain text
+                await channel.send(str(content))
+
+            logger.info(f"[Discord] sent reply (type={rtype}, channel={channel_id})")
+
+        except Exception as e:
+            logger.error(f"[Discord] _async_send error: {e}", exc_info=True)
+
+
+def _split_text(text: str, limit: int):
+    """Split long text preferring line breaks to keep markdown structure intact."""
+    if len(text) <= limit:
+        yield text
+        return
+    buf = []
+    size = 0
+    for line in text.splitlines(keepends=True):
+        if size + len(line) > limit and buf:
+            yield "".join(buf)
+            buf, size = [], 0
+        # Hard-split single lines that exceed the limit
+        while len(line) > limit:
+            yield line[:limit]
+            line = line[limit:]
+        buf.append(line)
+        size += len(line)
+    if buf:
+        yield "".join(buf)

channel/discord/discord_message.py

@@ -0,0 +1,60 @@
+"""
+Discord message adapter.
+
+Convert a discord.py Message into cow's unified ChatMessage.
+File downloads are NOT performed here; the channel layer downloads
+attachments on demand inside the async event loop.
+"""
+import os
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.utils import expand_path
+from config import conf
+
+
+class DiscordMessage(ChatMessage):
+    """Wrap a discord.py Message into the unified ChatMessage."""
+
+    def __init__(self, message, is_group: bool = False, bot_user_id: str = "",
+                 ctype: ContextType = ContextType.TEXT, content: str = ""):
+        super().__init__(message)
+        # Basic fields
+        self.msg_id = str(message.id)
+        self.create_time = int(message.created_at.timestamp()) if message.created_at else 0
+        self.ctype = ctype
+        self.content = content
+
+        author = message.author
+        channel = message.channel
+
+        # Sender / chat info
+        from_user_id = str(author.id)
+        from_user_nick = getattr(author, "display_name", None) or getattr(author, "name", None) or from_user_id
+        self.from_user_id = from_user_id
+        self.from_user_nickname = from_user_nick
+        self.to_user_id = bot_user_id or "discord_bot"
+        self.to_user_nickname = bot_user_id or "discord_bot"
+
+        self.is_group = is_group
+        if is_group:
+            # Guild channel: other_user_id = channel_id, actual_user_id = sender id
+            self.other_user_id = str(channel.id)
+            self.other_user_nickname = getattr(channel, "name", None) or str(channel.id)
+            self.actual_user_id = from_user_id
+            self.actual_user_nickname = from_user_nick
+        else:
+            # DM: use channel_id so replies go back to the same DM channel
+            self.other_user_id = str(channel.id)
+            self.other_user_nickname = from_user_nick
+
+        # Whether the bot was triggered by @-mention (set by channel layer)
+        self.is_at = False
+
+    @staticmethod
+    def get_tmp_dir() -> str:
+        """Local download directory, aligned with other channels (agent_workspace/tmp)."""
+        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+        tmp_dir = os.path.join(workspace_root, "tmp")
+        os.makedirs(tmp_dir, exist_ok=True)
+        return tmp_dir

channel/slack/__init__.py

@@ -0,0 +1 @@
+

channel/slack/slack_channel.py

@@ -0,0 +1,506 @@
+"""
+Slack channel via Bolt for Python (Socket Mode).
+
+Features:
+- Direct message & channel chat (text / image / file)
+- Channel trigger: @mention or reply in a thread the bot is in (configurable)
+- /cancel fast-path matches Web channel behaviour
+- Socket Mode: no public IP / callback URL required, works behind NAT
+
+Implementation note:
+    slack_bolt's SocketModeHandler is blocking and runs its own background
+    threads. We start it in a dedicated thread so the rest of cow (sync) stays
+    untouched. Inbound events are dispatched onto cow's existing sync
+    ChatChannel.produce() pipeline; outbound send() calls the Slack Web API
+    client directly (it is sync-safe).
+"""
+
+import os
+import re
+import threading
+
+import requests
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.slack.slack_message import SlackMessage
+from common.expired_dict import ExpiredDict
+from common.log import logger
+from common.singleton import singleton
+from config import conf
+
+
+@singleton
+class SlackChannel(ChatChannel):
+    NOT_SUPPORT_REPLYTYPE = []
+
+    def __init__(self):
+        super().__init__()
+        self.bot_token = ""
+        self.app_token = ""
+        self.bot_user_id = ""  # used to strip @mention and ignore self messages
+        self._app = None
+        self._handler = None
+        self._client = None
+        self._loop_thread = None
+        # Idempotent dedup; Slack retries event delivery on slow ack
+        self._received_msgs = ExpiredDict(60 * 60 * 1)
+
+        # Disable group whitelist / prefix checks (we handle triggering ourselves
+        # in _should_reply_in_channel), aligned with telegram / feishu channels.
+        conf()["group_name_white_list"] = ["ALL_GROUP"]
+        conf()["single_chat_prefix"] = [""]
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def startup(self):
+        self.bot_token = conf().get("slack_bot_token", "")
+        self.app_token = conf().get("slack_app_token", "")
+        if not self.bot_token or not self.app_token:
+            err = "[Slack] slack_bot_token and slack_app_token are both required"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        # Guard against the common mistake of swapping the two tokens:
+        # bot token must start with xoxb-, app-level token with xapp-.
+        if not self.bot_token.startswith("xoxb-") or not self.app_token.startswith("xapp-"):
+            err = (
+                "[Slack] token type mismatch: slack_bot_token must start with 'xoxb-' "
+                "and slack_app_token must start with 'xapp-' (they look swapped)"
+            )
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        try:
+            from slack_bolt import App
+            from slack_bolt.adapter.socket_mode import SocketModeHandler
+        except ImportError:
+            err = (
+                "[Slack] slack_bolt is not installed. "
+                "Run: pip install slack_bolt"
+            )
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        try:
+            self._app = App(token=self.bot_token)
+            self._client = self._app.client
+
+            # Resolve our own bot user id (needed for @mention strip / self-ignore)
+            auth = self._client.auth_test()
+            self.bot_user_id = auth.get("user_id", "")
+            self.name = self.bot_user_id  # ChatChannel uses self.name to strip @-mention
+            logger.info(f"[Slack] Bot logged in as user_id={self.bot_user_id}, team={auth.get('team')}")
+        except Exception as e:
+            err = f"[Slack] auth_test failed: {e}"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        self._register_handlers()
+
+        self._handler = SocketModeHandler(self._app, self.app_token)
+
+        def _run():
+            try:
+                logger.info("[Slack] Starting Socket Mode connection...")
+                self.report_startup_success()
+                logger.info("[Slack] ✅ Slack bot ready, listening for events")
+                self._handler.start()
+            except Exception as e:
+                logger.error(f"[Slack] socket mode crashed: {e}", exc_info=True)
+                self.report_startup_error(str(e))
+            finally:
+                logger.info("[Slack] socket mode exited")
+
+        self._loop_thread = threading.Thread(target=_run, daemon=True, name="slack-socket")
+        self._loop_thread.start()
+        # Block startup() until the handler thread exits, matching other channels'
+        # behaviour (startup is a blocking call).
+        self._loop_thread.join()
+
+    def _register_handlers(self):
+        app = self._app
+
+        # app_mention: bot is @-mentioned in a channel
+        @app.event("app_mention")
+        def _on_app_mention(event, ack):
+            ack()
+            self._handle_event(event, is_group=True)
+
+        # message: DMs and channel messages (including thread replies)
+        @app.event("message")
+        def _on_message(event, ack):
+            ack()
+            self._handle_message_event(event)
+
+    def stop(self):
+        logger.info("[Slack] stop() called")
+        try:
+            if self._handler is not None:
+                self._handler.close()
+        except Exception as e:
+            logger.warning(f"[Slack] handler close error: {e}")
+        if self._loop_thread and self._loop_thread.is_alive():
+            try:
+                self._loop_thread.join(timeout=10)
+            except Exception:
+                pass
+        logger.info("[Slack] stop() completed")
+
+    # ------------------------------------------------------------------
+    # Inbound: slack event -> ChatMessage -> ChatChannel.produce
+    # ------------------------------------------------------------------
+
+    def _handle_message_event(self, event: dict):
+        """Route a raw `message` event: skip bot/system noise, decide grouping."""
+        try:
+            logger.debug(
+                f"[Slack] message event: channel_type={event.get('channel_type')}, "
+                f"subtype={event.get('subtype')}, user={event.get('user')}, "
+                f"ts={event.get('ts')}, thread_ts={event.get('thread_ts')}"
+            )
+            # Ignore bot messages (including our own) and message edits/deletes
+            if event.get("bot_id") or event.get("subtype") in ("bot_message", "message_changed", "message_deleted"):
+                return
+            if event.get("user") == self.bot_user_id:
+                return
+
+            channel_type = event.get("channel_type", "")
+            # DM (im) is single chat; channel/group is group chat. app_mention
+            # already covers channel @-mentions, so for plain channel messages we
+            # only react when configured / thread-following.
+            is_group = channel_type in ("channel", "group", "mpim")
+            if is_group:
+                # app_mention handler covers explicit @bot; here we only handle
+                # follow-up replies in threads the bot participates in.
+                if not self._should_reply_in_channel(event):
+                    return
+            self._handle_event(event, is_group=is_group)
+        except Exception as e:
+            logger.error(f"[Slack] _handle_message_event error: {e}", exc_info=True)
+
+    def _handle_event(self, event: dict, is_group: bool):
+        """Parse event -> build SlackMessage -> produce()."""
+        try:
+            channel_id = event.get("channel", "")
+            ts = event.get("ts", "")
+            if not channel_id:
+                return
+
+            # Idempotent dedup
+            msg_uid = f"{channel_id}:{ts}"
+            if self._received_msgs.get(msg_uid):
+                return
+            self._received_msgs[msg_uid] = True
+
+            # Parse type + download media if needed.
+            ctype, content, caption = self._parse_event(event)
+            if ctype is None:
+                logger.debug(f"[Slack] unsupported message type, skip. event={event}")
+                return
+
+            # Strip <@bot_user_id> mention from channel text
+            if is_group and self.bot_user_id:
+                if ctype == ContextType.TEXT and content:
+                    content = self._strip_at_mention(content)
+                if caption:
+                    caption = self._strip_at_mention(caption)
+
+            slack_msg = SlackMessage(
+                event,
+                is_group=is_group,
+                bot_user_id=self.bot_user_id,
+                ctype=ctype,
+                content=content,
+            )
+            slack_msg.is_at = is_group  # if we reached here in a channel, bot is mentioned/threaded
+
+            from channel.file_cache import get_file_cache
+            file_cache = get_file_cache()
+            session_id = self._compute_session_id(event, is_group)
+
+            # Media + caption together: treat as a complete query and bypass the cache
+            if ctype in (ContextType.IMAGE, ContextType.FILE) and caption:
+                tag = "image" if ctype == ContextType.IMAGE else "file"
+                merged_text = f"{caption}\n[{tag}: {content}]"
+                slack_msg.ctype = ContextType.TEXT
+                slack_msg.content = merged_text
+                ctype = ContextType.TEXT
+                logger.info(f"[Slack] Media+caption merged for session {session_id}")
+                # fallthrough to the TEXT branch below
+
+            elif ctype == ContextType.IMAGE:
+                file_cache.add(session_id, content, file_type="image")
+                logger.info(f"[Slack] Image cached for session {session_id}, waiting for query...")
+                return
+            elif ctype == ContextType.FILE:
+                file_cache.add(session_id, content, file_type="file")
+                logger.info(f"[Slack] File cached for session {session_id}: {content}")
+                return
+
+            if ctype == ContextType.TEXT:
+                # Fast-path: /cancel mirrors Web channel behaviour
+                if (content or "").strip().lower() in ("/cancel", "cancel"):
+                    self._do_cancel(session_id, channel_id, event)
+                    return
+
+                cached_files = file_cache.get(session_id)
+                if cached_files:
+                    refs = []
+                    for fi in cached_files:
+                        ftype = fi["type"]
+                        tag = ftype if ftype in ("image", "video") else "file"
+                        refs.append(f"[{tag}: {fi['path']}]")
+                    slack_msg.content = (slack_msg.content or "") + "\n" + "\n".join(refs)
+                    file_cache.clear(session_id)
+                    logger.info(f"[Slack] Attached {len(cached_files)} cached file(s) to query")
+
+            # Reply in the originating thread when present, else start one on this msg
+            thread_ts = event.get("thread_ts") or ts
+
+            context = self._compose_context(
+                slack_msg.ctype,
+                slack_msg.content,
+                isgroup=is_group,
+                msg=slack_msg,
+                # Replies go back into the thread, no manual @mention needed
+                no_need_at=True,
+            )
+            if context:
+                context["session_id"] = session_id
+                context["receiver"] = channel_id
+                context["slack_channel"] = channel_id
+                context["slack_thread_ts"] = thread_ts if is_group else None
+                self.produce(context)
+            logger.debug(f"[Slack] received: type={ctype}, content={str(slack_msg.content)[:80]}")
+        except Exception as e:
+            logger.error(f"[Slack] _handle_event error: {e}", exc_info=True)
+
+    def _do_cancel(self, session_id: str, channel_id: str, event: dict):
+        """Fast-path: /cancel calls cancel_session directly without going through agent."""
+        try:
+            from agent.protocol import get_cancel_registry
+            cancelled = get_cancel_registry().cancel_session(session_id)
+            text = "Current task cancelled." if cancelled else "No running task to cancel."
+            thread_ts = event.get("thread_ts") or event.get("ts")
+            self._client.chat_postMessage(channel=channel_id, text=text, thread_ts=thread_ts)
+            logger.info(f"[Slack] /cancel session={session_id}, cancelled={cancelled}")
+        except Exception as e:
+            logger.error(f"[Slack] /cancel error: {e}", exc_info=True)
+
+    def _parse_event(self, event: dict):
+        """Parse a slack event and return (ctype, content, caption).
+
+        - content is text for ContextType.TEXT, otherwise the local file path
+        - caption is the optional text accompanying a file; empty for plain text
+        """
+        text = (event.get("text") or "").strip()
+        files = event.get("files") or []
+
+        if files:
+            # Handle the first attachment; caption is the accompanying message text
+            f = files[0]
+            mimetype = (f.get("mimetype") or "").lower()
+            url = f.get("url_private_download") or f.get("url_private")
+            name = f.get("name") or f.get("id") or "file"
+            if not url:
+                return (None, None, "")
+            path = self._download_file(url, name)
+            if not path:
+                return (None, None, "")
+            if mimetype.startswith("image/"):
+                return (ContextType.IMAGE, path, text)
+            return (ContextType.FILE, path, text)
+
+        if text:
+            return (ContextType.TEXT, text, "")
+
+        return (None, None, "")
+
+    def _download_file(self, url: str, name: str):
+        """Download a Slack private file (requires bot token auth) to local tmp dir."""
+        try:
+            headers = {"Authorization": f"Bearer {self.bot_token}"}
+            resp = requests.get(url, headers=headers, timeout=60, stream=True)
+            resp.raise_for_status()
+            tmp_dir = SlackMessage.get_tmp_dir()
+            # Sanitize the name and keep it unique-ish via the url tail
+            safe_name = re.sub(r"[^\w.\-]", "_", name)
+            local_path = os.path.join(tmp_dir, safe_name)
+            with open(local_path, "wb") as fp:
+                for chunk in resp.iter_content(chunk_size=8192):
+                    if chunk:
+                        fp.write(chunk)
+            logger.debug(f"[Slack] downloaded {name} -> {local_path}")
+            return local_path
+        except Exception as e:
+            logger.error(f"[Slack] download_file failed ({name}): {e}")
+            return None
+
+    # ------------------------------------------------------------------
+    # Channel trigger logic
+    # ------------------------------------------------------------------
+
+    def _should_reply_in_channel(self, event: dict) -> bool:
+        """Decide whether to reply to a plain channel message (no @mention).
+
+        app_mention already handles explicit @bot, so here we only deal with
+        follow-up messages. `all` replies to every message; `mention_or_reply`
+        replies inside threads the bot already participates in.
+        """
+        mode = conf().get("slack_group_trigger", "mention_or_reply")
+        if mode == "all":
+            return True
+        if mode == "mention_only":
+            return False
+        # mention_or_reply: follow up only within an existing thread
+        return bool(event.get("thread_ts"))
+
+    def _strip_at_mention(self, content: str) -> str:
+        """Strip <@BOT_USER_ID> from channel text."""
+        if not content or not self.bot_user_id:
+            return content
+        pattern = re.compile(r"<@" + re.escape(self.bot_user_id) + r">", re.IGNORECASE)
+        return pattern.sub("", content).strip()
+
+    @staticmethod
+    def _compute_session_id(event: dict, is_group: bool) -> str:
+        channel_id = event.get("channel", "")
+        user_id = event.get("user", "")
+        if is_group:
+            if conf().get("group_shared_session", True):
+                return f"slack_channel_{channel_id}"
+            return f"slack_channel_{channel_id}_{user_id}"
+        return f"slack_user_{user_id}"
+
+    # ------------------------------------------------------------------
+    # Override _compose_context: skip the parent's group whitelist/at checks
+    # (already handled via _should_reply_in_channel). Same idea as telegram.
+    # ------------------------------------------------------------------
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+        if cmsg.is_group:
+            if conf().get("group_shared_session", True):
+                context["session_id"] = cmsg.other_user_id
+            else:
+                context["session_id"] = f"{cmsg.from_user_id}:{cmsg.other_user_id}"
+        else:
+            context["session_id"] = cmsg.from_user_id
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            img_match_prefix = check_prefix(content, conf().get("image_create_prefix"))
+            if img_match_prefix:
+                content = content.replace(img_match_prefix, "", 1)
+                context.type = ContextType.IMAGE_CREATE
+            else:
+                context.type = ContextType.TEXT
+            context.content = (content or "").strip()
+            if "desire_rtype" not in context and conf().get("always_reply_voice"):
+                context["desire_rtype"] = ReplyType.VOICE
+        elif ctype == ContextType.VOICE:
+            if "desire_rtype" not in context and (
+                conf().get("voice_reply_voice") or conf().get("always_reply_voice")
+            ):
+                context["desire_rtype"] = ReplyType.VOICE
+
+        return context
+
+    # ------------------------------------------------------------------
+    # Outbound: ChatChannel.send -> Slack Web API
+    # ------------------------------------------------------------------
+
+    def send(self, reply: Reply, context: Context):
+        """Called from cow's sync main thread; Slack Web client is sync-safe."""
+        if self._client is None:
+            logger.warning("[Slack] client not ready, drop reply")
+            return
+
+        channel_id = context.get("slack_channel")
+        thread_ts = context.get("slack_thread_ts")
+        if not channel_id:
+            logger.warning("[Slack] no slack_channel in context, drop reply")
+            return
+
+        try:
+            self._do_send(reply, channel_id, thread_ts)
+            logger.info(f"[Slack] sent reply (type={reply.type}, channel={channel_id})")
+        except Exception as e:
+            logger.error(f"[Slack] send failed: {e}", exc_info=True)
+
+    def _do_send(self, reply: Reply, channel_id: str, thread_ts):
+        rtype = reply.type
+        content = reply.content
+
+        if rtype in (ReplyType.TEXT, ReplyType.INFO, ReplyType.ERROR):
+            text = str(content) if content is not None else ""
+            if not text:
+                return
+            # Slack caps a message around 40k chars; split conservatively
+            for chunk in _split_text(text, 3500):
+                self._client.chat_postMessage(channel=channel_id, text=chunk, thread_ts=thread_ts)
+
+        elif rtype == ReplyType.IMAGE:
+            # Already a local BytesIO; upload it directly
+            content.seek(0)
+            self._client.files_upload_v2(
+                channel=channel_id, file=content, filename="image.png", thread_ts=thread_ts,
+            )
+
+        elif rtype == ReplyType.IMAGE_URL:
+            url = str(content)
+            if url.startswith("file://"):
+                local = url[7:]
+                self._client.files_upload_v2(
+                    channel=channel_id, file=local, thread_ts=thread_ts,
+                )
+            else:
+                # Post the URL as text; Slack will unfurl it as an image preview
+                self._client.chat_postMessage(channel=channel_id, text=url, thread_ts=thread_ts)
+
+        elif rtype in (ReplyType.VOICE, ReplyType.FILE):
+            local = content[7:] if isinstance(content, str) and content.startswith("file://") else content
+            caption = getattr(reply, "text_content", None) or None
+            self._client.files_upload_v2(
+                channel=channel_id, file=local, initial_comment=caption, thread_ts=thread_ts,
+            )
+
+        else:
+            # Fallback: send as plain text
+            self._client.chat_postMessage(channel=channel_id, text=str(content), thread_ts=thread_ts)
+
+
+def _split_text(text: str, limit: int):
+    """Split long text preferring line breaks to keep markdown structure intact."""
+    if len(text) <= limit:
+        yield text
+        return
+    buf = []
+    size = 0
+    for line in text.splitlines(keepends=True):
+        if size + len(line) > limit and buf:
+            yield "".join(buf)
+            buf, size = [], 0
+        # Hard-split single lines that exceed the limit
+        while len(line) > limit:
+            yield line[:limit]
+            line = line[limit:]
+        buf.append(line)
+        size += len(line)
+    if buf:
+        yield "".join(buf)

channel/slack/slack_message.py

@@ -0,0 +1,60 @@
+"""
+Slack message adapter.
+
+Convert a Slack event payload into cow's unified ChatMessage.
+File downloads are NOT performed here; the channel layer downloads files
+on demand because it needs the bot token for authenticated download URLs.
+"""
+import os
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.utils import expand_path
+from config import conf
+
+
+class SlackMessage(ChatMessage):
+    """Wrap a Slack event into the unified ChatMessage."""
+
+    def __init__(self, event: dict, is_group: bool = False, bot_user_id: str = "",
+                 ctype: ContextType = ContextType.TEXT, content: str = ""):
+        super().__init__(event)
+        # Basic fields
+        self.msg_id = event.get("client_msg_id") or event.get("ts") or ""
+        try:
+            self.create_time = int(float(event.get("ts", 0)))
+        except (TypeError, ValueError):
+            self.create_time = 0
+        self.ctype = ctype
+        self.content = content
+
+        # Sender / chat info
+        from_user_id = event.get("user", "unknown")
+        channel_id = event.get("channel", "")
+        self.from_user_id = from_user_id
+        self.from_user_nickname = from_user_id
+        self.to_user_id = bot_user_id or "slack_bot"
+        self.to_user_nickname = bot_user_id or "slack_bot"
+
+        self.is_group = is_group
+        if is_group:
+            # Channel chat: other_user_id = channel_id, actual_user_id = sender id
+            self.other_user_id = channel_id
+            self.other_user_nickname = channel_id
+            self.actual_user_id = from_user_id
+            self.actual_user_nickname = from_user_id
+        else:
+            # DM: use channel_id so replies go back to the same DM channel
+            self.other_user_id = channel_id or from_user_id
+            self.other_user_nickname = from_user_id
+
+        # Whether the bot was triggered by @-mention (set by channel layer)
+        self.is_at = False
+
+    @staticmethod
+    def get_tmp_dir() -> str:
+        """Local download directory, aligned with other channels (agent_workspace/tmp)."""
+        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+        tmp_dir = os.path.join(workspace_root, "tmp")
+        os.makedirs(tmp_dir, exist_ok=True)
+        return tmp_dir

channel/telegram/telegram_channel.py

@@ -0,0 +1,719 @@
+"""
+Telegram channel via Bot API (long polling mode).
+
+Features:
+- Single chat & group chat (text / photo / voice / video / document)
+- Group trigger: @mention or reply-to-bot (configurable)
+- /cancel fast-path matches Web channel behaviour
+- Auto-register bot commands menu on startup (mirrors Web slash menu)
+- Optional HTTP/SOCKS5 proxy support for restricted networks
+
+Implementation note:
+    python-telegram-bot is async-first. We run the bot inside a dedicated
+    thread with its own asyncio loop so the rest of cow (which is sync)
+    stays untouched. Inbound updates are dispatched onto cow's existing
+    sync ChatChannel.produce() pipeline; outbound send() schedules
+    coroutines back onto that loop via asyncio.run_coroutine_threadsafe.
+"""
+
+import asyncio
+import os
+import re
+import threading
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.telegram.telegram_message import TelegramMessage
+from common.expired_dict import ExpiredDict
+from common.log import logger
+from common.singleton import singleton
+from config import conf
+
+# Bot command menu, aligned with Web slash commands.
+# Top-level commands only; sub-commands are entered with a space (e.g. "/skill list").
+TELEGRAM_BOT_COMMANDS = [
+    ("help", "Show command help"),
+    ("status", "Show running status"),
+    ("context", "View/clear conversation context (sub: clear)"),
+    ("skill", "Manage skills (list/search/install/...)"),
+    ("memory", "Manage memory (sub: dream)"),
+    ("knowledge", "Manage knowledge base (list/on/off)"),
+    ("config", "Show current config"),
+    ("cancel", "Cancel running agent task"),
+    ("logs", "Show recent logs"),
+    ("version", "Show version"),
+]
+
+
+@singleton
+class TelegramChannel(ChatChannel):
+    NOT_SUPPORT_REPLYTYPE = []
+
+    def __init__(self):
+        super().__init__()
+        self.bot_token = ""
+        self.bot_username = ""  # used for @-mention matching
+        self._bot = None
+        self._application = None
+        self._loop = None
+        self._loop_thread = None
+        self._stop_event = threading.Event()
+        # Idempotent dedup; TG occasionally redelivers the same update on flaky networks
+        self._received_msgs = ExpiredDict(60 * 60 * 1)
+
+        # Disable group whitelist / prefix checks (we handle triggering ourselves
+        # in _should_reply_in_group), aligned with feishu / wecom_bot channels.
+        conf()["group_name_white_list"] = ["ALL_GROUP"]
+        conf()["single_chat_prefix"] = [""]
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def startup(self):
+        self.bot_token = conf().get("telegram_token", "")
+        if not self.bot_token:
+            err = "[Telegram] telegram_token is required"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        try:
+            from telegram.ext import (
+                Application,
+                MessageHandler,
+                CommandHandler,
+                filters,
+            )
+        except ImportError:
+            err = (
+                "[Telegram] python-telegram-bot is not installed. "
+                "Run: pip install python-telegram-bot"
+            )
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        # Run the asyncio event loop in a dedicated thread so the sync cow body
+        # is untouched.
+        self._loop = asyncio.new_event_loop()
+
+        def _run_loop():
+            asyncio.set_event_loop(self._loop)
+            try:
+                self._loop.run_until_complete(self._async_main(Application, MessageHandler, CommandHandler, filters))
+            except Exception as e:
+                logger.error(f"[Telegram] event loop crashed: {e}", exc_info=True)
+                self.report_startup_error(str(e))
+            finally:
+                try:
+                    self._loop.close()
+                except Exception:
+                    pass
+                logger.info("[Telegram] event loop exited")
+
+        self._loop_thread = threading.Thread(target=_run_loop, daemon=True, name="telegram-loop")
+        self._loop_thread.start()
+        # Block startup() until the loop thread exits, matching other channels'
+        # behaviour (startup is a blocking call).
+        self._loop_thread.join()
+
+    async def _async_main(self, Application, MessageHandler, CommandHandler, filters):
+        """Build Application, register handlers, and run polling."""
+        builder = Application.builder().token(self.bot_token)
+
+        # Proxy: prefer telegram_proxy config, fall back to HTTPS_PROXY env var
+        proxy_url = conf().get("telegram_proxy", "") or os.environ.get("HTTPS_PROXY", "")
+        if proxy_url:
+            try:
+                builder = builder.proxy(proxy_url).get_updates_proxy(proxy_url)
+                logger.info(f"[Telegram] using proxy: {proxy_url}")
+            except Exception as e:
+                logger.warning(f"[Telegram] proxy config failed, fallback to direct: {e}")
+
+        # Media uploads (photo/voice/video/document) over a proxy can be slow,
+        # bump read/write/connect/pool timeouts.
+        builder = (
+            builder
+            .read_timeout(60)
+            .write_timeout(120)
+            .connect_timeout(30)
+            .pool_timeout(30)
+        )
+
+        application = builder.build()
+        self._application = application
+        self._bot = application.bot
+
+        # Fetch our own username (needed for @-mention matching in groups)
+        try:
+            me = await self._bot.get_me()
+            self.bot_username = me.username or ""
+            self.name = self.bot_username  # ChatChannel uses self.name to strip @-mention
+            logger.info(f"[Telegram] Bot logged in as @{self.bot_username} (id={me.id})")
+        except Exception as e:
+            err = f"[Telegram] get_me failed: {e}"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        # Register the command menu (failure is non-fatal)
+        if conf().get("telegram_register_commands", True):
+            try:
+                from telegram import BotCommand
+                cmds = [BotCommand(name, desc) for name, desc in TELEGRAM_BOT_COMMANDS]
+                await self._bot.set_my_commands(cmds)
+                logger.info(f"[Telegram] Registered {len(cmds)} bot commands")
+            except Exception as e:
+                logger.warning(f"[Telegram] set_my_commands failed: {e}")
+
+        # Handlers:
+        # 1) /cancel uses the fast-path
+        application.add_handler(CommandHandler("cancel", self._on_cancel))
+        # 2) Normal messages (text + media)
+        application.add_handler(MessageHandler(filters.ALL & ~filters.COMMAND, self._on_message))
+        # 3) Other slash commands are forwarded as plain text for the agent to handle
+        application.add_handler(MessageHandler(filters.COMMAND, self._on_command_passthrough))
+
+        # Start polling. drop_pending_updates avoids replaying backlog after restart.
+        # Transient "Server disconnected" / RemoteProtocolError during get_updates
+        # are common over proxies/flaky networks; PTB's network loop auto-retries,
+        # so we only need to keep the noise down (see _quiet_polling_network_errors).
+        self._quiet_polling_network_errors()
+        logger.info("[Telegram] Starting long polling...")
+        await application.initialize()
+        await application.start()
+        await application.updater.start_polling(
+            drop_pending_updates=True,
+            # Long-poll hold time on the server side; smaller value = reconnect more
+            # often but each hung connection fails faster.
+            timeout=30,
+            # Retry forever on transient get_updates network errors instead of giving up.
+            bootstrap_retries=-1,
+        )
+        self.report_startup_success()
+        logger.info("[Telegram] ✅ Telegram bot ready, polling for updates")
+
+        # Block until stop()
+        try:
+            while not self._stop_event.is_set():
+                await asyncio.sleep(0.5)
+        finally:
+            try:
+                await application.updater.stop()
+                await application.stop()
+                await application.shutdown()
+            except Exception as e:
+                logger.warning(f"[Telegram] shutdown error: {e}")
+
+    @staticmethod
+    def _quiet_polling_network_errors():
+        """Downgrade PTB's noisy 'Exception happened while polling for updates' logs.
+
+        These transient get_updates errors (RemoteProtocolError / NetworkError /
+        TimedOut, typically over a proxy) are auto-retried by PTB's network loop,
+        so logging the full traceback at ERROR is just noise. We attach a filter
+        that drops these specific records while leaving real errors untouched.
+        """
+        import logging
+
+        class _PollingNoiseFilter(logging.Filter):
+            _NEEDLES = (
+                "Exception happened while polling for updates",
+                "Server disconnected without sending a response",
+            )
+
+            def filter(self, record: logging.LogRecord) -> bool:
+                try:
+                    msg = record.getMessage()
+                except Exception:
+                    return True
+                if any(n in msg for n in self._NEEDLES):
+                    # Keep a single-line breadcrumb at DEBUG, drop the traceback.
+                    logger.debug(f"[Telegram] transient polling network error (auto-retrying): {msg.splitlines()[0]}")
+                    return False
+                return True
+
+        noise_filter = _PollingNoiseFilter()
+        for name in ("telegram.ext.Updater", "telegram.ext._updater", "telegram.ext"):
+            logging.getLogger(name).addFilter(noise_filter)
+
+    def stop(self):
+        logger.info("[Telegram] stop() called")
+        self._stop_event.set()
+        if self._loop_thread and self._loop_thread.is_alive():
+            try:
+                self._loop_thread.join(timeout=10)
+            except Exception:
+                pass
+        logger.info("[Telegram] stop() completed")
+
+    # ------------------------------------------------------------------
+    # Inbound: telegram update -> ChatMessage -> ChatChannel.produce
+    # ------------------------------------------------------------------
+
+    async def _on_cancel(self, update, _context):
+        """Fast-path: /cancel calls cancel_session directly without going through agent."""
+        try:
+            from agent.protocol import get_cancel_registry
+            session_id = self._compute_session_id(update)
+            cancelled = get_cancel_registry().cancel_session(session_id)
+            text = "Current task cancelled." if cancelled else "No running task to cancel."
+            await update.effective_message.reply_text(text)
+            logger.info(f"[Telegram] /cancel session={session_id}, cancelled={cancelled}")
+        except Exception as e:
+            logger.error(f"[Telegram] /cancel error: {e}", exc_info=True)
+            try:
+                await update.effective_message.reply_text(f"⚠️ /cancel failed: {e}")
+            except Exception:
+                pass
+
+    async def _on_command_passthrough(self, update, _context):
+        """All non-/cancel commands fall through to plain message handling."""
+        await self._on_message(update, _context)
+
+    async def _on_message(self, update, _context):
+        """Telegram update entry: parse message -> build ChatMessage -> produce()."""
+        try:
+            message = update.effective_message
+            chat = update.effective_chat
+            if not message or not chat:
+                return
+
+            # Idempotent dedup
+            msg_uid = f"{chat.id}:{message.message_id}"
+            if self._received_msgs.get(msg_uid):
+                return
+            self._received_msgs[msg_uid] = True
+
+            is_group = chat.type in ("group", "supergroup")
+
+            # Debug log: helpful when group messages are silently dropped
+            if is_group:
+                logger.debug(
+                    f"[Telegram] group update received: chat_id={chat.id}, "
+                    f"text={(message.text or message.caption or '')[:40]!r}, "
+                    f"reply_to_bot={bool(message.reply_to_message and message.reply_to_message.from_user and message.reply_to_message.from_user.username == self.bot_username)}"
+                )
+
+            # Group trigger gate (silently drop if not triggered)
+            if is_group and not self._should_reply_in_group(update):
+                logger.debug(f"[Telegram] group message not triggered (need @{self.bot_username} or reply), skip")
+                return
+
+            # Parse message type + download media if needed.
+            # Media messages with caption return both the local path and the caption text.
+            ctype, content, caption = await self._parse_message(message)
+            if ctype is None:
+                logger.debug(f"[Telegram] unsupported message type, skip. msg={message}")
+                return
+
+            # Strip @bot mention for group text/caption
+            if is_group and self.bot_username:
+                if ctype == ContextType.TEXT and content:
+                    content = self._strip_at_mention(content)
+                if caption:
+                    caption = self._strip_at_mention(caption)
+
+            tg_msg = TelegramMessage(
+                update,
+                is_group=is_group,
+                bot_username=self.bot_username,
+                ctype=ctype,
+                content=content,
+            )
+            tg_msg.is_at = is_group  # If we got here in a group, the bot is mentioned/replied
+
+            # File cache: standalone media goes into cache, the next text query attaches them
+            from channel.file_cache import get_file_cache
+            file_cache = get_file_cache()
+            session_id = self._compute_session_id(update)
+
+            # Media + caption together: treat as a complete query and bypass the cache
+            if ctype in (ContextType.IMAGE, ContextType.FILE) and caption:
+                tag = "image" if ctype == ContextType.IMAGE else "file"
+                merged_text = f"{caption}\n[{tag}: {content}]"
+                tg_msg.ctype = ContextType.TEXT
+                tg_msg.content = merged_text
+                ctype = ContextType.TEXT
+                logger.info(f"[Telegram] Media+caption merged for session {session_id}")
+                # fallthrough to the TEXT branch below
+
+            elif ctype == ContextType.IMAGE:
+                file_cache.add(session_id, content, file_type="image")
+                logger.info(f"[Telegram] Image cached for session {session_id}, waiting for query...")
+                return
+            elif ctype == ContextType.FILE:
+                file_cache.add(session_id, content, file_type="file")
+                logger.info(f"[Telegram] File cached for session {session_id}: {content}")
+                return
+
+            if ctype == ContextType.TEXT:
+                cached_files = file_cache.get(session_id)
+                if cached_files:
+                    refs = []
+                    for fi in cached_files:
+                        ftype = fi["type"]
+                        tag = ftype if ftype in ("image", "video") else "file"
+                        refs.append(f"[{tag}: {fi['path']}]")
+                    tg_msg.content = (tg_msg.content or "") + "\n" + "\n".join(refs)
+                    file_cache.clear(session_id)
+                    logger.info(f"[Telegram] Attached {len(cached_files)} cached file(s) to query")
+
+            # Dispatch to cow main pipeline (reuses ChatChannel._compose_context routing)
+            context = self._compose_context(
+                tg_msg.ctype,
+                tg_msg.content,
+                isgroup=is_group,
+                msg=tg_msg,
+            )
+            if context:
+                context["session_id"] = session_id
+                context["receiver"] = str(chat.id)
+                context["telegram_chat_id"] = chat.id
+                context["telegram_reply_to_msg_id"] = message.message_id if is_group else None
+                self.produce(context)
+            logger.debug(f"[Telegram] received: type={ctype}, content={str(tg_msg.content)[:80]}")
+
+        except Exception as e:
+            logger.error(f"[Telegram] _on_message error: {e}", exc_info=True)
+
+    async def _parse_message(self, message):
+        """Parse a telegram message and return (ctype, content, caption).
+
+        - content is text for ContextType.TEXT, otherwise the local file path
+        - caption is the optional text accompanying a media message; empty for plain text
+        """
+        caption = (message.caption or "").strip()
+
+        if message.photo:
+            largest = message.photo[-1]
+            path = await self._download_file(largest.file_id, suffix=".jpg")
+            return (ContextType.IMAGE, path, caption) if path else (None, None, "")
+
+        if message.voice or message.audio:
+            audio_obj = message.voice or message.audio
+            suffix = ".ogg" if message.voice else (
+                "." + (audio_obj.mime_type.split("/")[-1] if getattr(audio_obj, "mime_type", "") else "mp3")
+            )
+            path = await self._download_file(audio_obj.file_id, suffix=suffix)
+            return (ContextType.VOICE, path, caption) if path else (None, None, "")
+
+        if message.video or message.video_note:
+            video_obj = message.video or message.video_note
+            path = await self._download_file(video_obj.file_id, suffix=".mp4")
+            return (ContextType.FILE, path, caption) if path else (None, None, "")
+
+        if message.document:
+            doc = message.document
+            ext = ""
+            if doc.file_name and "." in doc.file_name:
+                ext = "." + doc.file_name.rsplit(".", 1)[-1]
+            path = await self._download_file(doc.file_id, suffix=ext, original_name=doc.file_name)
+            if not path:
+                return (None, None, "")
+            # Image-typed documents (user picked "send as file") are treated as images
+            mime = (doc.mime_type or "").lower()
+            if mime.startswith("image/"):
+                return (ContextType.IMAGE, path, caption)
+            return (ContextType.FILE, path, caption)
+
+        if message.text:
+            return (ContextType.TEXT, message.text.strip(), "")
+
+        return (None, None, "")
+
+    async def _download_file(self, file_id: str, suffix: str = "", original_name: str = ""):
+        """Download via bot.get_file into the local tmp dir; return path or None on failure."""
+        try:
+            f = await self._bot.get_file(file_id)
+            tmp_dir = TelegramMessage.get_tmp_dir()
+            base = original_name or f"{file_id}{suffix or ''}"
+            # Prefix with file_id to avoid name collisions / weird chars
+            safe_name = f"{file_id}_{base}" if original_name else base
+            local_path = os.path.join(tmp_dir, safe_name)
+            await f.download_to_drive(custom_path=local_path)
+            logger.debug(f"[Telegram] downloaded file_id={file_id} -> {local_path}")
+            return local_path
+        except Exception as e:
+            logger.error(f"[Telegram] download_file failed (file_id={file_id}): {e}")
+            return None
+
+    # ------------------------------------------------------------------
+    # Group trigger logic
+    # ------------------------------------------------------------------
+
+    def _should_reply_in_group(self, update) -> bool:
+        """Decide whether to reply to a group message based on configuration."""
+        mode = conf().get("telegram_group_trigger", "mention_or_reply")
+        if mode == "all":
+            return True
+
+        message = update.effective_message
+        if not message:
+            return False
+
+        # 1) Mentioned
+        if self.bot_username and self._is_mentioned(message, self.bot_username):
+            return True
+
+        # 2) Reply to a bot message
+        if mode == "mention_or_reply":
+            reply = message.reply_to_message
+            if reply and reply.from_user and reply.from_user.username == self.bot_username:
+                return True
+
+        return False
+
+    @staticmethod
+    def _is_mentioned(message, bot_username: str) -> bool:
+        """Check whether entities/caption_entities contain a @mention of the bot."""
+        bot_at = "@" + bot_username.lower()
+        text = (message.text or message.caption or "").lower()
+        if bot_at in text:
+            return True
+        # Also check entities strictly to support text_mention (no-username @)
+        for ent in (message.entities or []) + (message.caption_entities or []):
+            if ent.type == "mention":
+                src = message.text or message.caption or ""
+                if src[ent.offset: ent.offset + ent.length].lower() == bot_at:
+                    return True
+        return False
+
+    def _strip_at_mention(self, content: str) -> str:
+        """Strip @bot_username from group text (case-insensitive)."""
+        if not content or not self.bot_username:
+            return content
+        pattern = re.compile(r"@" + re.escape(self.bot_username), re.IGNORECASE)
+        return pattern.sub("", content).strip()
+
+    @staticmethod
+    def _compute_session_id(update) -> str:
+        chat = update.effective_chat
+        user = update.effective_user
+        is_group = chat.type in ("group", "supergroup")
+        if is_group:
+            if conf().get("group_shared_session", True):
+                return f"tg_group_{chat.id}"
+            return f"tg_group_{chat.id}_{user.id}"
+        return f"tg_user_{user.id}"
+
+    # ------------------------------------------------------------------
+    # Override _compose_context: skip the parent's group whitelist/at checks
+    # (already handled in _on_message via _should_reply_in_group). Same idea
+    # as the feishu channel.
+    # ------------------------------------------------------------------
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+        if cmsg.is_group:
+            if conf().get("group_shared_session", True):
+                context["session_id"] = cmsg.other_user_id
+            else:
+                context["session_id"] = f"{cmsg.from_user_id}:{cmsg.other_user_id}"
+        else:
+            context["session_id"] = cmsg.from_user_id
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            img_match_prefix = check_prefix(content, conf().get("image_create_prefix"))
+            if img_match_prefix:
+                content = content.replace(img_match_prefix, "", 1)
+                context.type = ContextType.IMAGE_CREATE
+            else:
+                context.type = ContextType.TEXT
+            context.content = (content or "").strip()
+            if "desire_rtype" not in context and conf().get("always_reply_voice"):
+                context["desire_rtype"] = ReplyType.VOICE
+        elif ctype == ContextType.VOICE:
+            if "desire_rtype" not in context and (
+                conf().get("voice_reply_voice") or conf().get("always_reply_voice")
+            ):
+                context["desire_rtype"] = ReplyType.VOICE
+
+        return context
+
+    # ------------------------------------------------------------------
+    # Outbound: ChatChannel.send -> Telegram API
+    # ------------------------------------------------------------------
+
+    def send(self, reply: Reply, context: Context):
+        """Called from cow's sync main thread; we marshal the coroutine onto the loop thread."""
+        if self._loop is None or self._bot is None:
+            logger.warning("[Telegram] bot not ready, drop reply")
+            return
+
+        chat_id = context.get("telegram_chat_id")
+        reply_to = context.get("telegram_reply_to_msg_id")
+        if chat_id is None:
+            logger.warning("[Telegram] no telegram_chat_id in context, drop reply")
+            return
+
+        coro = self._async_send(reply, chat_id, reply_to)
+        try:
+            future = asyncio.run_coroutine_threadsafe(coro, self._loop)
+            # Media uploads through a proxy can be slow; let PTB's own timeouts win
+            future.result(timeout=180)
+        except Exception as e:
+            logger.error(f"[Telegram] send failed: {e}")
+
+    # Number of retries for transient network errors (proxy hiccups etc.)
+    _SEND_RETRIES = 2
+    _SEND_RETRY_BACKOFF = 2.0  # seconds
+
+    async def _send_with_retry(self, send_fn, *, label: str):
+        """Run a single Telegram API call with retries for transient network errors."""
+        from telegram.error import NetworkError, TimedOut
+        last_err = None
+        for attempt in range(self._SEND_RETRIES + 1):
+            try:
+                return await send_fn()
+            except (NetworkError, TimedOut) as e:
+                last_err = e
+                if attempt >= self._SEND_RETRIES:
+                    break
+                wait = self._SEND_RETRY_BACKOFF * (attempt + 1)
+                logger.warning(
+                    f"[Telegram] {label} transient error (attempt {attempt + 1}/"
+                    f"{self._SEND_RETRIES + 1}): {e}; retry in {wait}s"
+                )
+                await asyncio.sleep(wait)
+        raise last_err
+
+    async def _async_send(self, reply: Reply, chat_id, reply_to_msg_id):
+        try:
+            rtype = reply.type
+            content = reply.content
+
+            if rtype == ReplyType.TEXT or rtype == ReplyType.INFO or rtype == ReplyType.ERROR:
+                # Telegram caps a single text message at 4096 chars; auto-split
+                text = str(content) if content is not None else ""
+                if not text:
+                    return
+                for chunk in _split_text(text, 4000):
+                    await self._send_with_retry(
+                        lambda c=chunk: self._bot.send_message(
+                            chat_id=chat_id,
+                            text=c,
+                            reply_to_message_id=reply_to_msg_id,
+                            # Avoid failing the whole send if reply_to was deleted
+                            allow_sending_without_reply=True,
+                        ),
+                        label="send_message",
+                    )
+
+            elif rtype == ReplyType.IMAGE:
+                # Already a local BytesIO; send it directly
+                content.seek(0)
+                await self._send_with_retry(
+                    lambda: self._bot.send_photo(
+                        chat_id=chat_id,
+                        photo=content,
+                        reply_to_message_id=reply_to_msg_id,
+                        allow_sending_without_reply=True,
+                    ),
+                    label="send_photo",
+                )
+
+            elif rtype == ReplyType.IMAGE_URL:
+                url = str(content)
+                if url.startswith("file://"):
+                    local = url[7:]
+                    # Open inside the lambda so each retry gets a fresh stream
+                    async def _send_local_photo():
+                        with open(local, "rb") as f:
+                            return await self._bot.send_photo(
+                                chat_id=chat_id, photo=f,
+                                reply_to_message_id=reply_to_msg_id,
+                                allow_sending_without_reply=True,
+                            )
+                    await self._send_with_retry(_send_local_photo, label="send_photo(file)")
+                else:
+                    await self._send_with_retry(
+                        lambda: self._bot.send_photo(
+                            chat_id=chat_id, photo=url,
+                            reply_to_message_id=reply_to_msg_id,
+                            allow_sending_without_reply=True,
+                        ),
+                        label="send_photo(url)",
+                    )
+
+            elif rtype == ReplyType.VOICE:
+                local = content[7:] if isinstance(content, str) and content.startswith("file://") else content
+                async def _send_voice():
+                    with open(local, "rb") as f:
+                        return await self._bot.send_voice(
+                            chat_id=chat_id, voice=f,
+                            reply_to_message_id=reply_to_msg_id,
+                            allow_sending_without_reply=True,
+                        )
+                await self._send_with_retry(_send_voice, label="send_voice")
+
+            elif rtype == ReplyType.FILE:
+                # Videos go through send_video, everything else through send_document
+                local = content[7:] if isinstance(content, str) and content.startswith("file://") else content
+                # File replies may carry an accompanying text caption
+                caption = getattr(reply, "text_content", None) or None
+                is_video = isinstance(local, str) and local.lower().endswith(
+                    (".mp4", ".mov", ".avi", ".mkv", ".webm")
+                )
+
+                async def _send_file():
+                    with open(local, "rb") as f:
+                        if is_video:
+                            return await self._bot.send_video(
+                                chat_id=chat_id, video=f, caption=caption,
+                                reply_to_message_id=reply_to_msg_id,
+                                allow_sending_without_reply=True,
+                            )
+                        return await self._bot.send_document(
+                            chat_id=chat_id, document=f, caption=caption,
+                            reply_to_message_id=reply_to_msg_id,
+                            allow_sending_without_reply=True,
+                        )
+                await self._send_with_retry(_send_file, label="send_video" if is_video else "send_document")
+
+            else:
+                # Fallback: send as plain text
+                await self._send_with_retry(
+                    lambda: self._bot.send_message(
+                        chat_id=chat_id, text=str(content),
+                        reply_to_message_id=reply_to_msg_id,
+                        allow_sending_without_reply=True,
+                    ),
+                    label="send_message(fallback)",
+                )
+
+            logger.info(f"[Telegram] sent reply (type={rtype}, chat_id={chat_id})")
+
+        except Exception as e:
+            logger.error(f"[Telegram] _async_send error: {e}", exc_info=True)
+
+
+def _split_text(text: str, limit: int):
+    """Split long text preferring line breaks to keep markdown structure intact."""
+    if len(text) <= limit:
+        yield text
+        return
+    buf = []
+    size = 0
+    for line in text.splitlines(keepends=True):
+        if size + len(line) > limit and buf:
+            yield "".join(buf)
+            buf, size = [], 0
+        # Hard-split single lines that exceed the limit
+        while len(line) > limit:
+            yield line[:limit]
+            line = line[limit:]
+        buf.append(line)
+        size += len(line)
+    if buf:
+        yield "".join(buf)

channel/telegram/telegram_message.py

@@ -0,0 +1,62 @@
+"""
+Telegram message adapter.
+
+Convert a python-telegram-bot Update into cow's unified ChatMessage.
+File downloads are NOT performed here; the channel layer triggers
+bot.get_file() on demand because it requires the async event loop.
+"""
+import os
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.utils import expand_path
+from config import conf
+
+
+class TelegramMessage(ChatMessage):
+    """Wrap a Telegram Update into the unified ChatMessage."""
+
+    def __init__(self, update, is_group: bool = False, bot_username: str = "",
+                 ctype: ContextType = ContextType.TEXT, content: str = ""):
+        super().__init__(update)
+        message = update.effective_message
+        chat = update.effective_chat
+        user = update.effective_user
+
+        # Basic fields
+        self.msg_id = str(message.message_id) if message else ""
+        self.create_time = int(message.date.timestamp()) if message and message.date else 0
+        self.ctype = ctype
+        self.content = content
+
+        # Sender / chat info
+        from_user_id = str(user.id) if user else "unknown"
+        from_user_nick = (
+            user.full_name if user and user.full_name else (user.username if user else "unknown")
+        )
+        self.from_user_id = from_user_id
+        self.from_user_nickname = from_user_nick or from_user_id
+        self.to_user_id = bot_username or "telegram_bot"
+        self.to_user_nickname = bot_username or "telegram_bot"
+
+        self.is_group = is_group
+        if is_group:
+            # Group: other_user_id = group_id, actual_user_id = sender id
+            self.other_user_id = str(chat.id)
+            self.other_user_nickname = chat.title or str(chat.id)
+            self.actual_user_id = from_user_id
+            self.actual_user_nickname = self.from_user_nickname
+        else:
+            self.other_user_id = from_user_id
+            self.other_user_nickname = self.from_user_nickname
+
+        # Whether the bot was triggered by @-mention or reply (set by channel layer)
+        self.is_at = False
+
+    @staticmethod
+    def get_tmp_dir() -> str:
+        """Local download directory, aligned with other channels (agent_workspace/tmp)."""
+        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+        tmp_dir = os.path.join(workspace_root, "tmp")
+        os.makedirs(tmp_dir, exist_ok=True)
+        return tmp_dir

channel/terminal/terminal_channel.py

@@ -1,4 +1,7 @@
+import json
+import os
 import sys
+import time
 
 from bridge.context import *
 from bridge.reply import Reply, ReplyType
@@ -8,6 +11,164 @@ from common.log import logger
 from config import conf
 
 
+class _Style:
+    """ANSI escape codes for terminal styling. Disabled when not a tty."""
+
+    enabled = sys.stdout.isatty()
+
+    RESET = "\033[0m"
+    BOLD = "\033[1m"
+    DIM = "\033[2m"
+    ITALIC = "\033[3m"
+
+    GRAY = "\033[90m"
+    RED = "\033[31m"
+    GREEN = "\033[32m"
+    YELLOW = "\033[33m"
+    BLUE = "\033[34m"
+    MAGENTA = "\033[35m"
+    CYAN = "\033[36m"
+
+    @classmethod
+    def wrap(cls, text, *codes):
+        if not cls.enabled or not codes:
+            return text
+        return "".join(codes) + text + cls.RESET
+
+
+class TerminalAgentRenderer:
+    """Render agent stream events to the terminal in real time.
+
+    Reuses the same `on_event` mechanism as the web channel so the terminal
+    can show reasoning, tool calls and streaming answer text just like the web UI.
+    """
+
+    def __init__(self):
+        self._reasoning_active = False
+        self._answer_active = False
+        self._has_output = False
+        # Track tool execution start time as a fallback when the event omits it
+        self._tool_started_at = {}
+
+    def _print(self, text, end="", flush=True):
+        sys.stdout.write(text)
+        if end:
+            sys.stdout.write(end)
+        if flush:
+            sys.stdout.flush()
+        self._has_output = True
+
+    def _close_section(self):
+        """Finish the currently open streaming section (reasoning or answer)."""
+        if self._reasoning_active:
+            self._print("", end="\n")
+            self._reasoning_active = False
+        if self._answer_active:
+            self._print("", end="\n")
+            self._answer_active = False
+
+    def _format_arguments(self, arguments):
+        try:
+            if isinstance(arguments, (dict, list)):
+                text = json.dumps(arguments, ensure_ascii=False)
+            else:
+                text = str(arguments)
+        except Exception:
+            text = str(arguments)
+        # Keep tool input compact in the terminal
+        if len(text) > 300:
+            text = text[:300] + "…"
+        return text
+
+    def handle_event(self, event: dict):
+        try:
+            self._handle_event(event)
+        except Exception as e:
+            logger.debug(f"[Terminal] render event error: {e}")
+
+    def _handle_event(self, event: dict):
+        event_type = event.get("type")
+        data = event.get("data", {}) or {}
+
+        if event_type == "agent_start":
+            self._print("\n" + _Style.wrap("Agent: ", _Style.BOLD, _Style.GREEN), end="\n")
+
+        elif event_type == "reasoning_update":
+            delta = data.get("delta", "")
+            if not delta:
+                return
+            if self._answer_active:
+                self._close_section()
+            if not self._reasoning_active:
+                self._print(_Style.wrap("💭 思考  ", _Style.DIM, _Style.MAGENTA), end="\n")
+                self._reasoning_active = True
+            self._print(_Style.wrap(delta, _Style.DIM, _Style.ITALIC))
+
+        elif event_type == "message_update":
+            delta = data.get("delta", "")
+            if not delta:
+                return
+            if self._reasoning_active:
+                self._close_section()
+            self._answer_active = True
+            self._print(delta)
+
+        elif event_type == "tool_execution_start":
+            self._close_section()
+            tool_name = data.get("tool_name", "tool")
+            tool_id = data.get("tool_call_id")
+            arguments = data.get("arguments", {})
+            self._tool_started_at[tool_id] = time.time()
+            header = _Style.wrap(f"🔧 {tool_name}", _Style.BOLD, _Style.CYAN)
+            args_str = self._format_arguments(arguments)
+            self._print(f"{header} {_Style.wrap(args_str, _Style.GRAY)}", end="\n")
+
+        elif event_type == "tool_execution_end":
+            tool_name = data.get("tool_name", "tool")
+            tool_id = data.get("tool_call_id")
+            status = data.get("status", "success")
+            result = data.get("result", "")
+            exec_time = data.get("execution_time")
+            if exec_time is None and tool_id in self._tool_started_at:
+                exec_time = time.time() - self._tool_started_at.pop(tool_id, time.time())
+            success = status == "success"
+            icon = "✓" if success else "✗"
+            color = _Style.GREEN if success else _Style.RED
+            result_str = str(result)
+            if len(result_str) > 500:
+                result_str = result_str[:500] + "…"
+            # Indent multi-line tool output for readability
+            result_str = result_str.replace("\n", "\n   ")
+            cost = f" ({exec_time:.2f}s)" if isinstance(exec_time, (int, float)) else ""
+            self._print(
+                _Style.wrap(f"   {icon} {tool_name}{cost}", color) + "  " + _Style.wrap(result_str, _Style.GRAY),
+                end="\n",
+            )
+
+        elif event_type == "file_to_send":
+            self._close_section()
+            file_path = data.get("path", "")
+            file_name = data.get("file_name", "")
+            label = file_name or file_path
+            self._print(_Style.wrap(f"📎 文件: {label}", _Style.BLUE), end="\n")
+
+        elif event_type == "error":
+            self._close_section()
+            err_msg = data.get("error") or "unknown error"
+            self._print(_Style.wrap(f"❌ {err_msg}", _Style.BOLD, _Style.RED), end="\n")
+
+        elif event_type == "agent_cancelled":
+            self._close_section()
+            self._print(_Style.wrap("⏹ 已中止", _Style.YELLOW), end="\n")
+
+        elif event_type == "agent_end":
+            self._close_section()
+
+    def finish(self):
+        """Ensure any open section is closed at the end of a turn."""
+        self._close_section()
+
+
 class TerminalMessage(ChatMessage):
     def __init__(
         self,
@@ -29,17 +190,33 @@ class TerminalMessage(ChatMessage):
 class TerminalChannel(ChatChannel):
     NOT_SUPPORT_REPLYTYPE = [ReplyType.VOICE]
 
+    def __init__(self):
+        super().__init__()
+        # Per-request renderers keyed by request_id; used to detect whether
+        # agent text was already streamed so send() can avoid duplicate output.
+        self._renderers = {}
+        # Callback that restores TTY attributes on exit (set in startup).
+        self._restore_terminal = None
+
     def send(self, reply: Reply, context: Context):
-        print("\nBot:")
+        request_id = context.get("request_id") if context else None
+        renderer = self._renderers.pop(request_id, None) if request_id else None
+        streamed = renderer is not None and renderer._has_output
+
+        if renderer is not None:
+            renderer.finish()
+
         if reply.type == ReplyType.IMAGE:
             from PIL import Image
 
             image_storage = reply.content
             image_storage.seek(0)
             img = Image.open(image_storage)
+            if not streamed:
+                print("\nAgent: ")
             print("<IMAGE>")
             img.show()
-        elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
+        elif reply.type == ReplyType.IMAGE_URL:  # download image from url
             import io
 
             import requests
@@ -52,38 +229,122 @@ class TerminalChannel(ChatChannel):
                 image_storage.write(block)
             image_storage.seek(0)
             img = Image.open(image_storage)
+            if not streamed:
+                print("\nAgent: ")
             print(img_url)
             img.show()
         else:
-            print(reply.content)
-        print("\nUser:", end="")
+            # When agent already streamed the answer, skip re-printing the
+            # final text to avoid duplication; just emit a trailing newline.
+            if streamed:
+                print()
+            else:
+                print("\nAgent: ")
+                print(reply.content)
+        print("\nUser: ", end="")
         sys.stdout.flush()
         return
 
+    def _silence_console_logging(self):
+        """Mute console log output so background-thread logs (web/MCP/scheduler)
+        don't flood the interactive terminal. Logs still go to run.log in full.
+
+        Configurable via `terminal_log_level` (default ERROR). The file handler
+        is untouched, so run.log keeps the complete log.
+        """
+        import logging
+
+        level_name = str(conf().get("terminal_log_level", "ERROR")).upper()
+        level = getattr(logging, level_name, logging.ERROR)
+        root_logger = logging.getLogger("log")
+        for handler in root_logger.handlers:
+            # Only raise the level of the stdout/stderr stream handler;
+            # keep FileHandler at the logger's level so run.log stays complete.
+            if isinstance(handler, logging.StreamHandler) and not isinstance(handler, logging.FileHandler):
+                handler.setLevel(level)
+
+    def _install_terminal_guard(self):
+        """Save TTY attributes and register restore hooks so the terminal is
+        never left in a broken state (no echo / raw mode / leftover ANSI) after
+        the process exits, especially when Ctrl+C interrupts a blocking input().
+        """
+        if not sys.stdin.isatty():
+            return
+        try:
+            import atexit
+            import termios
+
+            saved_attrs = termios.tcgetattr(sys.stdin.fileno())
+
+            def _restore():
+                try:
+                    termios.tcsetattr(sys.stdin.fileno(), termios.TCSADRAIN, saved_attrs)
+                except Exception:
+                    pass
+                try:
+                    if _Style.enabled:
+                        sys.stdout.write(_Style.RESET)
+                        sys.stdout.flush()
+                except Exception:
+                    pass
+
+            self._restore_terminal = _restore
+            atexit.register(_restore)
+        except Exception as e:
+            # termios is unavailable on Windows; skip the guard there.
+            logger.debug(f"[Terminal] terminal guard not installed: {e}")
+            self._restore_terminal = None
+
     def startup(self):
         context = Context()
-        logger.setLevel("WARN")
-        print("\nPlease input your question:\nUser:", end="")
+        self._silence_console_logging()
+        self._install_terminal_guard()
+        print("\nPlease input your question:\nUser: ", end="")
         sys.stdout.flush()
         msg_id = 0
         while True:
             try:
                 prompt = self.get_input()
-            except KeyboardInterrupt:
-                print("\nExiting...")
-                sys.exit()
+            except (KeyboardInterrupt, EOFError):
+                self._shutdown()
             msg_id += 1
             trigger_prefixs = conf().get("single_chat_prefix", [""])
             if check_prefix(prompt, trigger_prefixs) is None:
-                prompt = trigger_prefixs[0] + prompt  # 给没触发的消息加上触发前缀
+                prompt = trigger_prefixs[0] + prompt  # add trigger prefix to untriggered messages
 
             context = self._compose_context(ContextType.TEXT, prompt, msg=TerminalMessage(msg_id, prompt))
             context["isgroup"] = False
             if context:
+                # Attach an agent event renderer so reasoning / tool calls /
+                # streaming answer show up live in the terminal (web-like UX).
+                request_id = str(msg_id)
+                context["request_id"] = request_id
+                renderer = TerminalAgentRenderer()
+                self._renderers[request_id] = renderer
+                context["on_event"] = renderer.handle_event
                 self.produce(context)
             else:
                 raise Exception("context is None")
 
+    def _shutdown(self):
+        """Restore terminal state and terminate the whole process.
+
+        startup() runs in a daemon sub-thread, so sys.exit() would only kill
+        this thread and leave the main process (and web/MCP/scheduler threads)
+        alive, holding the terminal in a half-occupied state -> laggy input.
+        We reset any leftover ANSI styling and hard-exit the process instead.
+        """
+        # Restore TTY attributes and reset any leftover ANSI styling
+        # (e.g. interrupted mid-stream output) before terminating.
+        if self._restore_terminal:
+            self._restore_terminal()
+        elif _Style.enabled:
+            sys.stdout.write(_Style.RESET)
+        sys.stdout.write("\nExiting...\n")
+        sys.stdout.flush()
+        # Hard-exit the entire process from a daemon thread.
+        os._exit(0)
+
     def get_input(self):
         """
         Multi-line input function

channel/web/chat.html

@@ -47,11 +47,30 @@
          This runs synchronously in <head> so the correct class is on <html>
          before any CSS or body rendering occurs. -->
     <script>
+    // Map an arbitrary locale string (zh-CN, en-US, fr ...) to 'zh' / 'en',
+    // or '' when unrecognized so callers can fall through to the next source.
+    window.__cowNormalizeLang__ = function(raw) {
+        if (!raw) return '';
+        var v = String(raw).trim().toLowerCase();
+        if (v === 'auto') return '';
+        if (v.indexOf('zh') === 0) return 'zh';
+        if (v.indexOf('en') === 0) return 'en';
+        return '';
+    };
+    // Resolve the console language by priority:
+    //   user choice (localStorage) -> backend-detected -> browser -> 'zh'.
+    window.__cowResolveLang__ = function() {
+        return window.__cowNormalizeLang__(localStorage.getItem('cow_lang'))
+            || window.__cowNormalizeLang__(window.__COW_DEFAULT_LANG__)
+            || window.__cowNormalizeLang__(navigator.language || (navigator.languages && navigator.languages[0]))
+            || 'zh';
+    };
     (function() {
+        // Backend-resolved default language (from cow_lang config / auto-detect).
+        window.__COW_DEFAULT_LANG__ = '{{COW_DEFAULT_LANG}}';
         var theme = localStorage.getItem('cow_theme') || 'dark';
         if (theme === 'dark') document.documentElement.classList.add('dark');
-        var lang = localStorage.getItem('cow_lang') || 'zh';
-        document.documentElement.setAttribute('lang', lang);
+        document.documentElement.setAttribute('lang', window.__cowResolveLang__());
     })();
     </script>
 </head>
@@ -640,6 +659,31 @@
                                     </div>
                                 </div>
 
+                                <!-- Language Config Card -->
+                                <div class="bg-white dark:bg-[#1A1A1A] rounded-xl border border-slate-200 dark:border-white/10 p-6">
+                                    <div class="flex items-center gap-3 mb-5">
+                                        <div class="w-9 h-9 rounded-lg bg-sky-50 dark:bg-sky-900/30 flex items-center justify-center">
+                                            <i class="fas fa-language text-sky-500 text-sm"></i>
+                                        </div>
+                                        <h3 class="font-semibold text-slate-800 dark:text-slate-100" data-i18n="config_language">语言</h3>
+                                    </div>
+                                    <div class="space-y-4">
+                                        <div>
+                                            <label class="flex items-center gap-1.5 text-sm font-medium text-slate-600 dark:text-slate-400 mb-1.5">
+                                                <span data-i18n="config_language">语言</span>
+                                                <span class="cfg-tip" data-tip-key="config_language_hint"><i class="fas fa-circle-question"></i></span>
+                                            </label>
+                                            <div id="cfg-lang-select" class="cfg-dropdown" tabindex="0">
+                                                <div class="cfg-dropdown-selected">
+                                                    <span class="cfg-dropdown-text">--</span>
+                                                    <i class="fas fa-chevron-down cfg-dropdown-arrow"></i>
+                                                </div>
+                                                <div class="cfg-dropdown-menu"></div>
+                                            </div>
+                                        </div>
+                                    </div>
+                                </div>
+
                             </div>
                         </div>
                     </div>

channel/web/static/js/console.js

@@ -91,9 +91,31 @@ const I18N = {
         example_knowledge_title: '知识库', example_knowledge_text: '查看知识库当前文档情况',
         example_skill_title: '技能系统', example_skill_text: '查看所有支持的工具和技能',
         example_web_title: '指令中心', example_web_text: '查看全部命令',
+        slash_help: '显示命令帮助',
+        slash_status: '查看运行状态',
+        slash_context: '查看对话上下文',
+        slash_context_clear: '清除对话上下文',
+        slash_skill_list: '查看已安装技能',
+        slash_skill_list_remote: '浏览技能广场',
+        slash_skill_search: '搜索技能',
+        slash_skill_install: '安装技能 (名称或 GitHub URL)',
+        slash_skill_uninstall: '卸载技能',
+        slash_skill_info: '查看技能详情',
+        slash_skill_enable: '启用技能',
+        slash_skill_disable: '禁用技能',
+        slash_memory_dream: '手动触发记忆蒸馏 (可指定天数, 默认3)',
+        slash_knowledge: '查看知识库统计',
+        slash_knowledge_list: '查看知识库文件树',
+        slash_knowledge_on: '开启知识库',
+        slash_knowledge_off: '关闭知识库',
+        slash_config: '查看当前配置',
+        slash_cancel: '中止当前正在运行的 Agent 任务',
+        slash_logs: '查看最近日志',
+        slash_version: '查看版本',
         input_placeholder: '输入消息，或输入 / 使用指令',
         config_title: '配置管理', config_desc: '管理模型和 Agent 配置',
         config_model: '模型配置', config_agent: 'Agent 配置',
+        config_language: '语言', config_language_hint: '界面展示、命令文案、系统提示词等使用的语言（与右上角切换同步）',
         config_model_advanced: '高级配置',
         config_channel: '通道配置',
         config_agent_enabled: 'Agent 模式',
@@ -265,9 +287,31 @@ const I18N = {
         example_knowledge_title: 'Knowledge', example_knowledge_text: 'Show me the current knowledge base',
         example_skill_title: 'Skills', example_skill_text: 'Show current tools and skills',
         example_web_title: 'Commands', example_web_text: 'Show all commands',
+        slash_help: 'Show this help',
+        slash_status: 'Show running status',
+        slash_context: 'Show conversation context',
+        slash_context_clear: 'Clear conversation context',
+        slash_skill_list: 'List installed skills',
+        slash_skill_list_remote: 'Browse Skill Hub',
+        slash_skill_search: 'Search skills',
+        slash_skill_install: 'Install a skill (name or GitHub URL)',
+        slash_skill_uninstall: 'Uninstall a skill',
+        slash_skill_info: 'Show skill details',
+        slash_skill_enable: 'Enable a skill',
+        slash_skill_disable: 'Disable a skill',
+        slash_memory_dream: 'Trigger memory distillation (optional days, default 3)',
+        slash_knowledge: 'Show knowledge base stats',
+        slash_knowledge_list: 'Show knowledge base file tree',
+        slash_knowledge_on: 'Enable knowledge base',
+        slash_knowledge_off: 'Disable knowledge base',
+        slash_config: 'Show current config',
+        slash_cancel: 'Abort the running Agent task',
+        slash_logs: 'Show recent logs',
+        slash_version: 'Show version',
         input_placeholder: 'Type a message, or press / for commands',
         config_title: 'Configuration', config_desc: 'Manage model and agent settings',
         config_model: 'Model Configuration', config_agent: 'Agent Configuration',
+        config_language: 'Language', config_language_hint: 'Language for the UI, command text, system prompts and more (synced with the top-right switch)',
         config_model_advanced: 'Advanced',
         config_channel: 'Channel Configuration',
         config_agent_enabled: 'Agent Mode',
@@ -361,7 +405,25 @@ const I18N = {
     }
 };
 
-let currentLang = localStorage.getItem('cow_lang') || 'zh';
+// Resolve language by priority: user choice (localStorage) -> backend-detected
+// (cow_lang) -> browser language -> 'zh'. Shares __cowResolveLang__ defined in
+// chat.html; falls back to a local resolver if loaded standalone.
+let currentLang = (typeof window.__cowResolveLang__ === 'function')
+    ? window.__cowResolveLang__()
+    : (function () {
+        const norm = (raw) => {
+            if (!raw) return '';
+            const v = String(raw).trim().toLowerCase();
+            if (v === 'auto') return '';
+            if (v.indexOf('zh') === 0) return 'zh';
+            if (v.indexOf('en') === 0) return 'en';
+            return '';
+        };
+        return norm(localStorage.getItem('cow_lang'))
+            || norm(window.__COW_DEFAULT_LANG__)
+            || norm(navigator.language)
+            || 'zh';
+    })();
 
 function t(key) {
     return (I18N[currentLang] && I18N[currentLang][key]) || (I18N.en[key]) || key;
@@ -394,14 +456,60 @@ function applyI18n() {
     if (langLabel) langLabel.textContent = currentLang === 'zh' ? '中文' : 'EN';
 }
 
-function toggleLanguage() {
-    currentLang = currentLang === 'zh' ? 'en' : 'zh';
+// Single entry point for switching language. Updates the in-memory language,
+// persists the user choice locally, re-renders the UI, and binds the choice to
+// the backend `cow_lang` config so logs / agent replies / CLI follow suit.
+function setLanguage(lang) {
+    const next = (lang === 'en') ? 'en' : 'zh';
+    if (next === currentLang) {
+        // Still persist + sync in case storage/backend drifted from the UI.
+        syncLanguageToBackend(next);
+        return;
+    }
+    currentLang = next;
     localStorage.setItem('cow_lang', currentLang);
     applyI18n();
     _applyInputTooltips();
     // Re-render views whose DOM is built in JS (data-i18n alone does not
     // cover strings interpolated via t() into innerHTML).
     try { rerenderDynamicViews(); } catch (e) {}
+    // Keep the language switch button and config selector visually in sync.
+    try { updateLangControls(); } catch (e) {}
+    syncLanguageToBackend(currentLang);
+}
+
+// Persist the language to the backend `cow_lang` config (best-effort; the UI
+// has already switched locally, so a network failure is non-blocking).
+function syncLanguageToBackend(lang) {
+    try {
+        fetch('/config', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ updates: { cow_lang: lang } })
+        }).catch(() => {});
+    } catch (e) {}
+}
+
+// Reflect the current language on both the top-right toggle and the config
+// selector (if present), so the two entry points stay synchronized.
+function updateLangControls() {
+    const langLabel = document.getElementById('lang-label');
+    if (langLabel) langLabel.textContent = currentLang === 'zh' ? '中文' : 'EN';
+    // The config language picker is the custom .cfg-dropdown component. Only
+    // sync it once it has been initialized (i.e. the config panel was opened).
+    const sel = document.getElementById('cfg-lang-select');
+    if (sel && sel._ddValue !== undefined && sel._ddValue !== currentLang) {
+        sel._ddValue = currentLang;
+        const textEl = sel.querySelector('.cfg-dropdown-text');
+        if (textEl) textEl.textContent = currentLang === 'zh' ? '中文' : 'English';
+        sel.querySelectorAll('.cfg-dropdown-item').forEach(i => {
+            i.classList.toggle('active', i.dataset.value === currentLang);
+        });
+    }
+}
+
+function toggleLanguage() {
+    setLanguage(currentLang === 'zh' ? 'en' : 'zh');
 }
 
 // Refresh JS-rendered views after a language switch. Each branch uses the
@@ -1298,28 +1406,30 @@ chatInput.addEventListener('compositionstart', () => { isComposing = true; });
 chatInput.addEventListener('compositionend', () => { setTimeout(() => { isComposing = false; }, 100); });
 
 // ── Slash Command Menu ───────────────────────────────────────
+// desc holds an i18n key, resolved via t() at render time so the menu follows
+// the current UI language.
 const SLASH_COMMANDS = [
-    { cmd: '/help',                desc: '显示命令帮助' },
-    { cmd: '/status',              desc: '查看运行状态' },
-    { cmd: '/context',             desc: '查看对话上下文' },
-    { cmd: '/context clear',       desc: '清除对话上下文' },
-    { cmd: '/skill list',          desc: '查看已安装技能' },
-    { cmd: '/skill list --remote', desc: '浏览技能广场' },
-    { cmd: '/skill search ',       desc: '搜索技能' },
-    { cmd: '/skill install ',      desc: '安装技能 (名称或 GitHub URL)' },
-    { cmd: '/skill uninstall ',    desc: '卸载技能' },
-    { cmd: '/skill info ',         desc: '查看技能详情' },
-    { cmd: '/skill enable ',       desc: '启用技能' },
-    { cmd: '/skill disable ',      desc: '禁用技能' },
-    { cmd: '/memory dream ',        desc: '手动触发记忆蒸馏 (可指定天数, 默认3)' },
-    { cmd: '/knowledge',            desc: '查看知识库统计' },
-    { cmd: '/knowledge list',      desc: '查看知识库文件树' },
-    { cmd: '/knowledge on',        desc: '开启知识库' },
-    { cmd: '/knowledge off',       desc: '关闭知识库' },
-    { cmd: '/config',              desc: '查看当前配置' },
-    { cmd: '/cancel',              desc: '中止当前正在运行的 Agent 任务' },
-    { cmd: '/logs',                desc: '查看最近日志' },
-    { cmd: '/version',             desc: '查看版本' },
+    { cmd: '/help',                desc: 'slash_help' },
+    { cmd: '/status',              desc: 'slash_status' },
+    { cmd: '/context',             desc: 'slash_context' },
+    { cmd: '/context clear',       desc: 'slash_context_clear' },
+    { cmd: '/skill list',          desc: 'slash_skill_list' },
+    { cmd: '/skill list --remote', desc: 'slash_skill_list_remote' },
+    { cmd: '/skill search ',       desc: 'slash_skill_search' },
+    { cmd: '/skill install ',      desc: 'slash_skill_install' },
+    { cmd: '/skill uninstall ',    desc: 'slash_skill_uninstall' },
+    { cmd: '/skill info ',         desc: 'slash_skill_info' },
+    { cmd: '/skill enable ',       desc: 'slash_skill_enable' },
+    { cmd: '/skill disable ',      desc: 'slash_skill_disable' },
+    { cmd: '/memory dream ',       desc: 'slash_memory_dream' },
+    { cmd: '/knowledge',           desc: 'slash_knowledge' },
+    { cmd: '/knowledge list',      desc: 'slash_knowledge_list' },
+    { cmd: '/knowledge on',        desc: 'slash_knowledge_on' },
+    { cmd: '/knowledge off',       desc: 'slash_knowledge_off' },
+    { cmd: '/config',              desc: 'slash_config' },
+    { cmd: '/cancel',              desc: 'slash_cancel' },
+    { cmd: '/logs',                desc: 'slash_logs' },
+    { cmd: '/version',             desc: 'slash_version' },
 ];
 
 const slashMenu = document.getElementById('slash-menu');
@@ -1373,7 +1483,7 @@ function renderSlashItems() {
         slashFiltered.map((c, i) =>
             `<div class="slash-menu-item${i === slashActiveIdx ? ' active' : ''}" data-idx="${i}">` +
             `<span class="cmd">${escapeHtml(c.cmd)}</span>` +
-            `<span class="desc">${escapeHtml(c.desc)}</span></div>`
+            `<span class="desc">${escapeHtml(t(c.desc))}</span></div>`
         ).join('');
 
     const activeEl = slashMenu.querySelector('.slash-menu-item.active');
@@ -3296,6 +3406,18 @@ function initConfigView(data) {
     document.getElementById('cfg-max-steps').value = data.agent_max_steps || 20;
     document.getElementById('cfg-enable-thinking').checked = data.enable_thinking === true;
 
+    // Reflect the current UI language (already resolved, may include the user's
+    // local choice) on the selector so it stays in sync with the top-right toggle.
+    const langSel = document.getElementById('cfg-lang-select');
+    if (langSel) {
+        initDropdown(
+            langSel,
+            [{ value: 'zh', label: '中文' }, { value: 'en', label: 'English' }],
+            currentLang,
+            (val) => setLanguage(val)
+        );
+    }
+
     const pwdInput = document.getElementById('cfg-password');
     const maskedPwd = data.web_password_masked || '';
     pwdInput.value = maskedPwd;
@@ -3903,7 +4025,7 @@ const MODELS_CAPABILITY_DEFS = [
       iconChip: 'bg-blue-50 dark:bg-blue-900/30',        iconGlyph: 'text-blue-500' },
     { id: 'image',     icon: 'fa-image',            editable: true,  needsModel: true,  titleKey: 'models_capability_image',     descKey: 'models_capability_image_desc',
       iconChip: 'bg-blue-50 dark:bg-blue-900/30',        iconGlyph: 'text-blue-500' },
-    { id: 'asr',       icon: 'fa-microphone',       editable: true,  needsModel: false, titleKey: 'models_capability_asr',       descKey: 'models_capability_asr_desc',
+    { id: 'asr',       icon: 'fa-microphone',       editable: true,  needsModel: true,  titleKey: 'models_capability_asr',       descKey: 'models_capability_asr_desc',
       iconChip: 'bg-amber-50 dark:bg-amber-900/30',      iconGlyph: 'text-amber-500' },
     { id: 'tts',       icon: 'fa-volume-high',      editable: true,  needsModel: true,  titleKey: 'models_capability_tts',       descKey: 'models_capability_tts_desc',
       iconChip: 'bg-amber-50 dark:bg-amber-900/30',      iconGlyph: 'text-amber-500' },

channel/web/web_channel.py

@@ -21,6 +21,7 @@ from channel.chat_channel import ChatChannel, check_prefix
 from channel.chat_message import ChatMessage
 from collections import OrderedDict
 from common import const
+from common import i18n
 from common.log import logger
 from common.singleton import singleton
 from config import conf
@@ -98,7 +99,7 @@ def _require_auth():
 def _cancel_reply_text(cancelled: int, lang: str) -> str:
     en = lang.startswith("en")
     if cancelled > 0:
-        return "🛑 Cancelled." if en else "🛑 已中止"
+        return "🛑 Cancelled" if en else "🛑 已中止"
     return "Nothing to cancel." if en else "当前没有可中止的任务。"
 
 
@@ -477,7 +478,10 @@ class WebChannel(ChatChannel):
                         )
                         q.put({
                             "type": "done",
-                            "content": "(模型未返回任何内容，请重试或换一种方式描述你的需求)",
+                            "content": i18n.t(
+                                "(模型未返回任何内容，请重试或换一种方式描述你的需求)",
+                                "(The model returned no content. Please retry or rephrase your request.)",
+                            ),
                             "request_id": request_id,
                             "timestamp": time.time(),
                         })
@@ -805,13 +809,13 @@ class WebChannel(ChatChannel):
                     if not fpath:
                         continue
                     if ftype == "image":
-                        file_refs.append(f"[图片: {fpath}]")
+                        file_refs.append(f"[{i18n.t('图片', 'Image')}: {fpath}]")
                     elif ftype == "video":
-                        file_refs.append(f"[视频: {fpath}]")
+                        file_refs.append(f"[{i18n.t('视频', 'Video')}: {fpath}]")
                     elif ftype == "directory":
-                        file_refs.append(f"[目录: {fpath}]")
+                        file_refs.append(f"[{i18n.t('目录', 'Directory')}: {fpath}]")
                     else:
-                        file_refs.append(f"[文件: {fpath}]")
+                        file_refs.append(f"[{i18n.t('文件', 'File')}: {fpath}]")
                 if file_refs:
                     prompt = prompt + "\n" + "\n".join(file_refs)
                     logger.info(f"[WebChannel] Attached {len(file_refs)} file(s) to message")
@@ -952,7 +956,7 @@ class WebChannel(ChatChannel):
             if request_id and request_id in self.sse_queues:
                 self.sse_queues[request_id].put({
                     "type": "cancelled",
-                    "content": "Cancelled" if lang.startswith("en") else "已中止",
+                    "content": "🛑 Cancelled" if lang.startswith("en") else "🛑 已中止",
                     "request_id": request_id,
                     "timestamp": time.time(),
                 })
@@ -1008,7 +1012,10 @@ class WebChannel(ChatChannel):
         """Serve the chat HTML page."""
         file_path = os.path.join(os.path.dirname(__file__), 'chat.html')  # 使用绝对路径
         with open(file_path, 'r', encoding='utf-8') as f:
-            return f.read()
+            html = f.read()
+        # Inject the backend-resolved default language so the console can use
+        # it on first load (when the user has no saved cow_lang preference).
+        return html.replace("{{COW_DEFAULT_LANG}}", i18n.get_language())
 
     def startup(self):
         configured_host = conf().get("web_host", "")
@@ -1018,26 +1025,30 @@ class WebChannel(ChatChannel):
 
         self._cleanup_stale_voice_recordings()
 
-        # 打印可用渠道类型提示
+        # Print available channel types
         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. weixin           - 微信")
-        logger.info("[WebChannel]   2. web              - 网页")
-        logger.info("[WebChannel]   3. terminal         - 终端")
-        logger.info("[WebChannel]   4. feishu           - 飞书")
-        logger.info("[WebChannel]   5. dingtalk         - 钉钉")
-        logger.info("[WebChannel]   6. wecom_bot        - 企微智能机器人")
-        logger.info("[WebChannel]   7. wechatcom_app    - 企微自建应用")
-        logger.info("[WebChannel]   8. wechatmp         - 个人公众号")
-        logger.info("[WebChannel]   9. wechatmp_service - 企业公众号")
-        logger.info("[WebChannel] ✅ Web控制台已运行")
-        logger.info(f"[WebChannel] 🌐 本地访问: http://localhost:{port}")
+            "[WebChannel] Available channels (edit `channel_type` in config.json to switch, separate multiple with commas):")
+        logger.info("[WebChannel]   1. web              - Web")
+        logger.info("[WebChannel]   2. terminal         - Terminal")
+        logger.info("[WebChannel]   3. weixin           - WeChat")
+        logger.info("[WebChannel]   4. feishu           - Feishu")
+        logger.info("[WebChannel]   5. dingtalk         - DingTalk")
+        logger.info("[WebChannel]   6. wecom_bot        - WeCom Bot")
+        logger.info("[WebChannel]   7. wechatcom_app    - WeCom App")
+        logger.info("[WebChannel]   8. wechat_kf        - WeChat Customer Service")
+        logger.info("[WebChannel]   9. wechatmp         - WeChat Official Account")
+        logger.info("[WebChannel]  10. wechatmp_service - WeChat Official Account (Service)")
+        logger.info("[WebChannel]  11. telegram         - Telegram")
+        logger.info("[WebChannel]  12. slack            - Slack")
+        logger.info("[WebChannel]  13. discord          - Discord")
+        logger.info("[WebChannel] ✅ Web console is running")
+        logger.info(f"[WebChannel] 🌐 Local access: http://localhost:{port}")
         if is_public_bind:
-            logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port} (将YOUR_IP替换为服务器IP)")
+            logger.info(f"[WebChannel] 🌍 Server access: http://YOUR_IP:{port} (replace YOUR_IP with your server IP)")
             if not _is_password_enabled():
-                logger.info("[WebChannel] ⚠️  当前监听 0.0.0.0 且未设置 web_password，公网部署建议在 config.json 中配置访问密码")
+                logger.info("[WebChannel] ⚠️  Listening on 0.0.0.0 without web_password set; set an access password in config.json for public deployment")
         else:
-            logger.info(f"[WebChannel] 🔒 当前仅监听 {host}，仅本机可访问。如需公网访问，请将 web_host 改为 0.0.0.0 并配置 web_password 密码")
+            logger.info(f"[WebChannel] 🔒 Listening on {host} only (local access). For public access, set web_host to 0.0.0.0 and configure web_password")
 
         try:
             import webbrowser
@@ -1315,7 +1326,20 @@ class FileServeHandler:
             file_path = params.path
             if not file_path or not os.path.isabs(file_path):
                 raise web.notfound()
-            file_path = os.path.normpath(file_path)
+            # Resolve symlinks and confine access to the allowed root dirs,
+            # so this endpoint can't be abused to read arbitrary files (e.g. /etc/passwd, ~/.ssh).
+            # Defaults to the user home dir plus the agent workspace; set web_file_serve_root="/"
+            # to allow the whole filesystem.
+            file_path = os.path.realpath(file_path)
+            serve_root = conf().get("web_file_serve_root", "~") or "~"
+            allowed_roots = [
+                os.path.realpath(os.path.expanduser(serve_root)),
+                os.path.realpath(os.path.expanduser(conf().get("agent_workspace", "~/cow"))),
+            ]
+            if os.sep not in allowed_roots and not any(
+                os.path.commonpath([file_path, root]) == root for root in allowed_roots
+            ):
+                raise web.notfound()
             if not os.path.isfile(file_path):
                 raise web.notfound()
             content_type = mimetypes.guess_type(file_path)[0] or "application/octet-stream"
@@ -1371,6 +1395,8 @@ class ChatHandler:
         cache_bust = str(int(time.time()))
         html = html.replace('assets/js/console.js', f'assets/js/console.js?v={cache_bust}')
         html = html.replace('assets/css/console.css', f'assets/css/console.css?v={cache_bust}')
+        # Inject the backend-resolved default language for first-load fallback.
+        html = html.replace("{{COW_DEFAULT_LANG}}", i18n.get_language())
         return html
 
 
@@ -1378,12 +1404,12 @@ class ConfigHandler:
 
     _RECOMMENDED_MODELS = [
         const.DEEPSEEK_V4_FLASH, const.DEEPSEEK_V4_PRO, const.DEEPSEEK_CHAT, const.DEEPSEEK_REASONER,
-        const.MINIMAX_M2_7_HIGHSPEED, const.MINIMAX_M2_7, const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING,
-        const.CLAUDE_4_6_SONNET, const.CLAUDE_4_7_OPUS, const.CLAUDE_4_6_OPUS, const.CLAUDE_4_5_SONNET,
+        const.MINIMAX_M3, const.MINIMAX_M2_7_HIGHSPEED, const.MINIMAX_M2_7,
+        const.CLAUDE_4_8_OPUS, const.CLAUDE_4_7_OPUS, const.CLAUDE_4_6_SONNET, const.CLAUDE_4_6_OPUS, const.CLAUDE_4_5_SONNET,
         const.GEMINI_35_FLASH, const.GEMINI_31_FLASH_LITE_PRE, const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE,
         const.GPT_55, const.GPT_54, const.GPT_54_MINI, const.GPT_54_NANO, const.GPT_5, const.GPT_41, const.GPT_4o,
         const.GLM_5_1, const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7,
-        const.QWEN36_PLUS, const.QWEN37_MAX, const.QWEN35_PLUS, const.QWEN3_MAX,
+        const.QWEN37_PLUS, const.QWEN37_MAX, const.QWEN36_PLUS,
         const.DOUBAO_SEED_2_PRO, const.DOUBAO_SEED_2_CODE,
         const.KIMI_K2_6, const.KIMI_K2_5, const.KIMI_K2,
         const.ERNIE_5_1, const.ERNIE_5, const.ERNIE_X1_1, const.ERNIE_45_TURBO_128K, const.ERNIE_45_TURBO_32K,
@@ -1416,7 +1442,7 @@ class ConfigHandler:
             "api_base_key": None,
             "api_base_default": None,
             "api_base_placeholder": "",
-            "models": [const.MINIMAX_M2_7, const.MINIMAX_M2_7_HIGHSPEED, const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING],
+            "models": [const.MINIMAX_M3, const.MINIMAX_M2_7, const.MINIMAX_M2_7_HIGHSPEED],
         }),
         ("claudeAPI", {
             "label": "Claude",
@@ -1424,7 +1450,7 @@ class ConfigHandler:
             "api_base_key": "claude_api_base",
             "api_base_default": "https://api.anthropic.com/v1",
             "api_base_placeholder": _PLACEHOLDER_V1,
-            "models": [const.CLAUDE_4_6_SONNET, const.CLAUDE_4_7_OPUS, const.CLAUDE_4_6_OPUS, const.CLAUDE_4_5_SONNET],
+            "models": [const.CLAUDE_4_8_OPUS, const.CLAUDE_4_7_OPUS, const.CLAUDE_4_6_SONNET, const.CLAUDE_4_6_OPUS, const.CLAUDE_4_5_SONNET],
         }),
         ("gemini", {
             "label": "Gemini",
@@ -1456,7 +1482,7 @@ class ConfigHandler:
             "api_base_key": None,
             "api_base_default": None,
             "api_base_placeholder": "",
-            "models": [const.QWEN36_PLUS, const.QWEN37_MAX, const.QWEN35_PLUS, const.QWEN3_MAX],
+            "models": [const.QWEN37_PLUS, const.QWEN37_MAX, const.QWEN36_PLUS],
         }),
         ("doubao", {
             "label": {"zh": "豆包", "en": "Doubao"},
@@ -1509,6 +1535,7 @@ class ConfigHandler:
     ])
 
     EDITABLE_KEYS = {
+        "cow_lang",
         "model", "bot_type", "use_linkai",
         "open_ai_api_base", "deepseek_api_base", "qianfan_api_base", "claude_api_base", "gemini_api_base",
         "zhipu_ai_api_base", "moonshot_base_url", "ark_base_url", "custom_api_base", "mimo_api_base",
@@ -1617,6 +1644,15 @@ class ConfigHandler:
 
             logger.info(f"[WebChannel] Config updated: {list(applied.keys())}")
 
+            # Apply a language change immediately so backend logs, agent
+            # replies and CLI output switch without a restart.
+            if "cow_lang" in applied:
+                try:
+                    i18n.resolve_language(applied["cow_lang"])
+                    logger.info(f"[WebChannel] Language switched to: {i18n.get_language()}")
+                except Exception as lang_err:
+                    logger.warning(f"[WebChannel] Failed to apply language: {lang_err}")
+
             # Reset Bridge so that bot routing reflects the new config.
             # Without this, Bridge keeps its cached bot instance (e.g. LinkAIBot)
             # even after the user switches bot_type / use_linkai / model in UI.
@@ -1684,6 +1720,28 @@ class ModelsHandler:
         ],
     }
 
+    # ASR engine catalog per provider. The first entry of each list is the
+    # runtime default (mirrors DEFAULT_ASR_MODEL in voice/*). Users can still
+    # pick "custom" in the UI to send any other model id.
+    _ASR_PROVIDER_MODELS = {
+        "openai": [
+            {"value": "gpt-4o-mini-transcribe", "hint": "默认 · 速度快"},
+            {"value": "gpt-4o-transcribe",      "hint": "更高准确率"},
+            {"value": "whisper-1",              "hint": "经典 Whisper"},
+        ],
+        "dashscope": [
+            {"value": "qwen3-asr-flash", "hint": "覆盖普通话、方言与主流外语"},
+        ],
+        "zhipu": [
+            {"value": "glm-asr-2512", "hint": "智谱语音识别"},
+        ],
+        # LinkAI gateway pins whisper-1 for ASR and ignores any other id,
+        # so expose only that to avoid misleading the user.
+        "linkai": [
+            {"value": "whisper-1", "hint": "网关固定使用"},
+        ],
+    }
+
     # Per-provider voice timbres. Entries can be a bare code string
     # (label = code) or {value, hint?} when a friendly secondary label
     # helps recognition. We keep `value` as the raw API code so power
@@ -1928,8 +1986,8 @@ class ModelsHandler:
         ],
         "doubao":    [const.DOUBAO_SEED_2_PRO],
         "moonshot":  [const.KIMI_K2_6],
-        "dashscope": [const.QWEN36_PLUS, const.QWEN35_PLUS, const.QWEN3_MAX],
-        "claudeAPI": [const.CLAUDE_4_6_SONNET, const.CLAUDE_4_7_OPUS, const.CLAUDE_4_6_OPUS],
+        "dashscope": [const.QWEN37_PLUS, const.QWEN36_PLUS],
+        "claudeAPI": [const.CLAUDE_4_8_OPUS, const.CLAUDE_4_7_OPUS, const.CLAUDE_4_6_SONNET, const.CLAUDE_4_6_OPUS],
         "gemini":    [const.GEMINI_35_FLASH, const.GEMINI_31_FLASH_LITE_PRE, const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE],
         "qianfan":   [const.ERNIE_45_TURBO_VL],
         # Zhipu's bot hard-codes the call to glm-5v-turbo regardless of what
@@ -1949,7 +2007,7 @@ class ModelsHandler:
         "linkai":    [
             const.GPT_41_MINI,
             const.GPT_54_MINI,
-            const.QWEN36_PLUS,
+            const.QWEN37_PLUS,
             const.DOUBAO_SEED_2_PRO,
             const.KIMI_K2_6,
             const.CLAUDE_4_6_SONNET,
@@ -2066,7 +2124,7 @@ class ModelsHandler:
     _VISION_AUTO_ORDER = [
         ("moonshot",  "moonshot_api_key",  const.KIMI_K2_6),
         ("doubao",    "ark_api_key",       const.DOUBAO_SEED_2_PRO),
-        ("dashscope", "dashscope_api_key", const.QWEN36_PLUS),
+        ("dashscope", "dashscope_api_key", const.QWEN37_PLUS),
         ("claudeAPI", "claude_api_key",    const.CLAUDE_4_6_SONNET),
         ("gemini",    "gemini_api_key",    const.GEMINI_35_FLASH),
         ("qianfan",   "qianfan_api_key",   const.ERNIE_45_TURBO_VL),
@@ -2204,8 +2262,9 @@ class ModelsHandler:
             "editable": True,
             "current_provider": explicit,
             "suggested_provider": suggested,
-            "current_model": "",
+            "current_model": (local_config.get("voice_to_text_model") or "") if explicit else "",
             "providers": cls._ASR_PROVIDERS,
+            "provider_models": cls._ASR_PROVIDER_MODELS,
         }
 
     @classmethod
@@ -2577,7 +2636,7 @@ class ModelsHandler:
         if capability == "vision":
             return self._set_vision(provider_id, model)
         if capability == "asr":
-            return self._set_simple("voice_to_text", provider_id)
+            return self._set_asr(provider_id, model)
         if capability == "tts":
             return self._set_tts(provider_id, model, (data.get("voice") or "").strip())
         if capability == "embedding":
@@ -2737,6 +2796,30 @@ class ModelsHandler:
             self._refresh_voice_routing()
         return json.dumps({"status": "success", key: value})
 
+    def _set_asr(self, provider_id: str, model: str) -> str:
+        local_config = conf()
+        file_cfg = self._read_file_config()
+        local_config["voice_to_text"] = provider_id
+        file_cfg["voice_to_text"] = provider_id
+        # Only overwrite the model when one is supplied. An empty model means
+        # "keep whatever is configured" so switching provider from the console
+        # never wipes a user's hand-set voice_to_text_model (runtime falls back
+        # to the engine default via `or DEFAULT_ASR_MODEL` regardless).
+        if model:
+            local_config["voice_to_text_model"] = model
+            file_cfg["voice_to_text_model"] = model
+        self._write_file_config(file_cfg)
+        logger.info(
+            f"[ModelsHandler] asr updated: provider={provider_id!r} "
+            f"model={model!r}"
+        )
+        self._refresh_voice_routing()
+        return json.dumps({
+            "status": "success",
+            "provider": provider_id,
+            "model": local_config.get("voice_to_text_model", ""),
+        })
+
     def _set_tts(self, provider_id: str, model: str, voice: str = "") -> str:
         local_config = conf()
         file_cfg = self._read_file_config()
@@ -2897,6 +2980,18 @@ class ChannelsHandler:
                 {"key": "wechatcomapp_port", "label": "Port", "type": "number", "default": 9898},
             ],
         }),
+        ("wechat_kf", {
+            "label": {"zh": "微信客服", "en": "WeChat Customer Service"},
+            "icon": "fa-headset",
+            "color": "emerald",
+            "fields": [
+                {"key": "wechat_kf_corp_id", "label": "Corp ID", "type": "text"},
+                {"key": "wechat_kf_secret", "label": "Secret", "type": "secret"},
+                {"key": "wechat_kf_token", "label": "Token", "type": "secret"},
+                {"key": "wechat_kf_aes_key", "label": "AES Key", "type": "secret"},
+                {"key": "wechat_kf_port", "label": "Port", "type": "number", "default": 9888},
+            ],
+        }),
         ("wechatmp", {
             "label": {"zh": "公众号", "en": "WeChat MP"},
             "icon": "fa-comment-dots",
@@ -2909,6 +3004,31 @@ class ChannelsHandler:
                 {"key": "wechatmp_port", "label": "Port", "type": "number", "default": 8080},
             ],
         }),
+        ("telegram", {
+            "label": {"zh": "Telegram", "en": "Telegram"},
+            "icon": "fa-paper-plane",
+            "color": "sky",
+            "fields": [
+                {"key": "telegram_token", "label": "Bot Token", "type": "secret"},
+            ],
+        }),
+        ("slack", {
+            "label": {"zh": "Slack", "en": "Slack"},
+            "icon": "fa-hashtag",
+            "color": "purple",
+            "fields": [
+                {"key": "slack_bot_token", "label": "Bot Token (xoxb-)", "type": "secret"},
+                {"key": "slack_app_token", "label": "App Token (xapp-)", "type": "secret"},
+            ],
+        }),
+        ("discord", {
+            "label": {"zh": "Discord", "en": "Discord"},
+            "icon": "fa-discord",
+            "color": "indigo",
+            "fields": [
+                {"key": "discord_token", "label": "Bot Token", "type": "secret"},
+            ],
+        }),
     ])
 
     @staticmethod

channel/wechat_kf/README.md

@@ -0,0 +1,115 @@
+# 微信客服（WeChat Customer Service）通道
+
+> 与 `channel/wechatcom/`（企微自建应用）是两个**独立的 CoW 通道**：
+>
+> - 自建应用：**面向企业内部成员**（员工通过企业微信 App 与机器人对话）。
+> - 微信客服：**面向外部微信用户**（普通微信用户通过链接/二维码进入对话）。
+>
+> 但底层都基于"企微自建应用"——本通道是**通过把一个企微自建应用绑定到微信客服账号**来实现 AI 接管对外咨询，详见 [LinkAI 微信客服接入文档](https://docs.link-ai.tech/platform/link-app/wechat-customer-service)。
+
+## 一、接入流程概览
+
+```
+┌─────────────────────┐    ┌─────────────────────┐    ┌──────────────────┐
+│ 1. 企业微信后台      │ →  │ 2. CoW 配置回调      │ →  │ 3. 绑定微信客服   │
+│   创建一个自建应用   │    │   端口 9888          │    │   账号           │
+└─────────────────────┘    └─────────────────────┘    └──────────────────┘
+                                                              ↓
+                                                   外部微信用户通过
+                                                   链接/二维码 →
+                                                   消息 → CoW Bot
+```
+
+> **重要**：建议**单独再创建一个企微自建应用**用于微信客服，**不要复用**已经接入员工内部使用的那个 `wechatcom_app` 应用，否则两个通道会争抢同一个回调地址。
+
+## 二、企业微信后台配置
+
+### 1. 创建企微自建应用
+
+进入 企业微信管理后台 → **应用管理** → **创建应用**。
+
+### 2. 收集字段
+
+| 字段 | 来源 | 对应 CoW 配置项 |
+|---|---|---|
+| 企业ID（CorpId） | 「我的企业」最下方 | `wechat_kf_corp_id` |
+| Secret | 进入应用详情 → 点击「查看」（会推送到管理员手机端，在手机上查看） | `wechat_kf_secret` |
+| Token | 应用「接收消息 → 设置API接收」 | `wechat_kf_token` |
+| EncodingAESKey | 应用「接收消息 → 设置API接收」 | `wechat_kf_aes_key` |
+
+> AgentId 在本通道**不需要**（消息发送走的是 `cgi-bin/kf/send_msg`，不依赖 agent_id）。
+
+### 3. 配置回调地址 + 可信 IP
+
+在应用「**接收消息 → 设置API接收**」里填：
+
+- URL：`http://<your-host>:9888/wxkf/`（公网必须可达）
+- Token / EncodingAESKey：与下方 `config.json` 一致
+
+回到应用详情页，把服务器公网 IP 填入「**企业可信IP**」。
+
+### 4. 绑定微信客服账号
+
+进入 企业微信后台 → **微信客服** → 创建客服账号 → **将该账号绑定到上一步创建的企微自建应用**。
+
+绑定完成后，进入 **微信客服 → 微信客服账号详情** 页面，在「**接入链接**」一栏：
+
+- 「复制链接」可拿到形如 `https://work.weixin.qq.com/kfid/kfcd83e5896b9ba07be` 的访问链接
+- 「生成二维码」可拿到对应二维码
+
+把链接或二维码推给微信客户使用即可。
+
+## 三、CoW 配置（`config.json`）
+
+```json
+{
+  "channel_type": "wechat_kf",
+
+  "wechat_kf_corp_id": "ww1234567890abcdef",
+  "wechat_kf_secret": "<企微应用的 Secret>",
+  "wechat_kf_token": "<接收消息 Token>",
+  "wechat_kf_aes_key": "<EncodingAESKey>",
+  "wechat_kf_port": 9888
+}
+```
+
+| 字段 | 说明 |
+|---|---|
+| `wechat_kf_corp_id` | 企业 ID |
+| `wechat_kf_secret` | **绑定到微信客服**的那个企微自建应用的 Secret |
+| `wechat_kf_token` | 该应用「接收消息」配置的 Token |
+| `wechat_kf_aes_key` | 该应用「接收消息」配置的 EncodingAESKey |
+| `wechat_kf_port` | 监听端口，默认 `9888` |
+
+也支持环境变量：`WECHAT_KF_CORP_ID` / `WECHAT_KF_SECRET` / `WECHAT_KF_TOKEN` / `WECHAT_KF_AES_KEY`。
+
+## 四、运行
+
+```bash
+python app.py
+```
+
+启动后日志里会看到：
+
+```
+[wechat_kf] WeCom customer-service channel started
+[wechat_kf] Listening on http://0.0.0.0:9888/wxkf/
+```
+
+回到企微后台「设置API接收」点击保存——会触发 `GET /wxkf/?...&echostr=...`，CoW 通过 `crypto.check_signature` 校验后返回明文 `echostr`，验证成功。
+
+## 五、支持的回复类型
+
+| ReplyType | 是否支持 | 备注 |
+|---|---|---|
+| `TEXT` / `INFO` / `ERROR` | ✅ | 自动按 2048 字节切片分段发送 |
+| `IMAGE`（本地） / `IMAGE_URL`（网络） | ✅ | 大图自动压缩到 10MB 以内 |
+| `VOICE` | ✅ | 转 amr 后发送，>60s 自动切片 |
+| `VIDEO_URL` | ✅ | 通过临时素材接口上传 |
+| `FILE` | ✅ | |
+
+## 六、参考文档
+
+- [LinkAI 微信客服接入文档](https://docs.link-ai.tech/platform/link-app/wechat-customer-service)
+- [企业微信开放接口 - 微信客服 - 接收消息](https://developer.work.weixin.qq.com/document/path/94670)
+- [企业微信开放接口 - 微信客服 - 发送消息](https://developer.work.weixin.qq.com/document/path/95122)

channel/wechat_kf/wechat_kf_channel.py

@@ -0,0 +1,603 @@
+# -*- coding=utf-8 -*-
+"""
+WeChat Customer Service (微信客服) channel for CoW.
+
+Differences from `channel/wechatcom/` (企微自建应用):
+    1. Audience: external WeChat users (not internal members).
+    2. Receiver fields: `external_userid` + `open_kfid` instead of a single
+       member `userid`.
+    3. Inbound flow: callback only delivers an event token, the actual
+       message bodies must be pulled via `cgi-bin/kf/sync_msg` with a
+       persistent cursor. See `wechat_kf_cursor_store.py`.
+    4. Outbound flow: messages are sent via `cgi-bin/kf/send_msg` (each
+       request must specify both `touser` and `open_kfid`); wechatpy has
+       no native helper, so we call the HTTP endpoint directly.
+"""
+import io
+import json
+import os
+import threading
+import time
+import xml.etree.ElementTree as ET
+from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
+from typing import Optional
+
+import requests
+import web
+from wechatpy.enterprise import WeChatClient
+from wechatpy.enterprise.crypto import WeChatCrypto
+from wechatpy.enterprise.exceptions import InvalidCorpIdException
+from wechatpy.exceptions import InvalidSignatureException, WeChatClientException
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel
+from channel.file_cache import get_file_cache
+from channel.wechat_kf.wechat_kf_cursor_store import CursorStore
+from channel.wechat_kf.wechat_kf_message import WechatKfMessage
+from common.log import logger
+from common.singleton import singleton
+from common.utils import (
+    compress_imgfile,
+    fsize,
+    remove_markdown_symbol,
+    split_string_by_utf8_length,
+)
+from config import conf
+
+try:
+    from voice.audio_convert import any_to_amr, split_audio
+except ImportError as e:  # voice features optional
+    logger.debug(
+        "[wechat_kf] import voice.audio_convert failed, voice will be disabled: {}".format(e)
+    )
+
+MAX_UTF8_LEN = 2048
+KF_API_BASE = "https://qyapi.weixin.qq.com/cgi-bin/kf"
+SYNC_MSG_LIMIT = 1000
+
+
+@singleton
+class WechatKfChannel(ChatChannel):
+    NOT_SUPPORT_REPLYTYPE = []
+
+    def __init__(self):
+        super().__init__()
+        self.corp_id = conf().get("wechat_kf_corp_id")
+        self.secret = conf().get("wechat_kf_secret")
+        self.token = conf().get("wechat_kf_token")
+        self.aes_key = conf().get("wechat_kf_aes_key")
+        self._http_server = None
+        logger.info(
+            "[wechat_kf] Initializing WeCom customer-service channel, corp_id: {}".format(
+                self.corp_id
+            )
+        )
+        self.crypto = WeChatCrypto(self.token, self.aes_key, self.corp_id)
+        # Use the stock wechatpy WeChatClient so that the access_token is
+        # cached and only refreshed when actually expired (~2h). The local
+        # `WechatComAppClient` subclass has a broken background refresh
+        # loop that re-fetches every 60s and a `fetch_access_token()`
+        # override that may return a dict instead of a string, which
+        # corrupts URLs and triggers errcode 40014.
+        self.client = WeChatClient(self.corp_id, self.secret)
+
+        # Persist sync_msg cursor under the user's home dir by default,
+        # so it survives `tmp/` cleanups and cwd changes across restarts.
+        cursor_path = os.path.expanduser(
+            conf().get("wechat_kf_cursor_path") or "~/.wechat_kf_cursors.json"
+        )
+        self.cursor_store = CursorStore(cursor_path)
+
+        # WeCom requires the callback HTTP response to return within ~5s,
+        # otherwise it retries the same notification. sync_msg pulling
+        # can easily exceed that, so we dispatch it to a background pool
+        # and let `Query.POST` reply success immediately.
+        self._callback_executor = ThreadPoolExecutor(
+            max_workers=4, thread_name_prefix="wxkf-cb"
+        )
+        # Per-open_kfid lock: serialize sync_msg for the same kf account
+        # so that callback retries (or rapid-fire events) don't race on
+        # the same cursor and produce duplicate replies.
+        self._kf_locks: dict = defaultdict(threading.Lock)
+        self._kf_locks_guard = threading.Lock()
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+    def startup(self):
+        urls = ("/wxkf/?", "channel.wechat_kf.wechat_kf_channel.Query")
+        app = web.application(urls, globals(), autoreload=False)
+        port = conf().get("wechat_kf_port", 9888)
+        logger.info("[wechat_kf] WeCom customer-service channel started")
+        logger.info("[wechat_kf] Listening on http://0.0.0.0:{}/wxkf/".format(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("[wechat_kf] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[wechat_kf] Error stopping HTTP server: {e}")
+            self._http_server = None
+        try:
+            self._callback_executor.shutdown(wait=False)
+        except Exception as e:
+            logger.warning(f"[wechat_kf] Error shutting down callback executor: {e}")
+
+    # ------------------------------------------------------------------
+    # Outbound — implementing the abstract `send` contract
+    # ------------------------------------------------------------------
+    def send(self, reply: Reply, context: Context):
+        receiver = context["receiver"]
+        msg = context.kwargs.get("msg")
+        external_userid = context.get("external_userid") or (msg.external_userid if msg else None)
+        open_kfid = context.get("open_kfid") or (msg.open_kfid if msg else None)
+
+        if not external_userid or not open_kfid:
+            logger.error(
+                "[wechat_kf] missing external_userid or open_kfid, cannot send: "
+                f"external_userid={external_userid}, open_kfid={open_kfid}"
+            )
+            return
+
+        if reply.type in [ReplyType.TEXT, ReplyType.ERROR, ReplyType.INFO]:
+            reply_text = remove_markdown_symbol(reply.content)
+            texts = split_string_by_utf8_length(reply_text, MAX_UTF8_LEN)
+            if len(texts) > 1:
+                logger.info(
+                    "[wechat_kf] text too long, split into {} parts".format(len(texts))
+                )
+            for i, text in enumerate(texts):
+                self._send_text(external_userid, open_kfid, text)
+                if i != len(texts) - 1:
+                    time.sleep(0.5)
+            logger.info("[wechat_kf] Do send text to {}: {}".format(receiver, reply_text))
+
+        elif reply.type == ReplyType.VOICE:
+            file_path = reply.content
+            try:
+                amr_file = os.path.splitext(file_path)[0] + ".amr"
+                any_to_amr(file_path, amr_file)
+                duration, files = split_audio(amr_file, 60 * 1000)
+                if len(files) > 1:
+                    logger.info(
+                        "[wechat_kf] voice too long {}s > 60s, split into {} parts".format(
+                            duration / 1000.0, len(files)
+                        )
+                    )
+                media_ids = []
+                for path in files:
+                    with open(path, "rb") as f:
+                        response = self.client.media.upload("voice", f)
+                    logger.debug("[wechat_kf] upload voice response: {}".format(response))
+                    media_ids.append(response["media_id"])
+            except ImportError as e:
+                logger.error("[wechat_kf] voice conversion failed: {}".format(e))
+                logger.error("[wechat_kf] please install pydub: pip install pydub")
+                return
+            except WeChatClientException as e:
+                logger.error("[wechat_kf] upload voice failed: {}".format(e))
+                return
+
+            try:
+                os.remove(file_path)
+                if amr_file != file_path:
+                    os.remove(amr_file)
+            except Exception:
+                pass
+
+            for media_id in media_ids:
+                self._send_voice(external_userid, open_kfid, media_id)
+                time.sleep(1)
+            logger.info("[wechat_kf] sendVoice={}, receiver={}".format(reply.content, receiver))
+
+        elif reply.type == ReplyType.IMAGE_URL:
+            img_url = reply.content
+            pic_res = requests.get(img_url, stream=True)
+            image_storage = io.BytesIO()
+            for block in pic_res.iter_content(1024):
+                image_storage.write(block)
+            sz = fsize(image_storage)
+            if sz >= 10 * 1024 * 1024:
+                logger.info("[wechat_kf] image too large, compressing, sz={}".format(sz))
+                image_storage = compress_imgfile(image_storage, 10 * 1024 * 1024 - 1)
+            image_storage.seek(0)
+            try:
+                response = self.client.media.upload("image", image_storage)
+            except WeChatClientException as e:
+                logger.error("[wechat_kf] upload image failed: {}".format(e))
+                return
+            self._send_image(external_userid, open_kfid, response["media_id"])
+            logger.info("[wechat_kf] sendImage url={}, receiver={}".format(img_url, receiver))
+
+        elif reply.type == ReplyType.IMAGE:
+            image_storage = reply.content
+            sz = fsize(image_storage)
+            if sz >= 10 * 1024 * 1024:
+                logger.info("[wechat_kf] image too large, compressing, sz={}".format(sz))
+                image_storage = compress_imgfile(image_storage, 10 * 1024 * 1024 - 1)
+            image_storage.seek(0)
+            try:
+                response = self.client.media.upload("image", image_storage)
+            except WeChatClientException as e:
+                logger.error("[wechat_kf] upload image failed: {}".format(e))
+                return
+            self._send_image(external_userid, open_kfid, response["media_id"])
+            logger.info("[wechat_kf] sendImage, receiver={}".format(receiver))
+
+        elif reply.type == ReplyType.VIDEO_URL:
+            video_url = reply.content
+            try:
+                response = self.client.media.upload(
+                    "video", requests.get(video_url, stream=True).content
+                )
+            except WeChatClientException as e:
+                logger.error("[wechat_kf] upload video failed: {}".format(e))
+                return
+            self._send_video(external_userid, open_kfid, response["media_id"])
+            logger.info("[wechat_kf] sendVideo url={}, receiver={}".format(video_url, receiver))
+
+        elif reply.type == ReplyType.FILE:
+            file_path = reply.content
+            try:
+                with open(file_path, "rb") as f:
+                    response = self.client.media.upload(
+                        "file", (os.path.basename(file_path), f.read())
+                    )
+            except WeChatClientException as e:
+                logger.error("[wechat_kf] upload file failed: {}".format(e))
+                return
+            self._send_file(external_userid, open_kfid, response["media_id"])
+            logger.info("[wechat_kf] sendFile={}, receiver={}".format(file_path, receiver))
+
+        else:
+            logger.warning("[wechat_kf] unsupported reply type: {}".format(reply.type))
+
+    # ------------------------------------------------------------------
+    # Inbound — pull messages by cursor
+    # ------------------------------------------------------------------
+    def _get_kf_lock(self, open_kfid: str) -> threading.Lock:
+        with self._kf_locks_guard:
+            return self._kf_locks[open_kfid]
+
+    def submit_callback(self, token: str, open_kfid: str):
+        """
+        Async entry point used by the HTTP handler. Submits the actual
+        sync_msg pulling to a background thread so the callback response
+        can return within WeCom's 5s deadline.
+        """
+        try:
+            self._callback_executor.submit(self._run_callback, token, open_kfid)
+        except RuntimeError as e:
+            # Executor may be shut down during process exit; fall back
+            # to inline execution so we don't silently drop the event.
+            logger.warning(f"[wechat_kf] executor unavailable, run inline: {e}")
+            self._run_callback(token, open_kfid)
+
+    def _run_callback(self, token: str, open_kfid: str):
+        # Block on the per-kfid lock so retried callbacks queue up
+        # behind the in-flight one. The queued worker will then call
+        # sync_msg with the (already advanced) cursor, which is cheap
+        # when there is nothing new and still picks up any messages
+        # that arrived after the previous worker's last pull.
+        lock = self._get_kf_lock(open_kfid)
+        with lock:
+            try:
+                self.consume_callback(token, open_kfid)
+            except Exception as e:
+                logger.exception(f"[wechat_kf] consume_callback error: {e}")
+
+    def consume_callback(self, token: str, open_kfid: str):
+        """
+        Called from the HTTP `Query.POST` handler whenever WeCom notifies
+        us that there are new messages for `open_kfid`. Pulls all new
+        messages via sync_msg and feeds them into `produce()`.
+        """
+        existing_cursor = self.cursor_store.get(open_kfid)
+
+        # First-time bootstrap: always skip history, otherwise WeCom would
+        # replay up to 14 days of messages on the very first callback and
+        # flood every user with auto-replies.
+        if not existing_cursor:
+            self._initialize_cursor(token, open_kfid)
+            return
+
+        msgs = self._pull_messages(token, open_kfid, existing_cursor)
+        if not msgs:
+            return
+        file_cache = get_file_cache()
+        for raw in msgs:
+            try:
+                kf_msg = WechatKfMessage(msg=raw, client=self.client)
+            except NotImplementedError as e:
+                logger.debug("[wechat_kf] {}".format(e))
+                continue
+
+            session_id = kf_msg.from_user_id
+
+            # Cache lone images/files and wait for the user's follow-up
+            # text. Agent mode never reads memory.USER_IMAGE_CACHE, so
+            # without this the attachment is effectively lost.
+            if kf_msg.ctype in (ContextType.IMAGE, ContextType.FILE):
+                ftype = "image" if kf_msg.ctype == ContextType.IMAGE else "file"
+                try:
+                    kf_msg.prepare()  # download to local tmp path
+                    file_cache.add(session_id, kf_msg.content, file_type=ftype)
+                    logger.info(
+                        "[wechat_kf] {} cached for session {}: {}".format(
+                            ftype, session_id, kf_msg.content
+                        )
+                    )
+                except Exception as e:
+                    logger.warning(f"[wechat_kf] cache {ftype} failed: {e}")
+                continue
+
+            # On a text turn, attach any pending images/files as references
+            # so the downstream agent can pick them up via the text content.
+            # Paths are already under agent_workspace/tmp (see
+            # WechatKfMessage._get_tmp_dir), so a relative ref also works.
+            if kf_msg.ctype == ContextType.TEXT:
+                cached_files = file_cache.get(session_id)
+                if cached_files:
+                    refs = []
+                    for fi in cached_files:
+                        ftype, fpath = fi["type"], fi["path"]
+                        if ftype == "image":
+                            refs.append(f"[图片: {fpath}]")
+                        else:
+                            refs.append(f"[文件: {fpath}]")
+                    kf_msg.content = kf_msg.content + "\n" + "\n".join(refs)
+                    file_cache.clear(session_id)
+
+            context = self._compose_context(
+                kf_msg.ctype,
+                kf_msg.content,
+                isgroup=False,
+                msg=kf_msg,
+            )
+            if context:
+                self.produce(context)
+            time.sleep(0.05)  # tiny gap between messages of the same batch
+
+    def _initialize_cursor(self, token: str, open_kfid: str):
+        """
+        Drain all current messages for this `open_kfid` without producing
+        any context, just to advance the cursor to "now". This prevents
+        a fresh deployment from replying to up to ~14 days of history.
+        """
+        next_cursor = ""
+        total_skipped = 0
+        while True:
+            data = self._call_sync_msg(token, open_kfid, next_cursor)
+            if data is None:
+                break
+            msg_list = data.get("msg_list") or []
+            total_skipped += len(msg_list)
+            cursor_after = data.get("next_cursor") or ""
+            if cursor_after:
+                self.cursor_store.set(open_kfid, cursor_after)
+            if not data.get("has_more"):
+                break
+            if not cursor_after or cursor_after == next_cursor:
+                break
+            next_cursor = cursor_after
+        logger.info(
+            "[wechat_kf] first-start bootstrap finished for open_kfid={}, "
+            "skipped {} historical messages".format(open_kfid, total_skipped)
+        )
+
+    def _pull_messages(self, token: str, open_kfid: str, next_cursor: Optional[str]) -> list:
+        """Loop sync_msg until `has_more` is false. Returns raw msg dicts."""
+        collected = []
+        cursor = next_cursor or ""
+        while True:
+            data = self._call_sync_msg(token, open_kfid, cursor)
+            if data is None:
+                break
+            for item in data.get("msg_list") or []:
+                # Only consume messages from external users; ignore replies
+                # generated by our own kf account, otherwise we would loop
+                # back into ourselves.
+                if not item.get("external_userid"):
+                    continue
+                if item.get("msgtype") in ("text", "image", "voice", "file"):
+                    collected.append(item)
+            cursor_after = data.get("next_cursor") or ""
+            if cursor_after:
+                self.cursor_store.set(open_kfid, cursor_after)
+            if not data.get("has_more"):
+                break
+            if not cursor_after or cursor_after == cursor:
+                break
+            cursor = cursor_after
+
+        if collected:
+            collected = _dedup_image_text_pair(collected)
+        logger.info(
+            "[wechat_kf] pulled {} messages for open_kfid={}".format(len(collected), open_kfid)
+        )
+        return collected
+
+    def _call_sync_msg(self, token: str, open_kfid: str, cursor: str) -> Optional[dict]:
+        # `client.access_token` is the cached string property; do not use
+        # `fetch_access_token()` here — wechatpy returns the raw response
+        # dict from that call, which corrupts the query string.
+        url = f"{KF_API_BASE}/sync_msg?access_token={self.client.access_token}"
+        payload = {
+            "token": token,
+            "open_kfid": open_kfid,
+            "limit": SYNC_MSG_LIMIT,
+        }
+        if cursor:
+            payload["cursor"] = cursor
+        try:
+            resp = requests.post(url, json=payload, timeout=10).json()
+        except Exception as e:
+            logger.error(f"[wechat_kf] sync_msg request failed: {e}")
+            return None
+
+        if resp.get("errcode") != 0:
+            logger.error(
+                f"[wechat_kf] sync_msg errcode={resp.get('errcode')}, "
+                f"errmsg={resp.get('errmsg')}, open_kfid={open_kfid}"
+            )
+            return None
+        return resp
+
+    # ------------------------------------------------------------------
+    # Outbound HTTP wrappers (kf/send_msg)
+    # ------------------------------------------------------------------
+    def _post_send_msg(self, payload: dict) -> dict:
+        url = f"{KF_API_BASE}/send_msg?access_token={self.client.access_token}"
+        try:
+            resp = requests.post(url, json=payload, timeout=10).json()
+        except Exception as e:
+            logger.error(f"[wechat_kf] send_msg request failed: {e}")
+            return {"errcode": -1, "errmsg": str(e)}
+        if resp.get("errcode") != 0:
+            logger.error(f"[wechat_kf] send_msg failed, payload={payload}, resp={resp}")
+        return resp
+
+    def _send_text(self, external_userid: str, open_kfid: str, content: str) -> dict:
+        return self._post_send_msg({
+            "touser": external_userid,
+            "open_kfid": open_kfid,
+            "msgtype": "text",
+            "text": {"content": content},
+        })
+
+    def _send_image(self, external_userid: str, open_kfid: str, media_id: str) -> dict:
+        return self._post_send_msg({
+            "touser": external_userid,
+            "open_kfid": open_kfid,
+            "msgtype": "image",
+            "image": {"media_id": media_id},
+        })
+
+    def _send_voice(self, external_userid: str, open_kfid: str, media_id: str) -> dict:
+        return self._post_send_msg({
+            "touser": external_userid,
+            "open_kfid": open_kfid,
+            "msgtype": "voice",
+            "voice": {"media_id": media_id},
+        })
+
+    def _send_video(self, external_userid: str, open_kfid: str, media_id: str) -> dict:
+        return self._post_send_msg({
+            "touser": external_userid,
+            "open_kfid": open_kfid,
+            "msgtype": "video",
+            "video": {"media_id": media_id},
+        })
+
+    def _send_file(self, external_userid: str, open_kfid: str, media_id: str) -> dict:
+        return self._post_send_msg({
+            "touser": external_userid,
+            "open_kfid": open_kfid,
+            "msgtype": "file",
+            "file": {"media_id": media_id},
+        })
+
+    def _send_link(self, external_userid: str, open_kfid: str, link_data: dict) -> dict:
+        return self._post_send_msg({
+            "touser": external_userid,
+            "open_kfid": open_kfid,
+            "msgtype": "link",
+            "link": link_data,
+        })
+
+
+def _dedup_image_text_pair(messages: list) -> list:
+    """
+    A WeChat user often sends an image immediately followed by a text
+    question (e.g. "what's in this picture?"). Only when the batch is
+    exactly that 2-message image+text pair within a 5s window do we
+    collapse it into a single [image, text] turn. Otherwise return
+    every message so rapid-fire texts/images are all processed —
+    cursor freshness is already guaranteed by sync_msg.
+    """
+    if not messages:
+        return []
+
+    if len(messages) == 2:
+        a, b = messages
+        types = {a["msgtype"], b["msgtype"]}
+        if types == {"image", "text"} and abs(a["send_time"] - b["send_time"]) <= 5:
+            img = a if a["msgtype"] == "image" else b
+            txt = b if a["msgtype"] == "image" else a
+            return [img, txt]
+
+    return messages
+
+
+# ----------------------------------------------------------------------
+# HTTP handlers (web.py)
+# ----------------------------------------------------------------------
+class Query:
+    def GET(self):
+        channel = WechatKfChannel()
+        params = web.input()
+        logger.info("[wechat_kf] verify params: {}".format(params))
+        try:
+            signature = params.msg_signature
+            timestamp = params.timestamp
+            nonce = params.nonce
+            echostr = params.echostr
+            echostr = channel.crypto.check_signature(signature, timestamp, nonce, echostr)
+        except (InvalidSignatureException, InvalidCorpIdException):
+            raise web.Forbidden()
+        return echostr
+
+    def POST(self):
+        channel = WechatKfChannel()
+        params = web.input()
+        try:
+            signature = params.msg_signature
+            timestamp = params.timestamp
+            nonce = params.nonce
+            raw_body = web.data()
+            decrypted = channel.crypto.decrypt_message(raw_body, signature, timestamp, nonce)
+        except (InvalidSignatureException, InvalidCorpIdException) as e:
+            logger.warning(f"[wechat_kf] invalid signature: {e}")
+            raise web.Forbidden()
+
+        # We need the Token + OpenKfId fields from the inner XML to call
+        # sync_msg. wechatpy's parsed object exposes neither, so we parse
+        # the raw XML directly.
+        try:
+            root = ET.fromstring(decrypted)
+        except ET.ParseError as e:
+            logger.error(f"[wechat_kf] xml parse error: {e}")
+            return "success"
+
+        msg_type = (root.findtext("MsgType") or "").strip()
+        event = (root.findtext("Event") or "").strip()
+        if msg_type != "event" or event != "kf_msg_or_event":
+            logger.debug(
+                f"[wechat_kf] ignored callback msg_type={msg_type}, event={event}"
+            )
+            return "success"
+
+        token = root.findtext("Token") or ""
+        open_kfid = root.findtext("OpenKfId") or ""
+        if not token or not open_kfid:
+            logger.warning(
+                f"[wechat_kf] callback missing token or open_kfid: {decrypted}"
+            )
+            return "success"
+
+        # Hand off to a background worker — WeCom requires the callback
+        # to return success within ~5 seconds, otherwise it will retry
+        # and we may race the same cursor window into duplicate replies.
+        channel.submit_callback(token, open_kfid)
+        return "success"

channel/wechat_kf/wechat_kf_cursor_store.py

@@ -0,0 +1,80 @@
+# -*- coding=utf-8 -*-
+"""
+Local-file based persistence for WeCom customer-service `next_cursor`.
+
+Why we need this:
+    The WeCom customer-service (微信客服) callback only notifies us that
+    "new messages exist". To actually fetch them we must call the
+    `cgi-bin/kf/sync_msg` endpoint with a `cursor` so that we only get
+    messages newer than the previously processed one. If we lose this
+    cursor (e.g. on process restart) WeCom will replay up to ~14 days of
+    history, which would cause the bot to flood users with duplicate
+    replies.
+
+This implementation deliberately avoids any external dependency
+(no Redis / no DB) — a single JSON file under the project's tmp dir is
+enough for a CoW-style single-process deployment.
+"""
+import json
+import os
+import threading
+from typing import Optional
+
+from common.log import logger
+
+
+class CursorStore:
+    """Thread-safe per-`open_kfid` cursor store backed by a JSON file."""
+
+    def __init__(self, file_path: str):
+        self._file_path = file_path
+        self._lock = threading.Lock()
+        self._data = self._load()
+
+    def _load(self) -> dict:
+        try:
+            if os.path.exists(self._file_path):
+                with open(self._file_path, "r", encoding="utf-8") as f:
+                    return json.load(f) or {}
+        except Exception as e:
+            logger.warning(f"[wechat_kf] failed to load cursor file {self._file_path}: {e}")
+        return {}
+
+    def _flush_locked(self):
+        # Atomic write: write to *.tmp first then rename, avoid corruption on crash.
+        tmp_path = self._file_path + ".tmp"
+        try:
+            os.makedirs(os.path.dirname(self._file_path) or ".", exist_ok=True)
+            with open(tmp_path, "w", encoding="utf-8") as f:
+                json.dump(self._data, f, ensure_ascii=False)
+            os.replace(tmp_path, self._file_path)
+            # Tighten permissions: cursor file lives in $HOME, restrict to owner.
+            # No-op on Windows.
+            try:
+                os.chmod(self._file_path, 0o600)
+            except Exception:
+                pass
+        except Exception as e:
+            logger.warning(f"[wechat_kf] failed to flush cursor file {self._file_path}: {e}")
+            try:
+                if os.path.exists(tmp_path):
+                    os.remove(tmp_path)
+            except Exception:
+                pass
+
+    def get(self, open_kfid: str) -> Optional[str]:
+        with self._lock:
+            return self._data.get(open_kfid)
+
+    def set(self, open_kfid: str, cursor: str):
+        if not cursor:
+            return
+        with self._lock:
+            if self._data.get(open_kfid) == cursor:
+                return
+            self._data[open_kfid] = cursor
+            self._flush_locked()
+
+    def has(self, open_kfid: str) -> bool:
+        with self._lock:
+            return open_kfid in self._data

channel/wechat_kf/wechat_kf_message.py

@@ -0,0 +1,134 @@
+# -*- coding=utf-8 -*-
+"""
+Adapter that turns a single `sync_msg` item from WeCom customer-service
+into a CoW `ChatMessage` object.
+"""
+import os
+import re
+
+from wechatpy.enterprise import WeChatClient
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.log import logger
+from common.utils import expand_path
+from config import conf
+
+
+def _get_tmp_dir() -> str:
+    """Save under agent_workspace/tmp/ so agent tools (e.g. `read`) can
+    resolve a relative path like `tmp/xxx.pdf` against their own
+    workspace root. Mirrors the convention used by weixin / wecom_bot.
+    """
+    ws_root = expand_path(conf().get("agent_workspace", "~/cow"))
+    tmp_dir = os.path.join(ws_root, "tmp")
+    os.makedirs(tmp_dir, exist_ok=True)
+    return tmp_dir
+
+
+def _extract_filename(content_disposition: str) -> str:
+    """Best-effort parse of `filename` / `filename*` from a Content-Disposition
+    header. Returns '' when nothing usable is found."""
+    if not content_disposition:
+        return ""
+    # RFC 5987 form: filename*=UTF-8''xxx
+    m = re.search(r"filename\*=(?:[^'\"]*'[^']*'\s*)?([^;]+)", content_disposition)
+    if m:
+        try:
+            from urllib.parse import unquote
+            return unquote(m.group(1).strip().strip('"'))
+        except Exception:
+            return m.group(1).strip().strip('"')
+    m = re.search(r'filename\s*=\s*"?([^";]+)"?', content_disposition)
+    return m.group(1).strip() if m else ""
+
+
+class WechatKfMessage(ChatMessage):
+    """
+    msg structure (from cgi-bin/kf/sync_msg):
+        {
+          "msgid": "...",
+          "send_time": 1700000000,
+          "origin": 3,
+          "msgtype": "text" | "image" | "voice" | ...,
+          "open_kfid": "wkxxxx",
+          "external_userid": "wmxxxx",
+          "text": {"content": "..."},
+          "image": {"media_id": "..."},
+          "voice": {"media_id": "..."},
+          ...
+        }
+    """
+
+    def __init__(self, msg: dict, client: WeChatClient = None, is_group: bool = False):
+        # NOTE: skip parent constructor because it expects a wechatpy parsed
+        # message object, while here we receive a raw dict from sync_msg.
+        super().__init__(msg)
+        self.is_group = is_group
+        self.msg_id = msg.get("msgid")
+        self.create_time = msg.get("send_time")
+        self.origin = msg.get("origin")
+        self.msgtype = msg.get("msgtype")
+        self.open_kfid = msg.get("open_kfid")
+        self.external_userid = msg.get("external_userid")
+
+        if self.msgtype == "text":
+            self.ctype = ContextType.TEXT
+            self.content = msg.get("text", {}).get("content", "")
+        elif self.msgtype == "image":
+            self.ctype = ContextType.IMAGE
+            media_id = msg.get("image", {}).get("media_id", "")
+            self.content = os.path.join(_get_tmp_dir(), media_id + ".jpg")
+
+            def download_image():
+                response = client.media.download(media_id)
+                if response.status_code == 200:
+                    with open(self.content, "wb") as f:
+                        f.write(response.content)
+                else:
+                    logger.info(f"[wechat_kf] Failed to download image, {response.content}")
+
+            self._prepare_fn = download_image
+        elif self.msgtype == "voice":
+            self.ctype = ContextType.VOICE
+            media_id = msg.get("voice", {}).get("media_id", "")
+            # WeCom returns amr by default; downstream voice pipeline will convert.
+            self.content = os.path.join(_get_tmp_dir(), media_id + ".amr")
+
+            def download_voice():
+                response = client.media.download(media_id)
+                if response.status_code == 200:
+                    with open(self.content, "wb") as f:
+                        f.write(response.content)
+                else:
+                    logger.info(f"[wechat_kf] Failed to download voice, {response.content}")
+
+            self._prepare_fn = download_voice
+        elif self.msgtype == "file":
+            self.ctype = ContextType.FILE
+            media_id = msg.get("file", {}).get("media_id", "")
+            # Provisional path; rewritten in download_file() once we have
+            # the original filename from Content-Disposition.
+            self.content = os.path.join(_get_tmp_dir(), media_id)
+
+            def download_file():
+                response = client.media.download(media_id)
+                if response.status_code == 200:
+                    filename = _extract_filename(
+                        response.headers.get("Content-Disposition", "")
+                    ) or media_id
+                    self.content = os.path.join(_get_tmp_dir(), filename)
+                    with open(self.content, "wb") as f:
+                        f.write(response.content)
+                else:
+                    logger.info(f"[wechat_kf] Failed to download file, {response.content}")
+
+            self._prepare_fn = download_file
+        else:
+            raise NotImplementedError(
+                f"[wechat_kf] Unsupported message type: {self.msgtype}"
+            )
+
+        self.from_user_id = self.external_userid
+        self.to_user_id = self.open_kfid
+        self.other_user_id = self.external_userid

channel/wechatmp/passive_reply.py

@@ -103,14 +103,21 @@ class Query:
                 task_running = True
                 waiting_until = request_time + 4
                 while time.time() < waiting_until:
-                    if from_user in channel.running:
-                        time.sleep(0.1)
-                    else:
+                    if from_user not in channel.running:
                         task_running = False
                         break
+                    # Task still running, but if it has already produced cached
+                    # segments (e.g. multi-turn thinking output), return them now
+                    # instead of forcing the user to wait for the whole task. The
+                    # remaining segments are fetched by the user's next message.
+                    if channel.cache_dict.get(from_user):
+                        break
+                    time.sleep(0.1)
 
                 reply_text = ""
-                if task_running:
+                # Only fall back to retry / "thinking" hint when the task is still
+                # running AND there is nothing cached to send yet.
+                if task_running and not channel.cache_dict.get(from_user):
                     if request_cnt < 3:
                         # waiting for timeout (the POST request will be closed by Wechat official server)
                         time.sleep(2)
@@ -131,8 +138,22 @@ class Query:
 
                 # Only one request can access to the cached data
                 try:
-                    (reply_type, reply_content) = channel.cache_dict[from_user].pop(0)
-                    if not channel.cache_dict[from_user]:  # If popping the message makes the list empty, delete the user entry from cache
+                    # WeChat passive reply allows only a single reply per request.
+                    # To avoid forcing the user to send an extra message for every
+                    # segment of multi-turn agent output, drain all consecutive
+                    # cached text segments at once and merge them into one reply.
+                    # Media (voice/image) can only be returned one at a time, so it
+                    # stops the merge and is returned on its own.
+                    cached = channel.cache_dict[from_user]
+                    if cached[0][0] == "text":
+                        reply_type = "text"
+                        merged_parts = []
+                        while cached and cached[0][0] == "text":
+                            merged_parts.append(cached.pop(0)[1])
+                        reply_content = "\n\n".join(merged_parts)
+                    else:
+                        (reply_type, reply_content) = cached.pop(0)
+                    if not channel.cache_dict[from_user]:  # If draining empties the list, delete the user entry from cache
                         del channel.cache_dict[from_user]
                 except IndexError:
                     return "success"

channel/wechatmp/wechatmp_channel.py

@@ -134,10 +134,16 @@ class WechatMPChannel(ChatChannel):
 
             elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
                 img_url = reply.content
-                pic_res = requests.get(img_url, stream=True)
                 image_storage = io.BytesIO()
-                for block in pic_res.iter_content(1024):
-                    image_storage.write(block)
+                if img_url.startswith("file://") or os.path.isfile(img_url):
+                    # Local file produced by the agent (e.g. a generated image)
+                    local_path = img_url[len("file://"):] if img_url.startswith("file://") else img_url
+                    with open(local_path, "rb") as f:
+                        image_storage.write(f.read())
+                else:
+                    pic_res = requests.get(img_url, stream=True)
+                    for block in pic_res.iter_content(1024):
+                        image_storage.write(block)
                 image_storage.seek(0)
                 image_type = imghdr.what(image_storage)
                 filename = receiver + "-" + str(context["msg"].msg_id) + "." + image_type
@@ -258,10 +264,16 @@ class WechatMPChannel(ChatChannel):
                 logger.info("[wechatmp] Do send voice to {}".format(receiver))
             elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
                 img_url = reply.content
-                pic_res = requests.get(img_url, stream=True)
                 image_storage = io.BytesIO()
-                for block in pic_res.iter_content(1024):
-                    image_storage.write(block)
+                if img_url.startswith("file://") or os.path.isfile(img_url):
+                    # Local file produced by the agent (e.g. a generated image)
+                    local_path = img_url[len("file://"):] if img_url.startswith("file://") else img_url
+                    with open(local_path, "rb") as f:
+                        image_storage.write(f.read())
+                else:
+                    pic_res = requests.get(img_url, stream=True)
+                    for block in pic_res.iter_content(1024):
+                        image_storage.write(block)
                 image_storage.seek(0)
                 image_type = imghdr.what(image_storage)
                 filename = receiver + "-" + str(context["msg"].msg_id) + "." + image_type

cli/VERSION

@@ -1 +1 @@
-2.0.9
+2.1.0

cli/commands/install.py

@@ -14,7 +14,7 @@ CHINA_MIRROR = "https://registry.npmmirror.com/-/binary/playwright"
 
 # stream(msg, fg=None) — fg is "yellow" | "green" | "red" | None
 StreamFn = Callable[[str, Optional[str]], None]
-# on_phase(msg) — coarse-grained progress for chat channels (Chinese)
+# on_phase(msg) — coarse-grained progress for chat channels (localized via i18n)
 PhaseFn = Callable[[str], None]
 
 
@@ -112,16 +112,27 @@ def run_install_browser(
         stream: Optional callback ``(message, fg)`` for each line. ``fg`` is
             ``yellow`` / ``green`` / ``red`` or None. Defaults to colored click output.
         on_phase: Optional callback for coarse progress (e.g. push to chat);
-            messages are short Chinese status lines.
+            messages are short status lines localized via i18n.
 
     Returns:
         0 on success, 1 on fatal failure (pip or chromium install failed).
     """
+    from cli.utils import get_cli_language
+
+    # Import `common` only after get_cli_language() runs ensure_sys_path(),
+    # so it works when `cow` is invoked from outside the project directory.
+    get_cli_language()  # resolve cow_lang so i18n.t reflects config
+    from common import i18n
+    _t = i18n.t
+
     stream = stream or _default_stream
     python = sys.executable
     legacy_mode = False
 
-    _phase(on_phase, "🔧 开始安装浏览器工具依赖（约几分钟，请耐心等待）…")
+    _phase(on_phase, _t(
+        "🔧 开始安装浏览器工具依赖（约几分钟，请耐心等待）…",
+        "🔧 Installing browser tool dependencies (a few minutes, please wait)…",
+    ))
 
     glibc = _get_glibc_version()
     if glibc and glibc < GLIBC_THRESHOLD:
@@ -136,27 +147,36 @@ def run_install_browser(
         stream("")
         _phase(
             on_phase,
-            f"ℹ️ 检测到 glibc {glibc_str}（较旧），将安装兼容版 Playwright {PLAYWRIGHT_LEGACY_VERSION}。",
+            _t(
+                f"ℹ️ 检测到 glibc {glibc_str}（较旧），将安装兼容版 Playwright {PLAYWRIGHT_LEGACY_VERSION}。",
+                f"ℹ️ Detected glibc {glibc_str} (older); installing compatible Playwright {PLAYWRIGHT_LEGACY_VERSION}.",
+            ),
         )
 
     target_version = PLAYWRIGHT_LEGACY_VERSION if legacy_mode else PLAYWRIGHT_VERSION
 
-    _phase(on_phase, "📦 [1/3] 正在安装 Playwright Python 包…")
+    _phase(on_phase, _t("📦 [1/3] 正在安装 Playwright Python 包…", "📦 [1/3] Installing Playwright Python package…"))
     stream("[1/3] Installing playwright Python package...", "yellow")
     ret = _pip_install(f"playwright=={target_version}", stream)
     if ret != 0:
         stream("Failed to install playwright package.", "red")
-        _phase(on_phase, "❌ [1/3] Playwright Python 包安装失败。")
+        _phase(on_phase, _t("❌ [1/3] Playwright Python 包安装失败。", "❌ [1/3] Failed to install Playwright Python package."))
         return 1
 
     installed = _get_installed_version()
     if installed:
         stream(f"  playwright {installed} installed.", "green")
     stream("")
-    _phase(on_phase, f"✅ [1/3] Playwright 包已安装（{installed or target_version}）。")
+    _phase(on_phase, _t(
+        f"✅ [1/3] Playwright 包已安装（{installed or target_version}）。",
+        f"✅ [1/3] Playwright package installed ({installed or target_version}).",
+    ))
 
     if sys.platform == "linux":
-        _phase(on_phase, "🔧 [2/3] 正在安装 Linux 系统依赖与轻量中文字体（文泉驿正黑，部分步骤可能需要 sudo）…")
+        _phase(on_phase, _t(
+            "🔧 [2/3] 正在安装 Linux 系统依赖与轻量中文字体（文泉驿正黑，部分步骤可能需要 sudo）…",
+            "🔧 [2/3] Installing Linux system deps and a lightweight CJK font (WenQuanYi Zen Hei; some steps may need sudo)…",
+        ))
         stream("[2/3] Installing system dependencies (Linux)...", "yellow")
         ret = subprocess.call([python, "-m", "playwright", "install-deps", "chromium"])
         if ret != 0:
@@ -183,14 +203,23 @@ def run_install_browser(
             stream("  CJK font (wqy-zenhei) installed.", "green")
         _phase(
             on_phase,
-            "✅ [2/3] Linux 依赖与字体步骤已执行（若有权限问题请查看服务器日志或手动执行提示命令）。",
+            _t(
+                "✅ [2/3] Linux 依赖与字体步骤已执行（若有权限问题请查看服务器日志或手动执行提示命令）。",
+                "✅ [2/3] Linux deps and font steps executed (on permission issues, check the server log or run the suggested commands manually).",
+            ),
         )
     else:
         stream(f"[2/3] Skipping system deps (not needed on {sys.platform}).", "yellow")
-        _phase(on_phase, f"ℹ️ [2/3] 当前系统（{sys.platform}）跳过 Linux 专用依赖。")
+        _phase(on_phase, _t(
+            f"ℹ️ [2/3] 当前系统（{sys.platform}）跳过 Linux 专用依赖。",
+            f"ℹ️ [2/3] Skipping Linux-specific deps on this platform ({sys.platform}).",
+        ))
     stream("")
 
-    _phase(on_phase, "🌐 [3/3] 正在下载并安装 Chromium（体积较大，请耐心等待）…")
+    _phase(on_phase, _t(
+        "🌐 [3/3] 正在下载并安装 Chromium（体积较大，请耐心等待）…",
+        "🌐 [3/3] Downloading and installing Chromium (large download, please wait)…",
+    ))
     stream("[3/3] Installing Chromium browser...", "yellow")
     cmd = [python, "-m", "playwright", "install", "chromium"]
 
@@ -209,27 +238,33 @@ def run_install_browser(
     if use_mirror:
         env["PLAYWRIGHT_DOWNLOAD_HOST"] = CHINA_MIRROR
         stream(f"  (using China mirror: {CHINA_MIRROR})", None)
-        _phase(on_phase, "📡 检测到国内 pip 源配置，Chromium 将优先走国内镜像下载。")
+        _phase(on_phase, _t(
+            "📡 检测到国内 pip 源配置，Chromium 将优先走国内镜像下载。",
+            "📡 Detected a China pip mirror; Chromium will be downloaded from the China mirror first.",
+        ))
 
     ret = subprocess.call(cmd, env=env)
 
     if ret != 0 and use_mirror:
         stream("  Mirror download failed, retrying with official CDN...", "yellow")
-        _phase(on_phase, "⚠️ 镜像下载失败，正在改用官方源重试…")
+        _phase(on_phase, _t(
+            "⚠️ 镜像下载失败，正在改用官方源重试…",
+            "⚠️ Mirror download failed; retrying with the official CDN…",
+        ))
         env_no_mirror = os.environ.copy()
         env_no_mirror.pop("PLAYWRIGHT_DOWNLOAD_HOST", None)
         ret = subprocess.call(cmd, env=env_no_mirror)
 
     if ret != 0:
         stream("Failed to install Chromium.", "red")
-        _phase(on_phase, "❌ [3/3] Chromium 安装失败。")
+        _phase(on_phase, _t("❌ [3/3] Chromium 安装失败。", "❌ [3/3] Failed to install Chromium."))
         return 1
 
     stream("")
-    _phase(on_phase, "✅ [3/3] Chromium 已安装。")
+    _phase(on_phase, _t("✅ [3/3] Chromium 已安装。", "✅ [3/3] Chromium installed."))
 
     stream("Verifying browser installation...", None)
-    _phase(on_phase, "🔍 正在验证 Playwright 能否正常加载…")
+    _phase(on_phase, _t("🔍 正在验证 Playwright 能否正常加载…", "🔍 Verifying that Playwright loads correctly…"))
     ret = subprocess.call(
         [python, "-c", "from playwright.sync_api import sync_playwright; print('OK')"],
         stderr=subprocess.DEVNULL,
@@ -240,14 +275,20 @@ def run_install_browser(
             "  Consider upgrading your OS or using Docker.",
             "yellow",
         )
-        _phase(on_phase, "⚠️ 验证未完全通过：本机可能仍无法使用浏览器工具，请查看日志或升级系统。")
+        _phase(on_phase, _t(
+            "⚠️ 验证未完全通过：本机可能仍无法使用浏览器工具，请查看日志或升级系统。",
+            "⚠️ Verification did not fully pass: the browser tool may still not work here; check the log or upgrade your system.",
+        ))
     else:
         stream("  Verification passed.", "green")
-        _phase(on_phase, "✅ 验证通过。")
+        _phase(on_phase, _t("✅ 验证通过。", "✅ Verification passed."))
 
     stream("")
     stream("Browser tool ready! Restart CowAgent to enable it.", "green")
-    _phase(on_phase, "🎉 全部步骤结束。请重启 CowAgent 后使用 browser 工具。")
+    _phase(on_phase, _t(
+        "🎉 全部步骤结束。请重启 CowAgent 后使用 browser 工具。",
+        "🎉 All steps finished. Restart CowAgent to use the browser tool.",
+    ))
     return 0
 
 

cli/commands/process.py

@@ -8,11 +8,28 @@ from typing import Optional
 
 import click
 
-from cli.utils import get_project_root
+from cli.utils import get_project_root, load_config_json
 
 _IS_WIN = sys.platform == "win32"
 
 
+def _is_terminal_only() -> bool:
+    """Whether terminal is the only configured channel.
+
+    Terminal needs an interactive stdin/tty, which is incompatible with the
+    background daemon mode (stdout/stdin detached). When terminal is the only
+    channel, `start` must run in the foreground so it can own the tty.
+    """
+    channel = load_config_json().get("channel_type", "")
+    if isinstance(channel, str):
+        names = [c.strip() for c in channel.split(",") if c.strip()]
+    elif isinstance(channel, (list, tuple)):
+        names = [str(c).strip() for c in channel if str(c).strip()]
+    else:
+        names = []
+    return names == ["terminal"]
+
+
 def _get_pid_file():
     return os.path.join(get_project_root(), ".cow.pid")
 
@@ -103,6 +120,12 @@ def start(foreground, no_logs):
 
     python = sys.executable
 
+    # Terminal-only setups need an interactive tty; force foreground so the
+    # terminal channel can read stdin instead of fighting the shell over the tty.
+    if not foreground and _is_terminal_only():
+        foreground = True
+        click.echo("Detected terminal-only channel, starting in foreground...")
+
     if foreground:
         click.echo("Starting CowAgent in foreground...")
         if _IS_WIN:
@@ -252,7 +275,14 @@ def update(ctx):
 def status():
     """Show CowAgent running status."""
     from cli import __version__
-    from cli.utils import load_config_json
+    from cli.utils import load_config_json, get_cli_language
+
+    # get_cli_language() calls ensure_sys_path(), which adds the project root
+    # to sys.path. Import `common` only AFTER that, otherwise it fails with
+    # ModuleNotFoundError when `cow` runs from outside the project dir.
+    get_cli_language()  # resolve cow_lang so i18n.t reflects config
+    from common import i18n
+    _t = i18n.t
 
     pid = _read_pid()
     if pid:
@@ -260,17 +290,19 @@ def status():
     else:
         click.echo(click.style("● CowAgent is not running", fg="red"))
 
-    click.echo(f"  版本: v{__version__}")
+    click.echo(_t(f"  版本: v{__version__}", f"  Version: v{__version__}"))
 
     cfg = load_config_json()
     if cfg:
         channel = cfg.get("channel_type", "unknown")
         if isinstance(channel, list):
             channel = ", ".join(channel)
-        click.echo(f"  通道: {channel}")
-        click.echo(f"  模型: {cfg.get('model', 'unknown')}")
+        click.echo(_t(f"  通道: {channel}", f"  Channel: {channel}"))
+        click.echo(_t(f"  模型: {cfg.get('model', 'unknown')}", f"  Model: {cfg.get('model', 'unknown')}"))
         mode = "Chat" if cfg.get("agent") is False else "Agent"
-        click.echo(f"  模式: {mode}")
+        click.echo(_t(f"  模式: {mode}", f"  Mode: {mode}"))
+        lang_label = "中文" if i18n.get_language() == "zh" else "English"
+        click.echo(_t(f"  语言: {lang_label}", f"  Language: {lang_label}"))
 
 
 @click.command()

cli/commands/skill.py

@@ -517,18 +517,26 @@ def _install_targz_bytes(content: bytes, name: str, skills_dir: str, result: Ins
 
 def _print_install_success(name: str, source: str):
     """Print a unified install success message with description and source."""
+    from cli.utils import get_cli_language
+
+    # Import `common` only after get_cli_language() runs ensure_sys_path(),
+    # so it works when `cow` is invoked from outside the project directory.
+    get_cli_language()  # resolve cow_lang so i18n.t reflects config
+    from common import i18n
+    _t = i18n.t
+
     skills_dir = get_skills_dir()
     config = load_skills_config()
     display = config.get(name, {}).get("display_name", "")
     desc = _read_skill_description(os.path.join(skills_dir, name))
     click.echo(click.style(f"✓ {name}", fg="green"))
     if display and display != name:
-        click.echo(f"  名称: {display}")
+        click.echo(_t(f"  名称: {display}", f"  Name: {display}"))
     if desc:
         if len(desc) > 60:
             desc = desc[:57] + "…"
-        click.echo(f"  描述: {desc}")
-    click.echo(f"  来源: {source}")
+        click.echo(_t(f"  描述: {desc}", f"  Description: {desc}"))
+    click.echo(_t(f"  来源: {source}", f"  Source: {source}"))
 
 
 def _validate_skill_name(name: str):

cli/utils.py

@@ -40,6 +40,22 @@ def load_config_json() -> dict:
         return {}
 
 
+def get_cli_language() -> str:
+    """Resolve the CLI UI language using the shared i18n detector.
+
+    Reads the `cow_lang` field from config.json (defaults to "auto") and runs
+    the same detection used by the running app, so CLI output matches.
+    """
+    ensure_sys_path()
+    try:
+        from common import i18n
+
+        configured = load_config_json().get("cow_lang", "auto")
+        return i18n.resolve_language(configured)
+    except Exception:
+        return "en"
+
+
 def load_skills_config() -> dict:
     """Load skills_config.json from the custom skills directory."""
     path = os.path.join(get_skills_dir(), "skills_config.json")

common/const.py

@@ -30,8 +30,9 @@ 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_8_OPUS = "claude-opus-4-8"      # Claude Opus 4.8 - Agent推荐模型
 CLAUDE_4_7_OPUS = "claude-opus-4-7"      # Claude Opus 4.7
-CLAUDE_4_6_OPUS = "claude-opus-4-6"      # Claude Opus 4.6 - Agent推荐模型
+CLAUDE_4_6_OPUS = "claude-opus-4-6"      # Claude Opus 4.6
 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
@@ -107,17 +108,15 @@ 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)
 QWEN36_PLUS = "qwen3.6-plus"  # Qwen3.6 Plus - Omni model (MultiModalConversation)
+QWEN37_PLUS = "qwen3.7-plus"  # Qwen3.7 Plus - Omni model (MultiModalConversation)
 QWEN37_MAX = "qwen3.7-max"  # Qwen3.7 Max - Agent推荐模型
 QWQ_PLUS = "qwq-plus"
 
 # MiniMax
-MINIMAX_M2_7 = "MiniMax-M2.7"  # MiniMax M2.7 - Latest
-MINIMAX_TEXT_01 = "MiniMax-Text-01"  # MiniMax 多模态 (vision)
+MINIMAX_M3 = "MiniMax-M3"  # MiniMax M3 - Latest (default)
+MINIMAX_M2_7 = "MiniMax-M2.7"  # MiniMax M2.7
 MINIMAX_M2_7_HIGHSPEED = "MiniMax-M2.7-highspeed"  # MiniMax M2.7 highspeed
-MINIMAX_M2_5 = "MiniMax-M2.5"  # MiniMax M2.5
-MINIMAX_M2_1 = "MiniMax-M2.1"  # MiniMax M2.1
-MINIMAX_M2_1_LIGHTNING = "MiniMax-M2.1-lightning"  # MiniMax M2.1 极速版
-MINIMAX_M2 = "MiniMax-M2"  # MiniMax M2
+MINIMAX_TEXT_01 = "MiniMax-Text-01"  # MiniMax 多模态 (vision)
 MINIMAX_ABAB6_5 = "abab6.5-chat"  # MiniMax abab6.5
 
 # GLM (智谱AI)
@@ -188,13 +187,13 @@ MODEL_LIST = [
               ERNIE_45_TURBO_VL, ERNIE_45_TURBO_VL_32K,
 
               # MiniMax
-              MiniMax, MINIMAX_M2_7, MINIMAX_M2_7_HIGHSPEED, MINIMAX_M2_5, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
+              MiniMax, MINIMAX_M3, MINIMAX_M2_7, MINIMAX_M2_7_HIGHSPEED, MINIMAX_ABAB6_5,
 
               # 小米 MiMo
               MIMO, MIMO_V2_5_PRO, MIMO_V2_5, MIMO_V2_PRO, MIMO_V2_OMNI, MIMO_V2_FLASH,
 
               # Claude
-              CLAUDE3, CLAUDE_4_6_SONNET, CLAUDE_4_7_OPUS, CLAUDE_4_6_OPUS, CLAUDE_4_OPUS, CLAUDE_4_5_SONNET, CLAUDE_4_SONNET, CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229,
+              CLAUDE3, CLAUDE_4_8_OPUS, CLAUDE_4_7_OPUS, 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",
 
@@ -217,7 +216,7 @@ MODEL_LIST = [
               GLM_4_0520, GLM_4_AIR, GLM_4_AIRX, GLM_4_7,
 
               # Qwen (通义千问)
-              QWEN37_MAX, QWEN36_PLUS, QWEN35_PLUS, QWEN3_MAX, QWEN_MAX, QWEN_PLUS, QWEN_TURBO, QWEN_LONG,
+              QWEN37_PLUS, QWEN37_MAX, QWEN36_PLUS, QWEN35_PLUS, QWEN3_MAX, QWEN_MAX, QWEN_PLUS, QWEN_TURBO, QWEN_LONG,
 
               # Doubao (豆包)
               DOUBAO, DOUBAO_SEED_2_CODE, DOUBAO_SEED_2_PRO, DOUBAO_SEED_2_LITE, DOUBAO_SEED_2_MINI,
@@ -243,3 +242,7 @@ DINGTALK = "dingtalk"
 WECOM_BOT = "wecom_bot"
 QQ = "qq"
 WEIXIN = "weixin"
+WECHAT_KF = "wechat_kf"
+TELEGRAM = "telegram"
+SLACK = "slack"
+DISCORD = "discord"

common/i18n.py

@@ -0,0 +1,177 @@
+# encoding:utf-8
+
+"""Lightweight global language detection and resolution.
+
+This module is the single source of truth for the runtime UI language used
+across the CLI, startup logs, error messages, agent prompts and channel
+replies. It must NOT import project config (to avoid circular imports) and
+must stay dependency-free so it can run at the earliest startup phase.
+
+Resolution priority (highest first):
+  1. Explicit `cow_lang` from config.json — also covers Docker/CI, since any
+     config key is overridable via its uppercase env var (e.g. COW_LANG=zh),
+     handled by config.load_config() before resolution. COW_LANG is a private
+     name to avoid clashing with the gettext-standard LANGUAGE variable.
+  2. macOS `defaults read -g AppleLocale` (system-level preference; a Chinese
+     system locale is a strong signal that beats a shell-default LANG)
+  3. Standard locale env vars: LC_ALL > LC_MESSAGES > LANG
+  4. Python locale module
+  5. Default -> English
+
+A value of "auto" (the default) triggers detection (steps 2-5). Explicitly
+setting "zh" or "en" locks the language and skips detection.
+"""
+
+import os
+import subprocess
+import sys
+
+# Supported language codes
+ZH = "zh"
+EN = "en"
+SUPPORTED = (ZH, EN)
+DEFAULT_LANG = EN
+
+# Resolved language cache; None until first resolution.
+_resolved_lang = None
+
+
+def _normalize(raw):
+    """Map an arbitrary locale-ish string to a supported code, or None.
+
+    Only Chinese is detected explicitly; everything else (including unknown
+    or empty values) yields None so the caller can fall through to the next
+    detection source.
+    """
+    if not raw:
+        return None
+    value = str(raw).strip().lower().replace("_", "-")
+    if value in ("auto", ""):
+        return None
+    # Chinese variants: zh, zh-cn, zh-hans, zh-hans-cn, zh-tw, zh-hk ...
+    if value.startswith("zh") or value.startswith("chinese"):
+        return ZH
+    if value.startswith("en") or value.startswith("english"):
+        return EN
+    return None
+
+
+def _detect_from_env():
+    """Detect language from standard locale environment variables.
+
+    Note: on macOS, `LANG` is often a shell default (e.g. en_US.UTF-8 set by
+    .zshrc) that does not reflect the user's real preference, so AppleLocale
+    is checked first (see detect_language). On Linux these vars are the
+    primary signal.
+
+    The cow_lang env override (COW_LANG=zh) is intentionally NOT read here:
+    it sets config["cow_lang"] and is handled via the explicit config path,
+    not auto-detection.
+    """
+    for key in ("LC_ALL", "LC_MESSAGES", "LANG"):
+        lang = _normalize(os.environ.get(key))
+        if lang:
+            return lang
+    return None
+
+
+def _detect_from_macos():
+    """macOS fallback: read the system-wide AppleLocale preference.
+
+    On macOS the terminal often does NOT export LANG, yet the system locale
+    is still meaningful (e.g. a Chinese Mac reports zh_CN). This recovers
+    that signal so Chinese users are not misdetected as English.
+    """
+    if sys.platform != "darwin":
+        return None
+    try:
+        out = subprocess.run(
+            ["defaults", "read", "-g", "AppleLocale"],
+            capture_output=True,
+            text=True,
+            timeout=2,
+        )
+        if out.returncode == 0:
+            return _normalize(out.stdout)
+    except Exception:
+        pass
+    return None
+
+
+def _detect_from_python_locale():
+    """Last-resort detection via Python's locale module."""
+    try:
+        import locale
+
+        for value in locale.getlocale():
+            lang = _normalize(value)
+            if lang:
+                return lang
+    except Exception:
+        pass
+    return None
+
+
+def detect_language():
+    """Run full auto-detection and return a supported language code.
+
+    Order (auto-detection only; explicit config["cow_lang"] is resolved
+    before this is reached):
+      1. macOS AppleLocale (system-level preference; a Chinese system locale
+         is a strong, low-false-positive signal that beats a shell-default
+         LANG like en_US.UTF-8)
+      2. locale env vars LC_ALL / LC_MESSAGES / LANG (primary signal on Linux)
+      3. Python locale module
+      4. default English
+    """
+    return (
+        _detect_from_macos()
+        or _detect_from_env()
+        or _detect_from_python_locale()
+        or DEFAULT_LANG
+    )
+
+
+def resolve_language(configured=None):
+    """Resolve the effective language from a configured value.
+
+    `configured` is the raw `cow_lang` value from config.json (may be None,
+    "auto", "zh" or "en"). An explicit "zh"/"en" locks the result; "auto"
+    or empty triggers detection. The result is cached globally.
+    """
+    global _resolved_lang
+    explicit = _normalize(configured)
+    if explicit:
+        _resolved_lang = explicit
+    else:
+        _resolved_lang = detect_language()
+    return _resolved_lang
+
+
+def set_language(lang):
+    """Force the resolved language (used by tests or per-request overrides)."""
+    global _resolved_lang
+    normalized = _normalize(lang)
+    _resolved_lang = normalized or DEFAULT_LANG
+    return _resolved_lang
+
+
+def get_language():
+    """Return the currently resolved language, detecting lazily if needed."""
+    global _resolved_lang
+    if _resolved_lang is None:
+        _resolved_lang = detect_language()
+    return _resolved_lang
+
+
+def is_zh():
+    return get_language() == ZH
+
+
+def t(zh_text, en_text):
+    """Pick a string by the current language. Tiny inline-translation helper.
+
+    Intended for one-off strings where a full message catalog is overkill:
+        t("已中止", "Cancelled")
+    """
+    return zh_text if get_language() == ZH else en_text

common/utils.py

@@ -117,6 +117,18 @@ def expand_path(path: str) -> str:
     return expanded
 
 
+def is_cloud_deployment() -> bool:
+    if os.environ.get("CLOUD_DEPLOYMENT_ID"):
+        return True
+    try:
+        from config import conf
+        if conf().get("cloud_deployment_id"):
+            return True
+    except Exception:
+        pass
+    return False
+
+
 def get_cloud_headers(api_key: str) -> dict:
     """
     Build standard headers for LinkAI API requests,

config-template.json

@@ -1,5 +1,6 @@
 {
-  "channel_type": "weixin",
+  "cow_lang": "auto",
+  "channel_type": "web",
   "model": "deepseek-v4-flash",
   "deepseek_api_key": "",
   "deepseek_api_base": "https://api.deepseek.com/v1",

config.py

@@ -7,196 +7,222 @@ import os
 import pickle
 
 from common.log import logger
+from common import i18n
 
-# 将所有可用的配置项写在字典里, 请使用小写字母
-# 此处的配置值无实际意义，程序不会读取此处的配置，仅用于提示格式，请将配置加入到config.json中
+# All available config keys are listed in this dict (use lowercase keys).
+# The values here are placeholders only; the program does NOT read them.
+# They merely document the expected format — put real values in config.json.
 available_setting = {
-    # openai api配置
+    # global UI language for CLI, startup logs, error messages, agent prompts
+    # and channel replies. Options: "auto" (detect from system locale, default),
+    # "zh" (Chinese) or "en" (English). An explicit value locks the language.
+    # value: auto/en/zh
+    "cow_lang": "auto",
+    # openai api config
     "open_ai_api_key": "",  # openai api key
-    # openai apibase，当use_azure_chatgpt为true时，需要设置对应的api base
+    # openai api base; when use_azure_chatgpt is true, set the matching api base
     "open_ai_api_base": "https://api.openai.com/v1",
     "claude_api_base": "https://api.anthropic.com/v1",  # claude api base
     "gemini_api_base": "https://generativelanguage.googleapis.com",  # gemini api base
     "custom_api_key": "",  # custom OpenAI-compatible provider api key (used when bot_type is "custom")
     "custom_api_base": "",  # custom OpenAI-compatible provider api base (used when bot_type is "custom")
-    "proxy": "",  # openai使用的代理
-    # chatgpt模型， 当use_azure_chatgpt为true时，其名称为Azure上model deployment名称
-    "model": "gpt-3.5-turbo",  # 可选择: gpt-4o, pt-4o-mini, gpt-4-turbo, claude-3-sonnet, wenxin, moonshot, qwen-turbo, xunfei, glm-4, minimax, gemini等模型，全部可选模型详见common/const.py文件
-    "bot_type": "",  # 可选配置，使用兼容openai格式的三方服务时候，需填"openai"或"custom"（custom模式下切换模型不会自动切换bot_type）。bot具体名称详见common/const.py文件，如不填根据model名称判断
-    "use_azure_chatgpt": False,  # 是否使用azure的chatgpt
-    "azure_deployment_id": "",  # azure 模型部署名称
-    "azure_api_version": "",  # azure api版本
-    # Bot触发配置
-    "single_chat_prefix": ["bot", "@bot"],  # 私聊时文本需要包含该前缀才能触发机器人回复
-    "single_chat_reply_prefix": "[bot] ",  # 私聊时自动回复的前缀，用于区分真人
-    "single_chat_reply_suffix": "",  # 私聊时自动回复的后缀，\n 可以换行
-    "group_chat_prefix": ["@bot"],  # 群聊时包含该前缀则会触发机器人回复
-    "no_need_at": False,  # 群聊回复时是否不需要艾特
-    "group_chat_reply_prefix": "",  # 群聊时自动回复的前缀
-    "group_chat_reply_suffix": "",  # 群聊时自动回复的后缀，\n 可以换行
-    "group_chat_keyword": [],  # 群聊时包含该关键词则会触发机器人回复
-    "group_at_off": False,  # 是否关闭群聊时@bot的触发
-    "group_name_white_list": ["ChatGPT测试群", "ChatGPT测试群2"],  # 开启自动回复的群名称列表
-    "group_name_keyword_white_list": [],  # 开启自动回复的群名称关键词列表
-    "group_chat_in_one_session": ["ChatGPT测试群"],  # 支持会话上下文共享的群名称
-    "group_shared_session": False,  # 群聊是否共享会话上下文（所有成员共享）。False时每个用户在群内有独立会话
-    "nick_name_black_list": [],  # 用户昵称黑名单
-    "group_welcome_msg": "",  # 配置新人进群固定欢迎语，不配置则使用随机风格欢迎
-    "trigger_by_self": False,  # 是否允许机器人触发
-    "text_to_image": "dall-e-2",  # 图片生成模型，可选 dall-e-2, dall-e-3
-    # Azure OpenAI dall-e-3 配置
-    "dalle3_image_style": "vivid", # 图片生成dalle3的风格，可选有 vivid, natural
-    "dalle3_image_quality": "hd", # 图片生成dalle3的质量，可选有 standard, hd
-    # Azure OpenAI DALL-E API 配置, 当use_azure_chatgpt为true时,用于将文字回复的资源和Dall-E的资源分开.
-    "azure_openai_dalle_api_base": "", # [可选] azure openai 用于回复图片的资源 endpoint，默认使用 open_ai_api_base
-    "azure_openai_dalle_api_key": "", # [可选] azure openai 用于回复图片的资源 key，默认使用 open_ai_api_key
-    "azure_openai_dalle_deployment_id":"", # [可选] azure openai 用于回复图片的资源 deployment id，默认使用 text_to_image
-    "image_proxy": True,  # 是否需要图片代理，国内访问LinkAI时需要
-    "image_create_prefix": ["画", "看", "找"],  # 开启图片回复的前缀
-    "concurrency_in_session": 1,  # 同一会话最多有多少条消息在处理中，大于1可能乱序
-    "image_create_size": "256x256",  # 图片大小,可选有 256x256, 512x512, 1024x1024 (dall-e-3默认为1024x1024)
+    "proxy": "",  # proxy used by openai
+    # chatgpt model; when use_azure_chatgpt is true, this is the Azure model deployment name
+    "model": "gpt-3.5-turbo",  # options: gpt-4o, gpt-4o-mini, gpt-4-turbo, claude-3-sonnet, wenxin, moonshot, qwen-turbo, xunfei, glm-4, minimax, gemini, etc. See common/const.py for the full list
+    "bot_type": "",  # optional; for OpenAI-compatible third-party services set "openai" or "custom" (in custom mode switching model won't auto-switch bot_type). See common/const.py for bot names; inferred from model name if left empty
+    "use_azure_chatgpt": False,  # whether to use Azure chatgpt
+    "azure_deployment_id": "",  # azure model deployment name
+    "azure_api_version": "",  # azure api version
+    # Bot trigger config
+    "single_chat_prefix": ["bot", "@bot"],  # text must contain this prefix to trigger a reply in single chat
+    "single_chat_reply_prefix": "[bot] ",  # auto-reply prefix in single chat, used to distinguish from a real person
+    "single_chat_reply_suffix": "",  # auto-reply suffix in single chat; \n inserts a line break
+    "group_chat_prefix": ["@bot"],  # messages containing this prefix trigger a reply in group chat
+    "no_need_at": False,  # whether replying in group chat does not require an @mention
+    "group_chat_reply_prefix": "",  # auto-reply prefix in group chat
+    "group_chat_reply_suffix": "",  # auto-reply suffix in group chat; \n inserts a line break
+    "group_chat_keyword": [],  # messages containing this keyword trigger a reply in group chat
+    "group_at_off": False,  # whether to disable @bot triggering in group chat
+    "group_name_white_list": ["group1", "group2"],  # group names where auto-reply is enabled
+    "group_name_keyword_white_list": [],  # group-name keywords where auto-reply is enabled
+    "group_chat_in_one_session": ["group1"],  # group names that share conversation context
+    "group_shared_session": False,  # whether group chat shares conversation context (all members share). When False each user has an independent session in the group
+    "nick_name_black_list": [],  # user nickname blacklist
+    "group_welcome_msg": "",  # fixed welcome message for new group members; uses a random style when empty
+    "trigger_by_self": False,  # whether the bot can be triggered by itself
+    "text_to_image": "dall-e-2",  # image generation model, options: dall-e-2, dall-e-3
+    # Azure OpenAI dall-e-3 config
+    "dalle3_image_style": "vivid", # dalle3 image style, options: vivid, natural
+    "dalle3_image_quality": "hd", # dalle3 image quality, options: standard, hd
+    # Azure OpenAI DALL-E API config; when use_azure_chatgpt is true, separates the text-reply resource from the DALL-E resource
+    "azure_openai_dalle_api_base": "", # [optional] azure openai endpoint for image replies; defaults to open_ai_api_base
+    "azure_openai_dalle_api_key": "", # [optional] azure openai key for image replies; defaults to open_ai_api_key
+    "azure_openai_dalle_deployment_id":"", # [optional] azure openai deployment id for image replies; defaults to text_to_image
+    "image_proxy": True,  # whether an image proxy is needed; required when accessing LinkAI from mainland China
+    "image_create_prefix": ["画", "看", "找"],  # prefixes that enable image replies
+    "concurrency_in_session": 1,  # max number of in-flight messages per session; values >1 may cause out-of-order replies
+    "image_create_size": "256x256",  # image size, options: 256x256, 512x512, 1024x1024 (dall-e-3 defaults to 1024x1024)
     "group_chat_exit_group": False,
-    # chatgpt会话参数
-    "expires_in_seconds": 3600,  # 无操作会话的过期时间
-    # 人格描述
-    "character_desc": "你是ChatGPT, 一个由OpenAI训练的大型语言模型, 你旨在回答并解决人们的任何问题，并且可以使用多种语言与人交流。",
-    "conversation_max_tokens": 1000,  # 支持上下文记忆的最多字符数
-    # chatgpt限流配置
-    "rate_limit_chatgpt": 20,  # chatgpt的调用频率限制
-    "rate_limit_dalle": 50,  # openai dalle的调用频率限制
-    # chatgpt api参数 参考https://platform.openai.com/docs/api-reference/chat/create
+    # chatgpt session params
+    "expires_in_seconds": 3600,  # idle session expiry time
+    # persona description (only used in chat mode)
+    "character_desc": "You are a helpful AI assistant. You aim to answer and solve any questions people have, and can communicate in multiple languages.",
+    "conversation_max_tokens": 1000,  # max characters of context memory
+    # chatgpt rate limit config
+    "rate_limit_chatgpt": 20,  # chatgpt call rate limit
+    "rate_limit_dalle": 50,  # openai dalle call rate limit
+    # chatgpt api params, see https://platform.openai.com/docs/api-reference/chat/create
     "temperature": 0.9,
     "top_p": 1,
     "frequency_penalty": 0,
     "presence_penalty": 0,
-    "request_timeout": 180,  # chatgpt请求超时时间，openai接口默认设置为600，对于难问题一般需要较长时间
-    "timeout": 120,  # chatgpt重试超时时间，在这个时间内，将会自动重试
-    # Baidu 文心一言参数
-    "baidu_wenxin_model": "eb-instant",  # 默认使用ERNIE-Bot-turbo模型
+    "request_timeout": 180,  # chatgpt request timeout; the openai api defaults to 600, hard questions usually need longer
+    "timeout": 120,  # chatgpt retry timeout; will auto-retry within this window
+    # Baidu Wenxin (ERNIE) params
+    "baidu_wenxin_model": "eb-instant",  # defaults to the ERNIE-Bot-turbo model
     "baidu_wenxin_api_key": "",  # Baidu api key
     "baidu_wenxin_secret_key": "",  # Baidu secret key
     "baidu_wenxin_prompt_enabled": False,  # Enable prompt if you are using ernie character model
     # Baidu Qianfan / ERNIE OpenAI-compatible API
     "qianfan_api_key": "",  # Baidu Qianfan API key in bce-v3 format
     "qianfan_api_base": "https://qianfan.baidubce.com/v2",  # Qianfan OpenAI-compatible API base
-    # 讯飞星火API
-    "xunfei_app_id": "",  # 讯飞应用ID
-    "xunfei_api_key": "",  # 讯飞 API key
-    "xunfei_api_secret": "",  # 讯飞 API secret
-    "xunfei_domain": "",  # 讯飞模型对应的domain参数，Spark4.0 Ultra为 4.0Ultra，其他模型详见: https://www.xfyun.cn/doc/spark/Web.html
-    "xunfei_spark_url": "",  # 讯飞模型对应的请求地址，Spark4.0 Ultra为 wss://spark-api.xf-yun.com/v4.0/chat，其他模型参考详见: https://www.xfyun.cn/doc/spark/Web.html
-    # claude 配置
+    # Xunfei Spark API
+    "xunfei_app_id": "",  # Xunfei app id
+    "xunfei_api_key": "",  # Xunfei API key
+    "xunfei_api_secret": "",  # Xunfei API secret
+    "xunfei_domain": "",  # Xunfei model domain param; for Spark4.0 Ultra it is 4.0Ultra, see https://www.xfyun.cn/doc/spark/Web.html for others
+    "xunfei_spark_url": "",  # Xunfei model request url; for Spark4.0 Ultra it is wss://spark-api.xf-yun.com/v4.0/chat, see https://www.xfyun.cn/doc/spark/Web.html for others
+    # claude config
     "claude_api_cookie": "",
     "claude_uuid": "",
     # claude api key
     "claude_api_key": "",
-    # 通义千问API, 获取方式查看文档 https://help.aliyun.com/document_detail/2587494.html
+    # Tongyi Qianwen API, see https://help.aliyun.com/document_detail/2587494.html for how to obtain
     "qwen_access_key_id": "",
     "qwen_access_key_secret": "",
     "qwen_agent_key": "",
     "qwen_app_id": "",
-    "qwen_node_id": "",  # 流程编排模型用到的id，如果没有用到qwen_node_id，请务必保持为空字符串
-    # 阿里灵积(通义新版sdk)模型api key
+    "qwen_node_id": "",  # id used by workflow-orchestration models; keep it an empty string if qwen_node_id is unused
+    # Alibaba Lingji (Tongyi new sdk) model api key
     "dashscope_api_key": "",
     # Google Gemini Api Key
     "gemini_api_key": "",
-    # Embedding 模型设置
-    "embedding_provider": "",  # 显式指定厂商：openai / linkai / dashscope / doubao / zhipu (与 bot_type 命名一致)
-    "embedding_model": "",     # 留空使用厂商默认 model
-    "embedding_dimensions": 0, # 留空/0 使用厂商默认维度（推荐统一 1024）
-    # 语音设置
-    "speech_recognition": True,  # 是否开启语音识别
-    "group_speech_recognition": False,  # 是否开启群组语音识别
-    "voice_reply_voice": False,  # 是否使用语音回复语音，需要设置对应语音合成引擎的api key
-    "always_reply_voice": False,  # 是否一直使用语音回复
-    "voice_to_text": "openai",  # 语音识别引擎，支持openai,baidu,google,azure,xunfei,ali
-    "text_to_voice": "openai",  # 语音合成引擎，支持openai,baidu,google,azure,xunfei,ali,pytts(offline),elevenlabs,edge(online)
+    # Embedding model config
+    "embedding_provider": "",  # explicitly set the provider: openai / linkai / dashscope / doubao / zhipu (aligned with bot_type naming)
+    "embedding_model": "",     # leave empty to use the provider's default model
+    "embedding_dimensions": 0, # leave empty/0 to use the provider's default dimension (1024 recommended for consistency)
+    # voice config
+    "speech_recognition": True,  # whether to enable speech recognition
+    "group_speech_recognition": False,  # whether to enable group speech recognition
+    "voice_reply_voice": False,  # whether to reply to voice with voice; requires the matching TTS engine api key
+    "always_reply_voice": False,  # whether to always reply with voice
+    "voice_to_text": "openai",  # speech recognition engine: openai,baidu,google,azure,xunfei,ali
+    "text_to_voice": "openai",  # TTS engine: openai,baidu,google,azure,xunfei,ali,pytts(offline),elevenlabs,edge(online)
     "text_to_voice_model": "tts-1",
     "tts_voice_id": "alloy",
-    # baidu 语音api配置， 使用百度语音识别和语音合成时需要
+    # baidu voice api config; required when using Baidu speech recognition and TTS
     "baidu_app_id": "",
     "baidu_api_key": "",
     "baidu_secret_key": "",
-    # 1536普通话(支持简单的英文识别) 1737英语 1637粤语 1837四川话 1936普通话远场
+    # 1536 Mandarin (with basic English) 1737 English 1637 Cantonese 1837 Sichuanese 1936 Mandarin far-field
     "baidu_dev_pid": 1536,
-    # azure 语音api配置， 使用azure语音识别和语音合成时需要
+    # azure voice api config; required when using Azure speech recognition and TTS
     "azure_voice_api_key": "",
     "azure_voice_region": "japaneast",
-    # elevenlabs 语音api配置
-    "xi_api_key": "",  # 获取ap的方法可以参考https://docs.elevenlabs.io/api-reference/quick-start/authentication
-    "xi_voice_id": "",  # ElevenLabs提供了9种英式、美式等英语发音id，分别是“Adam/Antoni/Arnold/Bella/Domi/Elli/Josh/Rachel/Sam”
-    # 服务时间限制
-    "chat_time_module": False,  # 是否开启服务时间限制
-    "chat_start_time": "00:00",  # 服务开始时间
-    "chat_stop_time": "24:00",  # 服务结束时间
-    # 翻译api
-    "translate": "baidu",  # 翻译api，支持baidu, youdao
-    # baidu翻译api的配置
-    "baidu_translate_app_id": "",  # 百度翻译api的appid
-    "baidu_translate_app_key": "",  # 百度翻译api的秘钥
-    # youdao翻译api的配置
-    "youdao_translate_app_key": "",  # 有道翻译api的应用ID
-    "youdao_translate_app_secret": "",  # 有道翻译api的应用密钥
-    # wechatmp的配置
-    "wechatmp_token": "",  # 微信公众平台的Token
-    "wechatmp_port": 8080,  # 微信公众平台的端口,需要端口转发到80或443
-    "wechatmp_app_id": "",  # 微信公众平台的appID
-    "wechatmp_app_secret": "",  # 微信公众平台的appsecret
-    "wechatmp_aes_key": "",  # 微信公众平台的EncodingAESKey，加密模式需要
-    # wechatcom的通用配置
-    "wechatcom_corp_id": "",  # 企业微信公司的corpID
-    # wechatcomapp的配置
-    "wechatcomapp_token": "",  # 企业微信app的token
-    "wechatcomapp_port": 9898,  # 企业微信app的服务端口,不需要端口转发
-    "wechatcomapp_secret": "",  # 企业微信app的secret
-    "wechatcomapp_agent_id": "",  # 企业微信app的agent_id
-    "wechatcomapp_aes_key": "",  # 企业微信app的aes_key
-    # 飞书配置
-    "feishu_port": 80,  # 飞书bot监听端口，仅webhook模式需要
-    "feishu_app_id": "",  # 飞书机器人应用APP Id
-    "feishu_app_secret": "",  # 飞书机器人APP secret
-    "feishu_token": "",  # 飞书 verification token，仅webhook模式需要
-    "feishu_event_mode": "websocket",  # 飞书事件接收模式: webhook(HTTP服务器) 或 websocket(长连接)
-    # 飞书流式回复（基于官方 cardkit 流式卡片 API，需要机器人开通 cardkit:card:write 权限，且飞书客户端 7.20+）
-    "feishu_stream_reply": True,  # 是否开启流式回复（打字机效果）。失败/老客户端自动降级为非流式或升级提示
-    # 钉钉配置
-    "dingtalk_client_id": "",  # 钉钉机器人Client ID 
-    "dingtalk_client_secret": "",  # 钉钉机器人Client Secret
+    # elevenlabs voice api config
+    "xi_api_key": "",  # see https://docs.elevenlabs.io/api-reference/quick-start/authentication for how to obtain the api key
+    "xi_voice_id": "",  # ElevenLabs offers 9 English voice ids: Adam/Antoni/Arnold/Bella/Domi/Elli/Josh/Rachel/Sam
+    # service time limit
+    "chat_time_module": False,  # whether to enable service-time limiting
+    "chat_start_time": "00:00",  # service start time
+    "chat_stop_time": "24:00",  # service stop time
+    # translation api
+    "translate": "baidu",  # translation api: baidu, youdao
+    # baidu translation api config
+    "baidu_translate_app_id": "",  # baidu translation api appid
+    "baidu_translate_app_key": "",  # baidu translation api secret key
+    # youdao translation api config
+    "youdao_translate_app_key": "",  # youdao translation api app id
+    "youdao_translate_app_secret": "",  # youdao translation api app secret
+    # wechatmp config
+    "wechatmp_token": "",  # WeChat Official Account token
+    "wechatmp_port": 8080,  # WeChat Official Account port; needs port forwarding to 80 or 443
+    "wechatmp_app_id": "",  # WeChat Official Account appID
+    "wechatmp_app_secret": "",  # WeChat Official Account appsecret
+    "wechatmp_aes_key": "",  # WeChat Official Account EncodingAESKey; required in encrypted mode
+    # wechatcom shared config
+    "wechatcom_corp_id": "",  # WeCom corp id
+    # wechatcomapp config
+    "wechatcomapp_token": "",  # WeCom app token
+    "wechatcomapp_port": 9898,  # WeCom app service port; no port forwarding needed
+    "wechatcomapp_secret": "",  # WeCom app secret
+    "wechatcomapp_agent_id": "",  # WeCom app agent_id
+    "wechatcomapp_aes_key": "",  # WeCom app aes_key
+    # WeChat Customer Service (wechat_kf) config
+    "wechat_kf_corp_id": "",  # corp_id of the company the WeChat Customer Service belongs to
+    "wechat_kf_token": "",  # WeChat Customer Service callback token
+    "wechat_kf_port": 9888,  # WeChat Customer Service callback service port
+    "wechat_kf_secret": "",  # WeChat Customer Service app secret
+    "wechat_kf_aes_key": "",  # WeChat Customer Service callback aes_key
+    "wechat_kf_cursor_path": "~/.wechat_kf_cursors.json",  # path for persisting the WeChat Customer Service sync_msg cursor
+    # Feishu config
+    "feishu_port": 80,  # Feishu bot listening port; only needed in webhook mode
+    "feishu_app_id": "",  # Feishu bot app id
+    "feishu_app_secret": "",  # Feishu bot app secret
+    "feishu_token": "",  # Feishu verification token; only needed in webhook mode
+    "feishu_event_mode": "websocket",  # Feishu event mode: webhook(HTTP server) or websocket(long connection)
+    # Feishu streaming reply (based on the official cardkit streaming-card API; requires the cardkit:card:write permission and Feishu client 7.20+)
+    "feishu_stream_reply": True,  # whether to enable streaming reply (typewriter effect); auto-downgrades to non-streaming or shows an upgrade prompt on failure/old clients
+    # DingTalk config
+    "dingtalk_client_id": "",  # DingTalk bot Client ID
+    "dingtalk_client_secret": "",  # DingTalk bot Client Secret
     "dingtalk_card_enabled": False,
-    # 企微智能机器人配置(长连接模式)
-    "wecom_bot_id": "",  # 企微智能机器人BotID
-    "wecom_bot_secret": "",  # 企微智能机器人长连接Secret
-    # 微信配置
-    "weixin_token": "",  # 微信登录后获取的bot_token，留空则启动时自动扫码登录
+    # WeCom smart bot config (long connection mode)
+    "wecom_bot_id": "",  # WeCom smart bot BotID
+    "wecom_bot_secret": "",  # WeCom smart bot long-connection secret
+    # Telegram config
+    "telegram_token": "",  # Bot token from @BotFather
+    "telegram_proxy": "",  # Optional HTTP/SOCKS5 proxy, e.g. http://127.0.0.1:7890 or socks5://127.0.0.1:1080 (empty falls back to env vars)
+    "telegram_group_trigger": "mention_or_reply",  # Group trigger: mention_or_reply(@ or reply, recommended) | mention_only(@ only) | all(every message)
+    "telegram_register_commands": True,  # Auto-register the BotFather command menu on startup (aligned with web slash commands)
+    # Slack config (Socket Mode, no public IP required)
+    "slack_bot_token": "",  # Bot User OAuth Token, like xoxb-...
+    "slack_app_token": "",  # App-Level Token (generated after enabling Socket Mode), like xapp-...
+    "slack_group_trigger": "mention_or_reply",  # Channel trigger: mention_or_reply(@ or reply in thread, recommended) | mention_only(@ only) | all(every message)
+    # Discord config (Gateway connection, no public IP required)
+    "discord_token": "",  # Discord Bot Token (generated on the Bot page of the Developer Portal)
+    "discord_group_trigger": "mention_or_reply",  # Channel trigger: mention_or_reply(@ or reply to bot, recommended) | mention_only(@ only) | all(every message)
+    # WeChat config
+    "weixin_token": "",  # bot_token obtained after WeChat login; leave empty to auto scan-login on startup
     "weixin_base_url": "https://ilinkai.weixin.qq.com",  # Weixin ilink API base URL
     "weixin_cdn_base_url": "https://novac2c.cdn.weixin.qq.com/c2c",  # CDN base URL
     "weixin_credentials_path": "~/.weixin_cow_credentials.json",  # credentials file path
-    # chatgpt指令自定义触发词
-    "clear_memory_commands": ["#清除记忆"],  # 重置会话指令，必须以#开头
-    # channel配置
-    "channel_type": "",  # 通道类型，支持多渠道同时运行。单个: "feishu"，多个: "feishu, dingtalk" 或 ["feishu", "dingtalk"]。可选值: web,feishu,dingtalk,wecom_bot,weixin,wechatmp,wechatmp_service,wechatcom_app
-    "web_console": True,  # 是否自动启动Web控制台（默认启动）。设为False可禁用
-    "subscribe_msg": "",  # 订阅消息, 支持: wechatmp, wechatmp_service, wechatcom_app
-    "debug": False,  # 是否开启debug模式，开启后会打印更多日志
-    "appdata_dir": "",  # 数据目录
-    # 插件配置
-    "plugin_trigger_prefix": "$",  # 规范插件提供聊天相关指令的前缀，建议不要和管理员指令前缀"#"冲突
-    # 是否使用全局插件配置
+    # custom trigger words for chatgpt commands
+    "clear_memory_commands": ["#清除记忆"],  # session-reset command; must start with #
+    # channel config
+    "channel_type": "",  # channel type; supports running multiple channels at once. Single: "feishu", multiple: "feishu, dingtalk" or ["feishu", "dingtalk"]. Options: web,feishu,dingtalk,wecom_bot,weixin,wechatmp,wechatmp_service,wechatcom_app,wechat_kf,telegram,slack,discord
+    "web_console": True,  # whether to auto-start the Web console (on by default). Set False to disable
+    "subscribe_msg": "",  # subscribe message; supported by: wechatmp, wechatmp_service, wechatcom_app
+    "debug": False,  # whether to enable debug mode; prints more logs when on
+    "appdata_dir": "",  # data directory
+    # plugin config
+    "plugin_trigger_prefix": "$",  # prefix for plugin chat commands; avoid clashing with the admin command prefix "#"
+    # whether to use the global plugin config
     "use_global_plugin_config": False,
-    "max_media_send_count": 3,  # 单次最大发送媒体资源的个数
-    "media_send_interval": 1,  # 发送图片的事件间隔，单位秒
-    # 智谱AI 平台配置
+    "max_media_send_count": 3,  # max number of media resources sent at once
+    "media_send_interval": 1,  # interval between sending images, in seconds
+    # Zhipu AI platform config
     "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",
-    # 豆包(火山方舟) 平台配置
+    # Doubao (Volcano Ark) platform config
     "ark_api_key": "",
     "ark_base_url": "https://ark.cn-beijing.volces.com/api/v3",
-    # 魔搭社区 平台配置
+    # ModelScope community platform config
     "modelscope_api_key": "",
     "modelscope_base_url": "https://api-inference.modelscope.cn/v1/chat/completions",
-    # LinkAI平台配置
+    # LinkAI platform config
     "use_linkai": False,
     "linkai_api_key": "",
     "linkai_app_code": "",
@@ -209,21 +235,22 @@ available_setting = {
     "Minimax_base_url": "",
     "deepseek_api_key": "",
     "deepseek_api_base": "https://api.deepseek.com/v1",
-    # 小米 MiMo 大模型
+    # Xiaomi MiMo LLM
     "mimo_api_key": "",
     "mimo_api_base": "https://api.xiaomimimo.com/v1",
     "web_host": "",  # Web console bind address; empty means auto
     "web_port": 9899,
     "web_password": "",  # Web console password; empty means no authentication required
     "web_session_expire_days": 30,  # Auth session expiry in days
-    "agent": True,  # 是否开启Agent模式
-    "agent_workspace": "~/cow",  # agent工作空间路径，用于存储skills、memory等
-    "agent_max_context_tokens": 50000,  # Agent模式下最大上下文tokens
-    "agent_max_context_turns": 20,  # Agent模式下最大上下文记忆轮次
-    "agent_max_steps": 20,  # Agent模式下单次运行最大决策步数
+    "web_file_serve_root": "~",  # Root dir the /api/file endpoint may serve; "/" allows the whole filesystem
+    "agent": True,  # whether to enable Agent mode
+    "agent_workspace": "~/cow",  # agent workspace path, used to store skills, memory, etc.
+    "agent_max_context_tokens": 50000,  # max context tokens in Agent mode
+    "agent_max_context_turns": 20,  # max context memory turns in Agent mode
+    "agent_max_steps": 20,  # max decision steps per run in Agent mode
     "enable_thinking": False,  # Enable deep-thinking mode for thinking-capable models
     "reasoning_effort": "high",  # Reasoning depth under thinking mode: "high" or "max"
-    "knowledge": True,  # 是否开启知识库功能
+    "knowledge": True,  # whether to enable the knowledge base feature
     "skill": {},  # Per-skill runtime config; nested keys flatten to SKILL_<NAME>_<KEY> env vars at startup
     "mcp_servers": [],  # MCP server list; each entry supports type "stdio" (local process) or "sse" (remote URL)
 }
@@ -236,7 +263,7 @@ class Config(dict):
             d = {}
         for k, v in d.items():
             self[k] = v
-        # user_datas: 用户数据，key为用户名，value为用户数据，也是dict
+        # user_datas: per-user data; key is the username, value is the user's data (also a dict)
         self.user_datas = {}
 
     def __getitem__(self, key):
@@ -246,11 +273,11 @@ class Config(dict):
         return super().__setitem__(key, value)
 
     def get(self, key, default=None):
-        # 跳过以下划线开头的注释字段
+        # skip comment fields starting with an underscore
         if key.startswith("_"):
             return super().get(key, default)
         
-        # 如果key不在available_setting中，直接走dict的get，返回config.json中实际加载的值（如不存在则返回default）
+        # if the key is not in available_setting, fall back to dict.get and return the value actually loaded from config.json (or default if absent)
         if key not in available_setting:
             return super().get(key, default)
         
@@ -317,7 +344,7 @@ def drag_sensitive(config):
 def load_config():
     global config
 
-    # 打印 ASCII Logo
+    # print ASCII logo
     logger.info("  ____                _                    _   ")
     logger.info(" / ___|_____      __ / \\   __ _  ___ _ __ | |_ ")
     logger.info("| |   / _ \\ \\ /\\ / // _ \\ / _` |/ _ \\ '_ \\| __|")
@@ -327,13 +354,13 @@ def load_config():
     logger.info("")
     config_path = "./config.json"
     if not os.path.exists(config_path):
-        logger.info("配置文件不存在，将使用config-template.json模板")
+        logger.info("config file not found, falling back to config-template.json")
         config_path = "./config-template.json"
 
     config_str = read_file(config_path)
     logger.debug("[INIT] config str: {}".format(drag_sensitive(config_str)))
 
-    # 将json字符串反序列化为dict类型。
+    # Deserialize the json string into a dict.
     # `object_pairs_hook` lets us catch users who accidentally typed the
     # same key twice (e.g. two `"tools"` blocks) — json.loads would
     # otherwise silently drop all but the last occurrence.
@@ -350,7 +377,7 @@ def load_config():
     # Some online deployment platforms (e.g. Railway) deploy project from github directly. So you shouldn't put your secrets like api key in a config file, instead use environment variables to override the default config.
     for name, value in os.environ.items():
         name = name.lower()
-        # 跳过以下划线开头的注释字段
+        # skip comment fields starting with an underscore
         if name.startswith("_"):
             continue
         if name in available_setting:
@@ -369,21 +396,26 @@ def load_config():
         logger.setLevel(logging.DEBUG)
         logger.debug("[INIT] set log level to DEBUG")
 
+    # Resolve the global UI language as early as possible so that every
+    # downstream layer (logs, CLI, agent prompts, channel replies) shares it.
+    resolved_lang = i18n.resolve_language(config.get("cow_lang", "auto"))
+
     logger.info("[INIT] load config: {}".format(drag_sensitive(config)))
 
-    # 打印系统初始化信息
+    # print system initialization info
     logger.info("[INIT] ========================================")
     logger.info("[INIT] System Initialization")
     logger.info("[INIT] ========================================")
+    logger.info("[INIT] Language: {}".format(resolved_lang))
     logger.info("[INIT] Channel: {}".format(config.get("channel_type", "unknown")))
     logger.info("[INIT] Model: {}".format(config.get("model", "unknown")))
 
-    # Agent模式信息
+    # Agent mode info
     if config.get("agent", True):
         workspace = config.get("agent_workspace", "~/cow")
         logger.info("[INIT] Mode: Agent (workspace: {})".format(workspace))
     else:
-        logger.info("[INIT] Mode: Chat (在config.json中设置 \"agent\":true 可启用Agent模式)")
+        logger.info("[INIT] Mode: Chat (set \"agent\":true in config.json to enable Agent mode)")
 
     logger.info("[INIT] Debug: {}".format(config.get("debug", False)))
     logger.info("[INIT] ========================================")
@@ -425,6 +457,11 @@ def load_config():
         "wechatmp_app_secret": "WECHATMP_APP_SECRET",
         "wechatcomapp_agent_id": "WECHATCOMAPP_AGENT_ID",
         "wechatcomapp_secret": "WECHATCOMAPP_SECRET",
+        "wechatcom_corp_id": "WECHATCOM_CORP_ID",
+        "wechat_kf_corp_id": "WECHAT_KF_CORP_ID",
+        "wechat_kf_secret": "WECHAT_KF_SECRET",
+        "wechat_kf_token": "WECHAT_KF_TOKEN",
+        "wechat_kf_aes_key": "WECHAT_KF_AES_KEY",
         "qq_app_id": "QQ_APP_ID",
         "qq_app_secret": "QQ_APP_SECRET",
         "weixin_token": "WEIXIN_TOKEN",
@@ -587,8 +624,8 @@ plugin_config = {}
 
 def write_plugin_config(pconf: dict):
     """
-    写入插件全局配置
-    :param pconf: 全量插件配置
+    Write the global plugin config.
+    :param pconf: the full plugin config
     """
     global plugin_config
     for k in pconf:
@@ -596,8 +633,8 @@ def write_plugin_config(pconf: dict):
 
 def remove_plugin_config(name: str):
     """
-    移除待重新加载的插件全局配置
-    :param name: 待重载的插件名
+    Remove the global config of a plugin pending reload.
+    :param name: name of the plugin to reload
     """
     global plugin_config
     plugin_config.pop(name.lower(), None)
@@ -605,12 +642,12 @@ def remove_plugin_config(name: str):
 
 def pconf(plugin_name: str) -> dict:
     """
-    根据插件名称获取配置
-    :param plugin_name: 插件名称
-    :return: 该插件的配置项
+    Get the config for a plugin by name.
+    :param plugin_name: plugin name
+    :return: the plugin's config
     """
     return plugin_config.get(plugin_name.lower())
 
 
-# 全局配置，用于存放全局生效的状态
+# global config holding globally-effective state
 global_config = {"admin_users": []}

docker/docker-compose.yml

@@ -8,6 +8,7 @@ services:
     ports:
       - "9899:9899"
     environment:
+      COW_LANG: 'auto'
       CHANNEL_TYPE: 'weixin'
       MODEL: 'deepseek-v4-flash'
       DEEPSEEK_API_KEY: ''

docs/channels/dingtalk.mdx

@@ -1,35 +1,35 @@
 ---
-title: 钉钉
-description: 将 CowAgent 接入钉钉应用
+title: DingTalk
+description: Integrate CowAgent into DingTalk application
 ---
 
-通过钉钉开放平台创建智能机器人应用，将 CowAgent 接入钉钉。
+Integrate CowAgent into DingTalk by creating an intelligent robot app on the DingTalk Open Platform.
 
-## 一、创建应用
+## 1. Create App
 
-1. 进入 [钉钉开发者后台](https://open-dev.dingtalk.com/fe/app#/corp/app)，登录后点击 **创建应用**，填写应用相关信息：
+1. Go to [DingTalk Developer Console](https://open-dev.dingtalk.com/fe/app#/corp/app), log in and click **Create App**, fill in the app information:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-create-app.png" width="800"/>
 
-2. 点击添加应用能力，选择 **机器人** 能力，点击 **添加**：
+2. Click **Add App Capability**, select **Robot** capability and click **Add**:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-add-bot.png" width="800"/>
 
-3. 配置机器人信息后点击 **发布**。发布后，点击 "**点击调试**"，会自动创建测试群聊，可在客户端查看：
+3. Configure the robot information and click **Publish**. After publishing, click "**Debug**" to automatically create a test group chat, which can be viewed in the client:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-config-bot.png" width="600"/>
 
-4. 点击 **版本管理与发布**，创建新版本发布：
+4. Click **Version Management & Release**, create a new version and publish:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-publish-bot.png" width="700"/>
 
-## 二、项目配置
+## 2. Project Configuration
 
-1. 点击 **凭证与基础信息**，获取 `Client ID` 和 `Client Secret`：
+1. Click **Credentials & Basic Info**, get the `Client ID` and `Client Secret`:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-get-secret.png" width="700"/>
 
-2. 将以下配置加入项目根目录的 `config.json` 文件：
+2. Add the following configuration to `config.json` in the project root:
 
 ```json
 {
@@ -39,18 +39,20 @@ description: 将 CowAgent 接入钉钉应用
 }
 ```
 
-3. 安装依赖：
+3. Install the dependency:
 
 ```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：
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-app-config.png" width="700"/>
+
+4. After starting the project, go to the DingTalk Developer Console, click **Event Subscription**, then click **Connection verified, verify channel**. When "**Connection successful**" is displayed, the configuration is complete:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-event-sub.png" width="700"/>
 
-## 三、使用
+## 3. Usage
 
-与机器人私聊或将机器人拉入企业群中均可开启对话：
+Chat privately with the robot or add it to an enterprise group to start a conversation:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-hosting-demo.png" width="650"/>

docs/channels/discord.mdx

@@ -0,0 +1,93 @@
+---
+title: Discord
+description: Integrate CowAgent with a Discord Bot
+---
+
+> Integrate CowAgent into Discord via a Discord Bot using the **Gateway** (persistent WebSocket). Supports direct messages (DM) and server channels (triggered by @mention or replying to the bot). The Gateway uses a persistent WebSocket connection — no public IP or callback URL required, works out of the box.
+
+## 1. Setup
+
+### Step 1: Create a Discord Application and Bot
+
+1. Open the [Discord Developer Portal](https://discord.com/developers/applications), click **New Application**, enter a name (e.g. `CowAgent`), and create it.
+2. Go to the **Bot** page in the left sidebar, click **Reset Token** to generate a Bot Token, then copy and store it safely (shown only once).
+
+<Tip>
+  This token is your bot's password — keep it secret. If it leaks, click **Reset Token** again on the Bot page to regenerate it.
+</Tip>
+
+### Step 2: Enable the Message Content Intent
+
+Reading message text in both DMs and channels depends on this privileged intent.
+
+1. On the **Bot** page, find **Privileged Gateway Intents**.
+2. Turn on **Message Content Intent** and save.
+
+<Note>
+  Without this intent enabled, incoming message content will be empty and the bot will not respond.
+</Note>
+
+### Step 3: Invite the Bot to a Server
+
+1. Go to **OAuth2 → URL Generator** in the left sidebar.
+2. Under **Scopes**, check `bot`.
+3. Under **Bot Permissions**, check at least: `Send Messages`, `Read Message History`, `Attach Files`, `View Channels`.
+4. Copy the generated authorization URL at the bottom, open it in a browser, and authorize it for your target server.
+
+<Note>
+  You can skip this step if you only need DMs, but you still need a DM channel with the bot (e.g. the user messages the bot directly).
+</Note>
+
+### Step 4: Connect to CowAgent
+
+<Tabs>
+  <Tab title="Web Console (Recommended)">
+    Open the Web Console (default `http://127.0.0.1:9899`), go to **Channels**, click **Add Channel**, choose **Discord**, paste the Bot Token, and click connect.
+  </Tab>
+  <Tab title="Config File">
+    Add the following to `config.json` and start Cow:
+
+    ```json
+    {
+      "channel_type": "discord",
+      "discord_token": "your-discord-bot-token",
+      "discord_group_trigger": "mention_or_reply"
+    }
+    ```
+
+    | Key | Description | Default |
+    | --- | --- | --- |
+    | `discord_token` | Bot Token generated on the Bot page of the Developer Portal | - |
+    | `discord_group_trigger` | Channel trigger: `mention_or_reply` (@ or reply to bot) / `mention_only` (@ only) / `all` (all messages) | `mention_or_reply` |
+  </Tab>
+</Tabs>
+
+The integration is ready when you see logs like:
+
+```
+[Discord] Bot logged in as CowAgent#1234 (id=123456789)
+[Discord] ✅ Discord bot ready, listening for messages
+```
+
+## 2. Capabilities
+
+| Feature | Support |
+| --- | --- |
+| Direct message (DM) | ✅ |
+| Server channel (@bot / reply to bot) | ✅ |
+| Text messages | ✅ send / receive |
+| Image messages | ✅ send / receive |
+| File messages | ✅ send / receive (PDF / Word / Excel, etc.) |
+
+<Note>
+  A single Discord message is capped at 2000 characters; long replies are automatically split across multiple messages by line breaks.
+</Note>
+
+## 3. Usage
+
+Once connected:
+
+- **Direct message (DM)**: find your bot in the server member list, click its avatar, and message it directly.
+- **Channel**: in a channel where the bot is invited, trigger it with `@your-bot hello` or by **replying to one of the bot's messages**.
+
+When sending an image or file, you can **add a text caption** (description / question) in the attachment input — the bot will answer based on both. Sending an attachment first and then a follow-up question also works; the two messages are merged automatically.

docs/channels/feishu.mdx

@@ -1,45 +1,44 @@
 ---
-title: 飞书
-description: 将 CowAgent 接入飞书应用
+title: Feishu (Lark)
+description: Integrate CowAgent into Feishu via a custom enterprise app
 ---
 
-> 通过飞书自建应用接入 CowAgent，支持单聊与群聊（@机器人），使用 WebSocket 长连接模式，无需公网 IP，支持流式打字机回复、语音消息收发。
+> Integrate CowAgent into Feishu via a custom enterprise app. Supports p2p chat and group chat (@bot), uses WebSocket long connection (no public IP needed), supports streaming typewriter replies and voice messages.
 
 <Note>
-  接入需要是飞书企业用户且具有企业管理权限。
+  You need to be a Feishu enterprise user with admin privileges.
 </Note>
 
-## 一、接入方式
+## 1. Setup
 
-### 方式一：扫码一键接入（推荐）
-
-启动 Cow 项目后在终端中即可完成扫码创建。或打开 Web 控制台（本地链接：http://127.0.0.1:9899 ），选择 **通道** 菜单，点击 **接入通道**，选择 **飞书**，点击 **一键创建飞书应用**，使用 **飞书 App** 扫描二维码即可自动完成应用创建并接入:
+### Option 1: One-click Scan to Create (Recommended)
 
+No need to manually create an app on the Feishu Developer Platform. Start the Cow project, open the web console (default `http://127.0.0.1:9899/`), go to **Channels**, click **Add Channel**, choose **Feishu**, then under the **Scan QR** tab click **One-click Create Feishu App** and scan with the **Feishu App** to complete app creation and connection automatically.
 
 <img src="https://cdn.link-ai.tech/doc/20260505181126.png" width="800"/>
 
-
 <Note>
-  1. `lark-oapi` 依赖版本需要 >=1.5.5
-  2. 扫码创建出的应用会自动预置全部所需权限（消息收发、卡片读写、群聊事件等）和事件订阅，无需到开发者后台手动配置。
+  1. Requires `lark-oapi` ≥ 1.5.5.
+  2. The created app comes with all required permissions (messaging, card read/write, group events, etc.) and event subscriptions pre-configured — no manual setup on the developer console needed. Currently only the Feishu mainland version is supported (Lark international not yet supported).
 </Note>
 
+When starting from CLI without `feishu_app_id` configured, the QR code is also printed to the terminal.
 
-### 方式二：手动创建接入
+### Option 2: Manual Setup
 
-需要先在飞书开放平台创建自建应用并配置权限，再通过 Web 控制台或配置文件接入。
+Manually create a custom app on the Feishu Developer Platform, then connect via Web Console or config file.
 
-**步骤一：创建应用**
+**Step 1: Create the App**
 
-1. 进入 [飞书开发平台](https://open.feishu.cn/app/)，点击 **创建企业自建应用**：
+1. Go to [Feishu Developer Platform](https://open.feishu.cn/app/), click **Create Enterprise Custom App**:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-create-app.jpg" width="500"/>
 
-2. 在 **添加应用能力** 中，为应用添加 **机器人** 能力：
+2. In **Add App Capabilities**, add the **Bot** capability:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-add-bot.jpg" width="800"/>
 
-3. 在 **权限管理** 中，将以下权限粘贴到输入框，全选并 **批量开通**：
+3. In **Permission Management**, paste the following permissions and **Batch Enable** all:
 
 ```
 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,cardkit:card:write
@@ -47,18 +46,18 @@ im:message,im:message.group_at_msg,im:message.group_at_msg:readonly,im:message.p
 
 <img src="https://cdn.link-ai.tech/doc/feishu-hosting-add-auth2.png" width="800"/>
 
-4. 在 **凭证与基础信息** 中获取 `App ID` 和 `App Secret`：
+4. Get `App ID` and `App Secret` from **Credentials & Basic Info**:
 
 <img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-appid-secret.jpg" width="800"/>
 
-**步骤二：接入 CowAgent**
+**Step 2: Connect to CowAgent**
 
 <Tabs>
-  <Tab title="Web 控制台">
-    打开 Web 控制台，选择 **通道** 菜单，点击 **接入通道**，选择 **飞书**，切换到「手动填写」Tab，输入 App ID 和 App Secret，点击接入即可。
+  <Tab title="Web Console">
+    Open the web console, go to **Channels**, click **Add Channel**, choose **Feishu**, switch to the **Manual** tab, enter App ID and App Secret, then click connect.
   </Tab>
-  <Tab title="配置文件">
-    在 `config.json` 中添加以下配置后启动程序：
+  <Tab title="Config File">
+    Add the following to `config.json` and start the program:
 
     ```json
     {
@@ -69,43 +68,43 @@ im:message,im:message.group_at_msg,im:message.group_at_msg:readonly,im:message.p
     }
     ```
 
-    | 参数 | 说明 | 默认值 |
+    | Parameter | Description | Default |
     | --- | --- | --- |
-    | `feishu_app_id` | 飞书应用 App ID | - |
-    | `feishu_app_secret` | 飞书应用 App Secret | - |
-    | `feishu_stream_reply` | 是否开启流式打字机回复 | `true` |
+    | `feishu_app_id` | Feishu app App ID | - |
+    | `feishu_app_secret` | Feishu app App Secret | - |
+    | `feishu_stream_reply` | Enable streaming typewriter reply | `true` |
   </Tab>
 </Tabs>
 
-**步骤三：发布应用**
+**Step 3: Publish the App**
 
-1. 启动 Cow 项目后，在飞书开放平台点击 **事件与回调**，选择 **长连接** 模式并保存：
+1. After Cow is running, go to **Events & Callbacks** in the Feishu Developer Platform, choose **Long Connection** mode and save:
 
 <img src="https://cdn.link-ai.tech/doc/202601311731183.png" width="600"/>
 
-2. 点击 **添加事件**，搜索 "接收消息"，选择 **接收消息 v2.0** 并确认。
+2. Click **Add Event**, search for "Receive Message" and choose **Receive Message v2.0**.
 
-3. 点击 **版本管理与发布**，创建版本并申请 **线上发布**，在飞书客户端审核通过：
+3. Click **Version Management & Release**, create a version and apply for **Production Release**. Approve the request in the Feishu client:
 
 <img src="https://cdn.link-ai.tech/doc/202601311807356.png" width="600"/>
 
-## 二、功能说明
+## 2. Features
 
-| 功能 | 支持情况 |
+| Feature | Status |
 | --- | --- |
-| 单聊 | ✅ |
-| 群聊（@机器人） | ✅ |
-| 文本消息 | ✅ 收发 |
-| 图片消息 | ✅ 收发 |
-| 语音消息 | ✅ 收发 |
-| 流式回复 | ✅（通过 `feishu_stream_reply` 配置控制，默认开启） |
+| P2P chat | ✅ |
+| Group chat (@bot) | ✅ |
+| Text messages | ✅ send/receive |
+| Image messages | ✅ send/receive |
+| Voice messages | ✅ send/receive |
+| Streaming reply | ✅ (powered by Feishu cardkit streaming card) |
 
 <Note>
-  流式回复需要机器人具备 `cardkit:card:write` 权限（一键创建已默认开通），且接收方飞书客户端版本 ≥ 7.20。低版本客户端会显示升级提示，权限或版本不满足时自动降级为普通文本回复。
+  Streaming reply requires the `cardkit:card:write` permission (already enabled by one-click creation) and Feishu client version ≥ 7.20. Older clients see an upgrade prompt; if the permission or version is not satisfied, replies fall back to plain text automatically.
 </Note>
 
-## 三、使用
+## 3. Usage
 
-完成接入后，在飞书中搜索机器人名称即可开始单聊对话。
+After connection, search for the bot name in Feishu to start a chat.
 
-如需在群聊中使用，将机器人添加到群中，@机器人发送消息即可。
+To use in groups, add the bot to a group and @-mention it.

docs/channels/index.mdx

@@ -1,39 +1,46 @@
 ---
-title: 通道概览
-description: CowAgent 支持的通道及能力矩阵
+title: Channels Overview
+description: Channels supported by CowAgent and their capability matrix
 ---
 
-CowAgent 支持接入多种聊天通道，启动时通过 `channel_type` 切换。Web 控制台默认开启，可与其他接入通道并行运行。
+CowAgent supports multiple chat channels. Switch between them at startup via `channel_type`. The Web Console is enabled by default and can run in parallel with other channels.
 
-## 能力矩阵
+## Capability Matrix
 
-下表汇总各通道支持的入站消息类型、机器人回复类型与群聊能力，方便按场景选择。
+The table below summarizes the inbound message types, bot reply types, and group chat capabilities supported by each channel, making it easy to choose by scenario.
 
-| 通道 | 文本 | 图片 | 文件 | 语音 | 群聊 |
+| Channel | Text | Image | File | Voice | Group Chat |
 | --- | :-: | :-: | :-: | :-: | :-: |
-| [微信](/channels/weixin) | ✅ | ✅ | ✅ | ✅ |  |
-| [Web 控制台](/channels/web) | ✅ | ✅ | ✅ | ✅ | |
-| [飞书](/channels/feishu) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [钉钉](/channels/dingtalk) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [企微智能机器人](/channels/wecom-bot) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [WeChat](/channels/weixin) | ✅ | ✅ | ✅ | ✅ |  |
+| [Web Console](/channels/web) | ✅ | ✅ | ✅ | ✅ | |
+| [Feishu](/channels/feishu) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [DingTalk](/channels/dingtalk) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [WeCom Bot](/channels/wecom-bot) | ✅ | ✅ | ✅ | ✅ | ✅ |
 | [QQ](/channels/qq) | ✅ | ✅ | ✅ | | ✅ |
-| [企业微信应用](/channels/wecom) | ✅ | ✅ | ✅ | ✅ | |
-| [公众号](/channels/wechatmp) | ✅ | ✅ | | ✅ | |
+| [WeCom App](/channels/wecom) | ✅ | ✅ | ✅ | ✅ | |
+| [Official Account](/channels/wechatmp) | ✅ | ✅ | | ✅ | |
+| [WeChat Customer Service](/channels/wechat-kf) | ✅ | ✅ | ✅ | ✅ | |
+| [Telegram](/channels/telegram) | ✅ | ✅ | ✅ | ✅ | ✅ |
+| [Slack](/channels/slack) | ✅ | ✅ | ✅ | | ✅ |
+| [Discord](/channels/discord) | ✅ | ✅ | ✅ | | ✅ |
 
-- **图片 / 文件 / 语音**列表示通道支持收发对应消息类型，具体细节详见各通道文档
-- **群聊**列指可识别并响应群消息
+- The **Image / File / Voice** columns indicate that the channel can send and receive the corresponding message types; see each channel's docs for details
+- The **Group Chat** column indicates the ability to recognize and respond to group messages
 
 <Tip>
-  每个通道的语音 / 图像能力依赖对应模型厂商的配置，详见 [模型概览](/models)。
+  The voice / image capabilities of each channel depend on the configuration of the corresponding model provider. See [Models Overview](/models/index) for details.
 </Tip>
 
-## 通道一览
+## Channel List
 
-- [Web 控制台](/channels/web) — 内置浏览器对话和管理面板，默认开启
-- [微信](/channels/weixin) — 通过个人微信扫码登录
-- [飞书](/channels/feishu) — 飞书自建机器人
-- [钉钉](/channels/dingtalk) — 钉钉自建机器人
-- [企微智能机器人](/channels/wecom-bot) — 企业微信智能机器人
-- [QQ](/channels/qq) — QQ 官方机器人开放平台
-- [企业微信应用](/channels/wecom) — 企业微信自建应用接入
-- [公众号](/channels/wechatmp) — 微信公众号（订阅号 / 服务号）
+- [Web Console](/channels/web) — built-in browser-based chat and management panel, enabled by default
+- [WeChat](/channels/weixin) — log in via personal WeChat QR scan
+- [Feishu](/channels/feishu) — Feishu custom bot
+- [DingTalk](/channels/dingtalk) — DingTalk custom bot
+- [WeCom Bot](/channels/wecom-bot) — WeCom AI Bot via WebSocket long connection
+- [QQ](/channels/qq) — QQ Official Bot open platform
+- [WeCom App](/channels/wecom) — WeCom custom app integration
+- [Official Account](/channels/wechatmp) — WeChat Official Account (subscription / service)
+- [Telegram](/channels/telegram) — global IM, 5-minute setup, no public IP needed
+- [Slack](/channels/slack) — team collaboration IM, Socket Mode integration, no public IP needed
+- [Discord](/channels/discord) — community IM, Gateway connection, no public IP needed

docs/channels/qq.mdx

@@ -1,44 +1,44 @@
 ---
-title: QQ 机器人
-description: 将 CowAgent 接入 QQ 机器人（WebSocket 长连接模式）
+title: QQ Bot
+description: Connect CowAgent to QQ Bot (WebSocket long connection)
 ---
 
-> 通过 QQ 开放平台的机器人接口接入 CowAgent，支持 QQ 单聊、QQ 群聊（@机器人）、频道消息和频道私信，无需公网 IP，使用 WebSocket 长连接模式。
+> Connect CowAgent via QQ Open Platform's bot API, supporting QQ direct messages, group chats (@bot), guild channel messages, and guild DMs. No public IP required — uses WebSocket long connection.
 
 <Note>
-  QQ 机器人通过 QQ 开放平台创建，使用 WebSocket 长连接接收消息，通过 OpenAPI 发送消息，无需公网 IP 和域名。
+  QQ Bot is created through the QQ Open Platform. It uses WebSocket long connection to receive messages and OpenAPI to send messages. No public IP or domain is required.
 </Note>
 
-## 一、创建 QQ 机器人
+## 1. Create a QQ Bot
 
-> 进入[QQ 开放平台](https://q.qq.com)，QQ扫码登录，如果未注册开放平台账号，请先完成[账号注册](https://q.qq.com/#/register)。
+> Visit the [QQ Open Platform](https://q.qq.com), sign in with QQ. If you haven't registered, please complete [account registration](https://q.qq.com/#/register) first.
 
-1.在 [QQ开放平台-机器人列表页](https://q.qq.com/#/apps)，点击创建机器人:
+1.Go to the [QQ Open Platform - Bot List](https://q.qq.com/#/apps), and click **Create Bot**:
 
 <img src="https://cdn.link-ai.tech/doc/20260317162900.png" width="800"/>
 
-2.填写机器人名称、头像等基本信息，完成创建：
+2.Fill in the bot name, avatar, and other basic information to complete the creation:
 
 <img src="https://cdn.link-ai.tech/doc/20260317163005.png" width="800"/>
 
-3.点击进入机器人配置页面，选择**开发管理**菜单，完成以下步骤：
+3.Enter the bot configuration page, go to **Development Management**, and complete the following steps:
 
-  - 复制并记录 **AppID**（机器人ID）
-  - 生成并记录 **AppSecret**（机器人秘钥）
+  - Copy and save the **AppID** (Bot ID)
+  - Generate and save the **AppSecret** (Bot Secret)
 
 <img src="https://cdn.link-ai.tech/doc/20260317164955.png" width="800"/>
 
-## 二、配置和运行
+## 2. Configuration and Running
 
-### 方式一：Web 控制台接入
+### Option A: Web Console
 
-启动 Cow项目后打开 Web 控制台 (本地链接为: http://127.0.0.1:9899/ )，选择 **通道** 菜单，点击 **接入通道**，选择 **QQ 机器人**，填写上一步保存的 AppID 和 AppSecret，点击接入即可。
+Start the program and open the Web console (local access: http://127.0.0.1:9899/). Go to the **Channels** tab, click **Connect Channel**, select **QQ Bot**, fill in the AppID and AppSecret from the previous step, and click Connect.
 
 <img src="https://cdn.link-ai.tech/doc/20260317165425.png" width="800"/>
 
-### 方式二：配置文件接入
+### Option B: Config File
 
-在 `config.json` 中添加以下配置：
+Add the following to your `config.json`:
 
 ```json
 {
@@ -48,41 +48,41 @@ description: 将 CowAgent 接入 QQ 机器人（WebSocket 长连接模式）
 }
 ```
 
-| 参数 | 说明 |
+| Parameter | Description |
 | --- | --- |
-| `qq_app_id` | QQ 机器人的 AppID，在开放平台开发管理中获取 |
-| `qq_app_secret` | QQ 机器人的 AppSecret，在开放平台开发管理中获取 |
+| `qq_app_id` | AppID of the QQ Bot, found in Development Management on the open platform |
+| `qq_app_secret` | AppSecret of the QQ Bot, found in Development Management on the open platform |
 
-配置完成后启动程序，日志显示 `[QQ] ✅ Connected successfully` 即表示连接成功。
+After configuration, start the program. The log message `[QQ] ✅ Connected successfully` indicates a successful connection.
 
 
-## 三、使用
+## 3. Usage
 
-在 QQ开放平台 - 管理 - **使用范围和人员** 菜单中，使用QQ客户端扫描 "添加到群和消息列表" 的二维码，即可开始与QQ机器人的聊天：
+In the QQ Open Platform, go to **Management → Usage Scope & Members**, scan the "Add to group and message list" QR code with your QQ client to start chatting with the bot:
 
 <img src="https://cdn.link-ai.tech/doc/20260317165947.png" width="800"/>
 
-对话效果：
+Chat example:
 <img src="https://cdn.link-ai.tech/doc/20260317171508.png" width="800"/>
 
-## 四、功能说明
+## 4. Supported Features
 
-> 注意：若需在群聊及频道中使用QQ机器人，需完成发布上架审核并在使用范围配置权限使用范围。
+> Note: To use the QQ bot in group chats and guild channels, you need to complete the publishing review and configure usage scope permissions.
 
-| 功能 | 支持情况 |
+| Feature | Status |
 | --- | --- |
-| QQ 单聊 | ✅ |
-| QQ 群聊（@机器人） | ✅ |
-| 频道消息（@机器人） | ✅ |
-| 频道私信 | ✅ |
-| 文本消息 | ✅ 收发 |
-| 图片消息 | ✅ 收发（群聊和单聊） |
-| 文件消息 | ✅ 发送（群聊和单聊） |
-| 定时任务 | ✅ 主动推送（每月每用户限 4 条） |
+| QQ Direct Messages | ✅ |
+| QQ Group Chat (@bot) | ✅ |
+| Guild Channel (@bot) | ✅ |
+| Guild DM | ✅ |
+| Text Messages | ✅ Send & Receive |
+| Image Messages | ✅ Send & Receive (group & direct) |
+| File Messages | ✅ Send (group & direct) |
+| Scheduled Tasks | ✅ Active push (4 per user per month) |
 
 
-## 五、注意事项
+## 5. Notes
 
-- **被动消息限制**：QQ 单聊被动消息有效期为 60 分钟，每条消息最多回复 5 次；QQ 群聊被动消息有效期为 5 分钟。
-- **主动消息限制**：单聊和群聊每月主动消息上限为 4 条，在使用定时任务功能时需要注意这个限制
-- **事件权限**：默认订阅 `GROUP_AND_C2C_EVENT`（QQ群/单聊）和 `PUBLIC_GUILD_MESSAGES`（频道公域消息），如需其他事件类型请在开放平台申请权限。
+- **Passive message limits**: QQ direct message replies are valid for 60 minutes (max 5 replies per message); group chat replies are valid for 5 minutes.
+- **Active message limits**: Both direct and group chats have a monthly limit of 4 active messages. Keep this in mind when using the scheduled tasks feature.
+- **Event permissions**: By default, `GROUP_AND_C2C_EVENT` (QQ group/direct) and `PUBLIC_GUILD_MESSAGES` (guild public messages) are subscribed. Apply for additional permissions on the open platform if needed.

docs/channels/slack.mdx

@@ -0,0 +1,118 @@
+---
+title: Slack
+description: Integrate CowAgent with a Slack App
+---
+
+> Integrate CowAgent into Slack via a Slack App in **Socket Mode**. Supports direct messages (DM) and channels (triggered by @mention or replying within a thread). Socket Mode uses a persistent WebSocket connection — no public IP or callback URL required, works out of the box.
+
+## 1. Setup
+
+### Step 1: Create a Slack App
+
+1. Open the [Slack API apps page](https://api.slack.com/apps), click **Create New App** → **From scratch**.
+2. Enter an **App Name** (e.g. `CowAgent`), pick the **Workspace** to install into, and create it.
+
+### Step 2: Enable Socket Mode and get the App Token
+
+1. In the left sidebar go to **Settings → Socket Mode** and turn on **Enable Socket Mode**.
+2. You will be prompted to generate an **App-Level Token** with the `connections:write` scope. Save this token starting with `xapp-`.
+
+<Tip>
+  Socket Mode receives events over a WebSocket connection, so you don't need to expose a public callback URL — ideal for local or intranet deployments.
+</Tip>
+
+### Step 3: Configure bot scopes and install
+
+1. Go to **Features → OAuth & Permissions**, click **Add an OAuth Scope** under **Bot Token Scopes**, and add the following scopes one by one:
+
+   ```
+   app_mentions:read
+   channels:history
+   chat:write
+   commands
+   files:read
+   files:write
+   groups:history
+   im:history
+   mpim:history
+   users:read
+   ```
+
+   <Note>
+     `files:read` / `files:write` are used for sending/receiving images and files; omit them if you only need text conversations.
+   </Note>
+
+2. Go to **Features → Event Subscriptions**, turn on **Enable Events**, and under **Subscribe to bot events** click **Add Bot User Event** to add:
+
+   ```
+   app_mention
+   message.im
+   message.channels
+   ```
+
+   <Note>
+     Add `message.groups` if you need to use the bot in private channels.
+   </Note>
+3. Go to **Features → App Home**, enable **Messages Tab** under **Show Tabs**, and check **Allow users to send Slash commands and messages from the messages tab**. Otherwise the DM input box is disabled and users cannot message the bot.
+4. Back in **OAuth & Permissions**, click **Install to Workspace**. After installing, copy the **Bot User OAuth Token** starting with `xoxb-`.
+
+<Tip>
+  If the Slack client still shows "Sending messages to this app has been turned off", make sure you completed the App Home step above, then refresh or restart the Slack client (remove the app from your conversations and reopen it if needed).
+</Tip>
+
+### Step 4: Connect to CowAgent
+
+<Tabs>
+  <Tab title="Web Console (Recommended)">
+    Open the Web Console (default `http://127.0.0.1:9899`), go to **Channels**, click **Add Channel**, choose **Slack**, paste the Bot Token (`xoxb-`) and App Token (`xapp-`), and click connect.
+  </Tab>
+  <Tab title="Config File">
+    Add the following to `config.json` and start Cow:
+
+    ```json
+    {
+      "channel_type": "slack",
+      "slack_bot_token": "xoxb-xxxxxxxxxxxx",
+      "slack_app_token": "xapp-xxxxxxxxxxxx",
+      "slack_group_trigger": "mention_or_reply"
+    }
+    ```
+
+    | Key | Description | Default |
+    | --- | --- | --- |
+    | `slack_bot_token` | Bot User OAuth Token, like `xoxb-...` | - |
+    | `slack_app_token` | App-Level Token (generated after enabling Socket Mode), like `xapp-...` | - |
+    | `slack_group_trigger` | Channel trigger: `mention_or_reply` (@ or reply in thread) / `mention_only` (@ only) / `all` (all messages) | `mention_or_reply` |
+  </Tab>
+</Tabs>
+
+The integration is ready when you see logs like:
+
+```
+[Slack] Bot logged in as user_id=U0XXXXXXX, team=Txxxxxxxx
+[Slack] ✅ Slack bot ready, listening for events
+```
+
+## 2. Capabilities
+
+| Feature | Support |
+| --- | --- |
+| Direct message (DM) | ✅ |
+| Channel (@bot / reply in thread) | ✅ |
+| Text messages | ✅ send / receive |
+| Image messages | ✅ send / receive |
+| File messages | ✅ send / receive (PDF / Word / Excel, etc.) |
+| Thread replies | ✅ replies are posted to the thread of the triggering message |
+
+<Note>
+  Slack organizes conversations into threads. The bot posts replies into the thread of the triggering message, keeping channels tidy.
+</Note>
+
+## 3. Usage
+
+Once connected:
+
+- **Direct message (DM)**: find your App under **Apps** in the Slack sidebar and message it directly.
+- **Channel**: invite the App into a channel (`/invite @your-app`), then trigger it with `@your-app hello`; continue the conversation by replying within the same thread.
+
+When sending an image or file, you can **add a text caption** (description / question) in the attachment input — the bot will answer based on both. Sending an attachment first and then a follow-up question also works; the two messages are merged automatically.

docs/channels/telegram.mdx

@@ -0,0 +1,111 @@
+---
+title: Telegram
+description: Integrate CowAgent with Telegram via the Bot API
+---
+
+> Integrate CowAgent into Telegram via the official Bot API. Supports private chat and group chat (triggered by @mention or replying to the bot). Uses Long Polling — no public IP required, works out of the box.
+
+
+## 1. Setup
+
+### Step 1: Create a Bot via BotFather
+
+1. Open the official account [@BotFather](https://t.me/BotFather) in Telegram.
+2. Send `/newbot` and follow the prompts:
+   - **Bot name** (display name, e.g. `My CowAgent Bot`)
+   - **Bot username** (must end with `bot`, e.g. `my_cowagent_bot`)
+3. Once created, BotFather returns an **HTTP API Token** (e.g. `123456789:ABCdefGhIJKlmNoPQRsTUVwxyZ`). Keep it safe.
+
+<Tip>
+  The token is the password of your bot — never share it. If it leaks, send `/revoke` to `@BotFather` to reset it.
+</Tip>
+
+### Step 2: (Group chat only) Disable Privacy Mode
+
+Skip this step if you only use private chat. Telegram bots run in **Privacy Mode** by default — in groups they can only see commands suffixed with `@bot` (e.g. `/start@your_bot`) and replies to bot messages; **plain `@bot hello` text messages are not delivered**, so the bot will appear unresponsive in groups.
+
+Send the following to `@BotFather`:
+
+1. `/setprivacy`
+2. Pick the bot you just created
+3. Choose `Disable`
+
+<Note>
+  If the bot is still silent in groups after this, try removing it from the group and adding it back.
+</Note>
+
+### Step 3: Connect to CowAgent
+
+<Tabs>
+  <Tab title="Web Console (Recommended)">
+    Open the Web Console (default `http://127.0.0.1:9899`), go to **Channels**, click **Add Channel**, choose **Telegram**, paste the Bot Token, and click connect.
+  </Tab>
+  <Tab title="Config File">
+    Add the following to `config.json` and start Cow:
+
+    ```json
+    {
+      "channel_type": "telegram",
+      "telegram_token": "123456789:ABCdefGhIJKlmNoPQRsTUVwxyZ",
+      "telegram_group_trigger": "mention_or_reply"
+    }
+    ```
+
+    | Key | Description | Default |
+    | --- | --- | --- |
+    | `telegram_token` | HTTP API Token returned by BotFather | - |
+    | `telegram_group_trigger` | Group trigger: `mention_or_reply` (@ or reply) / `mention_only` (@ only) / `all` (all messages) | `mention_or_reply` |
+    | `telegram_register_commands` | Whether to register the command menu with BotFather on startup | `true` |
+  </Tab>
+</Tabs>
+
+The integration is ready when you see logs like:
+
+```
+[Telegram] Bot logged in as @my_cowagent_bot (id=123456789)
+[Telegram] Registered 10 bot commands
+[Telegram] ✅ Telegram bot ready, polling for updates
+```
+
+## 2. Capabilities
+
+| Feature | Support |
+| --- | --- |
+| Private chat | ✅ |
+| Group chat (@bot / reply to bot) | ✅ |
+| Text messages | ✅ send / receive |
+| Image messages | ✅ send / receive |
+| Voice messages | ✅ send / receive (OGG/Opus) |
+| Video messages | ✅ send / receive |
+| File messages | ✅ send / receive (PDF / Word / Excel, etc.) |
+| Command menu | ✅ aligned with Web Console slash commands |
+
+### Command Menu
+
+On startup, the channel registers a command menu with BotFather. Typing `/` in Telegram shows a dropdown:
+
+| Command | Description |
+| --- | --- |
+| `/help` | Show command help |
+| `/status` | View runtime status |
+| `/context` | View conversation context (`/context clear` to clear) |
+| `/skill` | Skill management (`/skill list`, `/skill install`, ...) |
+| `/memory` | Memory management (`/memory dream`) |
+| `/knowledge` | Knowledge base (`/knowledge list` / `on` / `off`) |
+| `/config` | View current config |
+| `/cancel` | Cancel the running Agent task |
+| `/logs` | View recent logs |
+| `/version` | Show version |
+
+<Note>
+  Telegram's command menu only displays top-level commands; subcommands are entered with a space, e.g. `/skill list`, `/context clear`.
+</Note>
+
+## 3. Usage
+
+Once connected:
+
+- **Private chat**: search for your bot username (e.g. `@my_cowagent_bot`) in Telegram, click `Start` and chat away.
+- **Group chat**: add the bot to a group, then trigger it with `@bot hello` or by **replying to one of the bot's messages**. If the bot doesn't respond in groups, double-check Privacy Mode in [Step 2](#step-2-group-chat-only-disable-privacy-mode).
+
+When sending an image or file, you can **add a caption** (description / question) directly in the attachment input — the bot will answer based on both. Sending an attachment first and then a follow-up question also works; the two messages are merged automatically.

docs/channels/web.mdx

@@ -1,11 +1,11 @@
 ---
-title: Web 控制台
-description: 通过 Web 控制台使用 CowAgent
+title: Web Console
+description: Use CowAgent through the Web Console
 ---
 
-Web 控制台是 CowAgent 的默认通道，启动后会自动运行，通过浏览器即可与 Agent 对话，并支持在线管理模型、技能、记忆、通道等配置。
+The Web Console is CowAgent's default channel. It runs automatically once started, letting you chat with the Agent in a browser and manage models, skills, memory, channels, and other configuration online.
 
-## 配置
+## Configuration
 
 ```json
 {
@@ -17,78 +17,79 @@ Web 控制台是 CowAgent 的默认通道，启动后会自动运行，通过浏
 }
 ```
 
-| 参数 | 说明 | 默认值 |
+| Parameter | Description | Default |
 | --- | --- | --- |
-| `channel_type` | 设为 `web` | `web` |
-| `web_host` | Web 服务监听地址，默认监听 `127.0.0.1`（仅本机），如需公网访问请改为 `0.0.0.0` 并设置密码 | `""` |
-| `web_port` | Web 服务监听端口 | `9899` |
-| `web_password` | 访问密码，留空表示不启用密码保护；监听 `0.0.0.0` 时建议设置 | `""` |
-| `web_session_expire_days` | 登录会话有效天数 | `30` |
-| `enable_thinking` | 是否启用深度思考模式 | `false` |
+| `channel_type` | Set to `web` | `web` |
+| `web_host` | Web service listen address. Defaults to `127.0.0.1` (local only); set to `0.0.0.0` for public access and configure a password | `""` |
+| `web_port` | Web service listen port | `9899` |
+| `web_password` | Access password. Leave empty to disable password protection; recommended when listening on `0.0.0.0` | `""` |
+| `web_session_expire_days` | Login session validity in days | `30` |
+| `web_file_serve_root` | Root directory the web console can directly read/send files from. Defaults to the user home dir and agent workspace only; set to `/` to allow the whole filesystem | `"~"` |
+| `enable_thinking` | Whether to enable deep thinking mode | `false` |
 
-配置密码后，访问控制台时需先输入密码完成登录。登录状态默认保持 30 天，期间重启服务也无需重新登录。密码也支持在控制台的「配置」页面中在线修改。
+Once a password is configured, you must enter it to log in when accessing the console. The login session is kept for 30 days by default, so restarting the service during that period does not require re-login. The password can also be changed online from the "Configuration" page in the console.
 
-## 访问地址
+## Access URL
 
-启动项目后访问：
+After starting the project, visit:
 
-- 本地运行：`http://localhost:9899`
-- 服务器运行：`http://<server-ip>:9899`
+- Local: `http://localhost:9899`
+- Server: `http://<server-ip>:9899`
 
 <Note>
-  请确保服务器防火墙和安全组已放行对应端口。
+  Ensure the server firewall and security group allow the corresponding port.
 </Note>
 
-## 功能介绍
+## Features
 
-### 对话界面
+### Chat Interface
 
-支持流式输出，可实时展示 Agent 的思考过程（Reasoning）和工具调用过程（Tool Calls），更直观地观察 Agent 的决策过程。深度思考功能可通过配置或控制台的「Agent 配置」开关控制。
+Supports streaming output with real-time display of the Agent's reasoning process and tool calls, providing intuitive observation of the Agent's decision-making. Deep thinking can be toggled via configuration or the "Agent Configuration" switch in the console.
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260227180120.png" />
 
-#### 多会话管理
+#### Multi-Session Management
 
-对话界面支持多会话（Session）管理，所有会话记录持久化存储在数据库中：
+The chat interface supports multi-session management. All session records are persistently stored in the database:
 
-- **会话列表**：点击左侧历史会话图标可展开/收起会话列表面板，支持滚动加载全部历史会话
-- **AI 生成标题**：新会话在首轮对话完成后，自动调用模型生成简短的会话摘要标题
-- **新建会话**：点击会话列表顶部的「新对话」按钮或输入区的 `+` 按钮创建新会话
-- **删除会话**：点击会话项的删除按钮，确认后永久删除该会话及其所有消息
-- **清除上下文**：点击输入区的清除按钮，在当前会话中插入一条分隔线，分隔线以上的消息仍然展示但不再作为模型的上下文输入
+- **Session List**: Click the history icon on the left to expand/collapse the session list panel, with scroll-to-load support for all historical sessions
+- **AI-Generated Titles**: After the first exchange in a new session, the model is automatically called to generate a short summary title
+- **New Session**: Click the "New Chat" button at the top of the session list or the `+` button in the input area to create a new session
+- **Delete Session**: Click the delete button on a session item and confirm to permanently delete the session and all its messages
+- **Clear Context**: Click the clear button in the input area to insert a divider in the current session. Messages above the divider are still displayed but no longer included as context for the model
 
-### 模型管理
+### Model Management
 
-支持在线管理不同模型厂商的文本、图像、语音、向量模型配置，无需手动编辑配置文件：
+Manage text, image, voice, and embedding model configurations for different providers online — no need to edit config files manually:
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260521212949.png" />
 
-### 技能管理
+### Skill Management
 
-支持在线查看和管理 Agent 技能（Skills）：
+View and manage Agent skills (Skills) online:
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260227173403.png" />
 
-### 记忆管理
+### Memory Management
 
-支持在线查看和管理 Agent 记忆：
+View and manage Agent memory online:
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260227173349.png" />
 
-### 通道管理
+### Channel Management
 
-支持在线管理接入通道，支持实时连接/断开操作：
+Manage connected channels online with real-time connect/disconnect operations:
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260227173331.png" />
 
-### 定时任务
+### Scheduled Tasks
 
-支持在线查看和管理定时任务，包括一次性任务、固定间隔、Cron 表达式等多种调度方式的可视化管理：
+View and manage scheduled tasks online, including one-time tasks, fixed intervals, and Cron expressions:
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260227173704.png" />
 
-### 日志
+### Logs
 
-支持在线实时查看 Agent 运行日志，便于监控运行状态和排查问题：
+View Agent runtime logs in real time for monitoring and troubleshooting:
 
 <img width="850" src="https://cdn.link-ai.tech/doc/20260227173514.png" />

docs/channels/wechat-kf.mdx

@@ -0,0 +1,130 @@
+---
+title: WeChat Customer Service
+description: Integrate CowAgent into WeChat Customer Service
+---
+
+By binding a WeCom custom enterprise app to a WeChat Customer Service account, CowAgent can take over inbound inquiries from external WeChat users and serve them through links or QR codes embedded in WeChat Mini Programs, Official Accounts, Video Channels, and Video Channel stores.
+
+<Note>
+  WeChat Customer Service only supports Docker deployment or server Python deployment. A publicly reachable callback URL is required; local run mode is not supported.
+</Note>
+
+## 1. Prerequisites
+
+Required resources:
+
+1. A server with a public IP
+2. A registered and verified WeCom account
+3. WeChat Customer Service capability enabled
+
+<Note>
+  It is recommended to create a **dedicated** WeCom custom app for Customer Service rather than reusing the existing `wechatcom_app` one — otherwise the two channels will compete for the same callback URL.
+</Note>
+
+## 2. Create a WeCom Custom App
+
+1. In the [WeCom Admin Console](https://work.weixin.qq.com/wework_admin/frame#apps), go to **Application Management → Create Application**:
+
+<img src="https://cdn.link-ai.tech/doc/20260228103156.png" width="480"/>
+
+2. Click **My Enterprise** and find the **Corp ID** at the bottom of the page (it goes into `wechat_kf_corp_id`):
+
+<img src="https://cdn.link-ai.tech/doc/wechatcom-hosting-companyid.png" width="600"/>
+
+3. Open the app you just created and click **"View"** next to Secret. The Secret will be pushed to the admin's phone via the WeCom app, where it can be viewed:
+
+<img src="https://cdn.link-ai.tech/doc/wechatcom-hosting-agent-secret.png" width="600"/>
+
+4. Open the app's **Receive Messages → Set API Reception** page, click **"Random Generate"** to generate the **Token** and **EncodingAESKey**, and save them:
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/wechatcom-hosting-token-aeskey.jpg" width="600"/>
+
+<Note>
+  Saving the API reception configuration will fail at this point because the program has not started yet. Come back to save it after the project is running.
+</Note>
+
+## 3. Configuration and Run
+
+Fill in the 4 fields collected from the previous step (Corp ID / Secret / Token / EncodingAESKey):
+
+<Tabs>
+  <Tab title="Web Console">
+    Start the Cow project and open the Web Console. Go to the **Channels** menu, click **Connect**, choose **WeChat Customer Service**, fill in Corp ID / Secret / Token / AES Key (port defaults to 9888, configurable), and click Connect.
+
+    <img src="https://cdn.link-ai.tech/doc/cow-weixinkefu-web-control.png" width="800"/>
+  </Tab>
+  <Tab title="Config File">
+    Add the following configuration to `config.json` (each parameter maps to a field shown in the screenshots above):
+
+    ```json
+    {
+      "channel_type": "wechat_kf",
+      "wechat_kf_corp_id": "YOUR_CORP_ID",
+      "wechat_kf_secret": "YOUR_SECRET",
+      "wechat_kf_token": "YOUR_TOKEN",
+      "wechat_kf_aes_key": "YOUR_AES_KEY",
+      "wechat_kf_port": 9888
+    }
+    ```
+
+    | Parameter | Description |
+    | --- | --- |
+    | `wechat_kf_corp_id` | Corp ID |
+    | `wechat_kf_secret` | Secret of the WeCom custom app bound to Customer Service |
+    | `wechat_kf_token` | Token from the API reception config |
+    | `wechat_kf_aes_key` | EncodingAESKey from the API reception config |
+    | `wechat_kf_port` | Listening port, default 9888 |
+  </Tab>
+</Tabs>
+
+After connecting, start the program (the Web Console method restarts the channel automatically). When the log shows `Listening on http://0.0.0.0:9888/wxkf/`, the program is running successfully. You need to open this port externally (e.g., allow it in the cloud server security group).
+
+Then go back to **Receive Messages → Set API Reception** in the WeCom console and set the callback URL to `http://<your-host>:9888/wxkf/`, then click Save. After saving successfully, you also need to add the server IP to **Enterprise Trusted IPs**, otherwise messages cannot be sent or received:
+
+<img src="https://cdn.link-ai.tech/doc/wechat-com_config.png" width="600"/>
+
+<img src="https://cdn.link-ai.tech/doc/20260228103224.png" width="520"/>
+
+<Warning>
+  If URL verification fails or the configuration is unsuccessful:
+  1. Ensure the server firewall is disabled and the security group allows the listening port (default 9888)
+  2. Carefully check that Token, Secret, EncodingAESKey and other parameters are consistent, and the URL format is correct
+  3. Verified WeCom accounts must use a filed domain matching the entity
+</Warning>
+
+## 4. Bind a WeChat Customer Service Account
+
+In the WeCom Admin Console, go to **WeChat Customer Service**, create a customer service account, and bind it to the custom app you created above:
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/wxcustomer-hosting-step1.jpg" width="600"/>
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/wxcustomer-hosting-step2.jpg" width="600"/>
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/wxcustomer-hosting-step3.jpg" width="600"/>
+
+After binding, go to **WeChat Customer Service → Account Details**, and under **"Access Link"**:
+
+- Click **"Copy Link"** to get an access link like `https://work.weixin.qq.com/kfid/kfcd83e5896b9ba07be`
+- Click **"Generate QR Code"** to get the corresponding QR code
+
+Distribute the link or QR code to your WeChat customers:
+
+<img src="https://cdn.link-ai.tech/doc/wechat-customer-service_use.png" width="600"/>
+
+## 5. Usage
+
+After WeChat users enter the customer service conversation via the link or QR code, they can chat with the AI across multiple turns, with support for text, image, and voice messages:
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/wxcustomer-hosting-chat-demo.jpg" width="900"/>
+
+Beyond that, leveraging the official WeChat ecosystem, WeChat Customer Service can also be embedded into Official Accounts, Mini Programs, Video Channels and more. See the **WeChat Customer Service → Access Scenarios** section in the [WeCom Admin Console](https://work.weixin.qq.com/wework_admin/frame#/app/servicer) for details:
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/wxcustomer-hosting-interface-demo.png" width="800"/>
+
+## FAQ
+
+Make sure the following dependencies are installed:
+
+```bash
+pip install websocket-client pycryptodome
+```

docs/channels/wechatmp.mdx

@@ -1,22 +1,22 @@
 ---
-title: 微信公众号
-description: 将 CowAgent 接入微信公众号
+title: WeChat Official Account
+description: Integrate CowAgent with WeChat Official Accounts
 ---
 
-CowAgent 支持接入个人订阅号和企业服务号两种公众号类型。
+CowAgent supports both personal subscription accounts and enterprise service accounts.
 
-| 类型 | 要求 | 特点 |
+| Type | Requirements | Features |
 | --- | --- | --- |
-| **个人订阅号** | 个人可申请 | 收到消息时会回复一条提示，回复生成后需用户主动发消息获取 |
-| **企业服务号** | 企业申请，需通过微信认证开通客服接口 | 回复生成后可主动推送给用户 |
+| **Personal Subscription** | Available to individuals | Sends a placeholder reply first; users must send a message to retrieve the full response |
+| **Enterprise Service** | Enterprise with verified customer service API | Can proactively push replies to users |
 
 <Note>
-  公众号仅支持服务器和 Docker 部署，不支持本地运行。需额外安装扩展依赖：`pip3 install -r requirements-optional.txt`
+  Official Accounts only support server and Docker deployment, not local run mode. Install extended dependencies: `pip3 install -r requirements-optional.txt`
 </Note>
 
-## 一、个人订阅号
+## 1. Personal Subscription Account
 
-在 `config.json` 中添加以下配置：
+Add the following configuration to `config.json`:
 
 ```json
 {
@@ -30,34 +30,34 @@ CowAgent 支持接入个人订阅号和企业服务号两种公众号类型。
 }
 ```
 
-### 配置步骤
+### Setup Steps
 
-这些配置需要和 [微信公众号后台](https://mp.weixin.qq.com/advanced/advanced?action=dev&t=advanced/dev) 中的保持一致，进入页面后，在左侧菜单选择 **设置与开发 → 基本配置 → 服务器配置**，按下图进行配置：
+These configurations must be consistent with the [WeChat Official Account Platform](https://mp.weixin.qq.com/advanced/advanced?action=dev&t=advanced/dev). Navigate to **Settings & Development → Basic Configuration → Server Configuration** and configure as shown below:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103506.png" width="480"/>
 
-1. 在公众平台启用开发者密码（对应配置 `wechatmp_app_secret`），并将服务器 IP 填入白名单
-2. 按上图填写 `config.json` 中与公众号相关的配置，要与公众号后台的配置一致
-3. 启动程序，启动后会监听 80 端口（若无权限监听，则在启动命令前加上 `sudo`；若 80 端口已被占用，则关闭该占用进程）
-4. 在公众号后台 **启用服务器配置** 并提交，保存成功则表示已成功配置。注意 **"服务器地址(URL)"** 需要配置为 `http://{HOST}/wx` 的格式，其中 `{HOST}` 可以是服务器的 IP 或域名
+1. Enable the developer secret on the platform (corresponds to `wechatmp_app_secret`), and add the server IP to the whitelist
+2. Fill in the `config.json` with the official account parameters matching the platform configuration
+3. Start the program, which listens on port 80 (use `sudo` if you don't have permission; stop any process occupying port 80)
+4. **Enable server configuration** on the official account platform and submit. A successful save means the configuration is complete. Note that the **"Server URL"** must be in the format `http://{HOST}/wx`, where `{HOST}` can be the server IP or domain
 
-随后关注公众号并发送消息即可看到以下效果：
+After following the account and sending a message, you should see the following result:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103522.png" width="720"/>
 
-由于受订阅号限制，回复内容较短的情况下（15s 内），可以立即完成回复，但耗时较长的回复则会先回复一句 "正在思考中"，后续需要用户输入任意文字主动获取答案，而服务号则可以通过客服接口解决这一问题。
+Due to subscription account limitations, short replies (within 15s) can be returned immediately, but longer replies will first send a "Thinking..." placeholder, requiring users to send any text to retrieve the answer. Enterprise service accounts can solve this with the customer service API.
 
 <Tip>
-  **语音识别**：可利用微信自带的语音识别功能，需要在公众号管理页面的 "设置与开发 → 接口权限" 页面开启 "接收语音识别结果"。
+  **Voice Recognition**: You can use WeChat's built-in voice recognition. Enable "Receive Voice Recognition Results" under "Settings & Development → API Permissions" on the official account management page.
 </Tip>
 
-## 二、企业服务号
+## 2. Enterprise Service Account
 
-企业服务号与上述个人订阅号的接入过程基本相同，差异如下：
+The setup process for enterprise service accounts is essentially the same as personal subscription accounts, with the following differences:
 
-1. 在公众平台申请企业服务号并完成微信认证，在接口权限中确认已获得 **客服接口** 的权限
-2. 在 `config.json` 中设置 `"channel_type": "wechatmp_service"`，其他配置与上述订阅号相同
-3. 交互效果上，即使是较长耗时的回复，也可以主动推送给用户，无需用户手动获取
+1. Register an enterprise service account on the platform and complete WeChat certification. Confirm that the **Customer Service API** permission has been granted
+2. Set `"channel_type": "wechatmp_service"` in `config.json`; other configurations remain the same
+3. Even for longer replies, they can be proactively pushed to users without requiring manual retrieval
 
 ```json
 {

docs/channels/wecom-bot.mdx

@@ -1,56 +1,56 @@
 ---
-title: 企微智能机器人
-description: 将 CowAgent 接入企业微信智能机器人（长连接模式）
+title: WeCom Bot
+description: Connect CowAgent to WeCom AI Bot (WebSocket long connection)
 ---
 
-> 通过企业微信智能机器人接入CowAgent，支持企业内部单聊和内部群聊，无需公网 IP，使用 WebSocket 长连接模式，支持Markdown渲染和流式输出。
+> Connect CowAgent via WeCom AI Bot, supporting both internal direct messages and group chats. No public IP required — uses a WebSocket long connection, with Markdown rendering and streaming output.
 
 <Note>
-  智能机器人与企业微信自建应用是两种不同的接入方式。智能机器人使用 WebSocket 长连接，无需服务器公网 IP 和域名，配置更简单。
+  WeCom Bot and WeCom App are two different integration methods. WeCom Bot uses a WebSocket long connection and requires no public IP or domain, making setup much simpler.
 </Note>
 
-## 一、接入方式
+## 1. Connection methods
 
-### 方式一：扫码一键接入（推荐）
+### Option A: One-click QR scan (recommended)
 
-无需提前创建机器人，启动 Cow 项目后打开 Web 控制台（本地链接：http://127.0.0.1:9899/），选择 **通道** 菜单，点击**接入通道**，选择**企微智能机器人**，切换到「扫码接入」模式，使用**企业微信**扫码即可自动完成机器人创建和接入。
+No need to create the bot ahead of time. Start CowAgent and open the Web console (local URL: http://127.0.0.1:9899/), go to the **Channels** tab, click **Connect Channel**, choose **WeCom Bot**, switch to **QR scan** mode, and scan the QR code with **WeCom** — bot creation and connection complete automatically.
 
 <img src="https://cdn.link-ai.tech/doc/20260401121213.png" width="800"/>
 
 <Note>
-  扫码成功后，可在企业微信工作台 - **智能机器人**页面对机器人进行进一步配置，包括修改名称、头像、可见范围等。
+  After a successful scan, you can further configure the bot (name, avatar, visibility scope, etc.) in **WeCom Workbench → AI Bot**.
 </Note>
 
-### 方式二：手动创建接入
+### Option B: Manual creation
 
-需要先在企业微信中创建智能机器人并获取 Bot ID 和 Secret，再通过 Web 控制台或配置文件接入。
+Create the AI Bot in WeCom and obtain the Bot ID and Secret, then connect via the Web console or config file.
 
-**步骤一：创建智能机器人**
+**Step 1: Create the AI Bot**
 
-1. 打开企业微信客户端，进入工作台，点击**智能机器人**：
+1. Open the WeCom client, go to **Workbench**, and click **AI Bot**:
 
 <img src="https://cdn.link-ai.tech/doc/20260316180959.png" width="800"/>
 
-2. 点击创建机器人 - 手动创建：
+2. Click **Create Bot → Manual Creation**:
 
 <img src="https://cdn.link-ai.tech/doc/20260316181118.png" width="800"/>
 
-3. 右侧窗口拖到最下方，选择**API模式创建**：
+3. Scroll to the bottom of the right panel and select **API Mode**:
 
 <img src="https://cdn.link-ai.tech/doc/20260316181215.png" width="800"/>
 
-4. 设置机器人名称、头像、可见范围，并选择**长连接模式**，记录下 **Bot ID** 和 **Secret** 信息后点击保存。
+4. Set the bot name, avatar, and visibility scope. Choose **Long Connection** mode, save the **Bot ID** and **Secret**, then click Save.
 
-**步骤二：接入 CowAgent**
+**Step 2: Connect to CowAgent**
 
 <Tabs>
-  <Tab title="Web 控制台">
-    打开 Web 控制台，选择**通道**菜单，点击**接入通道**，选择**企微智能机器人**，切换到「手动填写」模式，输入 Bot ID 和 Secret，点击接入即可。
+  <Tab title="Web Console">
+    Open the Web console, go to the **Channels** tab, click **Connect Channel**, choose **WeCom Bot**, switch to **Manual** mode, enter the Bot ID and Secret, and click Connect.
 
     <img src="https://cdn.link-ai.tech/doc/20260316181711.png" width="800"/>
   </Tab>
-  <Tab title="配置文件">
-    在 `config.json` 中添加以下配置后启动程序：
+  <Tab title="Config File">
+    Add the following to `config.json`, then start CowAgent:
 
     ```json
     {
@@ -60,31 +60,31 @@ description: 将 CowAgent 接入企业微信智能机器人（长连接模式）
     }
     ```
 
-    | 参数 | 说明 |
+    | Parameter | Description |
     | --- | --- |
-    | `wecom_bot_id` | 智能机器人的 BotID |
-    | `wecom_bot_secret` | 智能机器人的 Secret |
+    | `wecom_bot_id` | Bot ID of the AI Bot |
+    | `wecom_bot_secret` | Secret of the AI Bot |
   </Tab>
 </Tabs>
 
-日志显示 `[WecomBot] Subscribe success` 即表示连接成功。
+The log line `[WecomBot] Subscribe success` confirms the connection is established.
 
-## 二、功能说明
+## 2. Supported features
 
-| 功能 | 支持情况 |
+| Feature | Status |
 | --- | --- |
-| 单聊 | ✅ |
-| 群聊（@机器人） | ✅ |
-| 文本消息 | ✅ 收发 |
-| 图片消息 | ✅ 收发 |
-| 文件消息 | ✅ 收发 |
-| 流式回复 | ✅ |
-| 定时任务主动推送 | ✅ |
+| Direct chat | ✅ |
+| Group chat (@bot) | ✅ |
+| Text messages | ✅ Send / Receive |
+| Image messages | ✅ Send / Receive |
+| File messages | ✅ Send / Receive |
+| Streaming replies | ✅ |
+| Scheduled push messages | ✅ |
 
-## 三、使用
+## 3. Usage
 
-在企业微信中搜索创建的机器人名称，即可开始单聊对话。
+Search for the bot's name inside WeCom to start a direct chat.
 
-如需在企微内部群聊中使用，将机器人添加到群中，@机器人发送消息即可。
+To use the bot in an internal group chat, add it to the group and @-mention it.
 
 <img src="https://cdn.link-ai.tech/doc/20260316182902.png" width="800"/>

docs/channels/wecom.mdx

@@ -1,48 +1,48 @@
 ---
-title: 企微自建应用
-description: 将 CowAgent 接入企业微信自建应用
+title: WeCom
+description: Integrate CowAgent into WeCom enterprise app
 ---
 
-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。
+Integrate CowAgent into WeCom through a custom enterprise app, supporting one-on-one chat for internal employees.
 
 <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。
+  WeCom only supports Docker deployment or server Python deployment. Local run mode is not supported.
 </Note>
 
-## 一、准备
+## 1. Prerequisites
 
-需要的资源：
+Required resources:
 
-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
+1. A server with public IP (overseas server, or domestic server with a proxy for international API access)
+2. A registered WeCom account (individual registration is possible but cannot be certified)
+3. Certified WeCom accounts additionally require a domain filed under the corresponding entity
 
-## 二、创建企业微信应用
+## 2. Create WeCom App
 
-1. 在 [企业微信管理后台](https://work.weixin.qq.com/wework_admin/frame#profile) 点击 **我的企业**，在最下方获取 **企业ID**（后续填写到 `wechatcom_corp_id` 字段中）。
+1. In the [WeCom Admin Console](https://work.weixin.qq.com/wework_admin/frame#profile), click **My Enterprise** and find the **Corp ID** at the bottom of the page. Save this ID for the `wechatcom_corp_id` configuration field.
 
-2. 切换到 **应用管理**，点击创建应用：
+2. Switch to **Application Management** and click Create Application:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103156.png" width="480"/>
 
-3. 进入应用创建页面，记录 `AgentId` 和 `Secret`：
+3. On the application creation page, record the `AgentId` and `Secret`:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103218.png" width="580"/>
 
-4. 点击 **设置API接收**，配置应用接口：
+4. Click **Set API Reception** to configure the application interface:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103211.png" width="520"/>
 
-- URL 格式为 `http://ip:port/wxcomapp`（认证企业需使用备案域名）
-- 随机获取 `Token` 和 `EncodingAESKey` 并保存
+- URL format: `http://ip:port/wxcomapp` (certified enterprises must use a filed domain)
+- Generate random `Token` and `EncodingAESKey` and save them for the configuration file
 
 <Note>
-  此时保存 API 接收配置会失败，因为程序还未启动，等项目运行后再回来保存。
+  The API reception configuration cannot be saved at this point because the program hasn't started yet. Come back to save it after the project is running.
 </Note>
 
-## 三、配置和运行
+## 3. Configuration and Run
 
-在 `config.json` 中添加以下配置（各参数与企业微信后台的对应关系见上方截图）：
+Add the following configuration to `config.json` (the mapping between each parameter and the WeCom console is shown in the screenshots above):
 
 ```json
 {
@@ -57,41 +57,41 @@ description: 将 CowAgent 接入企业微信自建应用
 }
 ```
 
-| 参数 | 说明 |
+| Parameter | Description |
 | --- | --- |
-| `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 |
+| `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 |
 
-配置完成后启动程序。当后台日志显示 `http://0.0.0.0:9898/` 时说明程序运行成功，需要将该端口对外开放（如在云服务器安全组中放行）。
+After configuration, start the program. When the log shows `http://0.0.0.0:9898/`, the program is running successfully. You need to open this port externally (e.g., allow it in the cloud server security group).
 
-程序启动后，回到企业微信后台保存 **消息服务器配置**，保存成功后还需将服务器 IP 添加到 **企业可信IP** 中，否则无法收发消息：
+After the program starts, return to the WeCom Admin Console to save the **Message Server Configuration**. After saving successfully, you also need to add the server IP to **Enterprise Trusted IPs**, otherwise messages cannot be sent or received:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103224.png" width="520"/>
 
 <Warning>
-  如遇到 URL 配置回调不通过或配置失败：
-  1. 确保服务器防火墙关闭且安全组放行监听端口
-  2. 仔细检查 Token、Secret Key 等参数配置是否一致，URL 格式是否正确
-  3. 认证企业微信需要配置与主体一致的备案域名
+  If the URL configuration callback fails or the configuration is unsuccessful:
+  1. Ensure the server firewall is disabled and the security group allows the listening port
+  2. Carefully check that Token, Secret Key and other parameter configurations are consistent, and that the URL format is correct
+  3. Certified WeCom accounts must configure a filed domain matching the entity
 </Warning>
 
-## 四、使用
+## 4. Usage
 
-在企业微信中搜索刚创建的应用名称，即可直接对话：
+Search for the app name you just created in WeCom to start chatting directly. You can run multiple instances listening on different ports to create multiple WeCom apps:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103228.png" width="720"/>
 
-如需让外部个人微信用户使用，可在 **我的企业 → 微信插件** 中分享邀请关注二维码，个人微信扫码关注后即可与应用对话：
+To allow external personal WeChat users to use the app, go to **My Enterprise → WeChat Plugin**, share the invite QR code. After scanning and following, personal WeChat users can join and chat with the app:
 
 <img src="https://cdn.link-ai.tech/doc/20260228103232.png" width="520"/>
 
-## 常见问题
+## FAQ
 
-需要确保已安装以下依赖：
+Make sure the following dependencies are installed:
 
 ```bash
 pip install websocket-client pycryptodome

docs/channels/weixin.mdx

@@ -1,21 +1,21 @@
 ---
-title: 微信
-description: 将 CowAgent 接入个人微信（基于官方接口）
+title: WeChat
+description: Connect CowAgent to personal WeChat (via the official API)
 ---
 
-> 接入个人微信，扫码登录即可使用，支持文本、图片、语音、文件、视频等消息的私聊收发。通过微信官方API进行接入，无安全风险，接入后会在会话中新增一个机器人助手，不影响当前账号的使用。
+> Connect CowAgent to your personal WeChat — scan to log in, no public IP required. Supports text, image, voice, file, and video messages in 1-on-1 chats. Backed by WeChat's official API; safe to use. After connecting, a bot assistant is added to your conversation list without affecting normal account usage.
 
-## 一、配置和运行
+## 1. Setup and run
 
-### 方式一：Web 控制台接入
+### Option A: Web console
 
-启动 Cow 项目后打开 Web 控制台 (本地链接为: http://127.0.0.1:9899/ )，选择 **通道** 菜单，点击 **接入通道**，选择 **微信**，点击接入后按照提示扫码登录。
+Start CowAgent and open the Web console (local URL: http://127.0.0.1:9899/). Go to the **Channels** tab, click **Connect Channel**, select **WeChat**, and follow the prompts to scan in.
 
 <img src="https://cdn.link-ai.tech/doc/20260322195114.png" width="800" />
 
-### 方式二：配置文件接入
+### Option B: Config file
 
-在 `config.json` 中设置 `channel_type` 为 `weixin`：
+Set `channel_type` to `weixin` in `config.json`:
 
 ```json
 {
@@ -23,52 +23,49 @@ description: 将 CowAgent 接入个人微信（基于官方接口）
 }
 ```
 
-启动程序后，终端会显示二维码，使用微信扫码授权即可完成登录。
+After starting CowAgent, a QR code is displayed in the terminal. Scan it with WeChat to complete login.
 
 <img src="https://cdn.link-ai.tech/doc/20260322195509.png" width="800" />
 
-
 <Note>
-  1. 兼容历史配置：`channel_type` 设为 `wx` 同样可以启用微信通道。
-  2. 注意微信客户端需要更新至 8.0.69 版本或以上
+  1. For backward compatibility, setting `channel_type` to `wx` also activates the WeChat channel.
+  2. The WeChat client must be on version **8.0.69** or higher.
 </Note>
 
-## 二、使用说明
+## 2. Usage
 
-微信扫码并进行授权确认后，即可完成接入并开始对话。接入微信后会在对话中创建出一个机器人助理，不会对已有账号的正常使用有任何影响。
+Once authorized, the integration completes and you can start chatting. A bot assistant is created in your WeChat conversation list, leaving normal account usage unaffected.
 
-> 你可以通过搜索"微信ClawBot"随时找到这个机器人，还可以修改这个机器人的头像、备注等信息，将机器人置顶在消息列表等。
+> You can find the bot at any time by searching for **"微信ClawBot"**. You may also rename it, change its avatar, pin it to the top of your conversation list, and so on.
 
 <img src="https://cdn.link-ai.tech/doc/83ae8251d896219fde4803f4205205be.jpg" width="250" />
 
+## 3. Login
 
+### QR code login
 
-## 三、登录说明
+On first startup, a QR code appears in the terminal (valid for around 2 minutes). Scan it with WeChat and confirm on your phone to log in.
 
-### 扫码登录
+- The QR code refreshes automatically when it expires
+- The `qrcode` dependency is already included in `requirements.txt`, so the QR code renders directly in the terminal after install
 
-首次启动时，终端会显示一个二维码（有效期约 2 分钟）。使用微信扫描二维码并在手机上确认后即可完成登录。
+### Credential persistence
 
-- 二维码过期后会自动刷新并重新显示
-- `requirements.txt` 中已默认包含 `qrcode` 依赖，安装后可在终端直接渲染二维码图案
+After a successful login, credentials are saved to `~/.weixin_cow_credentials.json`. Subsequent startups reuse the saved credentials with no need to re-scan.
 
-### 凭证保存
+To force a re-login, delete the credentials file and restart.
 
-登录成功后，凭证会自动保存至 `~/.weixin_cow_credentials.json`，下次启动时无需重新扫码。
+### Session expiry
 
-如需重新登录，删除该凭证文件后重启程序即可。
+When the WeChat session expires (errcode `-14`), CowAgent automatically clears old credentials and initiates a new QR login — no manual intervention required.
 
-### Session 过期
+## 4. Supported features
 
-当微信 session 过期时（errcode -14），程序会自动清除旧凭证并重新发起扫码登录，无需手动干预。
-
-## 四、功能说明
-
-| 功能 | 支持情况 |
+| Feature | Status |
 | --- | --- |
-| 单聊 | ✅ |
-| 文本消息 | ✅ 收发 |
-| 图片消息 | ✅ 收发 |
-| 文件消息 | ✅ 收发 |
-| 视频消息 | ✅ 收发 |
-| 语音消息 | ✅ 接收 (自带语音识别) |
+| Direct messages | ✅ |
+| Text messages | ✅ Send & Receive |
+| Image messages | ✅ Send & Receive |
+| File messages | ✅ Send & Receive |
+| Video messages | ✅ Send & Receive |
+| Voice messages | ✅ Receive (built-in speech recognition) |

docs/cli/general.mdx

@@ -1,17 +1,17 @@
 ---
-title: 常用命令
-description: 查看状态、管理配置和上下文等常用命令
+title: General Commands
+description: View status, manage config, and control context with commonly used commands
 ---
 
-以下命令支持在对话中使用 `/` 前缀，也支持在终端中使用 `cow` 前缀（部分命令仅对话可用）。
+The following commands can be used in chat with the `/` prefix or in the terminal with the `cow` prefix (some are chat-only).
 
 <Tip>
-  在 Web 控制台中输入 `/` 会自动弹出命令提示，支持键盘上下选择和 Tab 补全。
+  In the Web console, typing `/` brings up an autocomplete menu with keyboard navigation and Tab completion.
 </Tip>
 
 ## help
 
-显示所有可用命令的帮助信息。
+Show help information for all available commands.
 
 ```text
 /help
@@ -19,29 +19,15 @@ description: 查看状态、管理配置和上下文等常用命令
 
 ## status
 
-查看当前会话和服务的运行状态，包括进程信息、模型配置、会话消息数量和已加载技能数量。
+View current session and service status, including process info, model configuration, message count, and loaded skills.
 
 ```text
 /status
 ```
 
-输出示例：
-
-```
-🐮 CowAgent Status
-
-Process: PID 12345 | Running 2h 15m
-Version: 2.0.4
-Channel: web
-Model:   MiniMax-M2.5
-Mode:    agent
-
-Session: 12 messages | 8 skills loaded
-```
-
 ## cancel
 
-中止当前会话正在运行的 Agent 任务。在 Agent 执行长时间任务（例如多轮工具调用、长流式输出）时，可随时发送 `/cancel`，Agent 会在下一次工具执行前停止。Web 端、微信、企业微信、飞书等各通道均可使用。
+Abort the agent task currently running in this session. When the agent is busy with a long task (e.g. multi-turn tool calls or a long streaming response), send `/cancel` and the agent will stop before the next tool execution. Available across all channels — Web, WeChat, WeCom, Feishu, etc.
 
 ```text
 /cancel
@@ -49,67 +35,67 @@ Session: 12 messages | 8 skills loaded
 
 ## config
 
-查看或修改运行时配置。修改后立即生效，无需重启服务。
+View or modify runtime configuration. Changes take effect immediately without restarting.
 
-**查看所有可配置项：**
+**View all configurable items:**
 
 ```text
 /config
 ```
 
-**查看单个配置项：**
+**View a single item:**
 
 ```text
 /config model
 ```
 
-**修改配置项：**
+**Modify a config item:**
 
 ```text
 /config model deepseek-v4-flash
 ```
 
-**支持修改的配置项：**
+**Configurable items:**
 
-| 配置项 | 说明 | 示例值 |
+| Item | Description | Example |
 | --- | --- | --- |
-| `model` | AI 模型名称 | `deepseek-v4-flash` |
-| `agent_max_context_tokens` | 最大上下文 tokens | `40000` |
-| `agent_max_context_turns` | 最大上下文记忆轮次 | `30` |
-| `agent_max_steps` | 单次任务最大决策步数 | `15` |
-| `enable_thinking` | 是否启用深度思考模式 | `true` / `false` |
+| `model` | AI model name | `deepseek-v4-flash` |
+| `agent_max_context_tokens` | Max context tokens | `40000` |
+| `agent_max_context_turns` | Max context memory turns | `30` |
+| `agent_max_steps` | Max decision steps per task | `15` |
+| `enable_thinking` | Enable deep thinking mode | `true` / `false` |
 
 <Note>
-  修改 `model` 时，系统会自动匹配对应的模型调用方式。配置会写入 `config.json` 并持久保存。
+  When changing `model`, the system automatically matches the corresponding model API. Configuration is persisted to `config.json`.
 </Note>
 
 ## context
 
-查看当前会话的上下文信息，包括消息数量、内容长度等统计。
+View current session context statistics, including message count and content length.
 
 ```text
 /context
 ```
 
-**清空当前会话上下文：**
+**Clear current session context:**
 
 ```text
 /context clear
 ```
 
 <Tip>
-  清空上下文后，Agent 会"忘记"之前的对话内容，适用于切换话题或释放上下文空间。
+  Clearing context makes the Agent "forget" previous conversation, useful for switching topics or freeing context space.
 </Tip>
 
 ## logs
 
-查看最近的服务日志，默认显示最近 20 行，最多 50 行。
+View recent service logs. Shows the last 20 lines by default, up to 50.
 
 ```text
 /logs
 ```
 
-**指定行数：**
+**Specify line count:**
 
 ```text
 /logs 50
@@ -117,7 +103,7 @@ Session: 12 messages | 8 skills loaded
 
 ## version
 
-显示当前 CowAgent 版本号。
+Show the current CowAgent version.
 
 ```text
 /version

docs/cli/index.mdx

@@ -1,31 +1,31 @@
 ---
-title: 命令总览
-description: CowAgent 命令系统 — 终端 CLI 和对话命令
+title: Commands Overview
+description: CowAgent command system — Terminal CLI and chat commands
 ---
 
-CowAgent 提供两种命令交互方式：
+CowAgent provides two ways to interact via commands:
 
-- **终端CLI** — 在系统终端中执行 `cow <命令>`，用于服务管理、技能管理等运维操作
-- **对话命令** — 在对话中输入 `/<命令>` 或 `cow <命令>`，用于查看状态、管理技能、调整配置等
+- **Terminal CLI** — Run `cow <command>` in your system terminal for service management, skill management, and other operations
+- **Chat Commands** — Type `/<command>` or `cow <command>` in any conversation to check status, manage skills, adjust configuration, etc.
 
-## 终端命令
+## Cow CLI
 
-通过一键安装脚本部署后，`cow` 命令会自动可用。手动安装的用户需要在项目根目录下额外执行：
+After deploying with the one-click install script, the `cow` command is automatically available. For manual installations, run:
 
 ```bash
 pip install -e .
 ```
 
-安装后即可在任意位置使用 `cow` 命令：
+Then use the `cow` command from anywhere:
 
 ```bash
 cow help
 ```
 
-输出示例：
+Example output:
 
 ```
-CowAgent CLI
+🐮 CowAgent CLI
 
 Usage: cow <command>
 
@@ -49,35 +49,33 @@ Others:
   version   Show version
 ```
 
-## 对话命令
+## Chat Commands
 
-在 Web 控制台或任意接入渠道的对话中，支持输入以 `/` 开头的命令：
+In the Web console or any connected channel, type `/` to see command suggestions. Supported commands:
 
-| 命令 | 说明 |
+| Command | Description |
 | --- | --- |
-| `/help` | 显示命令帮助 |
-| `/status` | 查看服务状态和配置 |
-| `/cancel` | 中止当前正在运行的 Agent 任务 |
-| `/config` | 查看或修改运行时配置 |
-| `/skill` | 管理技能（安装、卸载、启用、禁用等） |
-| `/memory dream [N]` | 手动触发记忆蒸馏（默认 3 天，最大 30） |
-| `/knowledge` | 查看知识库统计信息 |
-| `/knowledge list` | 查看知识库目录结构 |
-| `/knowledge on\|off` | 开启或关闭知识库 |
-| `/context` | 查看当前会话上下文信息 |
-| `/context clear` | 清空当前会话上下文 |
-| `/logs` | 查看最近日志 |
-| `/version` | 显示版本号 |
+| `/help` | Show command help |
+| `/status` | View service status and configuration |
+| `/cancel` | Abort the currently running agent task |
+| `/config` | View or modify runtime configuration |
+| `/skill` | Manage skills (install, uninstall, enable, disable, etc.) |
+| `/memory dream [N]` | Manually trigger memory distillation (default 3 days, max 30) |
+| `/knowledge` | View knowledge base statistics |
+| `/knowledge list` | View knowledge base directory structure |
+| `/knowledge on\|off` | Enable or disable knowledge base |
+| `/context` | View current session context info |
+| `/context clear` | Clear current session context |
+| `/logs` | View recent logs |
+| `/version` | Show version number |
 
 <Tip>
-  对话命令中 `/start`、`/stop`、`/restart` 等服务管理命令会提示到终端中执行，因为它们涉及进程操作。
+  Service management commands like `/start`, `/stop`, `/restart` will prompt you to use them in the terminal instead, as they involve process operations.
 </Tip>
 
-## 命令对照表
+## Command Availability
 
-以下是各命令在终端和对话中的可用性：
-
-| 命令 | 终端 (`cow`) | 对话 (`/`) |
+| Command | Terminal (`cow`) | Chat (`/`) |
 | --- | :---: | :---: |
 | help | ✓ | ✓ |
 | version | ✓ | ✓ |
@@ -86,13 +84,13 @@ Others:
 | cancel | ✗ | ✓ |
 | config | ✗ | ✓ |
 | context | — | ✓ |
-| memory (子命令) | ✗ | ✓ |
-| knowledge (子命令) | ✓ | ✓ |
-| skill (子命令) | ✓ | ✓ |
+| memory (subcommands) | ✗ | ✓ |
+| knowledge (subcommands) | ✓ | ✓ |
+| skill (subcommands) | ✓ | ✓ |
 | start / stop / restart | ✓ | ✗ |
 | update | ✓ | ✗ |
 | install-browser | ✓ | ✗ |
 
 <Note>
-  `context` 在终端中仅提示到对话中使用。`config` 仅支持在对话中修改。
+  `context` only shows a hint in the terminal to use it in chat. `config` is only available in chat.
 </Note>

docs/cli/memory-knowledge.mdx

@@ -1,63 +1,49 @@
 ---
-title: 记忆与知识库
-description: 记忆蒸馏和知识库管理命令
+title: Memory & Knowledge
+description: Memory distillation and knowledge base management commands
 ---
 
 ## memory
 
-管理 Agent 的长期记忆系统。
+Manage the Agent's long-term memory system.
 
 ### memory dream
 
-手动触发记忆蒸馏（Deep Dream），整理近期的天级记忆，蒸馏合并到 MEMORY.md，并生成梦境日记。
+Manually trigger memory distillation (Deep Dream) — consolidate recent daily memories into MEMORY.md and generate a dream diary.
 
 ```text
 /memory dream [N]
 ```
 
-- `N`：整理近 N 天的记忆，默认 3 天，最大 30 天
-- 蒸馏在后台异步执行，完成后会在对话中通知结果
-- 无需等待 Agent 初始化，首次对话前即可使用
+- `N`: Consolidate the last N days of memory (default 3, max 30)
+- Runs asynchronously in the background; you'll be notified in chat when complete
+- Works without Agent initialization — can be used before the first conversation
 
-**示例：**
+**Examples:**
 
 ```text
-/memory dream       # 整理近 3 天
-/memory dream 7     # 整理近 7 天
-/memory dream 30    # 整理近 30 天（全量）
+/memory dream       # Consolidate last 3 days
+/memory dream 7     # Consolidate last 7 days
+/memory dream 30    # Consolidate last 30 days (full)
 ```
 
-蒸馏完成后，Web 端会收到带有跳转链接的通知，可直接查看更新后的 MEMORY.md 和梦境日记。
+On the Web console, the completion notification includes clickable links to view the updated MEMORY.md and dream diary.
 
 <Tip>
-  系统每天 23:55 会自动执行一次蒸馏（lookback 1 天）。手动触发适用于首次部署后的历史整理，或需要立即更新记忆时使用。
+  The system automatically runs distillation daily at 23:55 (lookback 1 day). Manual trigger is useful for consolidating historical memories after first deployment, or when you need an immediate memory update.
 </Tip>
 
 ## knowledge
 
-查看和管理个人知识库。默认显示知识库统计信息。
+View and manage the personal knowledge base. Shows statistics by default.
 
 ```text
 /knowledge
 ```
 
-输出示例：
-
-```
-📚 知识库
-
-- 状态：已开启
-- 页面数：12
-- 总大小：45.2 KB
-- 分类明细：
-  - concepts/: 5 篇
-  - entities/: 4 篇
-  - sources/: 3 篇
-```
-
 ### knowledge list
 
-查看知识库目录树结构。
+View the knowledge base directory tree.
 
 ```text
 /knowledge list
@@ -65,7 +51,7 @@ description: 记忆蒸馏和知识库管理命令
 
 ### knowledge on / off
 
-开启或关闭知识库。关闭后不再注入知识提示词和索引知识文件。
+Enable or disable the knowledge base. When disabled, knowledge prompts and file indexing are not injected.
 
 ```text
 /knowledge on
@@ -73,5 +59,5 @@ description: 记忆蒸馏和知识库管理命令
 ```
 
 <Note>
-  终端 CLI 中 `cow knowledge` 和 `cow knowledge list` 可用，但 `on|off` 仅支持在对话中使用（需实时生效）。
+  In the terminal CLI, `cow knowledge` and `cow knowledge list` are available, but `on|off` is only supported in chat (requires runtime effect).
 </Note>

docs/cli/process.mdx

@@ -1,28 +1,28 @@
 ---
-title: 进程管理
-description: 使用 cow 命令管理 CowAgent 进程的启动、停止、重启、更新等操作
+title: Process Management
+description: Manage CowAgent process lifecycle with cow commands
 ---
 
-进程管理命令用于控制 CowAgent 后台进程的生命周期。这些命令仅在终端中可用。
+Process management commands control the CowAgent background process. These commands are only available in the terminal.
 
 ## start
 
-启动 CowAgent 服务。默认以后台进程方式运行，并自动跟踪日志输出。
+Start the CowAgent service. Runs as a background daemon by default and automatically tails logs.
 
 ```bash
 cow start
 ```
 
-**选项：**
+**Options:**
 
-| 选项 | 说明 |
+| Option | Description |
 | --- | --- |
-| `-f`, `--foreground` | 前台运行，不以后台守护进程方式启动 |
-| `--no-logs` | 启动后不自动跟踪日志 |
+| `-f`, `--foreground` | Run in foreground, not as a background daemon |
+| `--no-logs` | Don't tail logs after starting |
 
 ## stop
 
-停止正在运行的 CowAgent 服务。
+Stop the running CowAgent service.
 
 ```bash
 cow stop
@@ -30,97 +30,86 @@ cow stop
 
 ## restart
 
-重启 CowAgent 服务（先停止再启动）。
+Restart the CowAgent service (stop then start).
 
 ```bash
 cow restart
 ```
 
-**选项：**
+**Options:**
 
-| 选项 | 说明 |
+| Option | Description |
 | --- | --- |
-| `--no-logs` | 重启后不自动跟踪日志 |
+| `--no-logs` | Don't tail logs after restart |
 
 ## update
 
-更新代码并重启服务。自动执行以下流程：
+Update code and restart the service. Automatically performs:
 
-1. 拉取最新代码（`git pull`）
-2. 停止当前服务
-3. 更新 Python 依赖
-4. 重新安装 CLI
-5. 启动服务
+1. Pull latest code (`git pull`)
+2. Stop current service
+3. Update Python dependencies
+4. Reinstall CLI
+5. Start service
 
 ```bash
 cow update
 ```
 
 <Warning>
-  如果 `git pull` 失败（如存在本地未提交的修改），更新会中止，服务不受影响。
+  If `git pull` fails (e.g., uncommitted local changes), the update aborts and the service remains unaffected.
 </Warning>
 
 ## status
 
-查看 CowAgent 服务运行状态，包括进程信息、版本号、当前配置的模型和通道。
+Check CowAgent service status, including process info, version, and current model/channel configuration.
 
 ```bash
 cow status
 ```
 
-输出示例：
-
-```
-🐮 CowAgent Status
-  Status:   ● Running (PID: 12345)
-  Version:  2.0.4
-  Channel:  web
-  Model:    MiniMax-M2.5
-  Mode:     agent
-```
-
 ## logs
 
-查看服务日志。
+View service logs.
 
 ```bash
 cow logs
 ```
 
-**选项：**
+**Options:**
 
-| 选项 | 说明 | 默认值 |
+| Option | Description | Default |
 | --- | --- | --- |
-| `-f`, `--follow` | 持续跟踪日志输出 | 否 |
-| `-n`, `--lines` | 显示最近 N 行 | 50 |
+| `-f`, `--follow` | Continuously tail log output | No |
+| `-n`, `--lines` | Show last N lines | 50 |
 
-示例：
+Examples:
 
 ```bash
-# 查看最近 100 行日志
+# View last 100 lines
 cow logs -n 100
 
-# 持续跟踪日志
+# Continuously tail logs
 cow logs -f
 ```
 
 ## install-browser
 
-安装 Playwright 和 Chromium 浏览器，用于启用 [浏览器工具](/tools/browser)。
+Install Playwright and Chromium browser for the [browser tool](/tools/browser).
 
 ```bash
 cow install-browser
 ```
 
 <Tip>
-  仅在需要使用浏览器工具（如网页浏览、截图等）时才需要安装。
+  Only needed when using browser tools (web browsing, screenshots, etc.).
 </Tip>
 
-## run.sh 兼容
+## run.sh Compatibility
 
-如果未安装 Cow CLI，也可以使用 `run.sh` 脚本管理服务：
+If Cow CLI is not installed, you can use `run.sh` to manage the service:
 
-| cow 命令 | run.sh 等效命令 |
+| cow command | run.sh equivalent |
 | --- | --- |
 | `cow start` | `./run.sh start` |
 | `cow stop` | `./run.sh stop` |
@@ -130,5 +119,5 @@ cow install-browser
 | `cow logs` | `./run.sh logs` |
 
 <Note>
-  推荐使用 `cow` 命令，它提供更简洁的语法和更丰富的功能。通过一键安装脚本部署时 `cow` 命令会自动安装。
+  The `cow` command is recommended — it provides cleaner syntax and richer features. It is automatically installed via the one-click install script.
 </Note>

docs/cli/skill.mdx

@@ -1,190 +1,182 @@
 ---
-title: 技能管理
-description: 通过命令安装、卸载、启用、禁用和管理技能
+title: Skill Management
+description: Install, uninstall, enable, disable, and manage skills via commands
 ---
 
-技能管理命令用于安装、查询和管理 CowAgent 的技能。在对话中使用 `/skill <子命令>`，在终端中使用 `cow skill <子命令>`。
+Skill management commands are used to install, query, and manage CowAgent skills. Use `/skill <subcommand>` in chat or `cow skill <subcommand>` in the terminal.
 
 ## list
 
-列出已安装的技能及其状态。
+List installed skills and their status.
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill list
 ```
 
-```bash 终端
+```bash Terminal
 cow skill list
 ```
 </CodeGroup>
 
-输出示例：
+Example output:
 
 ```
-📦 已安装的技能 (3/4)
+📦 Installed skills (3/4)
 
 ✅ pptx
    Use this skill any time a .pptx file is involved…
-   来源: cowhub
+   Source: cowhub
 
 ✅ skill-creator
    Create, install, or update skills…
-   来源: builtin
+   Source: builtin
 
-⏸️ image-vision (已禁用)
-   图片理解和视觉分析
-   来源: builtin
+⏸️ image-vision (disabled)
+   Image understanding and visual analysis
+   Source: builtin
 ```
 
-**浏览技能广场**（查看 Hub 上所有可安装的技能）：
+**Browse the Skill Hub** (view all available skills):
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill list --remote
 ```
 
-```bash 终端
+```bash Terminal
 cow skill list --remote
 ```
 </CodeGroup>
 
-**选项：**
+**Options:**
 
-| 选项 | 说明 | 默认值 |
+| Option | Description | Default |
 | --- | --- | --- |
-| `--remote`, `-r` | 浏览 Skill Hub 远程技能列表 | 否 |
-| `--page` | 远程列表分页页码 | 1 |
+| `--remote`, `-r` | Browse Skill Hub remote skill list | No |
+| `--page` | Page number for remote listing | 1 |
 
 ## search
 
-在技能广场中搜索技能。
+Search for skills on the Skill Hub.
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill search pptx
 ```
 
-```bash 终端
+```bash Terminal
 cow skill search pptx
 ```
 </CodeGroup>
 
 ## install
 
-安装技能。通过统一的 `install` 命令，可一键安装来自 **Cow 技能广场、GitHub、ClawHub** 以及任意 URL（zip 压缩包、SKILL.md 链接）上的技能，无需手动下载和配置。
+Install skills with a single `install` command from Cow Skill Hub, GitHub, ClawHub, or any URL (zip archives, SKILL.md links) — no manual download or configuration required.
 
-**从 Cow 技能广场安装（推荐）：**
+**From Skill Hub (recommended):**
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill install pptx
 ```
 
-```bash 终端
+```bash Terminal
 cow skill install pptx
 ```
 </CodeGroup>
 
-**从 GitHub 安装：**
+**From GitHub:**
 
 <CodeGroup>
-```text 对话
-# 安装仓库中的所有技能（自动扫描包含 SKILL.md 的子目录）
+```text Chat
+# Install all skills in a repo (auto-discovers subdirectories with SKILL.md)
 /skill install larksuite/cli
 
-# 指定子目录，只安装单个技能
+# Specify a subdirectory to install a single skill
 /skill install https://github.com/larksuite/cli/tree/main/skills/lark-im
 
-# 使用 # 指定子目录
+# Use # to specify a subdirectory
 /skill install larksuite/cli#skills/lark-minutes
 ```
 
-```bash 终端
-# 安装仓库中的所有技能（自动扫描包含 SKILL.md 的子目录）
+```bash Terminal
+# Install all skills in a repo (auto-discovers subdirectories with SKILL.md)
 cow skill install larksuite/cli
 
-# 指定子目录，只安装单个技能
+# Specify a subdirectory to install a single skill
 cow skill install https://github.com/larksuite/cli/tree/main/skills/lark-im
 
-# 使用 # 指定子目录
+# Use # to specify a subdirectory
 cow skill install larksuite/cli#skills/lark-minutes
 ```
 </CodeGroup>
 
-支持完整的 GitHub URL 和 `owner/repo` 简写。对于 mono-repo（一个仓库中包含多个技能），不指定子目录时会自动发现并批量安装所有技能；指定子目录时只安装该目录下的技能。
+Supports full GitHub URLs and `owner/repo` shorthand. For mono-repos (multiple skills in one repository), omitting the subdirectory auto-discovers and batch-installs all skills; specifying a subdirectory installs only that skill.
 
-**从 ClawHub 安装：**
+**From ClawHub:**
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill install clawhub:baidu-search
 ```
 
-```bash 终端
+```bash Terminal
 cow skill install clawhub:baidu-search
 ```
 </CodeGroup>
 
-**从 URL 安装：**
+**From URL:**
 
 <CodeGroup>
-```text 对话
-# 从 zip 压缩包安装（支持单个或批量）
+```text Chat
+# Install from a zip archive (single or batch)
 /skill install https://cdn.link-ai.tech/skills/pptx.zip
 
-# 从 SKILL.md 链接安装
+# Install from a SKILL.md link
 /skill install https://example.com/path/to/SKILL.md
 ```
 
-```bash 终端
-# 从 zip 压缩包安装（支持单个或批量）
+```bash Terminal
+# Install from a zip archive (single or batch)
 cow skill install https://cdn.link-ai.tech/skills/pptx.zip
 
-# 从 SKILL.md 链接安装
+# Install from a SKILL.md link
 cow skill install https://example.com/path/to/SKILL.md
 ```
 </CodeGroup>
 
-支持从 zip / tar.gz 压缩包 URL 安装，解压后自动扫描包含 `SKILL.md` 的目录，支持单个或批量安装。也支持直接从 `SKILL.md` 文件链接安装，会自动解析技能名称和描述。
-
-安装成功后会显示技能名称、描述和来源，例如：
-
-```
-✅ baidu-search
-   百度搜索：使用百度搜索引擎检索信息…
-   来源: clawhub
-```
+Supports installing from zip / tar.gz archive URLs — automatically extracts and discovers directories containing `SKILL.md`, with support for single or batch install. Also supports installing directly from a `SKILL.md` file URL, automatically parsing the skill name and description.
 
 ## uninstall
 
-卸载已安装的技能。
+Uninstall an installed skill.
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill uninstall pptx
 ```
 
-```bash 终端
+```bash Terminal
 cow skill uninstall pptx
 ```
 </CodeGroup>
 
 <Warning>
-  卸载操作会删除技能目录下的所有文件，此操作不可恢复。
+  Uninstalling deletes all files in the skill directory. This action cannot be undone.
 </Warning>
 
 ## enable / disable
 
-启用或禁用技能，禁用后技能不会被 Agent 调用。
+Enable or disable a skill. Disabled skills will not be invoked by the Agent.
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill enable pptx
 /skill disable pptx
 ```
 
-```bash 终端
+```bash Terminal
 cow skill enable pptx
 cow skill disable pptx
 ```
@@ -192,27 +184,27 @@ cow skill disable pptx
 
 ## info
 
-查看已安装技能的详细信息，包括 `SKILL.md` 内容预览。
+View details of an installed skill, including a preview of its `SKILL.md`.
 
 <CodeGroup>
-```text 对话
+```text Chat
 /skill info pptx
 ```
 
-```bash 终端
+```bash Terminal
 cow skill info pptx
 ```
 </CodeGroup>
 
-## 技能来源
+## Skill Sources
 
-安装的技能会记录来源信息，可通过 `/skill list` 查看：
+Installed skills track their origin, viewable via `/skill list`:
 
-| 来源标识 | 说明 |
+| Source | Description |
 | --- | --- |
-| `builtin` | 项目内置技能 |
-| `cowhub` | 从 CowAgent Skill Hub 安装 |
-| `github` | 从 GitHub URL 直接安装 |
-| `clawhub` | 从 ClawHub 安装 |
-| `url` | 从 SKILL.md URL 安装 |
-| `local` | 本地创建的技能 |
+| `builtin` | Built-in project skills |
+| `cowhub` | Installed from CowAgent Skill Hub |
+| `github` | Installed directly from a GitHub URL |
+| `clawhub` | Installed from ClawHub |
+| `url` | Installed from a SKILL.md URL |
+| `local` | Locally created skills |

docs/docs.json

@@ -33,23 +33,36 @@
       "github": "https://github.com/zhayujie/CowAgent"
     }
   },
+  "redirects": [
+    {
+      "source": "/en/:slug*",
+      "destination": "/:slug*",
+      "permanent": true
+    }
+  ],
   "navigation": {
     "languages": [
       {
-        "language": "zh",
+        "language": "en",
         "default": true,
         "navbar": {
           "links": [
-            { "label": "官网", "href": "https://cowagent.ai/?lang=zh" },
-            { "label": "GitHub", "href": "https://github.com/zhayujie/CowAgent" }
+            {
+              "label": "Website",
+              "href": "https://cowagent.ai/"
+            },
+            {
+              "label": "GitHub",
+              "href": "https://github.com/zhayujie/CowAgent"
+            }
           ]
         },
         "tabs": [
           {
-            "tab": "项目介绍",
+            "tab": "Introduction",
             "groups": [
               {
-                "group": "概览",
+                "group": "Overview",
                 "pages": [
                   "intro/index",
                   "intro/architecture",
@@ -59,10 +72,10 @@
             ]
           },
           {
-            "tab": "快速开始",
+            "tab": "Get Started",
             "groups": [
               {
-                "group": "安装部署",
+                "group": "Installation",
                 "pages": [
                   "guide/quick-start",
                   "guide/manual-install",
@@ -72,17 +85,17 @@
             ]
           },
           {
-            "tab": "模型",
+            "tab": "Models",
             "groups": [
               {
-                "group": "模型配置",
+                "group": "Model Configuration",
                 "pages": [
                   "models/index",
                   "models/deepseek",
-                  "models/minimax",
                   "models/claude",
                   "models/gemini",
                   "models/openai",
+                  "models/minimax",
                   "models/glm",
                   "models/qwen",
                   "models/doubao",
@@ -97,16 +110,16 @@
             ]
           },
           {
-            "tab": "工具",
+            "tab": "Tools",
             "groups": [
               {
-                "group": "工具系统",
+                "group": "Tools System",
                 "pages": [
                   "tools/index"
                 ]
               },
               {
-                "group": "内置工具",
+                "group": "Built-in Tools",
                 "pages": [
                   "tools/read",
                   "tools/write",
@@ -121,7 +134,7 @@
                 ]
               },
               {
-                "group": "可选工具",
+                "group": "Optional Tools",
                 "pages": [
                   "tools/web-search",
                   "tools/vision",
@@ -129,7 +142,7 @@
                 ]
               },
               {
-                "group": "MCP 工具",
+                "group": "MCP Tools",
                 "pages": [
                   "tools/mcp"
                 ]
@@ -137,10 +150,10 @@
             ]
           },
           {
-            "tab": "技能",
+            "tab": "Skills",
             "groups": [
               {
-                "group": "技能系统",
+                "group": "Skills System",
                 "pages": [
                   "skills/index",
                   "skills/install",
@@ -149,7 +162,7 @@
                 ]
               },
               {
-                "group": "内置技能",
+                "group": "Built-in Skills",
                 "pages": [
                   "skills/skill-creator",
                   "skills/knowledge-wiki",
@@ -159,10 +172,10 @@
             ]
           },
           {
-            "tab": "记忆",
+            "tab": "Memory",
             "groups": [
               {
-                "group": "记忆系统",
+                "group": "Memory System",
                 "pages": [
                   "memory/index",
                   "memory/context",
@@ -172,10 +185,10 @@
             ]
           },
           {
-            "tab": "知识",
+            "tab": "Knowledge",
             "groups": [
               {
-                "group": "知识库",
+                "group": "Knowledge Base",
                 "pages": [
                   "knowledge/index"
                 ]
@@ -183,29 +196,33 @@
             ]
           },
           {
-            "tab": "通道",
+            "tab": "Channels",
             "groups": [
               {
-                "group": "接入渠道",
+                "group": "Platforms",
                 "pages": [
                   "channels/index",
-                  "channels/weixin",
                   "channels/web",
+                  "channels/telegram",
+                  "channels/slack",
+                  "channels/discord",
+                  "channels/weixin",
                   "channels/feishu",
                   "channels/dingtalk",
                   "channels/wecom-bot",
                   "channels/qq",
                   "channels/wecom",
+                  "channels/wechat-kf",
                   "channels/wechatmp"
                 ]
               }
             ]
           },
           {
-            "tab": "命令",
+            "tab": "CLI",
             "groups": [
               {
-                "group": "命令系统",
+                "group": "Command System",
                 "pages": [
                   "cli/index",
                   "cli/process",
@@ -217,12 +234,13 @@
             ]
           },
           {
-            "tab": "版本",
+            "tab": "Releases",
             "groups": [
               {
-                "group": "发布记录",
+                "group": "Release Notes",
                 "pages": [
                   "releases/overview",
+                  "releases/v2.1.0",
                   "releases/v2.0.9",
                   "releases/v2.0.8",
                   "releases/v2.0.7",
@@ -240,202 +258,213 @@
         ]
       },
       {
-        "language": "en",
+        "language": "zh",
         "navbar": {
           "links": [
-            { "label": "Website", "href": "https://cowagent.ai/" },
-            { "label": "GitHub", "href": "https://github.com/zhayujie/CowAgent" }
+            {
+              "label": "官网",
+              "href": "https://cowagent.ai/?lang=zh"
+            },
+            {
+              "label": "GitHub",
+              "href": "https://github.com/zhayujie/CowAgent"
+            }
           ]
         },
         "tabs": [
           {
-            "tab": "Introduction",
+            "tab": "项目介绍",
             "groups": [
               {
-                "group": "Overview",
+                "group": "概览",
                 "pages": [
-                  "en/intro/index",
-                  "en/intro/architecture",
-                  "en/intro/features"
+                  "zh/intro/index",
+                  "zh/intro/architecture",
+                  "zh/intro/features"
                 ]
               }
             ]
           },
           {
-            "tab": "Get Started",
+            "tab": "快速开始",
             "groups": [
               {
-                "group": "Installation",
+                "group": "安装部署",
                 "pages": [
-                  "en/guide/quick-start",
-                  "en/guide/manual-install",
-                  "en/guide/upgrade"
+                  "zh/guide/quick-start",
+                  "zh/guide/manual-install",
+                  "zh/guide/upgrade"
                 ]
               }
             ]
           },
           {
-            "tab": "Models",
+            "tab": "模型",
             "groups": [
               {
-                "group": "Model Configuration",
+                "group": "模型配置",
                 "pages": [
-                  "en/models/index",
-                  "en/models/deepseek",
-                  "en/models/minimax",
-                  "en/models/claude",
-                  "en/models/gemini",
-                  "en/models/openai",
-                  "en/models/glm",
-                  "en/models/qwen",
-                  "en/models/doubao",
-                  "en/models/kimi",
-                  "en/models/qianfan",
-                  "en/models/mimo",
-                  "en/models/linkai",
-                  "en/models/coding-plan",
-                  "en/models/custom"
+                  "zh/models/index",
+                  "zh/models/deepseek",
+                  "zh/models/minimax",
+                  "zh/models/claude",
+                  "zh/models/gemini",
+                  "zh/models/openai",
+                  "zh/models/glm",
+                  "zh/models/qwen",
+                  "zh/models/doubao",
+                  "zh/models/kimi",
+                  "zh/models/qianfan",
+                  "zh/models/mimo",
+                  "zh/models/linkai",
+                  "zh/models/coding-plan",
+                  "zh/models/custom"
                 ]
               }
             ]
           },
           {
-            "tab": "Tools",
+            "tab": "工具",
             "groups": [
               {
-                "group": "Tools System",
+                "group": "工具系统",
                 "pages": [
-                  "en/tools/index"
+                  "zh/tools/index"
                 ]
               },
               {
-                "group": "Built-in Tools",
+                "group": "内置工具",
                 "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",
-                  "en/tools/web-fetch",
-                  "en/tools/scheduler"
+                  "zh/tools/read",
+                  "zh/tools/write",
+                  "zh/tools/edit",
+                  "zh/tools/ls",
+                  "zh/tools/bash",
+                  "zh/tools/send",
+                  "zh/tools/memory",
+                  "zh/tools/env-config",
+                  "zh/tools/web-fetch",
+                  "zh/tools/scheduler"
                 ]
               },
               {
-                "group": "Optional Tools",
+                "group": "可选工具",
                 "pages": [
-                  "en/tools/web-search",
-                  "en/tools/vision",
-                  "en/tools/browser"
+                  "zh/tools/web-search",
+                  "zh/tools/vision",
+                  "zh/tools/browser"
                 ]
               },
               {
-                "group": "MCP Tools",
+                "group": "MCP 工具",
                 "pages": [
-                  "en/tools/mcp"
+                  "zh/tools/mcp"
                 ]
               }
             ]
           },
           {
-            "tab": "Skills",
+            "tab": "技能",
             "groups": [
               {
-                "group": "Skills System",
+                "group": "技能系统",
                 "pages": [
-                  "en/skills/index",
-                  "en/skills/install",
-                  "en/skills/create",
-                  "en/skills/hub"
+                  "zh/skills/index",
+                  "zh/skills/install",
+                  "zh/skills/create",
+                  "zh/skills/hub"
                 ]
               },
               {
-                "group": "Built-in Skills",
+                "group": "内置技能",
                 "pages": [
-                  "en/skills/skill-creator",
-                  "en/skills/knowledge-wiki",
-                  "en/skills/image-generation"
+                  "zh/skills/skill-creator",
+                  "zh/skills/knowledge-wiki",
+                  "zh/skills/image-generation"
                 ]
               }
             ]
           },
           {
-            "tab": "Memory",
+            "tab": "记忆",
             "groups": [
               {
-                "group": "Memory System",
+                "group": "记忆系统",
                 "pages": [
-                  "en/memory/index",
-                  "en/memory/context",
-                  "en/memory/deep-dream"
+                  "zh/memory/index",
+                  "zh/memory/context",
+                  "zh/memory/deep-dream"
                 ]
               }
             ]
           },
           {
-            "tab": "Knowledge",
+            "tab": "知识",
             "groups": [
               {
-                "group": "Knowledge Base",
+                "group": "知识库",
                 "pages": [
-                  "en/knowledge/index"
+                  "zh/knowledge/index"
                 ]
               }
             ]
           },
           {
-            "tab": "Channels",
+            "tab": "通道",
             "groups": [
               {
-                "group": "Platforms",
+                "group": "接入渠道",
                 "pages": [
-                  "en/channels/index",
-                  "en/channels/weixin",
-                  "en/channels/web",
-                  "en/channels/feishu",
-                  "en/channels/dingtalk",
-                  "en/channels/wecom-bot",
-                  "en/channels/qq",
-                  "en/channels/wecom",
-                  "en/channels/wechatmp"
+                  "zh/channels/index",
+                  "zh/channels/weixin",
+                  "zh/channels/web",
+                  "zh/channels/feishu",
+                  "zh/channels/dingtalk",
+                  "zh/channels/wecom-bot",
+                  "zh/channels/qq",
+                  "zh/channels/wecom",
+                  "zh/channels/wechat-kf",
+                  "zh/channels/wechatmp",
+                  "zh/channels/telegram",
+                  "zh/channels/slack",
+                  "zh/channels/discord"
                 ]
               }
             ]
           },
           {
-            "tab": "CLI",
+            "tab": "命令",
             "groups": [
               {
-                "group": "Command System",
+                "group": "命令系统",
                 "pages": [
-                  "en/cli/index",
-                  "en/cli/process",
-                  "en/cli/skill",
-                  "en/cli/memory-knowledge",
-                  "en/cli/general"
+                  "zh/cli/index",
+                  "zh/cli/process",
+                  "zh/cli/skill",
+                  "zh/cli/memory-knowledge",
+                  "zh/cli/general"
                 ]
               }
             ]
           },
           {
-            "tab": "Releases",
+            "tab": "版本",
             "groups": [
               {
-                "group": "Release Notes",
+                "group": "发布记录",
                 "pages": [
-                  "en/releases/overview",
-                  "en/releases/v2.0.9",
-                  "en/releases/v2.0.8",
-                  "en/releases/v2.0.7",
-                  "en/releases/v2.0.6",
-                  "en/releases/v2.0.5",
-                  "en/releases/v2.0.4",
-                  "en/releases/v2.0.3",
-                  "en/releases/v2.0.2",
-                  "en/releases/v2.0.1",
-                  "en/releases/v2.0.0"
+                  "zh/releases/overview",
+                  "zh/releases/v2.1.0",
+                  "zh/releases/v2.0.9",
+                  "zh/releases/v2.0.8",
+                  "zh/releases/v2.0.7",
+                  "zh/releases/v2.0.6",
+                  "zh/releases/v2.0.5",
+                  "zh/releases/v2.0.4",
+                  "zh/releases/v2.0.3",
+                  "zh/releases/v2.0.2",
+                  "zh/releases/v2.0.1",
+                  "zh/releases/v2.0.0"
                 ]
               }
             ]
@@ -446,8 +475,14 @@
         "language": "ja",
         "navbar": {
           "links": [
-            { "label": "ウェブサイト", "href": "https://cowagent.ai/" },
-            { "label": "GitHub", "href": "https://github.com/zhayujie/CowAgent" }
+            {
+              "label": "ウェブサイト",
+              "href": "https://cowagent.ai/"
+            },
+            {
+              "label": "GitHub",
+              "href": "https://github.com/zhayujie/CowAgent"
+            }
           ]
         },
         "tabs": [
@@ -602,7 +637,11 @@
                   "ja/channels/wecom-bot",
                   "ja/channels/qq",
                   "ja/channels/wecom",
-                  "ja/channels/wechatmp"
+                  "ja/channels/wechat-kf",
+                  "ja/channels/wechatmp",
+                  "ja/channels/telegram",
+                  "ja/channels/slack",
+                  "ja/channels/discord"
                 ]
               }
             ]
@@ -629,6 +668,7 @@
                 "group": "リリースノート",
                 "pages": [
                   "ja/releases/overview",
+                  "ja/releases/v2.1.0",
                   "ja/releases/v2.0.9",
                   "ja/releases/v2.0.8",
                   "ja/releases/v2.0.7",

docs/en/channels/dingtalk.mdx

@@ -1,58 +0,0 @@
----
-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), log in and click **Create App**, fill in the app information:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-create-app.png" width="800"/>
-
-2. Click **Add App Capability**, select **Robot** capability and click **Add**:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-add-bot.png" width="800"/>
-
-3. Configure the robot information and click **Publish**. After publishing, click "**Debug**" to automatically create a test group chat, which can be viewed in the client:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-config-bot.png" width="600"/>
-
-4. Click **Version Management & Release**, create a new version and publish:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-publish-bot.png" width="700"/>
-
-## 2. Project Configuration
-
-1. Click **Credentials & Basic Info**, get the `Client ID` and `Client Secret`:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-get-secret.png" width="700"/>
-
-2. Add the following configuration to `config.json` in the project root:
-
-```json
-{
-  "channel_type": "dingtalk",
-  "dingtalk_client_id": "YOUR_CLIENT_ID",
-  "dingtalk_client_secret": "YOUR_CLIENT_SECRET"
-}
-```
-
-3. Install the dependency:
-
-```bash
-pip3 install dingtalk_stream
-```
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-app-config.png" width="700"/>
-
-4. After starting the project, go to the DingTalk Developer Console, click **Event Subscription**, then click **Connection verified, verify channel**. When "**Connection successful**" is displayed, the configuration is complete:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-event-sub.png" width="700"/>
-
-## 3. Usage
-
-Chat privately with the robot or add it to an enterprise group to start a conversation:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-hosting-demo.png" width="650"/>

docs/en/channels/feishu.mdx

@@ -1,110 +0,0 @@
----
-title: Feishu (Lark)
-description: Integrate CowAgent into Feishu via a custom enterprise app
----
-
-> Integrate CowAgent into Feishu via a custom enterprise app. Supports p2p chat and group chat (@bot), uses WebSocket long connection (no public IP needed), supports streaming typewriter replies and voice messages.
-
-<Note>
-  You need to be a Feishu enterprise user with admin privileges.
-</Note>
-
-## 1. Setup
-
-### Option 1: One-click Scan to Create (Recommended)
-
-No need to manually create an app on the Feishu Developer Platform. Start the Cow project, open the web console (default `http://127.0.0.1:9899/`), go to **Channels**, click **Add Channel**, choose **Feishu**, then under the **Scan QR** tab click **One-click Create Feishu App** and scan with the **Feishu App** to complete app creation and connection automatically.
-
-<img src="https://cdn.link-ai.tech/doc/20260505181126.png" width="800"/>
-
-<Note>
-  1. Requires `lark-oapi` ≥ 1.5.5.
-  2. The created app comes with all required permissions (messaging, card read/write, group events, etc.) and event subscriptions pre-configured — no manual setup on the developer console needed. Currently only the Feishu mainland version is supported (Lark international not yet supported).
-</Note>
-
-When starting from CLI without `feishu_app_id` configured, the QR code is also printed to the terminal.
-
-### Option 2: Manual Setup
-
-Manually create a custom app on the Feishu Developer Platform, then connect via Web Console or config file.
-
-**Step 1: Create the App**
-
-1. Go to [Feishu Developer Platform](https://open.feishu.cn/app/), click **Create Enterprise Custom App**:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-create-app.jpg" width="500"/>
-
-2. In **Add App Capabilities**, add the **Bot** capability:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-add-bot.jpg" width="800"/>
-
-3. In **Permission Management**, paste the following permissions and **Batch Enable** all:
-
-```
-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,cardkit:card:write
-```
-
-<img src="https://cdn.link-ai.tech/doc/feishu-hosting-add-auth2.png" width="800"/>
-
-4. Get `App ID` and `App Secret` from **Credentials & Basic Info**:
-
-<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-appid-secret.jpg" width="800"/>
-
-**Step 2: Connect to CowAgent**
-
-<Tabs>
-  <Tab title="Web Console">
-    Open the web console, go to **Channels**, click **Add Channel**, choose **Feishu**, switch to the **Manual** tab, enter App ID and App Secret, then click connect.
-  </Tab>
-  <Tab title="Config File">
-    Add the following to `config.json` and start the program:
-
-    ```json
-    {
-      "channel_type": "feishu",
-      "feishu_app_id": "YOUR_APP_ID",
-      "feishu_app_secret": "YOUR_APP_SECRET",
-      "feishu_stream_reply": true
-    }
-    ```
-
-    | Parameter | Description | Default |
-    | --- | --- | --- |
-    | `feishu_app_id` | Feishu app App ID | - |
-    | `feishu_app_secret` | Feishu app App Secret | - |
-    | `feishu_stream_reply` | Enable streaming typewriter reply | `true` |
-  </Tab>
-</Tabs>
-
-**Step 3: Publish the App**
-
-1. After Cow is running, go to **Events & Callbacks** in the Feishu Developer Platform, choose **Long Connection** mode and save:
-
-<img src="https://cdn.link-ai.tech/doc/202601311731183.png" width="600"/>
-
-2. Click **Add Event**, search for "Receive Message" and choose **Receive Message v2.0**.
-
-3. Click **Version Management & Release**, create a version and apply for **Production Release**. Approve the request in the Feishu client:
-
-<img src="https://cdn.link-ai.tech/doc/202601311807356.png" width="600"/>
-
-## 2. Features
-
-| Feature | Status |
-| --- | --- |
-| P2P chat | ✅ |
-| Group chat (@bot) | ✅ |
-| Text messages | ✅ send/receive |
-| Image messages | ✅ send/receive |
-| Voice messages | ✅ send/receive |
-| Streaming reply | ✅ (powered by Feishu cardkit streaming card) |
-
-<Note>
-  Streaming reply requires the `cardkit:card:write` permission (already enabled by one-click creation) and Feishu client version ≥ 7.20. Older clients see an upgrade prompt; if the permission or version is not satisfied, replies fall back to plain text automatically.
-</Note>
-
-## 3. Usage
-
-After connection, search for the bot name in Feishu to start a chat.
-
-To use in groups, add the bot to a group and @-mention it.

docs/en/channels/index.mdx

@@ -1,39 +0,0 @@
----
-title: Channels Overview
-description: Channels supported by CowAgent and their capability matrix
----
-
-CowAgent supports multiple chat channels. Switch between them at startup via `channel_type`. The Web Console is enabled by default and can run in parallel with other channels.
-
-## Capability Matrix
-
-The table below summarizes the inbound message types, bot reply types, and group chat capabilities supported by each channel, making it easy to choose by scenario.
-
-| Channel | Text | Image | File | Voice | Group Chat |
-| --- | :-: | :-: | :-: | :-: | :-: |
-| [WeChat](/en/channels/weixin) | ✅ | ✅ | ✅ | ✅ |  |
-| [Web Console](/en/channels/web) | ✅ | ✅ | ✅ | ✅ | |
-| [Feishu](/en/channels/feishu) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [DingTalk](/en/channels/dingtalk) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [WeCom Bot](/en/channels/wecom-bot) | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [QQ](/en/channels/qq) | ✅ | ✅ | ✅ | | ✅ |
-| [WeCom App](/en/channels/wecom) | ✅ | ✅ | ✅ | ✅ | |
-| [Official Account](/en/channels/wechatmp) | ✅ | ✅ | | ✅ | |
-
-- The **Image / File / Voice** columns indicate that the channel can send and receive the corresponding message types; see each channel's docs for details
-- The **Group Chat** column indicates the ability to recognize and respond to group messages
-
-<Tip>
-  The voice / image capabilities of each channel depend on the configuration of the corresponding model provider. See [Models Overview](/en/models/index) for details.
-</Tip>
-
-## Channel List
-
-- [Web Console](/en/channels/web) — built-in browser-based chat and management panel, enabled by default
-- [WeChat](/en/channels/weixin) — log in via personal WeChat QR scan
-- [Feishu](/en/channels/feishu) — Feishu custom bot
-- [DingTalk](/en/channels/dingtalk) — DingTalk custom bot
-- [WeCom Bot](/en/channels/wecom-bot) — WeCom AI Bot via WebSocket long connection
-- [QQ](/en/channels/qq) — QQ Official Bot open platform
-- [WeCom App](/en/channels/wecom) — WeCom custom app integration
-- [Official Account](/en/channels/wechatmp) — WeChat Official Account (subscription / service)

docs/en/channels/qq.mdx

@@ -1,88 +0,0 @@
----
-title: QQ Bot
-description: Connect CowAgent to QQ Bot (WebSocket long connection)
----
-
-> Connect CowAgent via QQ Open Platform's bot API, supporting QQ direct messages, group chats (@bot), guild channel messages, and guild DMs. No public IP required — uses WebSocket long connection.
-
-<Note>
-  QQ Bot is created through the QQ Open Platform. It uses WebSocket long connection to receive messages and OpenAPI to send messages. No public IP or domain is required.
-</Note>
-
-## 1. Create a QQ Bot
-
-> Visit the [QQ Open Platform](https://q.qq.com), sign in with QQ. If you haven't registered, please complete [account registration](https://q.qq.com/#/register) first.
-
-1.Go to the [QQ Open Platform - Bot List](https://q.qq.com/#/apps), and click **Create Bot**:
-
-<img src="https://cdn.link-ai.tech/doc/20260317162900.png" width="800"/>
-
-2.Fill in the bot name, avatar, and other basic information to complete the creation:
-
-<img src="https://cdn.link-ai.tech/doc/20260317163005.png" width="800"/>
-
-3.Enter the bot configuration page, go to **Development Management**, and complete the following steps:
-
-  - Copy and save the **AppID** (Bot ID)
-  - Generate and save the **AppSecret** (Bot Secret)
-
-<img src="https://cdn.link-ai.tech/doc/20260317164955.png" width="800"/>
-
-## 2. Configuration and Running
-
-### Option A: Web Console
-
-Start the program and open the Web console (local access: http://127.0.0.1:9899/). Go to the **Channels** tab, click **Connect Channel**, select **QQ Bot**, fill in the AppID and AppSecret from the previous step, and click Connect.
-
-<img src="https://cdn.link-ai.tech/doc/20260317165425.png" width="800"/>
-
-### Option B: Config File
-
-Add the following to your `config.json`:
-
-```json
-{
-  "channel_type": "qq",
-  "qq_app_id": "YOUR_APP_ID",
-  "qq_app_secret": "YOUR_APP_SECRET"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `qq_app_id` | AppID of the QQ Bot, found in Development Management on the open platform |
-| `qq_app_secret` | AppSecret of the QQ Bot, found in Development Management on the open platform |
-
-After configuration, start the program. The log message `[QQ] ✅ Connected successfully` indicates a successful connection.
-
-
-## 3. Usage
-
-In the QQ Open Platform, go to **Management → Usage Scope & Members**, scan the "Add to group and message list" QR code with your QQ client to start chatting with the bot:
-
-<img src="https://cdn.link-ai.tech/doc/20260317165947.png" width="800"/>
-
-Chat example:
-<img src="https://cdn.link-ai.tech/doc/20260317171508.png" width="800"/>
-
-## 4. Supported Features
-
-> Note: To use the QQ bot in group chats and guild channels, you need to complete the publishing review and configure usage scope permissions.
-
-| Feature | Status |
-| --- | --- |
-| QQ Direct Messages | ✅ |
-| QQ Group Chat (@bot) | ✅ |
-| Guild Channel (@bot) | ✅ |
-| Guild DM | ✅ |
-| Text Messages | ✅ Send & Receive |
-| Image Messages | ✅ Send & Receive (group & direct) |
-| File Messages | ✅ Send (group & direct) |
-| Scheduled Tasks | ✅ Active push (4 per user per month) |
-
-
-## 5. Notes
-
-- **Passive message limits**: QQ direct message replies are valid for 60 minutes (max 5 replies per message); group chat replies are valid for 5 minutes.
-- **Active message limits**: Both direct and group chats have a monthly limit of 4 active messages. Keep this in mind when using the scheduled tasks feature.
-- **Event permissions**: By default, `GROUP_AND_C2C_EVENT` (QQ group/direct) and `PUBLIC_GUILD_MESSAGES` (guild public messages) are subscribed. Apply for additional permissions on the open platform if needed.

docs/en/channels/web.mdx

@@ -1,94 +0,0 @@
----
-title: Web Console
-description: Use CowAgent through the Web Console
----
-
-The Web Console is CowAgent's default channel. It runs automatically once started, letting you chat with the Agent in a browser and manage models, skills, memory, channels, and other configuration online.
-
-## Configuration
-
-```json
-{
-  "channel_type": "web",
-  "web_host": "0.0.0.0",
-  "web_port": 9899,
-  "web_password": "",
-  "enable_thinking": false
-}
-```
-
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `channel_type` | Set to `web` | `web` |
-| `web_host` | Web service listen address. Defaults to `127.0.0.1` (local only); set to `0.0.0.0` for public access and configure a password | `""` |
-| `web_port` | Web service listen port | `9899` |
-| `web_password` | Access password. Leave empty to disable password protection; recommended when listening on `0.0.0.0` | `""` |
-| `web_session_expire_days` | Login session validity in days | `30` |
-| `enable_thinking` | Whether to enable deep thinking mode | `false` |
-
-Once a password is configured, you must enter it to log in when accessing the console. The login session is kept for 30 days by default, so restarting the service during that period does not require re-login. The password can also be changed online from the "Configuration" page in the console.
-
-## Access URL
-
-After starting the project, visit:
-
-- Local: `http://localhost:9899`
-- Server: `http://<server-ip>:9899`
-
-<Note>
-  Ensure the server firewall and security group allow the corresponding port.
-</Note>
-
-## Features
-
-### Chat Interface
-
-Supports streaming output with real-time display of the Agent's reasoning process and tool calls, providing intuitive observation of the Agent's decision-making. Deep thinking can be toggled via configuration or the "Agent Configuration" switch in the console.
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260227180120.png" />
-
-#### Multi-Session Management
-
-The chat interface supports multi-session management. All session records are persistently stored in the database:
-
-- **Session List**: Click the history icon on the left to expand/collapse the session list panel, with scroll-to-load support for all historical sessions
-- **AI-Generated Titles**: After the first exchange in a new session, the model is automatically called to generate a short summary title
-- **New Session**: Click the "New Chat" button at the top of the session list or the `+` button in the input area to create a new session
-- **Delete Session**: Click the delete button on a session item and confirm to permanently delete the session and all its messages
-- **Clear Context**: Click the clear button in the input area to insert a divider in the current session. Messages above the divider are still displayed but no longer included as context for the model
-
-### Model Management
-
-Manage text, image, voice, and embedding model configurations for different providers online — no need to edit config files manually:
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260521212949.png" />
-
-### Skill Management
-
-View and manage Agent skills (Skills) online:
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260227173403.png" />
-
-### Memory Management
-
-View and manage Agent memory online:
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260227173349.png" />
-
-### Channel Management
-
-Manage connected channels online with real-time connect/disconnect operations:
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260227173331.png" />
-
-### Scheduled Tasks
-
-View and manage scheduled tasks online, including one-time tasks, fixed intervals, and Cron expressions:
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260227173704.png" />
-
-### Logs
-
-View Agent runtime logs in real time for monitoring and troubleshooting:
-
-<img width="850" src="https://cdn.link-ai.tech/doc/20260227173514.png" />

docs/en/channels/wechatmp.mdx

@@ -1,72 +0,0 @@
----
-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 | Sends a placeholder reply first; users must send a message to retrieve the full response |
-| **Enterprise Service** | Enterprise with verified customer service API | Can proactively push replies to users |
-
-<Note>
-  Official Accounts only support server and Docker deployment, not local run mode. Install extended dependencies: `pip3 install -r requirements-optional.txt`
-</Note>
-
-## 1. Personal Subscription Account
-
-Add the following configuration to `config.json`:
-
-```json
-{
-  "channel_type": "wechatmp",
-  "single_chat_prefix": [""],
-  "wechatmp_app_id": "wx73f9******d1e48",
-  "wechatmp_app_secret": "YOUR_APP_SECRET",
-  "wechatmp_aes_key": "",
-  "wechatmp_token": "YOUR_TOKEN",
-  "wechatmp_port": 80
-}
-```
-
-### Setup Steps
-
-These configurations must be consistent with the [WeChat Official Account Platform](https://mp.weixin.qq.com/advanced/advanced?action=dev&t=advanced/dev). Navigate to **Settings & Development → Basic Configuration → Server Configuration** and configure as shown below:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103506.png" width="480"/>
-
-1. Enable the developer secret on the platform (corresponds to `wechatmp_app_secret`), and add the server IP to the whitelist
-2. Fill in the `config.json` with the official account parameters matching the platform configuration
-3. Start the program, which listens on port 80 (use `sudo` if you don't have permission; stop any process occupying port 80)
-4. **Enable server configuration** on the official account platform and submit. A successful save means the configuration is complete. Note that the **"Server URL"** must be in the format `http://{HOST}/wx`, where `{HOST}` can be the server IP or domain
-
-After following the account and sending a message, you should see the following result:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103522.png" width="720"/>
-
-Due to subscription account limitations, short replies (within 15s) can be returned immediately, but longer replies will first send a "Thinking..." placeholder, requiring users to send any text to retrieve the answer. Enterprise service accounts can solve this with the customer service API.
-
-<Tip>
-  **Voice Recognition**: You can use WeChat's built-in voice recognition. Enable "Receive Voice Recognition Results" under "Settings & Development → API Permissions" on the official account management page.
-</Tip>
-
-## 2. Enterprise Service Account
-
-The setup process for enterprise service accounts is essentially the same as personal subscription accounts, with the following differences:
-
-1. Register an enterprise service account on the platform and complete WeChat certification. Confirm that the **Customer Service API** permission has been granted
-2. Set `"channel_type": "wechatmp_service"` in `config.json`; other configurations remain the same
-3. Even for longer replies, they can be proactively pushed to users without requiring manual retrieval
-
-```json
-{
-  "channel_type": "wechatmp_service",
-  "single_chat_prefix": [""],
-  "wechatmp_app_id": "YOUR_APP_ID",
-  "wechatmp_app_secret": "YOUR_APP_SECRET",
-  "wechatmp_aes_key": "",
-  "wechatmp_token": "YOUR_TOKEN",
-  "wechatmp_port": 80
-}
-```

docs/en/channels/wecom-bot.mdx

@@ -1,90 +0,0 @@
----
-title: WeCom Bot
-description: Connect CowAgent to WeCom AI Bot (WebSocket long connection)
----
-
-> Connect CowAgent via WeCom AI Bot, supporting both internal direct messages and group chats. No public IP required — uses a WebSocket long connection, with Markdown rendering and streaming output.
-
-<Note>
-  WeCom Bot and WeCom App are two different integration methods. WeCom Bot uses a WebSocket long connection and requires no public IP or domain, making setup much simpler.
-</Note>
-
-## 1. Connection methods
-
-### Option A: One-click QR scan (recommended)
-
-No need to create the bot ahead of time. Start CowAgent and open the Web console (local URL: http://127.0.0.1:9899/), go to the **Channels** tab, click **Connect Channel**, choose **WeCom Bot**, switch to **QR scan** mode, and scan the QR code with **WeCom** — bot creation and connection complete automatically.
-
-<img src="https://cdn.link-ai.tech/doc/20260401121213.png" width="800"/>
-
-<Note>
-  After a successful scan, you can further configure the bot (name, avatar, visibility scope, etc.) in **WeCom Workbench → AI Bot**.
-</Note>
-
-### Option B: Manual creation
-
-Create the AI Bot in WeCom and obtain the Bot ID and Secret, then connect via the Web console or config file.
-
-**Step 1: Create the AI Bot**
-
-1. Open the WeCom client, go to **Workbench**, and click **AI Bot**:
-
-<img src="https://cdn.link-ai.tech/doc/20260316180959.png" width="800"/>
-
-2. Click **Create Bot → Manual Creation**:
-
-<img src="https://cdn.link-ai.tech/doc/20260316181118.png" width="800"/>
-
-3. Scroll to the bottom of the right panel and select **API Mode**:
-
-<img src="https://cdn.link-ai.tech/doc/20260316181215.png" width="800"/>
-
-4. Set the bot name, avatar, and visibility scope. Choose **Long Connection** mode, save the **Bot ID** and **Secret**, then click Save.
-
-**Step 2: Connect to CowAgent**
-
-<Tabs>
-  <Tab title="Web Console">
-    Open the Web console, go to the **Channels** tab, click **Connect Channel**, choose **WeCom Bot**, switch to **Manual** mode, enter the Bot ID and Secret, and click Connect.
-
-    <img src="https://cdn.link-ai.tech/doc/20260316181711.png" width="800"/>
-  </Tab>
-  <Tab title="Config File">
-    Add the following to `config.json`, then start CowAgent:
-
-    ```json
-    {
-      "channel_type": "wecom_bot",
-      "wecom_bot_id": "YOUR_BOT_ID",
-      "wecom_bot_secret": "YOUR_SECRET"
-    }
-    ```
-
-    | Parameter | Description |
-    | --- | --- |
-    | `wecom_bot_id` | Bot ID of the AI Bot |
-    | `wecom_bot_secret` | Secret of the AI Bot |
-  </Tab>
-</Tabs>
-
-The log line `[WecomBot] Subscribe success` confirms the connection is established.
-
-## 2. Supported features
-
-| Feature | Status |
-| --- | --- |
-| Direct chat | ✅ |
-| Group chat (@bot) | ✅ |
-| Text messages | ✅ Send / Receive |
-| Image messages | ✅ Send / Receive |
-| File messages | ✅ Send / Receive |
-| Streaming replies | ✅ |
-| Scheduled push messages | ✅ |
-
-## 3. Usage
-
-Search for the bot's name inside WeCom to start a direct chat.
-
-To use the bot in an internal group chat, add it to the group and @-mention it.
-
-<img src="https://cdn.link-ai.tech/doc/20260316182902.png" width="800"/>

docs/en/channels/wecom.mdx

@@ -1,98 +0,0 @@
----
-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 (overseas server, or domestic server with a proxy for international API access)
-2. A registered WeCom account (individual registration is possible but cannot be certified)
-3. Certified WeCom accounts additionally require a domain filed under the corresponding entity
-
-## 2. Create WeCom App
-
-1. In the [WeCom Admin Console](https://work.weixin.qq.com/wework_admin/frame#profile), click **My Enterprise** and find the **Corp ID** at the bottom of the page. Save this ID for the `wechatcom_corp_id` configuration field.
-
-2. Switch to **Application Management** and click Create Application:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103156.png" width="480"/>
-
-3. On the application creation page, record the `AgentId` and `Secret`:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103218.png" width="580"/>
-
-4. Click **Set API Reception** to configure the application interface:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103211.png" width="520"/>
-
-- URL format: `http://ip:port/wxcomapp` (certified enterprises must use a filed domain)
-- Generate random `Token` and `EncodingAESKey` and save them for the configuration file
-
-<Note>
-  The API reception configuration cannot be saved at this point because the program hasn't started yet. Come back to save it after the project is running.
-</Note>
-
-## 3. Configuration and Run
-
-Add the following configuration to `config.json` (the mapping between each parameter and the WeCom console is shown in the screenshots above):
-
-```json
-{
-  "channel_type": "wechatcom_app",
-  "single_chat_prefix": [""],
-  "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 configuration, start the program. When the log shows `http://0.0.0.0:9898/`, the program is running successfully. You need to open this port externally (e.g., allow it in the cloud server security group).
-
-After the program starts, return to the WeCom Admin Console to save the **Message Server Configuration**. After saving successfully, you also need to add the server IP to **Enterprise Trusted IPs**, otherwise messages cannot be sent or received:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103224.png" width="520"/>
-
-<Warning>
-  If the URL configuration callback fails or the configuration is unsuccessful:
-  1. Ensure the server firewall is disabled and the security group allows the listening port
-  2. Carefully check that Token, Secret Key and other parameter configurations are consistent, and that the URL format is correct
-  3. Certified WeCom accounts must configure a filed domain matching the entity
-</Warning>
-
-## 4. Usage
-
-Search for the app name you just created in WeCom to start chatting directly. You can run multiple instances listening on different ports to create multiple WeCom apps:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103228.png" width="720"/>
-
-To allow external personal WeChat users to use the app, go to **My Enterprise → WeChat Plugin**, share the invite QR code. After scanning and following, personal WeChat users can join and chat with the app:
-
-<img src="https://cdn.link-ai.tech/doc/20260228103232.png" width="520"/>
-
-## FAQ
-
-Make sure the following dependencies are installed:
-
-```bash
-pip install websocket-client pycryptodome
-```

docs/en/channels/weixin.mdx

@@ -1,71 +0,0 @@
----
-title: WeChat
-description: Connect CowAgent to personal WeChat (via the official API)
----
-
-> Connect CowAgent to your personal WeChat — scan to log in, no public IP required. Supports text, image, voice, file, and video messages in 1-on-1 chats. Backed by WeChat's official API; safe to use. After connecting, a bot assistant is added to your conversation list without affecting normal account usage.
-
-## 1. Setup and run
-
-### Option A: Web console
-
-Start CowAgent and open the Web console (local URL: http://127.0.0.1:9899/). Go to the **Channels** tab, click **Connect Channel**, select **WeChat**, and follow the prompts to scan in.
-
-<img src="https://cdn.link-ai.tech/doc/20260322195114.png" width="800" />
-
-### Option B: Config file
-
-Set `channel_type` to `weixin` in `config.json`:
-
-```json
-{
-  "channel_type": "weixin"
-}
-```
-
-After starting CowAgent, a QR code is displayed in the terminal. Scan it with WeChat to complete login.
-
-<img src="https://cdn.link-ai.tech/doc/20260322195509.png" width="800" />
-
-<Note>
-  1. For backward compatibility, setting `channel_type` to `wx` also activates the WeChat channel.
-  2. The WeChat client must be on version **8.0.69** or higher.
-</Note>
-
-## 2. Usage
-
-Once authorized, the integration completes and you can start chatting. A bot assistant is created in your WeChat conversation list, leaving normal account usage unaffected.
-
-> You can find the bot at any time by searching for **"微信ClawBot"**. You may also rename it, change its avatar, pin it to the top of your conversation list, and so on.
-
-<img src="https://cdn.link-ai.tech/doc/83ae8251d896219fde4803f4205205be.jpg" width="250" />
-
-## 3. Login
-
-### QR code login
-
-On first startup, a QR code appears in the terminal (valid for around 2 minutes). Scan it with WeChat and confirm on your phone to log in.
-
-- The QR code refreshes automatically when it expires
-- The `qrcode` dependency is already included in `requirements.txt`, so the QR code renders directly in the terminal after install
-
-### Credential persistence
-
-After a successful login, credentials are saved to `~/.weixin_cow_credentials.json`. Subsequent startups reuse the saved credentials with no need to re-scan.
-
-To force a re-login, delete the credentials file and restart.
-
-### Session expiry
-
-When the WeChat session expires (errcode `-14`), CowAgent automatically clears old credentials and initiates a new QR login — no manual intervention required.
-
-## 4. Supported features
-
-| Feature | Status |
-| --- | --- |
-| Direct messages | ✅ |
-| Text messages | ✅ Send & Receive |
-| Image messages | ✅ Send & Receive |
-| File messages | ✅ Send & Receive |
-| Video messages | ✅ Send & Receive |
-| Voice messages | ✅ Receive (built-in speech recognition) |

docs/en/cli/general.mdx

@@ -1,110 +0,0 @@
----
-title: General Commands
-description: View status, manage config, and control context with commonly used commands
----
-
-The following commands can be used in chat with the `/` prefix or in the terminal with the `cow` prefix (some are chat-only).
-
-<Tip>
-  In the Web console, typing `/` brings up an autocomplete menu with keyboard navigation and Tab completion.
-</Tip>
-
-## help
-
-Show help information for all available commands.
-
-```text
-/help
-```
-
-## status
-
-View current session and service status, including process info, model configuration, message count, and loaded skills.
-
-```text
-/status
-```
-
-## cancel
-
-Abort the agent task currently running in this session. When the agent is busy with a long task (e.g. multi-turn tool calls or a long streaming response), send `/cancel` and the agent will stop before the next tool execution. Available across all channels — Web, WeChat, WeCom, Feishu, etc.
-
-```text
-/cancel
-```
-
-## config
-
-View or modify runtime configuration. Changes take effect immediately without restarting.
-
-**View all configurable items:**
-
-```text
-/config
-```
-
-**View a single item:**
-
-```text
-/config model
-```
-
-**Modify a config item:**
-
-```text
-/config model deepseek-v4-flash
-```
-
-**Configurable items:**
-
-| Item | Description | Example |
-| --- | --- | --- |
-| `model` | AI model name | `deepseek-v4-flash` |
-| `agent_max_context_tokens` | Max context tokens | `40000` |
-| `agent_max_context_turns` | Max context memory turns | `30` |
-| `agent_max_steps` | Max decision steps per task | `15` |
-| `enable_thinking` | Enable deep thinking mode | `true` / `false` |
-
-<Note>
-  When changing `model`, the system automatically matches the corresponding model API. Configuration is persisted to `config.json`.
-</Note>
-
-## context
-
-View current session context statistics, including message count and content length.
-
-```text
-/context
-```
-
-**Clear current session context:**
-
-```text
-/context clear
-```
-
-<Tip>
-  Clearing context makes the Agent "forget" previous conversation, useful for switching topics or freeing context space.
-</Tip>
-
-## logs
-
-View recent service logs. Shows the last 20 lines by default, up to 50.
-
-```text
-/logs
-```
-
-**Specify line count:**
-
-```text
-/logs 50
-```
-
-## version
-
-Show the current CowAgent version.
-
-```text
-/version
-```

docs/en/cli/index.mdx

@@ -1,96 +0,0 @@
----
-title: Commands Overview
-description: CowAgent command system — Terminal CLI and chat commands
----
-
-CowAgent provides two ways to interact via commands:
-
-- **Terminal CLI** — Run `cow <command>` in your system terminal for service management, skill management, and other operations
-- **Chat Commands** — Type `/<command>` or `cow <command>` in any conversation to check status, manage skills, adjust configuration, etc.
-
-## Cow CLI
-
-After deploying with the one-click install script, the `cow` command is automatically available. For manual installations, run:
-
-```bash
-pip install -e .
-```
-
-Then use the `cow` command from anywhere:
-
-```bash
-cow help
-```
-
-Example output:
-
-```
-🐮 CowAgent CLI
-
-Usage: cow <command>
-
-Service:
-  start     Start the CowAgent service
-  stop      Stop the CowAgent service
-  restart   Restart the CowAgent service
-  update    Update code and restart service
-  status    Show service status
-  logs      View service logs
-
-Skills:
-  skill     Manage skills (list / search / install / uninstall ...)
-
-Memory & Knowledge:
-  memory    Memory distillation (dream)
-  knowledge View knowledge base stats and structure
-
-Others:
-  help      Show this help message
-  version   Show version
-```
-
-## Chat Commands
-
-In the Web console or any connected channel, type `/` to see command suggestions. Supported commands:
-
-| Command | Description |
-| --- | --- |
-| `/help` | Show command help |
-| `/status` | View service status and configuration |
-| `/cancel` | Abort the currently running agent task |
-| `/config` | View or modify runtime configuration |
-| `/skill` | Manage skills (install, uninstall, enable, disable, etc.) |
-| `/memory dream [N]` | Manually trigger memory distillation (default 3 days, max 30) |
-| `/knowledge` | View knowledge base statistics |
-| `/knowledge list` | View knowledge base directory structure |
-| `/knowledge on\|off` | Enable or disable knowledge base |
-| `/context` | View current session context info |
-| `/context clear` | Clear current session context |
-| `/logs` | View recent logs |
-| `/version` | Show version number |
-
-<Tip>
-  Service management commands like `/start`, `/stop`, `/restart` will prompt you to use them in the terminal instead, as they involve process operations.
-</Tip>
-
-## Command Availability
-
-| Command | Terminal (`cow`) | Chat (`/`) |
-| --- | :---: | :---: |
-| help | ✓ | ✓ |
-| version | ✓ | ✓ |
-| status | ✓ | ✓ |
-| logs | ✓ | ✓ |
-| cancel | ✗ | ✓ |
-| config | ✗ | ✓ |
-| context | — | ✓ |
-| memory (subcommands) | ✗ | ✓ |
-| knowledge (subcommands) | ✓ | ✓ |
-| skill (subcommands) | ✓ | ✓ |
-| start / stop / restart | ✓ | ✗ |
-| update | ✓ | ✗ |
-| install-browser | ✓ | ✗ |
-
-<Note>
-  `context` only shows a hint in the terminal to use it in chat. `config` is only available in chat.
-</Note>

docs/en/cli/memory-knowledge.mdx

@@ -1,63 +0,0 @@
----
-title: Memory & Knowledge
-description: Memory distillation and knowledge base management commands
----
-
-## memory
-
-Manage the Agent's long-term memory system.
-
-### memory dream
-
-Manually trigger memory distillation (Deep Dream) — consolidate recent daily memories into MEMORY.md and generate a dream diary.
-
-```text
-/memory dream [N]
-```
-
-- `N`: Consolidate the last N days of memory (default 3, max 30)
-- Runs asynchronously in the background; you'll be notified in chat when complete
-- Works without Agent initialization — can be used before the first conversation
-
-**Examples:**
-
-```text
-/memory dream       # Consolidate last 3 days
-/memory dream 7     # Consolidate last 7 days
-/memory dream 30    # Consolidate last 30 days (full)
-```
-
-On the Web console, the completion notification includes clickable links to view the updated MEMORY.md and dream diary.
-
-<Tip>
-  The system automatically runs distillation daily at 23:55 (lookback 1 day). Manual trigger is useful for consolidating historical memories after first deployment, or when you need an immediate memory update.
-</Tip>
-
-## knowledge
-
-View and manage the personal knowledge base. Shows statistics by default.
-
-```text
-/knowledge
-```
-
-### knowledge list
-
-View the knowledge base directory tree.
-
-```text
-/knowledge list
-```
-
-### knowledge on / off
-
-Enable or disable the knowledge base. When disabled, knowledge prompts and file indexing are not injected.
-
-```text
-/knowledge on
-/knowledge off
-```
-
-<Note>
-  In the terminal CLI, `cow knowledge` and `cow knowledge list` are available, but `on|off` is only supported in chat (requires runtime effect).
-</Note>

docs/en/cli/process.mdx

@@ -1,123 +0,0 @@
----
-title: Process Management
-description: Manage CowAgent process lifecycle with cow commands
----
-
-Process management commands control the CowAgent background process. These commands are only available in the terminal.
-
-## start
-
-Start the CowAgent service. Runs as a background daemon by default and automatically tails logs.
-
-```bash
-cow start
-```
-
-**Options:**
-
-| Option | Description |
-| --- | --- |
-| `-f`, `--foreground` | Run in foreground, not as a background daemon |
-| `--no-logs` | Don't tail logs after starting |
-
-## stop
-
-Stop the running CowAgent service.
-
-```bash
-cow stop
-```
-
-## restart
-
-Restart the CowAgent service (stop then start).
-
-```bash
-cow restart
-```
-
-**Options:**
-
-| Option | Description |
-| --- | --- |
-| `--no-logs` | Don't tail logs after restart |
-
-## update
-
-Update code and restart the service. Automatically performs:
-
-1. Pull latest code (`git pull`)
-2. Stop current service
-3. Update Python dependencies
-4. Reinstall CLI
-5. Start service
-
-```bash
-cow update
-```
-
-<Warning>
-  If `git pull` fails (e.g., uncommitted local changes), the update aborts and the service remains unaffected.
-</Warning>
-
-## status
-
-Check CowAgent service status, including process info, version, and current model/channel configuration.
-
-```bash
-cow status
-```
-
-## logs
-
-View service logs.
-
-```bash
-cow logs
-```
-
-**Options:**
-
-| Option | Description | Default |
-| --- | --- | --- |
-| `-f`, `--follow` | Continuously tail log output | No |
-| `-n`, `--lines` | Show last N lines | 50 |
-
-Examples:
-
-```bash
-# View last 100 lines
-cow logs -n 100
-
-# Continuously tail logs
-cow logs -f
-```
-
-## install-browser
-
-Install Playwright and Chromium browser for the [browser tool](/en/tools/browser).
-
-```bash
-cow install-browser
-```
-
-<Tip>
-  Only needed when using browser tools (web browsing, screenshots, etc.).
-</Tip>
-
-## run.sh Compatibility
-
-If Cow CLI is not installed, you can use `run.sh` to manage the service:
-
-| cow command | run.sh equivalent |
-| --- | --- |
-| `cow start` | `./run.sh start` |
-| `cow stop` | `./run.sh stop` |
-| `cow restart` | `./run.sh restart` |
-| `cow update` | `./run.sh update` |
-| `cow status` | `./run.sh status` |
-| `cow logs` | `./run.sh logs` |
-
-<Note>
-  The `cow` command is recommended — it provides cleaner syntax and richer features. It is automatically installed via the one-click install script.
-</Note>

docs/en/cli/skill.mdx

@@ -1,210 +0,0 @@
----
-title: Skill Management
-description: Install, uninstall, enable, disable, and manage skills via commands
----
-
-Skill management commands are used to install, query, and manage CowAgent skills. Use `/skill <subcommand>` in chat or `cow skill <subcommand>` in the terminal.
-
-## list
-
-List installed skills and their status.
-
-<CodeGroup>
-```text Chat
-/skill list
-```
-
-```bash Terminal
-cow skill list
-```
-</CodeGroup>
-
-Example output:
-
-```
-📦 Installed skills (3/4)
-
-✅ pptx
-   Use this skill any time a .pptx file is involved…
-   Source: cowhub
-
-✅ skill-creator
-   Create, install, or update skills…
-   Source: builtin
-
-⏸️ image-vision (disabled)
-   Image understanding and visual analysis
-   Source: builtin
-```
-
-**Browse the Skill Hub** (view all available skills):
-
-<CodeGroup>
-```text Chat
-/skill list --remote
-```
-
-```bash Terminal
-cow skill list --remote
-```
-</CodeGroup>
-
-**Options:**
-
-| Option | Description | Default |
-| --- | --- | --- |
-| `--remote`, `-r` | Browse Skill Hub remote skill list | No |
-| `--page` | Page number for remote listing | 1 |
-
-## search
-
-Search for skills on the Skill Hub.
-
-<CodeGroup>
-```text Chat
-/skill search pptx
-```
-
-```bash Terminal
-cow skill search pptx
-```
-</CodeGroup>
-
-## install
-
-Install skills with a single `install` command from Cow Skill Hub, GitHub, ClawHub, or any URL (zip archives, SKILL.md links) — no manual download or configuration required.
-
-**From Skill Hub (recommended):**
-
-<CodeGroup>
-```text Chat
-/skill install pptx
-```
-
-```bash Terminal
-cow skill install pptx
-```
-</CodeGroup>
-
-**From GitHub:**
-
-<CodeGroup>
-```text Chat
-# Install all skills in a repo (auto-discovers subdirectories with SKILL.md)
-/skill install larksuite/cli
-
-# Specify a subdirectory to install a single skill
-/skill install https://github.com/larksuite/cli/tree/main/skills/lark-im
-
-# Use # to specify a subdirectory
-/skill install larksuite/cli#skills/lark-minutes
-```
-
-```bash Terminal
-# Install all skills in a repo (auto-discovers subdirectories with SKILL.md)
-cow skill install larksuite/cli
-
-# Specify a subdirectory to install a single skill
-cow skill install https://github.com/larksuite/cli/tree/main/skills/lark-im
-
-# Use # to specify a subdirectory
-cow skill install larksuite/cli#skills/lark-minutes
-```
-</CodeGroup>
-
-Supports full GitHub URLs and `owner/repo` shorthand. For mono-repos (multiple skills in one repository), omitting the subdirectory auto-discovers and batch-installs all skills; specifying a subdirectory installs only that skill.
-
-**From ClawHub:**
-
-<CodeGroup>
-```text Chat
-/skill install clawhub:baidu-search
-```
-
-```bash Terminal
-cow skill install clawhub:baidu-search
-```
-</CodeGroup>
-
-**From URL:**
-
-<CodeGroup>
-```text Chat
-# Install from a zip archive (single or batch)
-/skill install https://cdn.link-ai.tech/skills/pptx.zip
-
-# Install from a SKILL.md link
-/skill install https://example.com/path/to/SKILL.md
-```
-
-```bash Terminal
-# Install from a zip archive (single or batch)
-cow skill install https://cdn.link-ai.tech/skills/pptx.zip
-
-# Install from a SKILL.md link
-cow skill install https://example.com/path/to/SKILL.md
-```
-</CodeGroup>
-
-Supports installing from zip / tar.gz archive URLs — automatically extracts and discovers directories containing `SKILL.md`, with support for single or batch install. Also supports installing directly from a `SKILL.md` file URL, automatically parsing the skill name and description.
-
-## uninstall
-
-Uninstall an installed skill.
-
-<CodeGroup>
-```text Chat
-/skill uninstall pptx
-```
-
-```bash Terminal
-cow skill uninstall pptx
-```
-</CodeGroup>
-
-<Warning>
-  Uninstalling deletes all files in the skill directory. This action cannot be undone.
-</Warning>
-
-## enable / disable
-
-Enable or disable a skill. Disabled skills will not be invoked by the Agent.
-
-<CodeGroup>
-```text Chat
-/skill enable pptx
-/skill disable pptx
-```
-
-```bash Terminal
-cow skill enable pptx
-cow skill disable pptx
-```
-</CodeGroup>
-
-## info
-
-View details of an installed skill, including a preview of its `SKILL.md`.
-
-<CodeGroup>
-```text Chat
-/skill info pptx
-```
-
-```bash Terminal
-cow skill info pptx
-```
-</CodeGroup>
-
-## Skill Sources
-
-Installed skills track their origin, viewable via `/skill list`:
-
-| Source | Description |
-| --- | --- |
-| `builtin` | Built-in project skills |
-| `cowhub` | Installed from CowAgent Skill Hub |
-| `github` | Installed directly from a GitHub URL |
-| `clawhub` | Installed from ClawHub |
-| `url` | Installed from a SKILL.md URL |
-| `local` | Locally created skills |

docs/en/guide/manual-install.mdx

@@ -1,146 +0,0 @@
----
-title: Manual Install
-description: Deploy CowAgent manually (source code / Docker)
----
-
-## Source Code Deployment
-
-### 1. Clone the project
-
-```bash
-git clone https://github.com/zhayujie/CowAgent
-cd CowAgent/
-```
-
-<Tip>
-  For network issues, use the mirror: https://gitee.com/zhayujie/CowAgent
-</Tip>
-
-### 2. Install dependencies
-
-Core dependencies (required):
-
-```bash
-pip3 install -r requirements.txt
-```
-
-Optional dependencies (recommended):
-
-```bash
-pip3 install -r requirements-optional.txt
-```
-
-### 3. Install Cow CLI
-
-Install the command-line tool for managing services and skills:
-
-```bash
-pip3 install -e .
-```
-
-Then use the `cow` command:
-
-```bash
-cow help
-```
-
-<Note>
-  This step is recommended. After installation you can use `cow start`, `cow stop`, `cow update` to manage the service, and `cow skill` to manage skills. Without the CLI, you can use `./run.sh` or `python3 app.py` to run.
-</Note>
-
-### 4. 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.
-
-### 5. Run
-
-**Using Cow CLI (recommended):**
-
-```bash
-cow start
-```
-
-**Or run locally in foreground:**
-
-```bash
-python3 app.py
-```
-
-By default, the Web console starts. Access `http://localhost:9899` to chat.
-
-**Background run on server (without CLI):**
-
-```bash
-nohup python3 app.py & tail -f nohup.out
-```
-
-<Tip>
-  **Deploying on a server?** By default `web_host` only listens on `127.0.0.1` (local access). Set `web_host` to `0.0.0.0` in `config.json` to make the console reachable from outside, and set `web_password` to protect it. Don't forget to open port `9899` in your firewall or security group — ideally restricted to specific IPs.
-</Tip>
-
-## 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
-curl -O 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
-```
-
-<Tip>
-  **Running in Docker?** Set `WEB_HOST` to `0.0.0.0` in `docker-compose.yml` so the console is reachable from outside the container, and set `WEB_PASSWORD` to protect it. Make sure port `9899` is mapped to the host and open in your firewall or security group.
-</Tip>
-
-## Core Configuration
-
-```json
-{
-  "channel_type": "web",
-  "model": "deepseek-v4-flash",
-  "deepseek_api_key": "",
-  "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 | `deepseek-v4-flash` |
-| `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/CowAgent/blob/master/config.py).
-</Tip>

docs/en/guide/quick-start.mdx

@@ -1,58 +0,0 @@
----
-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
-
-<Tabs>
-  <Tab title="Linux / macOS">
-    ```bash
-    bash <(curl -fsSL https://cdn.link-ai.tech/code/cow/run.sh)
-    ```
-  </Tab>
-  <Tab title="Windows (PowerShell)">
-    ```powershell
-    irm https://cdn.link-ai.tech/code/cow/run.ps1 | iex
-    ```
-  </Tab>
-</Tabs>
-
-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 `~/CowAgent`
-4. Install Python dependencies and Cow CLI
-5. Guided configuration for AI model and channel
-6. Start service
-
-By default, the Web console starts after installation. Access `http://localhost:9899` to begin chatting.
-
-<Note>
-  **Deploying on a server?** By default `web_host` only listens on `127.0.0.1` (local access only). Set `web_host` to `0.0.0.0` in `config.json` to make the console reachable from outside, and set `web_password` to protect it. Don't forget to open port `9899` in your firewall or security group — ideally restricted to specific IPs.
-</Note>
-
-## Management Commands
-
-After installation, use the `cow` command to manage the service:
-
-| Command | Description |
-| --- | --- |
-| `cow start` | Start service |
-| `cow stop` | Stop service |
-| `cow restart` | Restart service |
-| `cow status` | Check run status |
-| `cow logs` | View real-time logs |
-| `cow update` | Update code and restart |
-| `cow install-browser` | Install browser tool dependencies |
-
-See the [Commands documentation](/en/cli/index) for more details.
-
-<Note>
-  If the `cow` command is not available, you can use `./run.sh <command>` (Linux/macOS) or `.\scripts\run.ps1 <command>` (Windows) as a fallback. Both are functionally equivalent.
-</Note>

docs/en/guide/upgrade.mdx

@@ -1,61 +0,0 @@
----
-title: Upgrade
-description: How to upgrade CowAgent
----
-
-## Recommended: One-line upgrade
-
-Use `cow update` to pull the latest code and restart the service in one step:
-
-```bash
-cow update
-```
-
-The command runs the following automatically:
-
-1. Pull the latest code (`git pull`)
-2. Stop the running service
-3. Update Python dependencies
-4. Reinstall the CLI
-5. Start the service
-
-<Note>
-  If the Cow CLI is not installed, `./run.sh update` performs the same operations.
-</Note>
-
-## Manual upgrade
-
-Run the following inside the project root:
-
-```bash
-git pull
-pip3 install -r requirements.txt
-pip3 install -e .
-```
-
-Then restart the service:
-
-```bash
-# Using Cow CLI (recommended)
-cow restart
-
-# Or using run.sh
-./run.sh restart
-
-# Or restart manually with nohup
-kill $(ps -ef | grep app.py | grep -v grep | awk '{print $2}')
-nohup python3 app.py & tail -f nohup.out
-```
-
-## Docker upgrade
-
-Run the following in the directory containing `docker-compose.yml`:
-
-```bash
-sudo docker compose pull
-sudo docker compose up -d
-```
-
-<Tip>
-  Back up `config.json` before upgrading. For Docker deployments, mount the workspace directory as a volume to persist data across upgrades.
-</Tip>

docs/en/intro/architecture.mdx

@@ -1,86 +0,0 @@
----
-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.jsdelivr.net/gh/zhayujie/cowagent-assets@main/architecture/en/architecture.jpg" alt="CowAgent Architecture" />
-
-| Module | Description |
-| --- | --- |
-| **Plan** | Understands user intent, decomposes complex tasks into multi-step plans, and iteratively invokes tools until the goal is achieved |
-| **Memory** | Automatically persists important information as core memory and daily memory, with hybrid keyword and vector retrieval for cross-session context continuity |
-| **Knowledge** | Organizes structured knowledge by topic. The Agent autonomously distills valuable information into Markdown pages, maintaining indexes and cross-references to build a growing knowledge network |
-| **Tools** | Core capability for Agent to access OS resources. 10+ built-in tools including file read/write, terminal, browser, scheduler, memory search, web search, and more |
-| **Skills** | Loads and manages Skills. Supports one-click installation from Skill Hub, GitHub, and more, or custom skill creation through conversation |
-| **Models** | Model layer with unified access to OpenAI, Claude, Gemini, DeepSeek, MiniMax, GLM, Qwen, and other mainstream LLMs |
-| **Channels** | Message channel layer for receiving and sending messages. Supports Web console, WeChat, Feishu, DingTalk, WeCom, WeChat Official Account, and more with a unified protocol |
-| **CLI** | Command-line system providing terminal commands (`cow`) and chat commands (`/`) for process management, skill installation, configuration, knowledge base management, and more |
-
-## 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 & Knowledge** — Store important information in long-term memory and organize structured knowledge into the knowledge base
-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.md          # Core memory
-├── memory/            # Long-term memory storage
-│   └── YYYY-MM-DD.md  # Daily memory
-├── knowledge/         # Personal knowledge base
-│   ├── index.md       # Knowledge index
-│   └── <category>/    # Topic-based pages
-└── skills/            # Custom skills
-    ├── skill-1/
-    └── skill-2/
-```
-
-Secret keys are stored separately in `~/.cow` directory for security:
-
-```
-~/.cow/
-└── .env               # Secret keys for skills
-```
-
-## Core Configuration
-
-Configure Agent mode parameters in `config.json`:
-
-```json
-{
-  "agent": true,
-  "agent_workspace": "~/cow",
-  "agent_max_context_tokens": 50000,
-  "agent_max_context_turns": 20,
-  "agent_max_steps": 20,
-  "enable_thinking": false
-}
-```
-
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `agent` | Enable Agent mode | `true` |
-| `agent_workspace` | Workspace path | `~/cow` |
-| `agent_max_context_tokens` | Max context tokens | `50000` |
-| `agent_max_context_turns` | Max context turns | `20` |
-| `agent_max_steps` | Max decision steps per task | `20` |
-| `enable_thinking` | Enable deep-thinking mode | `false` |
-| `knowledge` | Enable personal knowledge base | `true` |
-| `knowledge` | Enable personal knowledge base | `true` |

docs/en/intro/features.mdx

@@ -1,139 +0,0 @@
----
-title: Features
-description: CowAgent long-term memory, task planning, skills system, CLI commands, and browser tool in detail
----
-
-## 1. Long-term Memory
-
-The memory system enables the Agent to remember important information over time, using a three-tier memory flow: conversation context (short-term) → daily memory (mid-term) → MEMORY.md (long-term), forming a complete memory lifecycle.
-
-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. **Deep Dream** distillation runs daily, consolidating scattered daily memories into refined long-term memory and generating a narrative-style dream diary.
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260203000455.png" width="800" />
-</Frame>
-
-See [Long-term Memory](/en/memory) and [Deep Dream](/en/memory/deep-dream) for details.
-
-## 2. Personal Knowledge Base
-
-> The knowledge base system enables the Agent to continuously accumulate and organize structured knowledge. Unlike memory which records along a timeline, the knowledge base is organized by topics, transforming articles, conversation insights, and learning materials into interconnected Markdown pages that form a continuously growing knowledge network.
-
-The Agent automatically organizes valuable information from conversations into knowledge pages, maintaining cross-references and indexes. The Web console provides document browsing and knowledge graph visualization. Knowledge is stored in `~/cow/knowledge/` within the workspace.
-
-- **Auto-organization**: The Agent autonomously extracts and organizes structured knowledge during conversations, maintaining indexes and cross-references
-- **Knowledge graph**: Automatically builds a knowledge graph from cross-references between pages, with interactive graph visualization in the Web console
-- **Chat integration**: Knowledge document links referenced in Agent replies can be clicked directly in the Web console for viewing
-- **CLI management**: Use `/knowledge` commands to view stats, browse directory, and toggle the feature with `/knowledge on|off`
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260413105435.png" width="800" />
-</Frame>
-
-See [Personal Knowledge Base](/en/knowledge) for details.
-
-## 3. 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, browser, file send, scheduler, memory search, web search, environment config, and more.
-
-### 3.1 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>
-
-### 3.2 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/20260318211018.png" width="800" />
-</Frame>
-
-### 3.3 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>
-
-### 3.4 Browser
-
-The built-in `browser` tool allows the Agent to control a Chromium browser to visit web pages, fill forms, click elements, and take screenshots, with support for dynamic JS-rendered pages. Run `cow install-browser` to install with one command, automatically adapting to server (headless) and desktop environments:
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260401110103.png" width="800" />
-</Frame>
-
-### 3.5 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>
-
-## 4. 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.
-
-- [Skill Hub](https://skills.cowagent.ai/): An open skill marketplace featuring official, community, and third-party skills. Install with one command.
-- **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.
-
-Install skills: `/skill install <name>` or `cow skill install <name>`, supporting Skill Hub, GitHub, ClawHub, URL, and more.
-
-### 4.1 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>
-
-### 4.2 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>
-
-### 4.3 Skill Hub
-
-Visit [skills.cowagent.ai](https://skills.cowagent.ai/) to browse all available skills, or use commands in conversation:
-
-```text
-/skill list --remote          # Browse Skill Hub
-/skill search <keyword>       # Search skills
-/skill install <name>          # Install with one command
-```
-
-Also supports installing skills from GitHub, ClawHub, LinkAI, and other third-party platforms. See [Install Skills](/en/skills/install) for details.
-
-<img src="https://cdn.link-ai.tech/doc/20260401110103.png" width="750" />
-
-## 5. CLI Command System
-
-CowAgent provides two command interaction methods, covering service management, skill installation, configuration, and more:
-
-- **Terminal CLI:** Run `cow <command>` in the system terminal, supporting `start`, `stop`, `restart`, `update`, `status`, `logs`, `skill`, etc.
-- **Chat commands:** Type `/<command>` in conversation. The Web console shows a command menu when you type `/`.
-
-```bash
-cow start              # Start service
-cow stop               # Stop service
-cow update             # Update and restart
-cow skill install pptx # Install a skill
-cow install-browser    # Install browser tool
-```
-
-See [Command Overview](https://docs.cowagent.ai/en/cli) for details.

docs/en/intro/index.mdx

@@ -1,93 +0,0 @@
----
-title: Introduction
-description: CowAgent - Open-source super AI assistant and Agent Harness
----
-
-<div align="center">
-  <img src="https://cdn.link-ai.tech/doc/78c5dd674e2c828642ecc0406669fed7.png" alt="CowAgent" width="450px"/>
-</div>
-
-**CowAgent** is an open-source super AI assistant and Agent Harness. It proactively plans tasks, runs tools and skills, and autonomously grows with memory and knowledge.
-
-CowAgent is lightweight, easy to deploy, and built to extend. Plug in any major LLM provider, run it across Web and major IM platforms, 24/7 on a personal computer or server.
-
-<CardGroup cols={2}>
-  <Card title="GitHub" icon="github" href="https://github.com/zhayujie/CowAgent">
-    Open-source repository — Star and contribute
-  </Card>
-  <Card title="Try Online" icon="cloud" href="https://link-ai.tech/cowagent/create">
-    No setup required — experience CowAgent instantly
-  </Card>
-</CardGroup>
-
-## Core Capabilities
-
-<CardGroup cols={2}>
-  <Card title="Autonomous Task Planning" icon="brain" href="/en/intro/architecture">
-    Decomposes complex tasks and executes them step by step, looping over tools and skills until the goal is reached.
-  </Card>
-  <Card title="Long-term Memory" icon="database" href="/en/memory/index">
-    Three-tier architecture (context → daily → core), automatic Deep Dream distillation, hybrid keyword + vector retrieval.
-  </Card>
-  <Card title="Personal Knowledge Base" icon="book" href="/en/knowledge/index">
-    Auto-curates structured knowledge into a Markdown wiki, builds an evolving knowledge graph with visual browsing.
-  </Card>
-  <Card title="Skills System" icon="puzzle-piece" href="/en/skills/index">
-    A complete skill creation and execution engine. Install from Skill Hub or generate custom skills via natural-language conversation.
-  </Card>
-  <Card title="Multimodal Messaging" icon="image" href="/en/channels/web">
-    First-class support for text, images, voice, and files — recognition, generation, and delivery.
-  </Card>
-  <Card title="Tool System" icon="wrench" href="/en/tools/index">
-    Built-in file I/O, terminal, browser, scheduler, memory retrieval, web search, and more — with native MCP integration.
-  </Card>
-  <Card title="Command System" icon="terminal" href="/en/cli/index">
-    Terminal CLI and in-chat commands for process management, skill installation, configuration, and context inspection.
-  </Card>
-  <Card title="Pluggable Models" icon="microchip" href="/en/models/index">
-    Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, MiniMax, Doubao, and more — swap providers from the Web console with one click.
-  </Card>
-  <Card title="Multi-channel Integration" icon="server" href="/en/channels/index">
-    A single Agent simultaneously serves Web, WeChat, Feishu, DingTalk, WeCom, QQ, and Official Accounts.
-  </Card>
-</CardGroup>
-
-## Quick Start
-
-Run one of the commands below to install, configure, and start CowAgent in a single step:
-
-<Tabs>
-  <Tab title="Linux / macOS">
-    ```bash
-    bash <(curl -fsSL https://cdn.link-ai.tech/code/cow/run.sh)
-    ```
-  </Tab>
-  <Tab title="Windows (PowerShell)">
-    ```powershell
-    irm https://cdn.link-ai.tech/code/cow/run.ps1 | iex
-    ```
-  </Tab>
-</Tabs>
-
-Once started, open `http://localhost:9899` to access the **Web console** — the unified place to chat, configure providers, connect channels, and install skills.
-
-<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
-  </Card>
-</CardGroup>
-
-## Disclaimer
-
-1. This project is licensed under the [MIT License](https://github.com/zhayujie/CowAgent/blob/master/LICENSE) and is intended for technical research and learning. You are responsible for complying with applicable laws and regulations in your jurisdiction; the maintainers assume no liability for any consequences arising from use of this project.
-2. **Cost & safety:** Agent mode consumes substantially more tokens than plain chat — pick models that balance quality and cost. The Agent has access to your local operating system; deploy only in trusted environments.
-3. CowAgent is a pure open-source project and does not participate in, authorize, or issue any cryptocurrency.
-
-## Community
-
-Scan the WeChat QR code to join the open-source community group:
-
-<img width="140" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/open-community.png" />

docs/en/knowledge/index.mdx

@@ -1,93 +0,0 @@
----
-title: Personal Knowledge Base
-description: CowAgent personal knowledge base — structured knowledge accumulation, automatic organization, and knowledge graph
----
-
-The personal knowledge base is the Agent's long-term structured knowledge store, saved in the `knowledge/` directory within the workspace. Unlike memory, which is organized by timeline, the knowledge base organizes content by topic — articles, conversation insights, and learning materials are structured into interlinked Markdown pages, forming a continuously growing knowledge network.
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260413105435.png" width="800" />
-</Frame>
-
-## Core Concepts
-
-### Knowledge vs Memory
-
-| Dimension | Knowledge Base (knowledge/) | Long-term Memory (memory/) |
-| --- | --- | --- |
-| Organization | By topic, interlinked | By timeline, dated files |
-| Writing | Agent actively structures content | Auto-summarized on context trimming |
-| Content | Refined, structured knowledge | Raw conversation summaries |
-| Use cases | Study notes, tech docs, project knowledge | Conversation history, event records |
-
-### Directory Structure
-
-```
-~/cow/knowledge/
-├── index.md          # Knowledge index, entry point for all pages
-├── log.md            # Change log, records each write
-├── concepts/         # Conceptual knowledge
-│   └── machine-learning.md
-├── entities/         # Entity knowledge (people, orgs, tools)
-│   └── openai.md
-└── sources/          # Source knowledge (articles, papers)
-    └── llm-wiki.md
-```
-
-The directory structure is flexible — the Agent automatically creates appropriate category directories based on actual content. Users can also customize the organization.
-
-## Automatic Organization
-
-Knowledge writing is an autonomous Agent behavior, triggered in these scenarios:
-
-- **User shares an article or document** — The Agent automatically extracts key information and creates a structured knowledge page
-- **Conversation produces valuable conclusions** — The Agent organizes insights into knowledge pages and links them to existing knowledge
-- **User explicitly requests organization** — Users can guide the Agent to organize and update knowledge through conversation
-
-Each knowledge page includes cross-reference links to related pages, gradually building a knowledge graph.
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260413110104.png" width="800" />
-</Frame>
-
-## Knowledge Retrieval
-
-The Agent can retrieve knowledge during conversation through:
-
-- **Index lookup** — Quickly locate relevant pages via `knowledge/index.md`
-- **Semantic search** — Search knowledge content via the `memory_search` tool
-- **Direct read** — Read specific knowledge files via the `memory_get` tool
-
-## Web Console
-
-The web console provides a dedicated "Knowledge" module with:
-
-- **Document browsing** — Tree-style directory structure, searchable and collapsible, click to view content
-- **Knowledge graph** — Interactive graph visualizing relationships between knowledge pages
-- **Chat integration** — Knowledge document links referenced in Agent replies are clickable for direct navigation
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/17aad553d3e9e428c52ff9dc31726fda.png" width="800" />
-</Frame>
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260413105402.png" width="800" />
-</Frame>
-
-## CLI Commands
-
-Manage the knowledge base with the `/knowledge` command:
-
-| Command | Description |
-| --- | --- |
-| `/knowledge` | Show knowledge base statistics |
-| `/knowledge list` | Display file directory as a tree |
-| `/knowledge on` | Enable the knowledge base feature |
-| `/knowledge off` | Disable the knowledge base feature |
-
-## Configuration
-
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `knowledge` | Whether to enable the personal knowledge base | `true` |
-| `agent_workspace` | Workspace path; knowledge is stored under the `knowledge/` subdirectory | `~/cow` |

docs/en/memory/context.mdx

@@ -1,81 +0,0 @@
----
-title: Short-term Memory
-description: Conversation context — message management, compression strategies, and context operations
----
-
-Conversation context is the Agent's short-term memory, containing all messages in the current session (user input, Agent replies, tool calls and results). Proper context management is critical for the Agent's reasoning quality and cost control.
-
-## Context Structure
-
-Each conversation turn consists of:
-
-```
-User message → Agent thinking → Tool call → Tool result → ... → Agent final reply
-```
-
-A single turn may include multiple tool calls (controlled by `agent_max_steps`). All tool calls and results are retained in context until compressed or trimmed.
-
-## Key Configuration
-
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `agent_max_context_tokens` | Maximum context token budget | `50000` |
-| `agent_max_context_turns` | Maximum conversation turns in context | `20` |
-| `agent_max_steps` | Maximum decision steps per turn (tool call count) | `15` |
-
-Configurable via `config.json` or the `/config` chat command.
-
-## Compression Strategy
-
-When context exceeds limits, the system automatically compresses to free space. The process has multiple stages:
-
-### 1. Tool Result Truncation
-
-Before each decision loop, the system checks tool call results in historical turns. Results exceeding **20,000 characters** are truncated, keeping only the beginning and end with a truncation notice. Current turn results are not affected.
-
-### 2. Turn Trimming
-
-When conversation turns exceed `agent_max_context_turns`:
-
-- The **oldest half** of complete turns is trimmed (preserving tool call chain integrity)
-- Trimmed messages are summarized by LLM and **written to the daily memory file**
-- Once the LLM summary is ready, it is also **injected into the first user message** of the retained context, helping the model maintain conversational continuity
-- Summary injection runs asynchronously in the background and takes effect from the next turn onward
-
-### 3. Token Budget Trimming
-
-After turn trimming, if tokens still exceed the budget:
-
-- **Fewer than 5 turns**: All turns undergo **text compression** — each turn keeps only the first user text and last Agent reply, removing intermediate tool call chains
-- **5 or more turns**: The **first half** of turns is trimmed again, with discarded content written to memory and a context summary injected
-
-### 4. Overflow Emergency Handling
-
-When the model API returns a context overflow error:
-
-1. All current messages are summarized and written to memory
-2. Aggressive trimming is applied (tool results limited to 10K chars, user text to 10K, max 5 turns)
-3. If still overflowing, the entire conversation context is cleared
-
-## Session Persistence
-
-Conversation messages are persisted to a local database, automatically restored after service restart. Restore strategy:
-
-- Restores the most recent **`max(3, max_context_turns / 6)`** turns
-- Only retains each turn's **user text and Agent final reply**, not intermediate tool call chains
-- Sessions older than **30 days** are automatically cleaned up
-
-## Commands
-
-Use these commands in chat to manage context:
-
-| Command | Description |
-| --- | --- |
-| `/context` | View current context statistics (message count, role distribution, total characters) |
-| `/context clear` | Clear current session context |
-| `/config agent_max_context_tokens 80000` | Adjust context token budget |
-| `/config agent_max_context_turns 30` | Adjust context turn limit |
-
-<Tip>
-  After clearing context, the Agent "forgets" previous conversation content. Content that was already written to long-term memory can still be retrieved via memory search.
-</Tip>

docs/en/memory/deep-dream.mdx

@@ -1,90 +0,0 @@
----
-title: Deep Dream
-description: Deep Dream — automatic distillation from conversations to permanent memory
----
-
-Deep Dream is the core consolidation mechanism of CowAgent's memory system, responsible for distilling scattered daily memories into refined long-term memory and generating dream diaries.
-
-## Memory Flow
-
-CowAgent's memory progresses through three stages from short-term to long-term:
-
-```
-Conversation context (short-term) → Daily memory (mid-term) → MEMORY.md (long-term)
-```
-
-### 1. Conversation → Daily Memory
-
-When conversation context is trimmed or during the daily scheduled summary, the system uses LLM to summarize conversation content into key events, writing them to the daily memory file `memory/YYYY-MM-DD.md`.
-
-Triggers:
-- **Context trimming** — Trimmed content is summarized when turn or token limits are exceeded
-- **Daily schedule** — Automatically triggered at 23:55
-- **API overflow** — Emergency save of current conversation summary
-
-### 2. Daily Memory → MEMORY.md (Distillation)
-
-After the daily summary completes, Deep Dream automatically runs distillation:
-
-1. **Read materials** — Current `MEMORY.md` + today's daily memory
-2. **LLM distillation** — Deduplicate, merge, prune, extract new information
-3. **Overwrite MEMORY.md** — Output the refined long-term memory
-4. **Generate dream diary** — Record discoveries and insights from the consolidation
-
-### 3. Role of MEMORY.md
-
-`MEMORY.md` is injected into the system prompt for every conversation, keeping the Agent aware of user preferences, decisions, and key facts. Therefore it must stay concise — Deep Dream targets approximately 30 entries or fewer.
-
-## Distillation Rules
-
-Deep Dream follows these consolidation rules:
-
-| Operation | Description |
-| --- | --- |
-| **Merge & refine** | Combine similar entries into single high-density statements |
-| **Extract new** | Pull preferences, decisions, people, experiences from daily memory |
-| **Conflict update** | When new info contradicts old entries, newer info takes precedence |
-| **Clean invalid** | Remove temporary records, blank entries, formatting artifacts |
-| **Remove redundancy** | Delete old entries already covered by more refined statements |
-
-## Dream Diary
-
-Each distillation generates a dream diary saved at `memory/dreams/YYYY-MM-DD.md`, written in a narrative style recording:
-
-- Duplications or contradictions found
-- New insights extracted from daily memory
-- Cleanups and optimizations performed
-- Overall observations
-
-Dream diaries can be viewed in the Web console under "Memory → Dream Diary" tab.
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260414110032.png" width="800" />
-</Frame>
-
-## Manual Trigger
-
-In addition to the automatic daily run, you can manually trigger distillation in chat:
-
-```text
-/memory dream [N]
-```
-
-- `N`: Consolidate the last N days of memory (default 3, max 30)
-- Runs asynchronously in the background; you'll be notified in chat when complete
-- Web notifications include clickable links to view MEMORY.md and dream diary
-- Works without Agent initialization — can be used before the first conversation
-
-<Tip>
-  After first deployment, it's recommended to run `/memory dream 30` once to distill all historical daily memories into MEMORY.md.
-</Tip>
-
-## Safety Mechanisms
-
-| Mechanism | Description |
-| --- | --- |
-| **Skip on no content** | Distillation skipped when no daily memory exists, avoiding empty overwrites |
-| **Input dedup** | In scheduled tasks, automatically skipped when input materials haven't changed |
-| **Async execution** | Distillation runs in a background thread, never blocking conversation |
-| **Sequential guarantee** | In scheduled tasks, daily flush completes before distillation starts |
-| **No fabrication** | Prompt explicitly constrains consolidation to existing materials only |

docs/en/memory/index.mdx

@@ -1,71 +0,0 @@
----
-title: Long-term Memory
-description: CowAgent long-term memory system — file persistence, automatic writing, and hybrid retrieval
----
-
-Long-term memory is stored in workspace files, persisting across sessions. The Agent loads historical memory on demand via retrieval tools during conversation, and automatically writes conversation summaries to long-term memory when context is trimmed.
-
-<img src="https://cdn.link-ai.tech/doc/memory-architecture-en.jpg" alt="Memory Architecture" />
-
-## Memory Types
-
-### Core Memory (MEMORY.md)
-
-Stored in `~/cow/MEMORY.md`, containing long-term user preferences, important decisions, key facts, and other information that doesn't fade over time. The Agent reads and writes this file via tools to maintain long-term knowledge.
-
-### Daily Memory (memory/YYYY-MM-DD.md)
-
-Stored in `~/cow/memory/` directory, named by date (e.g., `2026-03-08.md`), recording daily conversation summaries and key events. Files are only created on first write to avoid generating empty files.
-
-### Dream Diary (memory/dreams/YYYY-MM-DD.md)
-
-A byproduct of the Deep Dream (memory distillation) process, recording discoveries, deduplication operations, and new insights from each consolidation. Stored in `~/cow/memory/dreams/` directory, named by date.
-
-## Automatic Writing
-
-The Agent automatically persists conversation content to long-term memory through the following mechanisms:
-
-- **On context trimming** — When conversation turns or tokens exceed the configured limit, the oldest half of the context is trimmed, and the discarded content is summarized by LLM into key information and written to the daily memory file. The summary is also asynchronously injected into the retained context for conversational continuity
-- **Daily scheduled summary** — A full summary is automatically triggered at 23:55 every day, ensuring memory is preserved even on low-activity days (skipped if content hasn't changed)
-- [Deep Dream (memory distillation)](/en/memory/deep-dream) — Runs automatically after the daily summary, distilling daily memories into MEMORY.md and generating a dream diary
-- **On API context overflow** — When the model API returns a context overflow error, the current conversation summary is saved as an emergency measure
-
-All memory writes run asynchronously in a background thread (LLM summarization + file writing), never blocking normal conversation replies.
-
-## Memory Retrieval
-
-The memory system supports hybrid retrieval modes:
-
-- **Keyword retrieval** — FTS5 full-text index matching with BM25 ranking
-- **Vector retrieval** — Embedding-based 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. Results are ranked by a combined score (default: 0.7 vector weight + 0.3 keyword weight). Daily memory scores decay over time (30-day half-life), while core memory does not decay.
-
-## Related Files
-
-Files related to memory in the workspace (default `~/cow`):
-
-| File | Description |
-| --- | --- |
-| `AGENT.md` | Agent personality and behavior settings |
-| `USER.md` | User identity information and preferences |
-| `RULE.md` | Custom rules and constraints |
-| `MEMORY.md` | Core memory (long-term) |
-| `memory/YYYY-MM-DD.md` | Daily memory (created on demand) |
-| `memory/dreams/YYYY-MM-DD.md` | Dream diary (auto-generated by Deep Dream) |
-
-## Web Console
-
-The memory management page in the Web console allows browsing memory files and dream diaries, with tab switching support:
-
-<Frame>
-  <img src="https://cdn.link-ai.tech/doc/20260414171014.png" width="800" />
-</Frame>
-
-## Configuration
-
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `agent_workspace` | Workspace path, memory files stored under this directory | `~/cow` |
-| `agent_max_context_tokens` | Max context tokens; when exceeded, content is trimmed and summarized into memory | `50000` |
-| `agent_max_context_turns` | Max context turns; when exceeded, content is trimmed and summarized into memory | `20` |

docs/en/models/claude.mdx

@@ -1,49 +0,0 @@
----
-title: Claude
-description: Anthropic Claude model configuration (Text Chat + Image Understanding)
----
-
-Claude is provided by Anthropic and supports both text chat and image understanding. The mainstream Sonnet / Opus models natively support vision, so no separate Vision model needs to be specified.
-
-<Tip>
-  All capabilities below can be configured in one place via the "Model Management" page in the Web Console, with no need to manually edit the configuration file.
-</Tip>
-
-## Text Chat
-
-```json
-{
-  "model": "claude-sonnet-4-6",
-  "claude_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Supports `claude-sonnet-4-6`, `claude-opus-4-7`, `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 one in the [Claude Console](https://console.anthropic.com/settings/keys) |
-| `claude_api_base` | Optional, defaults to `https://api.anthropic.com/v1`. Can be changed to a third-party proxy |
-
-### Model Selection
-
-| Model | Use Case |
-| --- | --- |
-| `claude-sonnet-4-6` | Default recommended, balanced cost and speed |
-| `claude-opus-4-7` | Complex reasoning and long-running tasks; best quality but higher cost |
-| `claude-sonnet-4-5` / `claude-sonnet-4-0` | Previous-generation flagships at a lower price |
-
-## Image Understanding
-
-Once `claude_api_key` is configured, the Agent's Vision tool automatically uses the Claude main model to recognize images, with no extra setup required.
-
-To manually specify a Vision model, set it explicitly in the configuration file:
-
-```json
-{
-  "tools": {
-    "vision": {
-      "model": "claude-sonnet-4-6"
-    }
-  }
-}
-```

docs/en/models/coding-plan.mdx

@@ -1,139 +0,0 @@
----
-title: Coding Plan
-description: Coding Plan model configuration
----
-
-> Coding Plan is a monthly subscription package offered by various providers, ideal for high-frequency Agent usage. CowAgent supports all Coding Plan providers via OpenAI-compatible mode.
-
-<Note>
-  Coding Plan API Base and API Key are usually separate from the standard pay-as-you-go ones. Please obtain them from each provider's platform.
-</Note>
-
-## General Configuration
-
-All providers can be accessed via the OpenAI-compatible protocol, and can be quickly configured through the web console. Set the model provider to **OpenAI**, select a custom model and enter the model code, then fill in the corresponding provider's API Base and API Key:
-
-<img src="https://cdn.link-ai.tech/doc/20260318113134.png" width="800"/>
-
-You can also configure directly in `config.json`:
-
-```json
-{
-  "bot_type": "openai",
-  "model": "MODEL_NAME",
-  "open_ai_api_base": "PROVIDER_CODING_PLAN_API_BASE",
-  "open_ai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `bot_type` | Must be `openai` (OpenAI-compatible mode) |
-| `model` | Model name supported by the provider |
-| `open_ai_api_base` | Provider's Coding Plan API Base URL |
-| `open_ai_api_key` | Provider's Coding Plan API Key |
-
----
-
-## Alibaba Cloud
-
-```json
-{
-  "bot_type": "openai",
-  "model": "qwen3.5-plus",
-  "open_ai_api_base": "https://coding.dashscope.aliyuncs.com/v1",
-  "open_ai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | `qwen3.5-plus`, `qwen3-max-2026-01-23`, `qwen3-coder-next`, `qwen3-coder-plus`, `glm-5`, `glm-4.7`, `kimi-k2.5`, `MiniMax-M2.5` |
-| `open_ai_api_base` | `https://coding.dashscope.aliyuncs.com/v1` |
-| `open_ai_api_key` | Coding Plan specific key (not shared with pay-as-you-go) |
-
-Reference: [Quick Start](https://help.aliyun.com/zh/model-studio/coding-plan-quickstart?spm=a2c4g.11186623.help-menu-2400256.d_0_2_1.70115203zi5Igc), [Model List](https://help.aliyun.com/zh/model-studio/coding-plan)
-
----
-
-## MiniMax
-
-```json
-{
-  "bot_type": "openai",
-  "model": "MiniMax-M2.5",
-  "open_ai_api_base": "https://api.minimaxi.com/v1",
-  "open_ai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | `MiniMax-M2.5`, `MiniMax-M2.5-highspeed`, `MiniMax-M2.1`, `MiniMax-M2` |
-| `open_ai_api_base` | China: `https://api.minimaxi.com/v1`; Global: `https://api.minimax.io/v1` |
-| `open_ai_api_key` | Coding Plan specific key (not shared with pay-as-you-go) |
-
-Reference: [China Key](https://platform.minimaxi.com/docs/coding-plan/quickstart), [Model List](https://platform.minimaxi.com/docs/guides/pricing-coding-plan), [Global Key](https://platform.minimax.io/docs/coding-plan/quickstart)
-
----
-
-## GLM
-
-```json
-{
-  "bot_type": "openai",
-  "model": "glm-4.7",
-  "open_ai_api_base": "https://open.bigmodel.cn/api/coding/paas/v4",
-  "open_ai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | `glm-5`, `glm-4.7`, `glm-4.6`, `glm-4.5`, `glm-4.5-air` |
-| `open_ai_api_base` | China: `https://open.bigmodel.cn/api/coding/paas/v4`; Global: `https://api.z.ai/api/coding/paas/v4` |
-| `open_ai_api_key` | Shared with standard API |
-
-Reference: [China Quick Start](https://docs.bigmodel.cn/cn/coding-plan/quick-start), [Global Quick Start](https://docs.z.ai/devpack/quick-start)
-
----
-
-## Kimi
-
-```json
-{
-  "bot_type": "moonshot",
-  "model": "kimi-for-coding",
-  "moonshot_base_url": "https://api.kimi.com/coding/v1",
-  "moonshot_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Use `kimi-for-coding` for auto-updating model, or specify a model such as `kimi-k2.6` |
-| `moonshot_base_url` | `https://api.kimi.com/coding/v1` |
-| `moonshot_api_key` | Coding Plan specific key (not shared with pay-as-you-go) |
-
-Reference: [Key & Docs](https://www.kimi.com/code/docs/)
-
----
-
-## Volcengine
-
-```json
-{
-  "bot_type": "openai",
-  "model": "Doubao-Seed-2.0-Code",
-  "open_ai_api_base": "https://ark.cn-beijing.volces.com/api/coding/v3",
-  "open_ai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | `Doubao-Seed-2.0-Code`, `Doubao-Seed-2.0-pro`, `Doubao-Seed-2.0-lite`, `Doubao-Seed-Code`, `MiniMax-M2.5`, `Kimi-K2.5`, `GLM-4.7`, `DeepSeek-V3.2` |
-| `open_ai_api_base` | `https://ark.cn-beijing.volces.com/api/coding/v3` |
-| `open_ai_api_key` | Shared with standard API |
-
-Reference: [Quick Start](https://www.volcengine.com/docs/82379/1928261?lang=zh)

docs/en/models/custom.mdx

@@ -1,62 +0,0 @@
----
-title: Custom
-description: Custom vendor configuration for third-party API proxies and local models
----
-
-For model services accessed via the OpenAI-compatible protocol or locally deployed models, such as:
-
-- **Third-party API proxies**: call multiple models through a unified API base
-- **Local models**: models deployed locally with tools like Ollama, vLLM, LocalAI
-- **Private deployments**: model services deployed inside an enterprise
-
-<Note>
-  Difference from the `openai` vendor: when a custom vendor is selected, switching models via `/config model` does not automatically switch the vendor type — the custom API address is always used.
-</Note>
-
-## Text Chat
-
-### Third-party API proxy
-
-```json
-{
-  "bot_type": "custom",
-  "model": "",
-  "custom_api_key": "YOUR_API_KEY",
-  "custom_api_base": "https://{your-proxy.com}/v1"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `bot_type` | Must be set to `custom` |
-| `model` | Model name; any model name supported by the proxy service |
-| `custom_api_key` | API key provided by the proxy service |
-| `custom_api_base` | API endpoint provided by the proxy service; must be OpenAI-compatible |
-
-### Local models
-
-Local models usually do not require an API key — only the API base needs to be filled in:
-
-```json
-{
-  "bot_type": "custom",
-  "model": "qwen3.5:27b",
-  "custom_api_base": "http://localhost:11434/v1"
-}
-```
-
-Common local deployment tools and their default endpoints:
-
-| Tool | Default API Base |
-| --- | --- |
-| [Ollama](https://ollama.com) | `http://localhost:11434/v1` |
-| [vLLM](https://docs.vllm.ai) | `http://localhost:8000/v1` |
-| [LocalAI](https://localai.io) | `http://localhost:8080/v1` |
-
-### Switching Models
-
-Switching models under a custom vendor only changes `model` — `bot_type` and the API endpoint remain unchanged:
-
-```
-/config model qwen3.5:27b
-```

docs/en/models/deepseek.mdx

@@ -1,72 +0,0 @@
----
-title: DeepSeek
-description: DeepSeek model configuration (Text Chat + Thinking Mode)
----
-
-DeepSeek is one of the default recommended vendors in Agent mode, focused on cost-effective text chat and task planning.
-
-## Text Chat
-
-```json
-{
-  "model": "deepseek-v4-flash",
-  "deepseek_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Supports `deepseek-v4-flash` (Default), `deepseek-v4-pro` |
-| `deepseek_api_key` | Create one on the [DeepSeek Platform](https://platform.deepseek.com/api_keys) |
-| `deepseek_api_base` | Optional, defaults to `https://api.deepseek.com/v1`. Can be changed to a third-party proxy |
-
-### Model Selection
-
-| Model | Use Case |
-| --- | --- |
-| `deepseek-v4-flash` | Default recommended; fast and low cost |
-| `deepseek-v4-pro` | Smarter; better for complex tasks |
-
-## Thinking Mode
-
-The V4 series (`deepseek-v4-flash` / `deepseek-v4-pro`) supports an explicit "thinking mode": before producing the final answer, the model emits a chain of thought (`reasoning_content`) to improve answer quality.
-
-### Toggle
-
-Controlled by the global `enable_thinking` config, and can also be toggled from the Web Console's configuration page:
-
-```json
-{
-  "enable_thinking": true
-}
-```
-
-- `true`: the model thinks before answering across all channels. The Web Console displays the thinking process; IM channels (WeChat / WeCom / DingTalk / Feishu) do not show it but still get better answers.
-- `false`: thinking is disabled, responses are faster, and time-to-first-token is lower.
-
-### Reasoning Effort
-
-Under thinking mode, `reasoning_effort` controls reasoning intensity:
-
-```json
-{
-  "enable_thinking": true,
-  "reasoning_effort": "high"
-}
-```
-
-| Value | Use Case |
-| --- | --- |
-| `high` (Default) | Day-to-day Agent tasks; balanced reasoning and speed |
-| `max` | Complex coding, long-horizon planning, strictly constrained tasks; deeper reasoning but more time and output tokens |
-
-`reasoning_effort` only takes effect when `enable_thinking` is `true`; it is ignored automatically when the model does not support thinking mode.
-
-### Behavior Notes
-
-- **Sampling parameters**: in thinking mode, `temperature`, `top_p`, `presence_penalty`, and `frequency_penalty` are ignored by the server (without errors). CowAgent automatically skips them.
-- **Multi-turn tool calls**: when the history contains tool calls, DeepSeek requires every assistant message to include `reasoning_content`. CowAgent handles this automatically, so toggling thinking mode across turns will not cause errors.
-
-<Tip>
-  `deepseek-v4-flash` is used by default; switch to `deepseek-v4-pro` for complex tasks; enable `enable_thinking` when deep reasoning is needed.
-</Tip>

docs/en/models/doubao.mdx

@@ -1,66 +0,0 @@
----
-title: Doubao
-description: Doubao (Volcengine Ark) model configuration (Text / Image Understanding / Image Generation / Embedding)
----
-
-Doubao (Volcengine Ark) supports text chat, image understanding, image generation (Seedream), and embedding. A single `ark_api_key` enables all capabilities.
-
-<Tip>
-  All capabilities below can be configured in one place via the "Model Management" page in the Web Console, with no need to manually edit the configuration file.
-</Tip>
-
-## Text Chat
-
-```json
-{
-  "model": "doubao-seed-2-0-pro-260215",
-  "ark_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Can be `doubao-seed-2-0-pro-260215`, `doubao-seed-2-0-code-preview-260215`, `doubao-seed-2-0-lite-260215`, etc. |
-| `ark_api_key` | Create one in the [Volcengine 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` |
-
-## Image Understanding
-
-Once `ark_api_key` is configured, the Agent's Vision tool automatically uses `doubao-seed-2-0-pro-260215` to recognize images, with no extra setup required.
-
-To manually specify a Vision model:
-
-```json
-{
-  "tools": {
-    "vision": {
-      "model": "doubao-seed-2-0-pro-260215"
-    }
-  }
-}
-```
-
-## Image Generation
-
-```json
-{
-  "skills": {
-    "image-generation": {
-      "model": "seedream-5.0-lite"
-    }
-  }
-}
-```
-
-Available models: `seedream-5.0-lite`, `seedream-4.5`.
-
-## Embedding
-
-```json
-{
-  "embedding_provider": "doubao",
-  "embedding_model": "doubao-embedding-vision-251215"
-}
-```
-
-The default model is `doubao-embedding-vision-251215` (multimodal embedding); the dimension (1024 or 2048) can be set via `embedding_dimensions` in the configuration file. After changing the embedding, run `/memory rebuild-index` to rebuild the index.

docs/en/models/gemini.mdx

@@ -1,59 +0,0 @@
----
-title: Gemini
-description: Google Gemini model configuration (Text Chat + Image Understanding + Image Generation)
----
-
-Google Gemini supports text chat, image understanding, and image generation (Nano Banana series). A single `gemini_api_key` enables all capabilities.
-
-<Tip>
-  All capabilities below can be configured in one place via the "Model Management" page in the Web Console, with no need to manually edit the configuration file.
-</Tip>
-
-## Text Chat
-
-```json
-{
-  "model": "gemini-3.5-flash",
-  "gemini_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Recommended: `gemini-3.5-flash`; also supports `gemini-3.1-pro-preview`, `gemini-3.1-flash-lite-preview`, `gemini-3-flash-preview`, `gemini-3-pro-preview`, etc. See [official docs](https://ai.google.dev/gemini-api/docs/models) |
-| `gemini_api_key` | Create one in [Google AI Studio](https://aistudio.google.com/app/apikey) |
-| `gemini_api_base` | Optional, defaults to `https://generativelanguage.googleapis.com`. Can be changed to a third-party proxy |
-
-## Image Understanding
-
-All Gemini models natively support vision. Once `gemini_api_key` is configured, the Agent's Vision tool automatically uses the main model to recognize images, with no extra setup required.
-
-To manually specify a Vision model:
-
-```json
-{
-  "tools": {
-    "vision": {
-      "model": "gemini-3.1-flash-lite-preview"
-    }
-  }
-}
-```
-
-## Image Generation
-
-```json
-{
-  "skills": {
-    "image-generation": {
-      "model": "gemini-3.1-flash-image-preview"
-    }
-  }
-}
-```
-
-| Model ID | Alias |
-| --- | --- |
-| `gemini-3.1-flash-image-preview` | Nano Banana 2 |
-| `gemini-3-pro-image-preview` | Nano Banana Pro |
-| `gemini-2.5-flash-image` | Nano Banana |

docs/en/models/glm.mdx

@@ -1,56 +0,0 @@
----
-title: GLM
-description: Zhipu AI GLM model configuration (Text / Image Understanding / Speech-to-Text / Embedding)
----
-
-Zhipu AI supports text chat, image understanding, speech-to-text (ASR), and embedding. A single `zhipu_ai_api_key` enables all capabilities.
-
-<Tip>
-  All capabilities below can be configured in one place via the "Model Management" page in the Web Console, with no need to manually edit the configuration file.
-</Tip>
-
-## Text Chat
-
-```json
-{
-  "model": "glm-5.1",
-  "zhipu_ai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Can be `glm-5.1`, `glm-5-turbo`, `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 one in the [Zhipu AI Console](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) |
-| `zhipu_ai_api_base` | Optional, defaults to `https://open.bigmodel.cn/api/paas/v4` |
-
-## Image Understanding
-
-Zhipu's chat models (`glm-5.1`, `glm-5-turbo`, etc.) do not support vision; vision calls are uniformly routed to `glm-5v-turbo`. Once `zhipu_ai_api_key` is configured, the Agent's Vision tool automatically uses this model, with no need to specify it explicitly in the configuration file.
-
-## Speech-to-Text (ASR)
-
-```json
-{
-  "voice_to_text": "zhipu",
-  "voice_to_text_model": "glm-asr-2512"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `voice_to_text` | Set to `zhipu` to enable Zhipu ASR |
-| `voice_to_text_model` | Optional, defaults to `glm-asr-2512` |
-
-Credentials are automatically reused from `zhipu_ai_api_key`. Audio files should be smaller than 25MB; oversized files may be rejected by the server.
-
-## Embedding
-
-```json
-{
-  "embedding_provider": "zhipu",
-  "embedding_model": "embedding-3"
-}
-```
-
-Available models: `embedding-3`, `embedding-2`. After changing the embedding, run `/memory rebuild-index` to rebuild the index.

docs/en/models/index.mdx

@@ -1,38 +0,0 @@
----
-title: Models Overview
-description: Model vendors supported by CowAgent and their capability matrix
----
-
-CowAgent supports a wide range of mainstream large language models. Model interfaces live under the project's `models/` directory. Beyond text chat, several vendors also provide vision understanding, image generation, speech-to-text, text-to-speech, and embeddings — all of which can be invoked on demand in the Agent flow.
-
-## Capability Matrix
-
-A snapshot of each vendor's capabilities. "Text" refers to the main chat model; the remaining columns show which Agent capabilities the vendor can power.
-
-| Vendor | Representative Models | Text | Vision | Image Gen | STT | TTS | Embedding |
-| --- | --- | :-: | :-: | :-: | :-: | :-: | :-: |
-| [DeepSeek](/en/models/deepseek) | deepseek-v4-flash / pro | ✅ | | | | | |
-| [MiniMax](/en/models/minimax) | MiniMax-M2.7 | ✅ | ✅ | ✅ | | ✅ | |
-| [Claude](/en/models/claude) | claude-opus-4-7 | ✅ | ✅ | | | | |
-| [Gemini](/en/models/gemini) | gemini-3.5-flash | ✅ | ✅ | ✅ | | | |
-| [OpenAI](/en/models/openai) | gpt-5.5, o-series | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [GLM](/en/models/glm) | glm-5.1, glm-5v-turbo | ✅ | ✅ | | ✅ | | ✅ |
-| [Qwen](/en/models/qwen) | qwen3.7-max | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [Doubao](/en/models/doubao) | doubao-seed-2.0 series | ✅ | ✅ | ✅ | | | ✅ |
-| [Kimi](/en/models/kimi) | kimi-k2.6 | ✅ | ✅ | | | | |
-| [ERNIE](/en/models/qianfan) | ernie-5.1 | ✅ | ✅ | | | | |
-| [MiMo](/en/models/mimo) | mimo-v2.5-pro / v2.5 | ✅ | ✅ | | | ✅ | |
-| [LinkAI](/en/models/linkai) | 100+ models from multiple vendors | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-| [Custom](/en/models/custom) | Local models / third-party proxies | ✅ | | | | | |
-
-<Tip>
-  Every capability in the Web console (Vision / Image / STT / TTS / Embedding / Web Search) can be configured independently with its own vendor and model — there is no forced binding between them.
-</Tip>
-
-## How to Configure
-
-**Option 1 (recommended):** Manage models and capabilities online via the [Web console](/en/channels/web), with no need to edit the configuration file:
-
-<img width="900" src="https://cdn.jsdelivr.net/gh/zhayujie/cowagent-assets@main/screenshots/en/web-console-models-config.png" />
-
-**Option 2:** Edit `config.json` manually and fill in the model name and API key for the selected vendor. Every model also supports OpenAI-compatible access — just set `bot_type` to `openai` and configure `open_ai_api_base` and `open_ai_api_key`.

docs/en/models/kimi.mdx

@@ -1,41 +0,0 @@
----
-title: Kimi
-description: Kimi (Moonshot) model configuration (Text Chat + Image Understanding)
----
-
-Kimi is provided by Moonshot and supports both text chat and image understanding. The `kimi-k2.x` series natively supports vision.
-
-<Tip>
-  All capabilities below can be configured in one place via the "Model Management" page in the Web Console, with no need to manually edit the configuration file.
-</Tip>
-
-## Text Chat
-
-```json
-{
-  "model": "kimi-k2.6",
-  "moonshot_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `model` | Can be `kimi-k2.6`, `kimi-k2.5`, `kimi-k2`, `moonshot-v1-8k`, `moonshot-v1-32k`, `moonshot-v1-128k` |
-| `moonshot_api_key` | Create one in the [Moonshot Console](https://platform.moonshot.cn/console/api-keys) |
-| `moonshot_base_url` | Optional, defaults to `https://api.moonshot.cn/v1` |
-
-## Image Understanding
-
-Once `moonshot_api_key` is configured, the Agent's Vision tool automatically uses `kimi-k2.6` to recognize images, with no extra setup required.
-
-To manually specify a Vision model:
-
-```json
-{
-  "tools": {
-    "vision": {
-      "model": "kimi-k2.6"
-    }
-  }
-}
-```

docs/en/models/linkai.mdx

@@ -1,103 +0,0 @@
----
-title: LinkAI
-description: Access text, vision, image, speech, and embedding capabilities through the LinkAI platform
----
-
-A single `linkai_api_key` gives you access to all capabilities of mainstream vendors such as OpenAI, Claude, Gemini, DeepSeek, MiniMax, Qwen, Kimi, and Doubao.
-
-<Tip>
-  All capabilities below can be configured in one place via the "Model Management" page in the Web Console, with no need to manually edit the configuration file.
-</Tip>
-
-## Text Chat
-
-```json
-{
-  "use_linkai": true,
-  "linkai_api_key": "YOUR_API_KEY"
-}
-```
-
-| Parameter | Description |
-| --- | --- |
-| `use_linkai` | Set to `true` to enable |
-| `linkai_api_key` | Create one in the [Console](https://link-ai.tech/console/interface) |
-| `model` | Can be any code from the [model list](https://link-ai.tech/console/models) |
-
-See [Model Service](https://link-ai.tech/console/models) for more.
-
-## Image Understanding
-
-Once configured, the Agent's Vision tool automatically calls multimodal models via the gateway, with no extra setup required. To manually specify a Vision model:
-
-```json
-{
-  "tools": {
-    "vision": {
-      "model": "gpt-5.4-mini"
-    }
-  }
-}
-```
-
-Available models: `gpt-4.1-mini`, `gpt-5.4-mini`, `qwen3.6-plus`, `doubao-seed-2-0-pro-260215`, `kimi-k2.6`, `claude-sonnet-4-6`, `gemini-3.1-flash-lite-preview`, etc.
-
-## Image Generation
-
-```json
-{
-  "skills": {
-    "image-generation": {
-      "model": "gpt-image-2"
-    }
-  }
-}
-```
-
-| Model ID | Alias |
-| --- | --- |
-| `gpt-image-2` | OpenAI |
-| `gemini-3.1-flash-image-preview` | Nano Banana 2 |
-| `gemini-3-pro-image-preview` | Nano Banana Pro |
-| `seedream-5.0-lite` | ByteDance Doubao Seedream |
-
-## Speech-to-Text (ASR)
-
-```json
-{
-  "voice_to_text": "linkai"
-}
-```
-
-ASR uses Whisper by default; credentials are automatically reused from `linkai_api_key`.
-
-## Text-to-Speech (TTS)
-
-The TTS gateway supports multiple underlying engines. The engine is selected by `text_to_voice_model`, and the available voices change with the engine.
-
-```json
-{
-  "text_to_voice": "linkai",
-  "text_to_voice_model": "doubao",
-  "tts_voice_id": "BV001_streaming"
-}
-```
-
-| `text_to_voice_model` | Engine |
-| --- | --- |
-| `tts-1` | OpenAI · Multi-language (voices like `alloy` / `nova` / `echo`, etc.) |
-| `doubao` | ByteDance Doubao · Rich Chinese voices |
-| `baidu` | Baidu · Chinese broadcaster voices |
-
-Voices differ by engine; we recommend selecting them visually in the Web Console under "Model Management → Text-to-Speech".
-
-## Embedding
-
-```json
-{
-  "embedding_provider": "linkai",
-  "embedding_model": "text-embedding-3-small"
-}
-```
-
-The default model is `text-embedding-3-small` (OpenAI-compatible). After changing the embedding, run `/memory rebuild-index` to rebuild the index.