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agent 服务开发框架：装饰器 + DI 声明四类能力，主 agent 编排执行；每次 run 产出结构化结果与可观测调用树（trace、成本、指标），交付可直接上线的服务。","maintainers":[{"name":"migor","email":"retrychx@163.com"}],"readme":"# Agentia\n\n[中文](./README.md) · [English](./README.en.md)\n\n[![CI](https://github.com/retrychx/agentia/actions/workflows/ci.yml/badge.svg)](https://github.com/retrychx/agentia/actions/workflows/ci.yml) [![npm](https://img.shields.io/npm/v/@migor/agentia)](https://www.npmjs.com/package/@migor/agentia) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)\n\n面向应用开发的声明式 agent 服务开发框架：装饰器 + DI 声明四类能力，主 agent 编排执行；每次 run 产出结构化结果与可观测调用树（trace、成本、指标），交付可直接上线的服务。\n\n- [安装与配置](#安装与配置)\n- [快速开始](#快速开始)\n- [四类能力](#四类能力)\n- [运行时特性](#运行时特性)\n- [可观测性与成本](#可观测性与成本)\n- [集成](#集成)\n- [API 速查](#api-速查)\n- [开发本仓库](#开发本仓库)\n- [延伸阅读](#延伸阅读)\n\n## 安装与配置\n\n```bash\nnpx @migor/cli create my-app         # 脚手架（含 .env / .env.example）\ncd my-app && npm install             # 框架与 CLI 都装进工程\n$EDITOR .env                         # 填 ANTHROPIC_API_KEY（也可直接 export，真实环境变量优先）\nnpm run dev                          # = agentia dev：本地 inspector 面板（输入 prompt / 选能力 / 选工作目录）\n# 可选：ANTHROPIC_BASE_URL（兼容端点）、AGENTIA_MODEL（缺省 claude-opus-5）\n\n# 工程内直接用：npx agentia g tool fetch-weather / npx agentia doctor / npx agentia --version\n#   （脚手架把 @migor/cli 装进 devDependencies ⇒ 走本地 bin，离线可用、版本与工程一同 pin）\n# 首次创建必须带 scope —— npm 上另有一个别人的 `agentia` 包，短名会装错东西。\n# 想全局装（到处都能敲 agentia）：npm i -g @migor/cli\n```\n\n> 框架**不自动**读 `.env`：脚手架 `src/app.ts` 的 `loadEnvFile()` 负责把它读进 `process.env`。\n> 放在**装配模块**（app.ts）而不是启动入口（main.ts），是因为 `agentia dev` 只 import app.ts、\n> 从不执行 main.ts —— 写错一侧会让 `npm run dev` 静默读不到 `.env` 而 `npm start` 读得到。\n> 真实环境变量优先（CI / docker / 命令行永远赢过文件），`loadEnvFile({ override: true })` 才反过来。\n\n> **版本**：`0.9.3`（`@migor/agentia` 与 `@migor/cli` 均已发布到 npm）。`examples/` **刻意**用\n> `file:../..` 指向本仓库而不是 npm 版本 —— 跑的是**工作区代码**，理由见\n> [`examples/README.md`](examples/README.md) 的「依赖说明」。\n\n> **运行时**：Node ≥ 18（`engines` 唯一要求，CI 在 18/20/22 上守）。按 Node 设计并测试，\n> **未对 Deno / edge 做验证**。唯一碰内置模块的 store 是 `SqliteTaskStore`（需 Node ≥ 22.5 的\n> `node:sqlite`）—— 未提供时**构造期给可读报错**，不影响包本身被导入。\n\n\n## 快速开始\n\n两条路线，按需选一即可（也可混用）。\n\n### 路线 A：CLI 脚手架\n\n```bash\nagentia create my-app                  # 脚手架新项目（依赖 @migor/agentia）\ncd my-app && npm install\nagentia g tool weather                 # 生成 src/tools/weather/index.ts 并登记 src/registry.ts\nagentia g subagent doc-reviewer        # 生成 src/subagents/doc-reviewer/{index.ts,system.md}\nagentia g skill note-writer            # 生成 src/skills/note-writer/index.ts\nagentia g prompt style-guide           # 生成 src/prompts/style-guide/{index.ts,asset.md}\nagentia dev                            # 本地 inspector 面板：输入 prompt 驱动一次 run + 看 trace\n```\n\n**目录约定**：四分类目录，一能力一文件夹 —— `src/tools/` · `src/skills/` · `src/prompts/` · `src/subagents/`。\n目录名就是类型，不用记别名。每个文件夹的 `index.ts` default export 一个 provider 类，DI token 缺省 = 文件夹名；\n长文本放文件夹内 `.md`，代码里用 `asset(import.meta.url, './system.md')` 读取。\n\n### 路线 B：手写装配\n\n不依赖 CLI，直接声明能力并装配成应用：\n\n```ts\nimport { Tool, createApp, SystemPrompt, RunContext } from '@migor/agentia';\n\nclass WeatherTools {\n  // 工具名缺省取方法名（建议 snake_case）；入参 = 模型按 schema 解析的结构化 input\n  @Tool({\n    description: '查询城市天气',\n    schema: {\n      type: 'object',\n      properties: { city: { type: 'string' } },\n      required: ['city'],\n      additionalProperties: false,\n    },\n    strict: true,\n  })\n  get_weather(input: { city: string }): string {\n    // 方法体内随时可读本次 run 的上下文（blackboard）。\n    // 想让键有补全/校验：在一次 declare module 里合并 Blackboard（见 docs/usage-guide.md §5.1）；\n    // 未声明时键为 string、值为 unknown，这里按需断言。\n    const token = RunContext.current()?.get('authToken') as string | undefined;\n    return `city=${input.city};token=${token ?? 'none'}`;\n  }\n}\n\nconst app = createApp({\n  name: 'weather-app',\n  providers: [{ provide: 'weather', useClass: WeatherTools }],\n  system: new SystemPrompt().add('role', '你是天气助手。', true),\n});\n\nconst { run, result } = await app.run(\n  [{ role: 'user', content: '上海天气如何?' }],\n  { blackboard: { authToken: 'sk-...' } }, // 预置本次 run 的上下文\n);\nconsole.log(result.finalText); // 最终文本\nconsole.log(result.trace);     // 调用树 + 每步 token/成本（traceId == runId）\n```\n\n装配还有**目录扫描**这条路（与显式注册表可混用）：\n\n```ts\n// 按给定顺序扫各目录（返回 Promise）；也可用 CLI 维护的 src/registry.ts 显式注册\nconst app = await createApp({\n  name: 'my-app',\n  discover: ['src/tools', 'src/skills', 'src/prompts', 'src/subagents'],\n  system,\n});\n```\n\n## 四类能力\n\n主 agent 按 `description` 从同一张「能力菜单」自选；装饰器只决定**谁控制流程**：\n\n| 能力 | 装饰器 | 谁决定流程 | 典型用途 |\n|---|---|---|---|\n| 工具 | `@Tool` | 你的代码（一次调用 = 一个函数） | 确定性操作：查库、算数、调 API |\n| 技能 | `@Skill` | 你的代码（脚本式，显式 `ctx.llm()`） | 「先取数 → 再让模型写 → 再加工」的固定流程 |\n| 子 agent | `@SubAgent` | **模型自己**（独立循环 + 裁剪上下文） | 自主多步、且中间过程不该污染主上下文 |\n| 提示资产 | `@Prompt` | 模型拉取（按需注入的文本） | 长文规范/模板，平时不进上下文 |\n\n`@Tool` 的写法见上方路线 B；其余三类：\n\n```ts\nimport { Skill, SubAgent, Prompt, asset, type SkillContext } from '@migor/agentia';\n\n// @Skill：代码控制的流程，模型调用只发生在显式 ctx.llm()\nclass Notes {\n  @Skill({ description: '按主题整理要点' })\n  async note_writer(input: { topic: string }, ctx: SkillContext): Promise<string> {\n    const r = await ctx.llm({ prompt: `就「${input.topic}」给出三个要点` });\n    return r.text; // 返回值即产物，以 tool_result 交回主 agent\n  }\n}\n\n// @SubAgent：独立循环 + 裁剪上下文，只有最终报告回流主上下文（方法体不执行）\nclass Pipeline {\n  @SubAgent({\n    name: 'reviewer',\n    description: '审查给定文档并输出书面评审意见',\n    schema: { type: 'object', properties: { doc: { type: 'string' } }, required: ['doc'], additionalProperties: false },\n    system: '你是评审 agent。结论必须以“审查通过/不通过”开头。',\n    tools: ['weather'], // 子 agent 自己的工具菜单（provider token）\n  })\n  reviewer(_input: { doc: string }): void {}\n}\n\n// @Prompt：纯文本资产，模型判定需要时拉取进上下文\nclass Assets {\n  @Prompt({ description: '品牌基调文案规范' })\n  style_guide(): string {\n    return asset(import.meta.url, './asset.md'); // 从能力文件夹读 .md\n  }\n}\n```\n\n四类一起装配（`providers` 里逐个登记；token 即子 agent / 技能引用工具的菜单名）：\n\n```ts\nimport { createApp, SystemPrompt } from '@migor/agentia';\n\nconst app = createApp({\n  name: 'weather-app',\n  providers: [\n    { provide: 'weather', useClass: WeatherTools },\n    { provide: 'notes', useClass: Notes },\n    { provide: 'pipeline', useClass: Pipeline },\n    { provide: 'assets', useClass: Assets },\n  ],\n  system: new SystemPrompt().add('role', '你是天气助手。', true),\n});\n```\n\n## 运行时特性\n\n### 触发方式\n\n同一份应用，三种触发任选：\n\n```ts\nimport { AsyncRunner, FileTaskStore, Scheduler } from '@migor/agentia';\n\n// 同步 RPC\nconst { run, result } = await app.run(messages);\n\n// 异步任务（幂等键去重，失败可重提，可落盘续跑）\nconst runner = new AsyncRunner(app, { store: new FileTaskStore('./tasks.jsonl'), concurrency: 4 });\nconst task = runner.submit('总结一下今天的新闻', { idempotencyKey: 'daily-2026-09-10' });\nawait runner.awaitTask(task.taskId);\n\n// 定时\nconst scheduler = new Scheduler(runner);\nscheduler.every(60_000, '巡检一次', { idempotencyKey: 'patrol' });\n```\n\n### 长上下文预算\n\n```ts\nimport { createBudgetPolicy } from '@migor/agentia';\n\nconst app = createApp({\n  // ...\n  contextPolicy: createBudgetPolicy({\n    budgetTokens: 60_000,    // 超预算先丢旧工具对，仍超且有 summarize 才压缩\n    summarize: (text) => mySummarizer(text), // 可选，框架不替你调模型\n  }),\n});\n```\n\n### 结构化结果\n\n```ts\nconst { result } = await app.run(messages, {\n  resultSchema: {\n    type: 'object',\n    properties: { pass: { type: 'boolean' }, reason: { type: 'string' } },\n    required: ['pass', 'reason'],\n    additionalProperties: false,\n  },\n});\nconsole.log(result.typed); // 校验过的结构化结果，不再从文本里猜 JSON\n// schema 也可来自 zod（peer 可选）：fromZod(z.toJSONSchema(S), S)\n```\n\n### 中间件\n\n挂在每一次能力调用前后的洋葱链——鉴权、限流、缓存、审计都走这里：\n\n```ts\nconst app = createApp({\n  // ...\n  middleware: [\n    async (call, next) => {\n      if (call.capability.name === 'danger_op' && !RunContext.current()?.get('isAdmin')) {\n        return 'forbidden'; // 短路，不调 next 即拦截\n      }\n      return next(); // next(newInput) 还可改写入参\n    },\n  ],\n});\n```\n\n## 可观测性与成本\n\n一次 run == 一条 trace（`traceId === runId`），**Turn 0 起内建** —— 不是外挂的第三方追踪 SDK 集成：\n\n```ts\nconst { run, result } = await app.run(messages);\nresult.trace.spans;       // 调用树：llm.turn / 能力 span / 事件\nresult.trace.totalUsage;  // token 汇总（只累加 llm.turn；能力 span 是子孙聚合，不参与求和）\n```\n\n- **每步记账**：span 属性带 model、input/output/cache tokens、成本估计、状态、错误类型\n- **出口是一条缝**：`TraceSink { export(trace) }` —— run 收尾（成功/失败两条路径）都投递，sink 抛错不影响 run。落库 / 采样 / 脱敏都在缝外用 sink 组合（实码见 `examples/observability/`，说明见 `docs/observability.md`）\n- **指标**：`metricsSink` 满足 `TraceSink` 即可接入（Prometheus 文本 / OTLP metrics），零依赖\n- **成本**：`priceOverrides` 注入价目表；未定价模型显式发 `usage.unpriced` 事件；`createBudgetGuard` 做**硬管控**（超限 run 以 `budget_exceeded` 收尾、算失败）\n- **调优闭环**：`buildRunReport` 出「能力 / 模型的耗时·token·成本·错误率排行」，CLI `agentia report <trace.jsonl>` 直接渲染\n- **回放**：`traceToMessages` 把已完成的 trace 线性化喂回模型（调试基底）\n- **本地开发**：`agentia dev` 的 inspector 面板与 `@migor/trace-view` 共用同一份渲染器（避免两处漂移）\n- **导出 OTLP**：\n\n```ts\nimport { createOtlpExporter } from '@migor/agentia';\n\nawait createOtlpExporter({ endpoint: 'http://localhost:4318' }).export(result.trace);\n```\n\n> 权威口径见 `docs/usage-guide.md` 的「观测」与「成本硬管控」两节。\n\n## 集成\n\n### HTTP 宿主\n\n```ts\nimport { createServer } from 'node:http';\nimport { createHttpHandler } from '@migor/agentia';\n\n// POST /run（同步） POST /tasks（异步） GET /tasks/:id（轮询）\ncreateServer(createHttpHandler(app, { runner })).listen(8080);\n```\n\n### MCP（连接器出厂自带）\n\nMCP server 的工具映射成菜单项 —— 连接器（stdio / StreamableHTTP）**随框架发布**，只用标准库（`node:child_process` + 全局 `fetch`），不新增第三方依赖：\n\n```ts\nimport { createApp, createStdioMcpConnector, createStreamableHttpMcpConnector, mcpTools } from '@migor/agentia';\n\n// stdio：spawn 一个 MCP server 子进程\nconst mcp = createStdioMcpConnector(['uvx', 'mcp-server-time']);\nconst tools = await mcpTools(mcp, { server: 'time' }); // → mcp_time_get_current_time …\n\n// 远程 server：StreamableHTTP（一个 endpoint，鉴权走 headers）\nconst remote = createStreamableHttpMcpConnector('https://mcp.example/mcp', {\n  headers: { authorization: 'Bearer …' },\n});\n\n// 与本地 @Tool 同池：同过中间件链、同进重名查重\ncreateApp({ system, providers: [...], tools });\n```\n\n`McpClientLike` 这条缝仍然在：接官方 SDK / 远程 server / 自研传输时实现 `listTools()` + `callTool()` 两个方法即可（框架**不 import** MCP SDK）。\n\n### 多模型（OpenAI 兼容端点）\n\n```ts\nimport { createOpenAIClient } from '@migor/agentia';\n\nconst { result } = await app.run(messages, {\n  client: createOpenAIClient({ baseURL: 'https://api.deepseek.com' }),\n  model: 'deepseek-chat',\n});\n```\n\n> **本地模型（Ollama 等）**：Ollama 暴露 OpenAI 兼容端点，用同一个适配器即可 ——\n> `createOpenAIClient({ baseURL: 'http://localhost:11434/v1' })` + `model: 'qwen3'`，\n> **不需要任何新代码或新依赖**。\n\n\n### 跨 run 记忆\n\n```ts\nimport { InMemoryMemoryStore } from '@migor/agentia';\n\nconst { result } = await app.run(messages, {\n  memory: { store: new InMemoryMemoryStore(), keys: ['profile'] },\n});\n```\n\n## API 速查\n\n| 导出 | 用途 |\n|---|---|\n| `createApp` / `discoverProviders` | 装配应用 / 扫描能力目录 |\n| `defineModule` | 能力包（providers + middleware 打包分发） |\n| `Tool` / `Skill` / `SubAgent` / `Prompt` | 四类能力装饰器 |\n| `CapabilityMiddleware`（`middleware` 选项） | 能力调用拦截器链 |\n| `asset` | 读能力文件夹内的文本资产 |\n| `SystemPrompt` | 拼装系统提示（自动打缓存 breakpoint） |\n| `RunContext.current()` | 取本次 run 的 blackboard / runId |\n| `AsyncRunner` / `Scheduler` / `runSync` / `createHttpHandler` | 异步 / 定时 / 同步 / HTTP 触发 |\n| `FileTaskStore` / `SqliteTaskStore` / `RedisTaskStore` / `InMemoryTaskStore` | 任务记录存储 |\n| `createBudgetPolicy` | 长上下文预算护栏 |\n| `createAnthropicClient` | 默认 ModelClient（Anthropic）：自定义只传 `apiKey` / `baseURL`，不必直接依赖厂商 SDK |\n| `createOpenAIClient` | OpenAI 兼容端点适配（多模型） |\n| `InMemoryMemoryStore`（`memory` 选项） | 跨 run 记忆水合/回写 |\n| `createOtlpExporter` | trace 导出 OTLP |\n| `traceToMessages` | trace 重放基底：把完成的 run 还原成消息喂回模型调试 |\n| `fromZod` | zod schema 接入（peer 可选） |\n| `runAgent` / `executeRun` | 裸引擎入口（不走装配） |\n\n## 开发本仓库\n\n```bash\nnpm install\nnpm run build        # 编译框架（dist/）\nnpm run typecheck    # 类型检查\nnpm run lint         # lint + 格式检查（Biome）\nnpm test             # 单元测试（node:test）\nnpm run e2e          # 端到端：CLI 脚手架 → 装配 mock run；再真跑 examples/complete 与 examples/deploy（含崩溃续跑）\n```\n\n> 注：本仓库的 `npm run dev`（tsx）前需先 `npm approve-scripts` 批准 esbuild/tsx 的 postinstall；\n> 脚手架项目里的 `agentia dev` 不受此限。\n\n## 延伸阅读\n\n- **使用者向完整说明**（API 速查 / 类型链路 / 已知边界 / 反例）：[`docs/usage-guide.md`](docs/usage-guide.md)\n- 完整导出清单：[`src/index.ts`](src/index.ts)\n- 设计规格：[`docs/spec.md`](docs/spec.md)\n- 生产可观测配方（落库检索 / 日志关联 / 采样 / 脱敏）：[`docs/observability.md`](docs/observability.md)\n- 完整示例（四类能力 + 三种触发 + 全观测栈）：[`examples/complete/`](examples/complete/)\n- 最小部署示例：[`examples/deploy/`](examples/deploy/)\n- 官网：[agentia-web.pages.dev](https://agentia-web.pages.dev)（[在线 Playground](https://agentia-web.pages.dev/playground) · [文档](https://agentia-web.pages.dev/docs)）\n","readmeFilename":"README.md"}