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<strong>简体中文</strong>\n</p>\n\n[SemaClaw](https://github.com/midea-ai/SemaClaw) 是一个面向个人 AI Agent 的通用工程，构建在可复用的 Agent 运行时（[sema-code-core](https://github.com/midea-ai/sema-code-core)）之上。它提供了将原始运行时变成一个真正可用的个人 AI 系统所需的全部周边设施 —— 权限管理、记忆系统、定时任务、多 Agent 编排、频道适配、Web UI 等等。它同时也是一个参考实现，是社区评估并改进其底层工程决策的起点。\n\n---\n\n<p align=\"center\">\n  <img src=\"https://github.com/midea-ai/SemaClaw/releases/download/v0.1.1-preview/SemaClaw-demo.GIF\" alt=\"SemaClaw Demo\" width=\"720\" />\n</p>\n\n*SemaClaw 分析自身源码并自动生成了以上介绍动画 — 基于 [frontend-slides](https://github.com/zarazhangrui/frontend-slides) 和 [remotion](https://github.com/remotion-dev/remotion) skills（前序步骤使用 DeepSeek-Chat，Remotion 动画代码生成使用 Claude Sonnet 4.6）。* [观看完整演示视频](https://midea-ai.github.io/SemaClaw/assets/SemaClaw-demo.mp4)\n\n## 核心亮点\n\n- **三层上下文管理** —— 将工作上下文、长期记忆检索与按 Agent 划分的人格分区统一为同一个一致模型。\n- **Human-in-the-Loop 权限审批** —— `PermissionBridge` 是 harness 的原生原语，同时支持高风险工具调用的显式用户授权与 Agent 主动发起的澄清请求。\n- **四层插件架构** —— MCP 工具、子 Agent、Skills、Hooks，每一层对应一个明确的工程关注点，构成一个有原则的扩展面。\n- **插件市场** —— 从任意 Git 仓库或本地目录安装第三方插件包。每个插件可以将 Skills、子 Agent、Hooks 和 MCP 服务器打包在一起。插件默认关闭，在 Web UI 中逐个开启；支持添加多个来源并设置优先级排序。\n- **DAG Teams** —— 两阶段混合编排框架：将 LLM 驱动的动态任务分解，与确定性 DAG 执行结合起来，支持持久 Agent 与虚拟 Agent 的混合编排，内置 5 个可开箱即用的虚拟 subagent。\n- **可复用 Workflow** —— 把重复性 routine 固化成声明式 DAG 定义（Markdown + YAML），混编 agent 步骤（隔离 persona 会话）与确定性 script 步骤。支持参数化输入、`{{…}}` 模板（依赖自动推断）、workflow / step 两级 `guidance` 规则层，以及每个 workflow 独立的持久 workspace。可在对话中通过内置 `workflow` skill 编写定义，再从 CLI（`semaclaw workflow run`）或 Web UI 的 workflow 面板触发 —— 实时 DAG 图 + 运行历史 + 行内调参。运行只起隔离 session，绝不打扰正在聊天的 Agent。\n- **四模式定时任务** —— 纯通知 / 纯脚本 / 纯 Agent / 脚本+Agent 混合，按任务复杂度匹配执行模式，让 token 消耗与推理工作量成正比。\n- **Agentic Wiki** —— 将任务输出转化为结构化、可检索的 wiki 条目，与 Agent 记忆共同建立索引，形成一个会持续复利、能反哺未来 Agent 会话的个人知识库。\n- **Workbench：HTML as throwaway UI** —— 内置的 Agent 交付物渲染面板。`LaunchUI` 工具让 Agent 直接抛出交互式 HTML、富 Markdown、内嵌的 Web 服务（iframe）或 API 端点卡片 —— Web UI 工作台把每个 artifact 挂成可切换的历史 tab，免单独构建步骤。把\"HTML 当一次性 UI\"用起来：让 Agent **把结果展示给你**，而不只是描述给你听。\n- **多频道与 Web UI** —— 内置 Telegram、飞书、QQ 适配器，配套 WebSocket Gateway 与 React Web UI。\n\n---\n\n## 推荐插件包 —— Overture\n\n[**Overture**](https://github.com/NingyanZhu/Overture) 是一个示例插件市场，把 SemaClaw 进一步组装成可自我进化的个人 Agent 系统。从 **插件市场 → 添加源** 粘贴仓库 URL 即可安装。\n\n- **`notation`（记谱法）** —— 数据飞轮 hook。把每次工具调用、用户与结果落成结构化日志，作为下面两条自进化循环的信号源。\n- **`cadence`（基因） & `repertoire`（剧目库）** —— 两层自进化。`cadence` 演化 Agent 的*基线基因*（提示词、策略、默认启发式）；`repertoire` 演化*技能库* —— 增删、打磨、退役单个 skill。\n- **`timbre`（音色）** —— 人设插件包。每个 timbre 语气 / 价值取向 / 专业领域打包成一个可复用的人格档案，让任何 Agent 都能\"换装\"。把\"Agent 是谁\"和\"Agent 会什么\"解耦。\n\n---\n\n## 快速开始\n\n### 方式 A —— 从 npm 安装（推荐）\n\n```bash\n# 1. 全局安装\nnpm install -g semaclaw\n\n# 2. 启动\nsemaclaw\n```\n\n就这么简单。在浏览器打开 Web UI：**<http://127.0.0.1:18788/>**。\n\n> **安装失败？** 参见 [docs/INSTALL_TROUBLESHOOTING.md](docs/INSTALL_TROUBLESHOOTING.md) —— 收录了 macOS 上 `sharp` postinstall 失败等已知问题的修复步骤。\n\n> **首次启动需配置 LLM。** SemaClaw 不内置任何模型。打开 Web UI → **设置 → LLM**，添加一个 provider profile（OpenAI / Anthropic / DeepSeek / Qwen / ……），填写 `baseURL`、`apiKey`、`modelName` 。配置会持久化到 `~/.semaclaw/config.json`，并同步写入 `~/.semaclaw/semaclaw-model.conf`。在至少有一个 active profile 之前，任何调用 LLM 的 Agent 运行都会失败。\n\n如需启用消息频道（Telegram / 飞书 / QQ / 微信），在启动 `semaclaw` 之前，在当前工作目录创建 `.env` 文件即可。完整环境变量列表请参考 [docs/QUICK_START.md](docs/QUICK_START.md)。\n\n### 方式 B —— 从源码构建\n\n```bash\n# 1. 克隆\ngit clone https://github.com/midea-ai/SemaClaw.git\ncd SemaClaw\n\n# 2. 安装与构建\nnpm install\nnpm run build\nnpm run build:web\n\n# 3. 配置环境变量（可选）\ncp .env.example .env\n# 编辑 .env 以启用消息频道（Telegram / 飞书 / QQ / 微信）。\n# 如果不配置任何频道，SemaClaw 将以 Web UI 单机模式启动。\n\n# 4. 启动\nnpm start\n```\n\n启动后，在浏览器打开 Web UI：**<http://127.0.0.1:18788/>**，然后进入 **设置 → LLM** 至少添加一个 active 的 provider profile（参见方式 A 中的说明）。\n\n完整使用说明（环境变量、CLI 命令、运行时目录布局、架构细节）请参考 **[docs/QUICK_START.md](docs/QUICK_START.md)**。\n\n---\n\n## 文档\n\n| 文档 | 说明 |\n|---|---|\n| [快速开始与使用指南](docs/QUICK_START.md) | 安装、配置、CLI 命令、运行时布局、MCP 工具说明 |\n| [插件市场使用指南](docs/plugin-marketplace.md) | 添加插件来源，发现并启用包含 Skills / 子 Agent / Hooks / MCP 的插件包 |\n| [Hooks 使用指南](docs/hooks_guide.md) | 用 shell 脚本或 LLM 拦截 agent 生命周期事件 |\n| [Workflow 指南](skills/workflow/SKILL.md) | 编写可复用的 agent + script DAG workflow：定义格式、模板语法、observe 输出 |\n| [远程访问指南](docs/REMOTE_ACCESS.md) | 通过反向代理（Nginx / Caddy）安全暴露 Web UI |\n| [技术报告](https://arxiv.org/abs/2604.11548) | SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering |\n| [贡献指南](CONTRIBUTING.md) | *即将发布* |\n\n---\n\n## 项目结构\n\n```\nsemaclaw/\n├── src/\n│   ├── agent/          # Agent 生命周期、bridges、权限路由\n│   ├── channels/       # Telegram / 飞书 / QQ 适配器\n│   ├── gateway/        # GroupManager、MessageRouter、WebSocket Gateway\n│   ├── mcp/            # MCP servers（admin / schedule / memory / dispatch / ...）\n│   ├── memory/         # FTS5 + 向量混合搜索、每日日志\n│   ├── scheduler/      # Cron / interval / once 调度\n│   ├── workflow/       # 可复用 workflow 引擎（registry、DAG executor、run store）\n│   ├── wiki/           # Git 驱动的个人知识库\n│   ├── marketplace/    # 插件市场（来源管理、插件发现、MCP/Skills/子 Agent 注入）\n│   └── clawhub/        # ClaWHub 技能市场集成\n├── web/                # React + Vite Web UI\n├── skills/             # 内置技能\n└── docs/               # 详细文档\n```\n\n---\n\n## 参与贡献\n\n欢迎贡献。SemaClaw 的目的是推动个人 AI Agent 领域的共享工程基础 —— issue、PR、设计讨论都同样有价值。详情请见 [CONTRIBUTING.md](CONTRIBUTING.md) *（即将发布）*。\n\n---\n\n## 开源协议\n\n[MIT](LICENSE) © AIRC Sema Team\n\n---\n\n## 关于 Logo\n\nSemaClaw 的 logo 是一匹背上长着 **Claw 钳形翅膀**的马。设计灵感来自\"**以梦为马**\"—— 希望这个 AI harness 能承载用户天马行空的想象力，载着他们去任何想去的地方。\n\n名字本身也藏了几层含义：\n\n- **Sema** 取自 *semantic*（语义）的开头，而 *ma* 与中文「**马**」同音；\n- **harness** 这个词的本意正是「**马具**」—— 有驾驭约束的意思；\n- **Claw**（钳）与中文「**钱**」谐音，所以这匹马背上长着 Claw 翅膀，也藏着一份小小的祝福：**马上有钱** 🐎💰。\n\n愿这个项目能让每一位使用者都既能驰骋想象，也能马到功成。\n\n---\n\n## 致谢\n\nSemaClaw 构建在 [sema-code-core](https://github.com/midea-ai/sema-code-core) 之上 —— 它提供了底层 Agent 运行时。在产品形态上，SemaClaw 也受到了 [OpenClaw](https://github.com/openclaw/openclaw) 的启发，并接入了来自同一项目的 [ClaWHub](https://github.com/openclaw/clawhub) 插件市场。同时感谢本项目所依赖的更广阔的开源生态，包括 [Model Context Protocol](https://modelcontextprotocol.io)、[grammY](https://grammy.dev) 等许多优秀项目。\n\n---\n\n> SemaClaw 的目标不是定义个人 AI Agent 的最终架构 —— 而是推动一个共享的工程基础，让更好的架构能够在它之上被构建出来。\n","readmeFilename":"README.zh-CN.md"}