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YAML workflow engine — 267 CN + 184 EN + 5 more language libraries of AI roles, auto DAG parallelism, zero code. One sentence → multiple AI roles collaborate → complete plan in minutes. 11 LLM providers, 7 need no API key.","maintainers":[{"name":"jnmetacode","email":"ialanhacker@gmail.com"}],"readme":"# Agency Orchestrator\n\n**中文** | [English](./README.en.md)\n\n> **一句话，让多个 AI 角色自动协作，几分钟出完整方案。**\n>\n> **也是你的「一人公司」：你当老板，AI 当团队——自动组队、重大决策请你签字、按验收标准交付。**\n\n[![CI](https://github.com/jnMetaCode/agency-orchestrator/actions/workflows/ci.yml/badge.svg)](https://github.com/jnMetaCode/agency-orchestrator/actions)\n[![npm version](https://img.shields.io/npm/v/agency-orchestrator)](https://www.npmjs.com/package/agency-orchestrator)\n[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](./CONTRIBUTING.md)\n\n**一句话出结果 · 267 个专业 AI 角色 · YAML 零代码 · 11 种大模型 · 支持 key（推荐 DeepSeek），也有 7 种免 key 方式**\n\n> 📖 [完整上手教程](https://mp.weixin.qq.com/s/XcGbkMb6TM6NLQiL7ICwbw)（从安装到实战，10 分钟上手）&nbsp;·&nbsp; 🎓 **免费配套学习**：[从零学会 AI 编程](https://aiolaola.com/?utm_source=github&utm_campaign=orchestrator)（180 节实操课 + 三卷书 + 实战社区，永久免费）\n\n> 觉得有用？请点个 **Star** — 帮助更多人发现这个项目。\n\n<p align=\"center\">\n  <img src=\"./demo-studio-zh.gif\" alt=\"网页 Studio：一句话，AI 自动组队\" width=\"820\"><br/>\n  <em>网页 Studio：打一句话，AI 自动从 200+ 专家里组队并运行</em>\n</p>\n\n---\n\n## 网页 Studio（图形界面）\n\n不想敲命令行？本地跑一条 `ao web`，浏览器里勾选专家、运行工作流、查看产物、实时介入——全程图形界面，全中英双语。\n\n> 🆕 **「一人公司」系列模板**：做产品 / 做内容 / 做投研 + 全员大会，关键步骤带验收标准（`acceptance`），投研含老板签字闸门——交付的是可验收的工作成果，不承诺奇迹。\n> 🆕 **AI 自动组队**：不知道选哪些专家？角色页一句话、不选角色，AI 自动从全部专家里挑人组队并运行。\n> 🆕 **可视化画布**：工作流可在画布上拖拽节点 / 连线（自动防环）/ 改任务·角色 / 保存，运行时节点按状态实时点亮。\n> 🆕 **创意库**：内置图像生成提示词库（Nano Banana / Gemini，可搜索 / 分类 / 一键复制）。\n> 🆕 **零配置首跑**：本机已登录 Claude Code / Gemini CLI 等？AO 自动探测并直接用，连 API key 都不用配。\n> 🆕 **我的角色 + 提示生成**：「提示生成」页产出的 system prompt 一键存为自建角色（`~/.ao/roles`），组队页「我的」分类直接用；角色卡可 ☆ 收藏常用。\n> 🆕 **多语言角色库**：`npm i agency-agents-ko`（ko / ru / pt-br / id / ar）后，Studio「角色库」下拉一键切换，工作流 `agents_dir` 写包名即可跑。\n\n<p align=\"center\">\n  <img src=\"./docs/screenshots/studio-workflows-zh.png\" alt=\"Studio · 工作流模板：内置模板一键运行\" width=\"800\"><br/>\n  <em>工作流：内置模板一键运行，也能对比多个模板</em>\n</p>\n\n<p align=\"center\">\n  <img src=\"./docs/screenshots/studio-role-lib-ko.png\" alt=\"Studio · 角色组队：多语言角色库下拉（图为韩语库）\" width=\"800\"><br/>\n  <em>角色组队：☆ 收藏常用、「我的」自建角色，一键切换多语言角色库（图为韩语库）</em>\n</p>\n\n> 启动：`ao web`（本地，密钥只存你自己机器、绝不外传）。也有 [桌面客户端下载](https://github.com/jnMetaCode/agency-orchestrator/releases/latest)（Electron · macOS / Windows / Linux）。\n> 英文界面同样完整 → 见 [English README](./README.en.md)。\n\n---\n\n## 一句话出结果\n\n也可以纯命令行——一条命令，一句话出结果：\n\n```bash\nao compose \"我是一个程序员，想用AI做自媒体副业，目标月入2万，帮我做完整规划\" --run\n```\n\n<p align=\"center\">\n  <img src=\"./demo-zh.gif\" alt=\"ao compose 命令行演示\" width=\"700\">\n</p>\n\n5 个 AI 角色自动分工协作：\n\n```\n  工作流: 程序员AI自媒体副业规划\n  步骤数: 5 | 模型: claude-code\n  参与者: 🔭 趋势研究员 | 📱 平台分析师 | 💰 财务规划师 | ✍️ 内容策略师 | 📋 执行规划师\n──────────────────────────────────────────────────\n\n  ✅ 🔭 趋势研究员    31.3s  → 6个赛道竞争度/变现天花板/AI提效倍数对比\n  ✅ 📱 平台分析师    32.0s  → 6大平台三维评分，推荐\"小红书+公众号\"组合\n  ✅ 💰 财务规划师    31.8s  → 月入2万拆解：课程¥11,880 + 社群¥2,488 + 咨询¥4,000\n  ✅ ✍️ 内容策略师    44.6s  → 20个选题 + 4套标题模板 + 内容SOP\n  ✅ 📋 执行规划师    42.2s  → 90天行动计划，精确到每天做什么\n\n==================================================\n  完成: 5/5 步 | 182.1s | 6,493 tokens\n==================================================\n```\n\n**不用写代码，不用写配置，不用选角色。** 一句话 → AI 自动拆解任务 → 从 267 个角色中匹配 → 按 DAG 并行执行 → 输出完整方案。\n\n### 你能用它做什么\n\n```bash\nao compose \"帮我分析做一个AI记账工具的可行性\" --run             # 创业可行性分析\nao compose \"对比 Cursor、Windsurf 和 Copilot，给出选择建议\" --run  # 技术选型报告\nao compose \"写一篇关于 AI Agent 趋势的深度文章\" --run             # 深度长文写作\nao compose \"用 10 万块启动一个 AI 教育项目\" --run                 # 商业计划书\nao compose \"PR 代码审查，覆盖安全和性能\" --run                    # 代码审查报告\nao compose \"设计一个 SaaS 产品的定价策略\" --run                   # 定价分析\n```\n\n每个场景自动匹配不同的 AI 角色组合。\n\n---\n\n## 为什么需要 Agency Orchestrator\n\n跟一个 AI 聊天，它给你一个视角。但做任何决策，你需要产品的视角、技术的视角、财务的视角、营销的视角……\n\n**Agency Orchestrator = 让多个 AI 专家各干各的，最后汇总。相当于一个人 vs 一个团队。**\n\n| | ChatGPT / Claude | CrewAI / LangGraph | **Agency Orchestrator** |\n|---|--------|-----------|---------------------|\n| 角色数 | 1 个通用 | 自己写 | **267 个专业角色** |\n| 使用方式 | 对话 | 写 Python | **一句话 / YAML** |\n| API key | — | 必须 | **支持 key，也有 7 种免 key 方式** |\n| 依赖 | — | pip + 几十个包 | **npm + 2 个依赖** |\n| 并行 | — | 手动建图 | **DAG 自动检测** |\n| 中文角色 | — | 无 | **267 个** |\n| 价格 | 订阅制 | 开源 + API 费 | **DeepSeek 甜区极低成本，亦可免 key 起步** |\n\n## 3 步开始\n\n### 第 1 步：安装\n\n```bash\nnpm install -g agency-orchestrator\n```\n\n> **装 CLI 还是桌面端？**\n> - **桌面客户端**（[下载](https://github.com/jnMetaCode/agency-orchestrator/releases/latest)）**自带引擎与 Node，双击即用，无需** `npm i -g agency-orchestrator`。只有想在终端用 `ao` 命令、或接进脚本 / CI 时才需要装 CLI。\n> - 用 `--provider claude-code`（或 `gemini-cli` / `codex-cli` 等）时，需要**本机已安装并登录对应 CLI**；AO 会自动探测已装的，零配置直接用。用 API key 类（deepseek/openai…）则配好 key 即可，无需装任何 CLI。\n> - **自定义目录**：产物 / 数据目录用 `AO_DATA_DIR`（桌面端默认指向 userData），角色库用 `AO_AGENTS_DIR`，统一工作区用 `AO_HOME`。\n> - **Docker / NAS 部署**（amd64/arm64）：`docker run -d -p 8088:8088 -v ao-data:/data ghcr.io/jnmetacode/agency-orchestrator:latest`，打开 `http://主机IP:8088`，密钥在页面「供应商」里配（存进挂载卷，重启不丢）。也可用仓库根的 [docker-compose.yml](./docker-compose.yml) 一键起。\n\n### 第 2 步：一句话跑起来\n\n```bash\n# 用你已有的 Claude 会员（无需 API key）\nao compose \"帮我分析做一个AI记账工具的可行性\" --run --provider claude-code\n\n# 或用 DeepSeek（充 10 块跑很久）\nexport DEEPSEEK_API_KEY=\"你的key\"\nao compose \"帮我分析做一个AI记账工具的可行性\" --run\n```\n\n### 第 3 步：用内置模板或在 AI 编程工具中使用\n\n```bash\n# 用 32 个内置模板\nao run workflows/一人公司全员大会.yaml --input idea=\"帮打工人用AI写简历的求职神器\"\nao run workflows/dev/pr-review.yaml --input code=@src/main.ts\nao run workflows/story-creation.yaml -i premise=\"一个程序员发现AI开始回复不该知道的事情\"\n```\n\n也可以在 Cursor / Claude Code 中直接说\"帮我跑一个工作流\"——支持 **14 个 AI 编程工具**（[集成指南](./integrations/)）。\n\n## 更多真实演示\n\n```\n$ ao compose \"帮我分析抖音短视频赛道的创业机会\" --run\n\n  工作流: 抖音短视频赛道创业机会分析与商业方案制定\n  步骤数: 6 | 并发: 2 | 模型: deepseek-chat\n  参与者: 👔 老板 | 📊 市场调研员 | 🔍 用户研究员 | 🧭 产品经理 | 📣 营销主管 | 💰 财务总监\n──────────────────────────────────────────────────\n\n  ✅ 👔 老板          12.7s   → 战略方向与目标用户定位\n  ✅ 📊 市场调研员    45.2s   → 7亿日活用户数据、竞争格局分析\n  ✅ 🔍 用户研究员    38.1s   → 用户画像、痛点挖掘、付费意愿\n  ✅ 🧭 产品经理      41.3s   → MVP功能清单、内容矩阵、变现路径\n  ✅ 📣 营销主管      35.6s   → 冷启动方案、投放策略、用户漏斗\n  ✅ 💰 财务总监      28.4s   → 150万启动、首年400万收入、盈亏平衡分析\n\n==================================================\n  完成: 6/6 步 | 233.0s | 65,191 tokens\n==================================================\n```\n\n6 个角色中，市场调研员和用户研究员**自动并行执行**（从 DAG 依赖关系检测）。\n\n## 工作原理\n\n```yaml\nname: \"产品需求评审\"\nagents_dir: \"agency-agents-zh\"\n\nllm:\n  provider: \"deepseek\"          # 免 API key: claude-code / gemini-cli / copilot-cli / codex-cli / hermes-cli / ollama\n  model: \"deepseek-chat\"\n\nconcurrency: 2\n\ninputs:\n  - name: prd_content\n    required: true\n\nsteps:\n  - id: analyze\n    role: \"product/product-manager\"\n    task: \"分析以下 PRD，提取核心需求：\\n\\n{{prd_content}}\"\n    output: requirements\n\n  - id: tech_review\n    role: \"engineering/engineering-software-architect\"\n    task: \"评估技术可行性：\\n\\n{{requirements}}\"\n    output: tech_report\n    depends_on: [analyze]\n\n  - id: design_review\n    role: \"design/design-ux-researcher\"\n    task: \"评估用户体验风险：\\n\\n{{requirements}}\"\n    output: design_report\n    depends_on: [analyze]\n\n  - id: summary\n    role: \"product/product-manager\"\n    task: \"综合反馈输出结论：\\n\\n{{tech_report}}\\n\\n{{design_report}}\"\n    acceptance: \"1. 明确给出通过/不通过结论  2. 列出必须解决的问题\"  # 可选：验收标准，注入 prompt 并作评审依据\n    depends_on: [tech_review, design_review]\n```\n\n引擎自动：\n\n1. 解析 YAML → 构建 **DAG**（有向无环图）\n2. 检测并行 — `tech_review` 和 `design_review` 并发执行\n3. 通过 `{{变量}}` 在步骤间传递输出\n4. 从 [agency-agents-zh](https://github.com/jnMetaCode/agency-agents-zh) 加载角色定义作为 system prompt\n5. 失败自动重试（指数退避）\n6. 保存所有输出到 `ao-output/`\n\n```\nanalyze ──→ tech_review  ──→ summary\n         └→ design_review ──┘\n          (并行)\n```\n\n## 11 种 LLM — 7 种不需要 API key\n\n**你已经有这些会员了吧？直接就能跑：**\n\n| 你有... | YAML 配置 | 安装 CLI | 额外费用 |\n|---------|----------|---------|---------|\n| Claude Max/Pro（$20/月） | `provider: \"claude-code\"` | `npm i -g @anthropic-ai/claude-code` | **不花钱** |\n| Google 账号 | `provider: \"gemini-cli\"` | `npm i -g @google/gemini-cli` | **免费**（1000 次/天，Gemini 2.5 Pro） |\n| GitHub Copilot（$10/月） | `provider: \"copilot-cli\"` | `npm i -g @github/copilot` | **不花钱** |\n| ChatGPT Plus/Pro（$20/月） | `provider: \"codex-cli\"` | `npm i -g @openai/codex` | **不花钱** |\n| OpenClaw 账号 | `provider: \"openclaw-cli\"` | `npm i -g openclaw` | **不花钱** |\n| Hermes Agent（🔥 NousResearch 热门开源） | `provider: \"hermes-cli\"` | [安装指南](https://github.com/NousResearch/hermes-agent) | **免费** |\n| 一台电脑 | `provider: \"ollama\"` | [ollama.ai](https://ollama.ai) | **免费**（本地模型，见下方提示） |\n\n> ⚠️ **模型能力决定多智能体的价值**：我们用质量评测验证过（见 [EVAL_FINDINGS.md](EVAL_FINDINGS.md)）——**DeepSeek 这一档（够强又不贵）上，多智能体产出明显优于单次 prompt**；但**本地小模型（如 llama3 8B 级）能力不足时，多角色交接反而会放大漂移、产出不如单次**。追求质量请用 DeepSeek/Claude/Gemini 等有能力的模型；本地 Ollama 建议用 70B+ 模型。\n\n**也支持传统 API key（追求质量推荐 DeepSeek，性价比甜区）：**\n\n| 提供商 | 配置 | 环境变量 |\n|--------|------|---------|\n| DeepSeek | `provider: \"deepseek\"` | `DEEPSEEK_API_KEY` |\n| 火山引擎（豆包 / Kimi / GLM，赞助商） | `provider: \"volcengine\"` | `ARK_API_KEY` |\n| Claude API | `provider: \"claude\"` | `ANTHROPIC_API_KEY` |\n| OpenAI | `provider: \"openai\"` | `OPENAI_API_KEY` |\n\n**自定义 API（智谱、月之暗面、硅基流动等 OpenAI 兼容 API）：**\n\n```bash\nao init --provider openai --model 模型名 \\\n  --base-url https://你的API地址/v1 \\\n  --api-key 你的key\n```\n\n或手动编辑 `.env`：\n\n```env\nAO_PROVIDER=openai\nAO_MODEL=模型名\nOPENAI_BASE_URL=https://你的API地址/v1\nOPENAI_API_KEY=你的key\n```\n\n常见示例：\n\n| 平台 | base_url | model |\n|------|----------|-------|\n| 火山引擎 | `https://ark.cn-beijing.volces.com/api/coding/v3` | `ark-code-latest` |\n| 智谱 AI | `https://open.bigmodel.cn/api/paas/v4` | `glm-4` |\n| 硅基流动 | `https://api.siliconflow.cn/v1` | `deepseek-ai/DeepSeek-V3` |\n| 月之暗面 | `https://api.moonshot.cn/v1` | `moonshot-v1-8k` |\n\n> ⚠️ 注意：这些平台请使用 `provider: \"openai\"`，不要用 `provider: \"ollama\"`。Ollama 仅用于本地模型，不发送 API Key。\n\n> 💡 **网页 Studio 里更简单**：「供应商」面板支持直接**添加自定义供应商**（任意 OpenAI 兼容 endpoint，带 SiliconFlow / OpenRouter / 火山方舟 / 智谱 / Kimi 等常见预设，点一下自动填 base_url），加完即可在顶部下拉切换使用。Claude Code / Gemini CLI / Codex 卡片还支持配置**第三方中转**（填中转商的 base_url + token，跳过官方账号登录）。\n\n## CLI 命令\n\n```bash\nao demo                              # 零配置体验多智能体协作\nao init                              # （可选）复制 267 个中文角色到本地以便编辑\nao init --lang en                    # （可选）复制 184 个英文角色到本地以便编辑\nao init --workflow                    # 交互式创建工作流\nao compose \"一句话描述\"                # AI 智能编排工作流\nao compose \"一句话描述\" --run          # 编排并立即执行\nao team save <workflow.yaml>          # 把角色阵容存成可复用团队 (Loadout)\nao team list / show / rm              # 管理已保存的团队\nao run --team <名字> \"新任务\"          # 用已保存的团队跑新任务（锁定阵容）\nao prompt optimize \"提示词\"           # AI 优化提示词（--save 存为可复用资产）\nao prompt test / list / garden        # 测试 / 管理 / 起手模板（提示词沉淀）\nao skills [名字]                       # 列出/查看可挂到步骤的方法论 skill\nao run <workflow.yaml> [选项]          # 执行工作流\nao validate <workflow.yaml>           # 校验（不执行）\nao plan <workflow.yaml>               # 查看执行计划（DAG）\nao explain <workflow.yaml>            # 用自然语言解释执行计划\nao roles                             # 列出所有角色\nao serve                             # 启动 MCP Server（供 Claude Code / Cursor 调用）\n```\n\n| 参数 | 说明 |\n|------|------|\n| `--input key=value` | 传入输入变量 |\n| `--input key=@file` | 从文件读取变量值 |\n| `--output dir` | 输出目录（默认 `ao-output/`） |\n| `--resume <dir\\|last>` | 从上次运行恢复（加载已完成步骤的输出） |\n| `--from <step-id>` | 配合 `--resume`，从指定步骤重新执行 |\n| `--feedback \"意见\"` | 对话式返工：把修改意见交给 `--from` 指定的专家，让它带着「上一版产出 + 你的意见」在原稿基础上修改（不指定 `--resume` 时默认对上一次运行返工） |\n| `--watch` | 实时终端进度显示 |\n| `--quiet` | 静默模式 |\n\n### AI 智能编排（Compose）\n\n一句话描述需求，AI 自动从 267 个角色中选角色、设计 DAG、生成完整 workflow YAML：\n\n```bash\nao compose \"PR 代码审查，要覆盖安全和性能\"\n```\n\nAI 会自动：\n1. 从 267 角色中匹配出 Code Reviewer、Security Engineer、Performance Benchmarker\n2. 设计 DAG（三路并行 → 汇总）\n3. 生成带 `depends_on`、变量串联的完整 YAML\n4. 保存到 `workflows/` — 直接 `ao run` 就能跑\n\n支持 `--provider` 和 `--model` 参数（默认使用 DeepSeek）。\n\n### 团队 / Loadout（把跑得好的阵容存下来复用）\n\n`compose` 每次都是临时组队。如果某个角色阵容效果好，把它**存成团队**，套到任意新任务上——团队只保存「角色阵容」，与具体任务解耦：\n\n```bash\n# 从一个跑得好的工作流抽出阵容，存成团队\nao team save workflows/tech-blog.yaml --name 技术博客组\n\n# 让整队人接新活（自动用这几个角色重新设计步骤并运行）\nao run --team 技术博客组 \"写一篇关于 RISC-V 架构的科普\"\n\nao team list           # 查看已保存的团队\nao team show 技术博客组  # 查看阵容构成\n```\n\n`ao run --team` 的本质 = compose 时把可选角色**锁定**为团队那几个，所以既不会漏人、也不会幻觉出别的角色。团队存在 `~/.ao/teams/*.team.yaml`（纯 YAML，可直接拷贝分享），**命令行和网页 Studio 共用同一份**——Studio 里勾选角色后点「存为团队」，命令行立刻 `ao run --team` 可用，反之亦然。\n\n> 自带私有专家：设环境变量 `AO_AGENTS_DIR=/你的角色目录`，`run / compose / roles / web` 全部改用你自己的角色库。\n> 自建单个角色：放 `~/.ao/roles/<id>.md`（与内置库叠加），工作流里用 `my/<id>` 引用；Studio「角色组队 → 我的」可视化增删。\n> 多语言角色库：`npm i agency-agents-ko`（另有 `pt-br` / `ar` / `id` / `ru`），工作流 `agents_dir: \"agency-agents-ko\"`，Studio 角色页出现「角色库」下拉。\n>\n> 固定全局目录：设 `AO_HOME=~/.ao`（或任意目录），运行产物 `ao-output`、`compose`/`--team` 生成的工作流都落到那里，不再随执行目录散落（#20）。也可用 `AO_OUTPUT_DIR` / `AO_WORKFLOWS_DIR` 单独指定。不设则维持原行为（写到当前目录）。\n\n### 提示词优化（Prompt Lab）\n\n把「靠感觉」的提示词，变成可优化、可测试、可对比、可沉淀的资产：\n\n```bash\nao prompt optimize \"帮我写个朋友圈文案卖咖啡\" --save 咖啡文案   # AI 把它改写成更有效的提示词\nao prompt test \"你是专业翻译，只输出译文\" --mode system --input \"good morning\"  # 用样例实跑看输出\nao prompt list / show 咖啡文案     # 已保存的提示词 + 版本历史\nao prompt garden                  # 内置起手模板\n```\n\n`--mode system|user` 区分「角色/系统提示词」和「任务提示词」。优化只产出**更好的提示词**（不会直接去执行它）。网页 Studio 的「提示词」页还能原版 vs 优化版**并排对比 + AI 评分**。存在 `~/.ao/prompts/`（`AO_PROMPTS_DIR` 可改），命令行与 Studio 共用。\n\n### Skills（给步骤挂方法论）\n\n角色决定「谁来做」，skill 决定「怎么做」。给工作流某一步挂一个 **skill**（流程剧本），它的方法论会注入该步——比如让代码审查步骤遵循结构化审查法、让实现步骤走 TDD：\n\n```yaml\nsteps:\n  - id: review\n    role: \"engineering/engineering-code-reviewer\"\n    skill: \"chinese-code-review\"     # 单个；或 skills: [\"test-driven-development\", ...]\n    task: \"审查这段代码 {{code}}\"\n```\n\n```bash\nao skills                      # 列出全部可用 skill\nao skills test-driven-development   # 看某个 skill 的方法论\n```\n\nskill 内容直接用开源的 [superpowers-zh](https://github.com/jnMetaCode/superpowers-zh)（MIT，20 个,已作为依赖,零配置）；也可设 `AO_SKILLS_DIR=/你的skill目录` 挂自己的。\n\n### 迭代优化（Resume）\n\n跑完一轮觉得某步不满意？不用从头来。`--resume` 加载上一轮输出，`--from` 指定从哪步重跑：\n\n```bash\n# 第一轮：正常运行\nao run workflows/一人公司全员大会.yaml -i idea=\"用AI帮小商家做短视频\"\n\n# 觉得营销方案不够好？只重跑营销和后续步骤\nao run workflows/一人公司全员大会.yaml --resume last --from marketing_plan\n\n# 只改最终汇总\nao run workflows/一人公司全员大会.yaml --resume last --from launch_decision\n```\n\n每轮输出保存在 `ao-output/` 下独立目录，所有版本都保留，随时可以回溯。\n\n| 场景 | 命令 |\n|------|------|\n| 第一次运行 | `ao run workflow.yaml -i key=value` |\n| 从某步重跑 | `ao run workflow.yaml --resume last --from <步骤ID>` |\n| 只重跑失败的步骤 | `ao run workflow.yaml --resume last` |\n| 基于指定版本重跑 | `ao run workflow.yaml --resume ao-output/具体目录/ --from <步骤ID>` |\n\n### 对话式返工（Feedback）\n\n`--resume --from` 是「让某个专家重做」，但默认是**从零重写**。如果你只是想说一句「这里改一下」，用 `--feedback`——它把你的意见 + 这个专家**上一版的产出**一起交回去，让它在原稿基础上改，而不是推倒重来：\n\n```bash\n# 觉得故事结尾太平淡 —— 直接跟\"写故事\"那个专家说怎么改\nao run workflows/story-creation.yaml --from write_story \\\n  --feedback \"结尾太平淡，加一个反转，并收束前面埋的伏笔\"\n\n# 觉得营销方案不接地气\nao run workflows/一人公司全员大会.yaml --from marketing_plan \\\n  --feedback \"预算改成 5000 以内，渠道聚焦小红书 + 私域\"\n```\n\n不写 `--resume` 时默认对**上一次运行**返工（等价于 `--resume last`）。该专家改完后，它的下游步骤会自动用新产出重跑。\n\n## 编程 API\n\n```typescript\nimport { run } from 'agency-orchestrator';\n\nconst result = await run('workflow.yaml', {\n  prd_content: '你的 PRD 内容...',\n});\n\nconsole.log(result.success);     // true/false\nconsole.log(result.totalTokens); // { input: 1234, output: 5678 }\n```\n\n## MCP Server 模式\n\nAI 编程工具（Claude Code、Cursor 等）可通过 MCP 协议直接调用工作流操作，无需手动集成：\n\n```bash\nao serve              # 启动 MCP stdio 服务器\nao serve --verbose    # 带调试日志\n```\n\n配置 Claude Code（`settings.json`）：\n\n```json\n{\n  \"mcpServers\": {\n    \"agency-orchestrator\": {\n      \"command\": \"npx\",\n      \"args\": [\"agency-orchestrator\", \"serve\"]\n    }\n  }\n}\n```\n\n配置 Cursor（`.cursor/mcp.json`）：\n\n```json\n{\n  \"mcpServers\": {\n    \"agency-orchestrator\": {\n      \"command\": \"npx\",\n      \"args\": [\"agency-orchestrator\", \"serve\"]\n    }\n  }\n}\n```\n\n提供 6 个工具：`run_workflow`、`validate_workflow`、`list_workflows`、`plan_workflow`、`compose_workflow`、`list_roles`。\n\n## YAML Schema\n\n### 工作流\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `name` | string | 是 | 工作流名称 |\n| `agents_dir` | string | 是 | 角色目录路径 |\n| `llm.provider` | string | 是 | `claude-code` / `gemini-cli` / `copilot-cli` / `codex-cli` / `openclaw-cli` / `hermes-cli` / `ollama` / `claude` / `deepseek` / `openai` |\n| `llm.model` | string | 是 | 模型名称 |\n| `llm.max_tokens` | number | 否 | 默认 4096 |\n| `llm.timeout` | number | 否 | 步骤超时毫秒数（默认 API 120000 / CLI/ollama 600000）。因超时重试时自动 x1.5 递增，上限 3600000。`0` 表示不限时 |\n| `llm.retry` | number | 否 | 重试次数（默认 3） |\n| `concurrency` | number | 否 | 最大并行步骤数（默认 2） |\n| `inputs` | array | 否 | 输入变量定义 |\n| `steps` | array | 是 | 工作流步骤 |\n\n### 步骤\n\n| 字段 | 类型 | 必填 | 说明 |\n|------|------|------|------|\n| `id` | string | 是 | 步骤唯一标识 |\n| `role` | string | 是 | 角色路径（如 `\"engineering/engineering-sre\"`） |\n| `task` | string | 是 | 任务描述，支持 `{{变量}}` |\n| `output` | string | 否 | 输出变量名 |\n| `depends_on` | string[] | 否 | 依赖的步骤 ID |\n| `depends_on_mode` | string | 否 | `\"all\"`（默认）或 `\"any_completed\"`（任一完成即可） |\n| `condition` | string | 否 | 条件表达式，不满足则跳过（如 `\"{{var}} contains 技术\"`） |\n| `type` | string | 否 | `\"approval\"` 人工审批节点 / `\"human_input\"` 人工输入节点（跑到该步暂停，读取你的输入作为该步产出注入下游） |\n| `prompt` | string | 否 | `approval` / `human_input` 节点的提示文本 |\n| `loop` | object | 否 | 循环配置 |\n| `loop.back_to` | string | 否 | 循环回到的步骤 ID |\n| `loop.max_iterations` | number | 否 | 最大循环次数（1-10） |\n| `loop.exit_condition` | string | 否 | 退出条件表达式 |\n\n## 输出\n\n每次运行保存到 `ao-output/<名称>-<时间戳>/`：\n\n```\nao-output/产品需求评审-2026-03-22/\n├── summary.md          # 最终步骤输出\n├── steps/\n│   ├── 1-analyze.md\n│   ├── 2-tech_review.md\n│   ├── 3-design_review.md\n│   └── 4-summary.md\n└── metadata.json       # 耗时、token 用量、步骤状态\n```\n\n## 内置工作流模板（32 个）\n\n### 开发类（7 个）\n\n| 模板 | 角色 | 说明 |\n|------|------|------|\n| `dev/tech-design-review.yaml` | 架构师、后端架构师、安全工程师、代码审查员 | **技术方案评审**（设计→并行评审→结论） |\n| `dev/pr-review.yaml` | 代码审查员、安全工程师、性能基准师 | PR 评审（3 路并行→汇总） |\n| `dev/tech-debt-audit.yaml` | 架构师、代码审查员、测试分析师、Sprint 排序师 | 技术债务审计（并行→优先级排序） |\n| `dev/api-doc-gen.yaml` | 技术文档工程师、API 测试员 | API 文档生成（分析→验证→定稿） |\n| `dev/readme-i18n.yaml` | 内容创作者、技术文档工程师 | README 国际化 |\n| `dev/security-audit.yaml` | 安全工程师、威胁检测工程师 | 安全审计（并行→报告） |\n| `dev/release-checklist.yaml` | SRE、性能基准师、安全工程师、产品经理 | 发布 Go/No-Go 决策 |\n\n### 营销类（3 个）\n\n| 模板 | 角色 | 说明 |\n|------|------|------|\n| `marketing/competitor-analysis.yaml` | 趋势研究员、数据分析师、SEO 专家、高管摘要师 | **竞品分析报告**（研究→并行分析→摘要） |\n| `marketing/xiaohongshu-content.yaml` | 小红书专家、内容创作者、视觉叙事师、小红书运营 | **小红书种草笔记**（选题→并行创作→优化） |\n| `marketing/seo-content-matrix.yaml` | SEO 专家、策略师、内容创作者 | **SEO 内容矩阵**（关键词→策略→批量生成→审核） |\n\n### 数据 / 设计 / 运维类（7 个）\n\n| 模板 | 角色 | 说明 |\n|------|------|------|\n| `data/data-pipeline-review.yaml` | 数据工程师、数据库优化师、数据分析师 | 数据管道评审 |\n| `data/dashboard-design.yaml` | 数据分析师、UX 研究员、UI 设计师 | 仪表盘设计 |\n| `design/requirement-to-plan.yaml` | 产品经理、架构师、项目经理 | 需求→技术设计→任务拆分 |\n| `design/ux-review.yaml` | UX 研究员、无障碍审核员、UX 架构师 | UX 评审 |\n| `ops/incident-postmortem.yaml` | 事故指挥官、SRE、产品经理 | 事故复盘 |\n| `ops/sre-health-check.yaml` | SRE、性能基准师、基础设施运维师 | SRE 健康检查（3 路并行） |\n| `ops/weekly-report.yaml` | 会议助手、内容创作者、高管摘要师 | **周报/月报生成**（整理→亮点→定稿） |\n\n### 战略 / 法务 / HR 类（3 个）\n\n| 模板 | 角色 | 说明 |\n|------|------|------|\n| `strategy/business-plan.yaml` | 趋势研究员、财务预测师、产品经理、高管摘要师 | **商业计划书**（市场→并行分析→整合） |\n| `legal/contract-review.yaml` | 合同审查专家、法务合规员 | **合同审查**（逐条分析→合规检查→意见书） |\n| `hr/interview-questions.yaml` | 招聘专家、心理学家、后端架构师 | **面试题设计**（维度→并行出题→评分表） |\n\n### 通用类（12 个）\n\n| 模板 | 角色 | 说明 |\n|------|------|------|\n| `product-review.yaml` | 产品经理、架构师、UX 研究员 | 产品需求评审 |\n| `content-pipeline.yaml` | 策略师、创作者、增长黑客 | 内容创作流水线 |\n| `story-creation.yaml` | 叙事学家、心理学家、叙事设计师、创作者 | 协作小说创作（4 角色） |\n| `ai-opinion-article.yaml` | 趋势研究员、叙事设计师、心理学家、创作者 | AI 观点长文 |\n| `department-collab/code-review.yaml` | 代码审查员、安全工程师 | 代码评审（循环） |\n| `department-collab/hiring-pipeline.yaml` | HR、技术面试官、业务面试官 | 招聘流程 |\n| `department-collab/content-publish.yaml` | 内容创作者、品牌守护者 | 内容发布（循环） |\n| `department-collab/incident-response.yaml` | SRE、安全工程师、后端架构师 | 事故响应 |\n| `department-collab/marketing-campaign.yaml` | 策略师、创作者、审批人 | 营销活动（人工审批） |\n| `department-collab/ceo-org-delegation.yaml` | CEO、工程/市场/产品/HR 部门负责人 | **CEO 组织架构协作**（决策→部门并行→汇总） |\n| `一人公司全员大会.yaml` | CEO、市场调研员、用户研究员、产品经理、市场负责人、CFO | **一人公司全员大会**（CEO→6 部门并行→决策） |\n| `ai-startup-launch.yaml` | CEO、产品经理、架构师、市场负责人、财务顾问 | **SaaS 产品发布决策**（CEO→4 部门并行→发布计划） |\n\n## 生态与社区\n\n```\n你的 AI 会员 ──→ agency-orchestrator ──→ 400+ 个 AI 角色协作 ──→ 高质量输出\n                     │                  (267 中文 + 184 英文 + 5 语种)\n    ┌────────────────┼────────────────┐\n    ▼                ▼                ▼\n  14 个 AI 编程工具    CLI 模式         MCP Server\n  (Cursor/Claude     (自动化/CI/CD)   (Claude Code/\n   Code/Copilot...)                   Cursor 直接调用)\n```\n\n| 项目 | 定位 | 一句话 |\n|------|------|-------|\n| **本项目**（agency-orchestrator） | 🚀 编排引擎 | 一句话 → 267 专家协作，**几分钟出方案**（11 家 LLM / 7 免费） |\n| [agency-agents-zh](https://github.com/jnMetaCode/agency-agents-zh) ![](https://img.shields.io/github/stars/jnMetaCode/agency-agents-zh?style=flat&label=⭐) | 🎭 中文角色库 | 267 个**即插即用** AI 专家，含 52 中国原创（小红书 / 抖音 / 飞书 / 钉钉） |\n| [agency-agents](https://github.com/msitarzewski/agency-agents) | 🎭 英文角色库 | 184 个英文 AI 角色（by [@msitarzewski](https://github.com/msitarzewski)，MIT）——**已随 npm 包内置**，英文任务 `ao compose` 自动启用，无需单独安装 |\n| [ko](https://github.com/jnMetaCode/agency-agents-ko) · [ru](https://github.com/jnMetaCode/agency-agents-ru) · [pt-BR](https://github.com/jnMetaCode/agency-agents-pt-BR) · [id](https://github.com/jnMetaCode/agency-agents-id) · [ar](https://github.com/jnMetaCode/agency-agents-ar) | 🌍 多语言角色库 | 各 187 个（184 上游翻译 + 3 本地市场原创），`npm i agency-agents-<lang>` 后 Studio「角色库」下拉切换 |\n| [superpowers-zh](https://github.com/jnMetaCode/superpowers-zh) ![](https://img.shields.io/github/stars/jnMetaCode/superpowers-zh?style=flat&label=⭐) | 🧠 工作方法论 | 20 个 skills 教 AI 怎么干活（TDD / 调试 / 代码审查等） |\n| [ai-coding-guide](https://github.com/jnMetaCode/ai-coding-guide) | 📖 实战教程 | 66 个 Claude Code 技巧 + 9 款工具最佳实践 + 配置模板 |\n| [shellward](https://github.com/jnMetaCode/shellward) | 🛡️ 安全中间件 | 8 层防御 + DLP 数据流 + 注入检测，**零依赖**（含 MCP Server） |\n| 🆕 [ai-shortfilm-prompts](https://github.com/jnMetaCode/ai-shortfilm-prompts) | 🎬 视频提示词 | Mx-Shell《丧尸清道夫》5 段式方法论 + Skill，Seedance / 小云雀 / Sora / 可灵 / 即梦通用 |\n| 🆕 [codepet](https://github.com/jnMetaCode/codepet) ![](https://img.shields.io/github/stars/jnMetaCode/codepet?style=flat&label=⭐) | 🐾 桌面宠物 | 挂在桌面的桌宠，你写代码 / 用 Claude Code 它就涨经验、升级、换状态、冒话（Electron · 仅读元数据，本地优先） |\n\n### 交流\n\n<table>\n<tr>\n<td width=\"170\" align=\"center\">\n<img src=\"assets/qr-wechat.jpg\" width=\"150\" alt=\"微信公众号 AI不止语 二维码\"><br>\n<sub>微信扫码关注</sub>\n</td>\n<td>\n\n微信公众号 **「AI不止语」**（微信搜索 `AI_BuZhiYu`）— 技术问答 · 项目更新 · 实战文章\n\n| 渠道 | 加入方式 |\n|------|---------|\n| QQ 2群 | [点击加入](https://qm.qq.com/q/EeNQA9xCxy)（群号 1071280067） |\n| 微信群 | 关注公众号后回复「群」获取入群方式 |\n\n</td>\n</tr>\n</table>\n\n## 路线图\n\n- [x] **v0.1** — YAML 工作流、DAG 引擎、4 个 LLM 连接器、CLI、实时输出\n- [x] **v0.2** — 条件分支、循环迭代、人工审批、Resume 断点续跑、5 个部门协作模板\n- [x] **v0.3** — 9 个 AI 工具集成、20+ 工作流模板、`ao explain`、`ao init --workflow`、`--watch` 模式\n- [x] **v0.4** — MCP Server 模式（`ao serve`）、14 个 AI 工具集成、一键安装脚本、32 个工作流模板、**10 种 LLM（7 种免 API key：Claude Code / Gemini / Copilot / Codex / OpenClaw / Hermes / Ollama）**\n- [x] **v0.5** — `ao compose --run` 一句话出结果、实时流式输出、智能重试（指数退避）、步骤级模型覆盖、Agent 身份标识\n- [ ] **v0.6** — Web UI、可视化 DAG 编辑器、英文工作流模板、工作流市场\n\n## 贡献\n\n参见 [CONTRIBUTING.md](./CONTRIBUTING.md)，欢迎 PR！\n\n## 许可证\n\n[Apache-2.0](./LICENSE)\n","readmeFilename":"README.md"}