docs: reposition README around TypeScript-native, goal-to-result, lightweight differentiators
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README.md
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README.md
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# Open Multi-Agent
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Build AI agent teams that decompose goals into tasks automatically. Define agents with roles and tools, describe a goal — the framework plans the task graph, schedules dependencies, and runs everything in parallel.
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TypeScript framework for multi-agent orchestration. One `runTeam()` call from goal to result — the framework decomposes it into tasks, resolves dependencies, and runs agents in parallel.
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3 runtime dependencies. 27 source files. One `runTeam()` call from goal to result.
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3 runtime dependencies · 27 source files · Deploys anywhere Node.js runs
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[](https://github.com/JackChen-me/open-multi-agent/stargazers)
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[](./LICENSE)
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## Why Open Multi-Agent?
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- **Auto Task Decomposition** — Describe a goal in plain text. A built-in coordinator agent breaks it into a task DAG with dependencies and assignees — no manual orchestration needed.
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- **Multi-Agent Teams** — Define agents with different roles, tools, and even different models. They collaborate through a message bus and shared memory.
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- **Task DAG Scheduling** — Tasks have dependencies. The framework resolves them topologically — dependent tasks wait, independent tasks run in parallel.
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- **Goal In, Result Out** — `runTeam(team, "Build a REST API")`. A coordinator agent auto-decomposes the goal into a task DAG with dependencies and assignees, runs independent tasks in parallel, and synthesizes the final output. No manual task definitions or graph wiring required.
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- **TypeScript-Native** — Built for the Node.js ecosystem. `npm install`, import, run. No Python runtime, no subprocess bridge, no sidecar services. Embed in Express, Next.js, serverless functions, or CI/CD pipelines.
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- **Auditable and Lightweight** — 3 runtime dependencies (`@anthropic-ai/sdk`, `openai`, `zod`). 27 source files. The entire codebase is readable in an afternoon.
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- **Model Agnostic** — Claude, GPT, Gemma 4, and local models (Ollama, vLLM, LM Studio) in the same team. Swap models per agent via `baseURL`.
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- **Multi-Agent Collaboration** — Agents with different roles, tools, and models collaborate through a message bus and shared memory.
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- **Structured Output** — Add `outputSchema` (Zod) to any agent. Output is parsed as JSON, validated, and auto-retried once on failure. Access typed results via `result.structured`.
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- **Task Retry** — Set `maxRetries` on tasks for automatic retry with exponential backoff. Failed attempts accumulate token usage for accurate billing.
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- **Observability** — Optional `onTrace` callback emits structured spans for every LLM call, tool execution, task, and agent run — with timing, token usage, and a shared `runId` for correlation. Zero overhead when not subscribed, zero extra dependencies.
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- **In-Process Execution** — No subprocess overhead. Everything runs in one Node.js process. Deploy to serverless, Docker, CI/CD.
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## Quick Start
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README_zh.md
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# Open Multi-Agent
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构建能自动拆解目标的 AI 智能体团队。定义智能体的角色和工具，描述一个目标——框架自动规划任务图、调度依赖、并行执行。
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TypeScript 多智能体编排框架。一次 `runTeam()` 调用从目标到结果——框架自动拆解任务、解析依赖、并行执行。
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3 个运行时依赖，27 个源文件，一次 `runTeam()` 调用从目标到结果。
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3 个运行时依赖 · 27 个源文件 · Node.js 能跑的地方都能部署
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[](https://github.com/JackChen-me/open-multi-agent/stargazers)
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[](./LICENSE)
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## 为什么选择 Open Multi-Agent？
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- **自动任务拆解** — 用自然语言描述目标，内置的协调者智能体自动将其拆解为带依赖关系和分配的任务图——无需手动编排。
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- **多智能体团队** — 定义不同角色、工具甚至不同模型的智能体。它们通过消息总线和共享内存协作。
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- **任务 DAG 调度** — 任务之间存在依赖关系。框架进行拓扑排序——有依赖的任务等待，无依赖的任务并行执行。
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- **目标进，结果出** — `runTeam(team, "构建一个 REST API")`。协调者智能体自动将目标拆解为带依赖关系的任务图，分配给对应智能体，独立任务并行执行，最终合成输出。无需手动定义任务或编排流程图。
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- **TypeScript 原生** — 为 Node.js 生态而生。`npm install` 即用，无需 Python 运行时、无子进程桥接、无额外基础设施。可嵌入 Express、Next.js、Serverless 函数或 CI/CD 流水线。
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- **可审计、极轻量** — 3 个运行时依赖（`@anthropic-ai/sdk`、`openai`、`zod`），27 个源文件。一个下午就能读完全部源码。
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- **模型无关** — Claude、GPT、Gemma 4 和本地模型（Ollama、vLLM、LM Studio）可以在同一个团队中使用。通过 `baseURL` 即可接入任何 OpenAI 兼容服务。
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- **多智能体协作** — 定义不同角色、工具和模型的智能体，通过消息总线和共享内存协作。
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- **结构化输出** — 为任意智能体添加 `outputSchema`（Zod），输出自动解析为 JSON 并校验，校验失败自动重试一次。通过 `result.structured` 获取类型化结果。
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- **任务重试** — 为任务设置 `maxRetries`，失败时自动指数退避重试。所有尝试的 token 用量累计，确保计费准确。
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- **可观测性** — 可选的 `onTrace` 回调为每次 LLM 调用、工具执行、任务和智能体运行发出结构化 span 事件——包含耗时、token 用量和共享的 `runId` 用于关联追踪。未订阅时零开销，零额外依赖。
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- **进程内执行** — 没有子进程开销。所有内容在一个 Node.js 进程中运行。可部署到 Serverless、Docker、CI/CD。
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## 快速开始
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