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tool for AI multi-tool collaboration infrastructure — works with Claude Code, Codex, Antigravity CLI, and OpenCode","maintainers":[{"name":"fitframework","email":"codecaster365@outlook.com"}],"readme":"<p align=\"center\">\n  <img src=\"./assets/logo.svg\" alt=\"Agent Infra 标志\" width=\"200\">\n</p>\n\n<h1 align=\"center\">Agent Infra</h1>\n\n<p align=\"center\">\n  AI 编程代理的协作基础设施 —— 为 Claude Code、Codex、Antigravity CLI、OpenCode 提供 skills、工作流和沙箱。\n</p>\n\n<p align=\"center\">\n  <strong>从 Issue 到合并 PR，由风险选择相称的生命周期。</strong> 每个任务都先分析，每份实现都接受独立代码审查。\n</p>\n\n<p align=\"center\">\n  <a href=\"https://www.npmjs.com/package/@fitlab-ai/agent-infra\"><img src=\"https://img.shields.io/npm/v/@fitlab-ai/agent-infra\" alt=\"npm version\"></a>\n  <a href=\"https://www.npmjs.com/package/@fitlab-ai/agent-infra\"><img src=\"https://img.shields.io/npm/dm/@fitlab-ai/agent-infra\" alt=\"npm downloads\"></a>\n  <a href=\"License.txt\"><img src=\"https://img.shields.io/badge/License-MIT-blue.svg\" alt=\"License: MIT\"></a>\n  <a href=\"https://nodejs.org/\"><img src=\"https://img.shields.io/badge/Node.js-%3E%3D22.9.0-brightgreen?logo=node.js\" alt=\"Node.js >= 22.9.0\"></a>\n  <a href=\"https://github.com/fitlab-ai/agent-infra/releases\"><img src=\"https://img.shields.io/github/v/release/fitlab-ai/agent-infra\" alt=\"GitHub release\"></a>\n  <a href=\"https://codecov.io/gh/fitlab-ai/agent-infra\"><img src=\"https://codecov.io/gh/fitlab-ai/agent-infra/graph/badge.svg\" alt=\"codecov\"></a>\n  <a href=\"CONTRIBUTING.zh-CN.md\"><img src=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg\" alt=\"PRs Welcome\"></a>\n</p>\n\n<p align=\"center\">\n  <a href=\"./README.md\">English</a> · <strong>中文</strong>\n</p>\n\n## 为什么需要 agent-infra？\n\n越来越多的团队会在同一个仓库里混用 Claude Code、Codex、Antigravity CLI、OpenCode 等 AI TUI，但每个工具往往都会带来自己的命令体系、提示词习惯和本地约定。缺少共享层时，结果通常是工作流割裂、初始化重复、任务历史难以追踪。\n\nagent-infra 的目标就是把这层共享基础设施标准化。它为所有支持的 AI TUI 提供统一的任务生命周期、统一的 skill 词汇、统一的项目治理文件、隔离的开发沙箱以及统一的升级路径，让团队切换工具时不必重新发明流程。\n\n## 实战演示\n\n<p align=\"center\">\n  <img src=\"./assets/demo-init.gif\" alt=\"CLI 安装初始化演示\" width=\"720\">\n</p>\n\n> 演示动画通过 Git LFS 存储。克隆前请安装 Git LFS 并运行 `git lfs install`；\n> 已有检出可运行 `git lfs pull`。如果图片变成很小的文本指针或缺失，请先用\n> `git lfs ls-files` 核对跟踪状态，再重试 `git lfs pull`。\n\n完成初始化后，在你的 AI TUI 中打开项目并安装最新 skills：\n\n```bash\n/update-agent-infra\n```\n\n> AI 读取 `.agents/.airc.json`，自动定位已安装的模板根目录，并通过 `sync-templates.js` 确定性地同步最新的 skill 清单、managed 文件和注册表。\n\n**场景**：Issue #42 报告 *\"登录接口在邮箱包含加号时返回 500\"*。每个任务都从分析开始，并由分析根据可观察事实记录一条规范路径：\n\n| 路径 | 阶段 | 适用条件 |\n|------|------|----------|\n| 精简路径 | `analyze → code → review-code` | 范围和验收明确，不需要独立设计或文档审计决策 |\n| 标准路径 | `analyze → plan → code → review-code` | 实现需要明确跨模块契约、数据流或测试策略 |\n| 完整路径 | `analyze → review-analysis → plan → review-plan → code → review-code` | 存在具体的验收争议、高代价接口/schema/迁移选择，或真实外部/安全边界，需要独立审查 |\n\n文件数量、模块数量或可能存在的风险不能单独触发完整路径。后续 finding 只有在新证据成立时才会把工作返回分析、方案或实现阶段；每条路径都保留独立 `review-code`。\n\n假设分析认为应用层边界需要明确方案，因此选择**标准路径**：\n\n```bash\n/import-issue 42           # AI 读取 Issue，创建任务，提取需求\n/analyze-task --task <task-id>    # AI 扫描代码库、记录所选路径，输出 analysis.md\n/plan-task --task <task-id>       # AI 提出修复方案\n```\n\n> **你审查方案后用自然语言回复：**\n\n```\n方案方向没问题，但不要动数据库结构。\n只在应用层的 LoginService 里修复就行。\n```\n\n> AI 按你的要求重跑 `/plan-task` 更新方案并确认。\n\n```bash\n/code-task --task <task-id>       # AI 编写修复代码、测试，并创建本地 checkpoint commit\n/review-code --task <task-id>     # fresh 隔离 reviewer 报告一个次要问题\n/code-task --task <task-id>       # AI 修复次要问题、重新验证，并创建新的 checkpoint\n/review-code --task <task-id>     # reviewer 审查并通过当前 checkpoint\n/create-pr --task <task-id>       # 将已审查分支发布到任务绑定目标分支；PR 自动关联 Issue #42\n/complete-task --task <task-id>   # 任务归档\n```\n\n**一条由分析选择的路径、一次自然语言纠正和一次独立代码审查。** 生命周期会让证据与审查深度和任务风险相称。\n\n以上每条命令在 Claude Code、Codex、Antigravity CLI、OpenCode 中完全通用。任务进行到一半切换工具，工作流状态照常延续。每个 skill 背后做了什么，见 [内置 AI Skills](./docs/zh-CN/skills.md)。\n\n## 核心特性\n\n- **多 AI 协作**：为 Claude Code、Codex、Antigravity CLI、OpenCode 提供统一的协作模型\n- **引导 CLI + skill 驱动执行**：初始化一次，后续日常操作交给 AI skills\n- **IM 命令桥接**：把飞书消息路由到内置命令、任务只读视图、沙箱管理和受控生命周期 skill 执行\n- **双语文档**：英文为主文档，配套同步的中文版本\n- **模板源架构**：`templates/` 目录镜像最终渲染出的项目结构\n- **AI 辅助升级**：模板升级时可合并变更，同时尽量保留项目侧定制\n- **可审计的过程数据归档**：把本地任务与 GitHub 证据保存为只追加、可校验的快照\n\n### 沙箱控制边界\n\ndirect-host 生命周期和工作流命令在当前 CLI 进程中执行。任务沙箱把请求发送给所属 broker；broker 会先校验请求、manifest、generation、owner、lease 和 controller，再启动一次性 executor。系统不再使用宿主 socket、worker token 或用户级 task-control 服务。\n\n任务沙箱内的 task 目录只允许其绑定任务读写。产物命令只接受类型化 workflow 操作和规范的产物 basename。executor 使用 `O_NOFOLLOW` 打开候选文件，通过同一个文件描述符读取并计算摘要，检查可观察的元数据变化，再把已校验字节原子落入权威 task 目录。\n\n## 快速开始\n\n### 1. 安装 agent-infra\n\n**方式 A - npm（推荐）**\n\n```bash\nnpm install -g @fitlab-ai/agent-infra\n```\n\n**方式 B - Shell 脚本**\n\n```bash\n# 便捷封装：检测 Node.js 后，内部执行 npm install -g\ncurl -fsSL https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/install.sh | sh\n```\n\n**方式 C - Homebrew (macOS)**\n\n```bash\n# 新版 Homebrew 默认拒绝加载第三方 tap 的 formula，\n# 会导致升级被静默跳过。首次安装前先信任本 tap。\nbrew trust fitlab-ai/tap\nbrew install fitlab-ai/tap/agent-infra\n```\n\n### 更新 agent-infra\n\n```bash\nai update\n```\n\n`ai update` 用于更新已安装的 agent-infra CLI。在已初始化项目中，使用\n`ai sync` 刷新项目 seed files 和文件注册表。\n\n对应的包管理器命令仍然是：\n\n```bash\nnpm update -g @fitlab-ai/agent-infra\n# 或者通过 Homebrew 安装时：\nbrew upgrade agent-infra\n```\n\n查看当前版本：\n\n```bash\nai version\n# 或：agent-infra version\n```\n\n### 2. 初始化新项目\n\n```bash\ncd my-project\nai init\n# 或：agent-infra init\n```\n\nCLI 会收集项目元数据，让你选择启用的 Agent Client，安装该客户端需要的 `update-agent-infra` 种子命令，并生成 `.agents/.airc.json`。\n\n> `ai` 是 `agent-infra` 的简写命令，两者等价。\n\n### 3. 渲染完整基础设施\n\n在任意 AI TUI 中执行 `update-agent-infra`：\n\n| TUI | 命令 |\n|-----|------|\n| Claude Code | `/update-agent-infra` |\n| Codex | `$update-agent-infra` |\n| Antigravity CLI | `/update-agent-infra` |\n| OpenCode | `/update-agent-infra` |\n| TraeCode CLI | `/update-agent-infra` |\n\n该命令会检测当前打包模板版本并渲染所有受管理文件。首次安装和后续升级都使用同一条命令。\n\n## 核心命令\n\n以下命令用于独立管理五个内建 Agent Client：\n\n```bash\nai agent-client list\nai agent-client status\nai agent-client enable codex\nai agent-client disable antigravity-cli\nai agent-client configure\n```\n\n最常用的生命周期命令，按交付顺序排列。命令前缀因 TUI 而异（Claude Code/Antigravity/OpenCode/TraeCode 用 `/skill`，Codex 用 `$skill`），工作流语义保持一致。\n\n| 命令 | 用途 |\n|------|------|\n| `create-task` / `import-issue` | 从描述或 GitHub Issue 创建任务 |\n| `run-task` | 当所选客户端能提供已验证的 actual model/effort 证据时，用 fresh 隔离 executor/reviewer 续跑生命周期；Codex 现已具备实验性的 Hooks + App Server 证据通道，但在该通道接入 delegation receipt 前仍不会启用 orchestration |\n| `analyze-task` | 明确范围与风险，再选择精简、标准或完整路径 |\n| `review-analysis` | 仅在完整路径需要独立需求检查点时审查分析 |\n| `plan-task` / `review-plan` | 标准和完整路径需要设计方案；只有完整路径独立审查方案 |\n| `code-task` → `review-code` | 实现并测试；`code-task` 创建本地 checkpoint，再由结构化审查检视该 checkpoint |\n| `create-pr` → `complete-task` | 将已通过审查的 checkpoint 发布到任务绑定目标分支，并在合并和最终门禁通过后归档 |\n\n启动时可提供原子角色策略，例如：`$run-task --task 42 --executor-model <id> --executor-reasoning-effort <值> --reviewer-model <id> --reviewer-reasoning-effort <值>`。两个角色可以使用同一模型。完全没有显式策略时，`run-task` 读取当前 Agent Client 可选的 `agentClients[].orchestration`；两个来源都不完整时，会在创建任何 run 前展示宿主模型选择指引。重入使用已持久化的 run 策略。升级 agent-infra 前必须完成或清空 active run；不受支持的磁盘状态会失败关闭且不改写。\n\n完整清单（任务状态、发布、安全、项目维护等 skill）见 [内置 AI Skills](./docs/zh-CN/skills.md)。\n\n## 安装效果\n\n安装完成后，项目将获得完整的 AI 协作基础设施：\n\n```text\nmy-project/\n├── .agents/               # 共享 AI 协作配置\n│   ├── .airc.json         # 中央配置文件\n│   ├── workspace/         # 任务工作区（git 忽略）\n│   ├── skills/            # 内置 AI skills\n│   ├── workflows/         # 4 个预置工作流\n│   └── templates/         # 任务与产物模板\n├── .claude/               # Claude Code 配置与命令\n├── .opencode/             # OpenCode 配置与命令\n└── AGENTS.md              # 通用 AI agent 指令\n```\n\n## 文档\n\n深度指南位于 [`docs/zh-CN/`](./docs/zh-CN/README.md)：\n\n- [过程数据归档](./docs/zh-CN/process-data.md) — 采集、校验、审计、修复、导出、隐私与备份指南\n- [架构概览](./docs/zh-CN/architecture.md) — 引导 CLI、端到端流程、分层架构\n- [平台支持](./docs/zh-CN/platform-support.md) — macOS、Linux、Windows；沙箱引擎与资源配置\n- [沙箱](./docs/zh-CN/sandbox.md) — 沙箱 aliases、宿主-沙箱文件交换、用户级 dotfiles 通道\n- [Benchmark 契约与威胁模型](./docs/zh-CN/benchmark.md) — 版本化 benchmark schema、信任边界与安全保证\n- [飞书桥接](./docs/zh-CN/feishu-bridge.md) — 配置飞书长连接 adapter、命令路由、权限和 TUI 选择\n- [内置 AI Skills](./docs/zh-CN/skills.md) — 按使用场景分组的完整 skill 清单\n- [自定义 Skills](./docs/zh-CN/custom-skills.md) — 创建并同步项目专属 skill\n- [自定义 TUI 配置](./docs/zh-CN/custom-tui.md) — 适配非内置的 AI TUI\n- [预置工作流](./docs/zh-CN/workflows.md) — 分阶段交付链路与示例流程\n- [配置参考](./docs/zh-CN/configuration.md) — `.agents/.airc.json`、外部源、版本管理\n- [文件管理策略](./docs/zh-CN/file-management.md) — managed / merged / ejected 更新策略\n\n## 参与贡献\n\n开发规范请参阅 [CONTRIBUTING.zh-CN.md](CONTRIBUTING.zh-CN.md)。\n\n## 许可协议\n\n[MIT](License.txt)\n\n## 社区\n\n- 微信公众号：`FitFramework`\n- QQ 技术交流群：`1029802553`\n\n<p align=\"center\">\n  <img src=\"./assets/qrcode_for_wechat_gh.png\" alt=\"FitFramework 微信公众号二维码\" width=\"250\">\n  <img src=\"./assets/qrcode_for_qq_01.png\" alt=\"FitFramework QQ 技术交流群二维码\" width=\"250\">\n</p>\n","readmeFilename":"README.zh-CN.md"}