Revert "ci: add release workflow triggered by v*.*.* tags"

Strategic rewrite following docs/project-evaluation-2026-04-09.md.
README.md and README_zh.md updated in lockstep.

Top fold changes:
- New tagline positioning against CrewAI and LangGraph
- Replace 11-bullet "Why" with 3 bullets (runTeam / 3 deps / multi-model)
- New Philosophy section with "we build / we don't build / tracking"
- "We don't build" limited to handoffs and checkpointing (softened);
  Cloud/Studio bullet dropped to preserve future Hosted option
- New "How is this different from X?" FAQ covering LangGraph JS, CrewAI,
  and Vercel AI SDK
- New "Used by" section with three early-stage integrations, framed
  honestly for a new project (temodar-agent, rentech-quant-platform,
  cybersecurity SOC home lab)

Examples section:
- Shrink 15-row catalog table to 4 featured entries + link to examples/
- Featured: 02 team collaboration, 06 local model, 09 structured output,
  11 trace observability
- Eliminates maintenance debt of updating the table on every new example

Refinements during alignment pass:
- Launch date corrected to 2026-04-01 (matches first commit timestamp)
- Surface Gemini @google/genai peer dep in top fold and Providers table
- Rephrase "Agent handoffs" bullet to avoid reading as single-agent framework
- Update prose example to Opus 4.6 / GPT-5.4 / local Gemma 4
- Quick Start code example shortened ~30% (developer/reviewer collapsed
  to stubs, still demonstrates multi-agent team shape)
- Remove CrewAI endorsement stats (48K stars / Andrew Ng / $18M) to keep
  comparisons technical
- Drop Star History cache-buster since growth has stabilized; bump
  contributors cache-buster to max=20 so all 8 contributors render
- Delete Author section; shrink Contributing to Examples + Documentation

Small carry-over fixes:
- Fix duplicated task_complete line in Quick Start output sample
- Add AgentPool.runParallel() note to Three Ways to Run
- Update source file count 33 → 35

Kept unchanged per scope:
- Architecture diagram, Built-in Tools, Supported Providers

Does not touch source code or package.json.

README.md

@@ -1,8 +1,10 @@
 # Open Multi-Agent
 
-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.
+The lightweight multi-agent orchestration engine for TypeScript. Three runtime dependencies, zero config, goal to result in one `runTeam()` call.
 
-3 runtime dependencies · 33 source files · Deploys anywhere Node.js runs · Mentioned in [Latent Space](https://www.latent.space/p/ainews-a-quiet-april-fools) AI News
+CrewAI is Python. LangGraph makes you draw the graph by hand. `open-multi-agent` is the `npm install` you drop into an existing Node.js backend when you need a team of agents to work on a goal together. Nothing more, nothing less.
+
+3 runtime dependencies · 35 source files · Deploys anywhere Node.js runs · Mentioned in [Latent Space](https://www.latent.space/p/ainews-a-quiet-april-fools) AI News (top AI engineering newsletter, 170k+ subscribers)
 
 [![GitHub stars](https://img.shields.io/github/stars/JackChen-me/open-multi-agent)](https://github.com/JackChen-me/open-multi-agent/stargazers)
 [![license](https://img.shields.io/github/license/JackChen-me/open-multi-agent)](./LICENSE)
@@ -11,19 +13,51 @@ TypeScript framework for multi-agent orchestration. One `runTeam()` call from go
 
 **English** | [中文](./README_zh.md)
 
-## Why Open Multi-Agent?
+## What you actually get
 
-- **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.
-- **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.
-- **Auditable and Lightweight** — 3 runtime dependencies (`@anthropic-ai/sdk`, `openai`, `zod`). 33 source files. The entire codebase is readable in an afternoon.
-- **Model Agnostic** — Claude, GPT, Gemma 4, and local models (Ollama, vLLM, LM Studio, llama.cpp server) in the same team. Swap models per agent via `baseURL`.
-- **Multi-Agent Collaboration** — Agents with different roles, tools, and models collaborate through a message bus and shared memory.
-- **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`.
-- **Task Retry** — Set `maxRetries` on tasks for automatic retry with exponential backoff. Failed attempts accumulate token usage for accurate billing.
-- **Human-in-the-Loop** — Optional `onApproval` callback on `runTasks()`. After each batch of tasks completes, your callback decides whether to proceed or abort remaining work.
-- **Lifecycle Hooks** — `beforeRun` / `afterRun` on `AgentConfig`. Intercept the prompt before execution or post-process results after. Throw from either hook to abort.
-- **Loop Detection** — `loopDetection` on `AgentConfig` catches stuck agents repeating the same tool calls or text output. Configurable action: warn (default), terminate, or custom callback.
-- **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.
+- **Goal to result in one call.** `runTeam(team, "Build a REST API")` kicks off a coordinator agent that decomposes the goal into a task DAG, resolves dependencies, runs independent tasks in parallel, and synthesizes the final output. No graph to draw, no tasks to wire up.
+- **TypeScript-native, three runtime dependencies.** `@anthropic-ai/sdk`, `openai`, `zod`. That is the whole runtime. Embed in Express, Next.js, serverless functions, or CI/CD pipelines. No Python runtime, no subprocess bridge, no cloud sidecar.
+- **Multi-model teams.** Claude, GPT, Gemini, Grok, Copilot, or any OpenAI-compatible local model (Ollama, vLLM, LM Studio, llama.cpp) in the same team. Run the architect on Opus 4.6, the developer on GPT-5.4, the reviewer on local Gemma 4, all in one `runTeam()` call. Gemini ships as an optional peer dependency: `npm install @google/genai` to enable.
+
+Other features (structured output, task retry, human-in-the-loop, lifecycle hooks, loop detection, observability) live below the fold and in [`examples/`](./examples/).
+
+## Philosophy: what we build, what we don't
+
+Our goal is to be the simplest multi-agent framework for TypeScript. Simplicity does not mean closed. We believe the long-term value of a framework is the size of the network it connects to, not its feature checklist.
+
+**We build:**
+- A coordinator that decomposes a goal into a task DAG.
+- A task queue that runs independent tasks in parallel and cascades failures to dependents.
+- A shared memory and message bus so agents can see each other's output.
+- Multi-model teams where each agent can use a different LLM provider.
+
+**We don't build:**
+- **Agent handoffs.** If agent A needs to transfer mid-conversation to agent B, use [OpenAI Agents SDK](https://github.com/openai/openai-agents-python). In our model, each agent owns one task end-to-end, with no mid-conversation transfers.
+- **State persistence / checkpointing.** Not planned for now. Adding a storage backend would break the three-dependency promise, and our workflows run in seconds to minutes, not hours. If real usage shifts toward long-running workflows, we will revisit.
+
+**Tracking:**
+- **MCP support.** Next up, see [#86](https://github.com/JackChen-me/open-multi-agent/issues/86).
+- **A2A protocol.** Watching, will move when production adoption is real.
+
+See [`DECISIONS.md`](./DECISIONS.md) for the full rationale.
+
+## How is this different from X?
+
+**vs. [LangGraph JS](https://github.com/langchain-ai/langgraphjs).** LangGraph is declarative graph orchestration: you define nodes, edges, and conditional routing, then `compile()` and `invoke()`. `open-multi-agent` is goal-driven: you declare a team and a goal, a coordinator decomposes it into a task DAG at runtime. LangGraph gives you total control of topology (great for fixed production workflows). This gives you less typing and faster iteration (great for exploratory multi-agent work). LangGraph also has mature checkpointing; we do not.
+
+**vs. [CrewAI](https://github.com/crewAIInc/crewAI).** CrewAI is the mature Python choice. If your stack is Python, use CrewAI. `open-multi-agent` is TypeScript-native: three runtime dependencies, embeds directly in Node.js without a subprocess bridge. Roughly comparable capability on the orchestration side. Choose on language fit.
+
+**vs. [Vercel AI SDK](https://github.com/vercel/ai).** AI SDK is the LLM call layer: a unified TypeScript client for 60+ providers with streaming, tool calls, and structured outputs. It does not orchestrate multi-agent teams. `open-multi-agent` sits on top when you need that. They compose: use AI SDK for single-agent work, reach for this when you need a team.
+
+## Used by
+
+`open-multi-agent` is a new project (launched 2026-04-01, MIT, 5,500+ stars). The ecosystem is still forming, so the list below is short and honest:
+
+- **[temodar-agent](https://github.com/xeloxa/temodar-agent)** (~50 stars). WordPress security analysis platform by [Ali Sünbül](https://github.com/xeloxa). Uses our built-in tools (`bash`, `file_*`, `grep`) directly in its Docker runtime. Confirmed production use.
+- **[rentech-quant-platform](https://github.com/rookiecoderasz/rentech-quant-platform).** Multi-agent quant trading research platform. Five pipelines plus MCP integrations, built on top of `open-multi-agent`. Early signal, very new.
+- **Cybersecurity SOC (home lab).** A private setup running Qwen 2.5 + DeepSeek Coder entirely offline via Ollama, building an autonomous SOC pipeline on Wazuh + Proxmox. Early user, not yet public.
+
+Using `open-multi-agent` in production or a side project? [Open a discussion](https://github.com/JackChen-me/open-multi-agent/discussions) and we will list it here.
 
 ## Quick Start
 
@@ -38,6 +72,7 @@ Set the API key for your provider. Local models via Ollama require no API key
 - `ANTHROPIC_API_KEY`
 - `OPENAI_API_KEY`
 - `GEMINI_API_KEY`
+- `XAI_API_KEY` (for Grok)
 - `GITHUB_TOKEN` (for Copilot)
 
 Three agents, one goal — the framework handles the rest:
@@ -53,19 +88,8 @@ const architect: AgentConfig = {
   tools: ['file_write'],
 }
 
-const developer: AgentConfig = {
-  name: 'developer',
-  model: 'claude-sonnet-4-6',
-  systemPrompt: 'You implement what the architect designs.',
-  tools: ['bash', 'file_read', 'file_write', 'file_edit'],
-}
-
-const reviewer: AgentConfig = {
-  name: 'reviewer',
-  model: 'claude-sonnet-4-6',
-  systemPrompt: 'You review code for correctness and clarity.',
-  tools: ['file_read', 'grep'],
-}
+const developer: AgentConfig = { /* same shape, tools: ['bash', 'file_read', 'file_write', 'file_edit'] */ }
+const reviewer: AgentConfig = { /* same shape, tools: ['file_read', 'grep'] */ }
 
 const orchestrator = new OpenMultiAgent({
   defaultModel: 'claude-sonnet-4-6',
@@ -94,8 +118,8 @@ task_complete architect
 task_start developer
 task_start developer              // independent tasks run in parallel
 task_complete developer
-task_start reviewer               // unblocked after implementation
 task_complete developer
+task_start reviewer               // unblocked after implementation
 task_complete reviewer
 agent_complete coordinator        // synthesizes final result
 Success: true
@@ -110,29 +134,18 @@ Tokens: 12847 output tokens
 | Auto-orchestrated team | `runTeam()` | Give a goal, framework plans and executes |
 | Explicit pipeline | `runTasks()` | You define the task graph and assignments |
 
+For MapReduce-style fan-out without task dependencies, use `AgentPool.runParallel()` directly. See [example 07](examples/07-fan-out-aggregate.ts).
+
 ## Examples
 
-All examples are runnable scripts in [`examples/`](./examples/). Run any of them with `npx tsx`:
+15 runnable scripts in [`examples/`](./examples/). Start with these four:
 
-```bash
-npx tsx examples/01-single-agent.ts
-```
+- [02 — Team Collaboration](examples/02-team-collaboration.ts): `runTeam()` coordinator pattern.
+- [06 — Local Model](examples/06-local-model.ts): Ollama and Claude in one pipeline via `baseURL`.
+- [09 — Structured Output](examples/09-structured-output.ts): any agent returns Zod-validated JSON.
+- [11 — Trace Observability](examples/11-trace-observability.ts): `onTrace` spans for LLM calls, tools, and tasks.
 
-| Example | What it shows |
-|---------|---------------|
-| [01 — Single Agent](examples/01-single-agent.ts) | `runAgent()` one-shot, `stream()` streaming, `prompt()` multi-turn |
-| [02 — Team Collaboration](examples/02-team-collaboration.ts) | `runTeam()` auto-orchestration with coordinator pattern |
-| [03 — Task Pipeline](examples/03-task-pipeline.ts) | `runTasks()` explicit dependency graph (design → implement → test + review) |
-| [04 — Multi-Model Team](examples/04-multi-model-team.ts) | `defineTool()` custom tools, mixed Anthropic + OpenAI providers, `AgentPool` |
-| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot as an LLM provider |
-| [06 — Local Model](examples/06-local-model.ts) | Ollama + Claude in one pipeline via `baseURL` (works with vLLM, LM Studio, etc.) |
-| [07 — Fan-Out / Aggregate](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 analysts in parallel, then synthesize |
-| [08 — Gemma 4 Local](examples/08-gemma4-local.ts) | `runTasks()` + `runTeam()` with local Gemma 4 via Ollama — zero API cost |
-| [09 — Structured Output](examples/09-structured-output.ts) | `outputSchema` (Zod) on AgentConfig — validated JSON via `result.structured` |
-| [10 — Task Retry](examples/10-task-retry.ts) | `maxRetries` / `retryDelayMs` / `retryBackoff` with `task_retry` progress events |
-| [11 — Trace Observability](examples/11-trace-observability.ts) | `onTrace` callback — structured spans for LLM calls, tools, tasks, and agents |
-| [12 — Grok](examples/12-grok.ts) | Same as example 02 (`runTeam()` collaboration) with Grok (`XAI_API_KEY`) |
-| [13 — Gemini](examples/13-gemini.ts) | Gemini adapter smoke test with `gemini-2.5-flash` (`GEMINI_API_KEY`) |
+Run any with `npx tsx examples/02-team-collaboration.ts`.
 
 ## Architecture
 
@@ -247,6 +260,8 @@ Tools added via `agent.addTool()` are always available regardless of filtering.
 | Ollama / vLLM / LM Studio | `provider: 'openai'` + `baseURL` | — | Verified |
 | llama.cpp server | `provider: 'openai'` + `baseURL` | — | Verified |
 
+Gemini requires `npm install @google/genai` (optional peer dependency).
+
 Verified local models with tool-calling: **Gemma 4** (see [example 08](examples/08-gemma4-local.ts)).
 
 Any OpenAI-compatible API should work via `provider: 'openai'` + `baseURL` (DeepSeek, Groq, Mistral, Qwen, MiniMax, etc.). **Grok now has first-class support** via `provider: 'grok'`.
@@ -295,27 +310,22 @@ const grokAgent: AgentConfig = {
 
 Issues, feature requests, and PRs are welcome. Some areas where contributions would be especially valuable:
 
-- **Provider integrations** — Verify and document OpenAI-compatible providers (DeepSeek, Groq, Qwen, MiniMax, etc.) via `baseURL`. See [#25](https://github.com/JackChen-me/open-multi-agent/issues/25). For providers that are NOT OpenAI-compatible (e.g. Gemini), a new `LLMAdapter` implementation is welcome — the interface requires just two methods: `chat()` and `stream()`.
 - **Examples** — Real-world workflows and use cases.
 - **Documentation** — Guides, tutorials, and API docs.
 
-## Author
-
-> JackChen — Ex PM (¥100M+ revenue), now indie builder. Follow on [X](https://x.com/JackChen_x) for AI Agent insights.
-
 ## Contributors
 
 <a href="https://github.com/JackChen-me/open-multi-agent/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent&v=20260408" />
+  <img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent&max=20&v=20260411" />
 </a>
 
 ## Star History
 
 <a href="https://star-history.com/#JackChen-me/open-multi-agent&Date">
  <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&theme=dark&v=20260408" />
-   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&v=20260408" />
-   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&v=20260408" />
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date" />
  </picture>
 </a>
 

README_zh.md

@@ -1,8 +1,10 @@
 # Open Multi-Agent
 
-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。
+面向 TypeScript 的轻量多智能体编排引擎。3 个运行时依赖，零配置，一次 `runTeam()` 调用从目标到结果。
 
-3 个运行时依赖 · 33 个源文件 · Node.js 能跑的地方都能部署 · 被 [Latent Space](https://www.latent.space/p/ainews-a-quiet-april-fools) AI News 提及（AI 工程领域头部 Newsletter，17 万+订阅者）
+CrewAI 是 Python。LangGraph 需要你自己画图。`open-multi-agent` 是你现有 Node.js 后端里 `npm install` 一下就能用的那一层。当你需要让一支 agent 团队围绕一个目标协作时，只提供这个，不多不少。
+
+3 个运行时依赖 · 35 个源文件 · Node.js 能跑的地方都能部署 · 被 [Latent Space](https://www.latent.space/p/ainews-a-quiet-april-fools) AI News 提及（AI 工程领域头部 Newsletter，17 万+订阅者）
 
 [![GitHub stars](https://img.shields.io/github/stars/JackChen-me/open-multi-agent)](https://github.com/JackChen-me/open-multi-agent/stargazers)
 [![license](https://img.shields.io/github/license/JackChen-me/open-multi-agent)](./LICENSE)
@@ -11,19 +13,51 @@ TypeScript 多智能体编排框架。一次 `runTeam()` 调用从目标到结
 
 [English](./README.md) | **中文**
 
-## 为什么选择 Open Multi-Agent？
+## 你真正得到的三件事
 
-- **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。
-- **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。
-- **可审计、极轻量** — 3 个运行时依赖（`@anthropic-ai/sdk`、`openai`、`zod`），33 个源文件。一个下午就能读完全部源码。
-- **模型无关** — Claude、GPT、Gemma 4 和本地模型（Ollama、vLLM、LM Studio、llama.cpp server）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。
-- **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。
-- **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。
-- **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。
-- **人机协同** — `runTasks()` 支持可选的 `onApproval` 回调。每批任务完成后，由你的回调决定是否继续执行后续任务。
-- **生命周期钩子** — `AgentConfig` 上的 `beforeRun` / `afterRun`。在执行前拦截 prompt，或在执行后处理结果。从钩子中 throw 可中止运行。
-- **循环检测** — `AgentConfig` 上的 `loopDetection` 可检测智能体重复相同工具调用或文本输出的卡死循环。可配置行为：警告（默认）、终止、或自定义回调。
-- **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。
+- **一次调用从目标到结果。** `runTeam(team, "构建一个 REST API")` 启动一个协调者 agent，把目标拆成任务 DAG，解析依赖，独立任务并行执行，最终合成输出。不需要画图，不需要手动连任务。
+- **TypeScript 原生，3 个运行时依赖。** `@anthropic-ai/sdk`、`openai`、`zod`。这就是全部运行时。可嵌入 Express、Next.js、Serverless 函数或 CI/CD 流水线。没有 Python 运行时，没有子进程桥接，没有云端 sidecar。
+- **多模型团队。** Claude、GPT、Gemini、Grok、Copilot，或任何 OpenAI 兼容的本地模型（Ollama、vLLM、LM Studio、llama.cpp）可以在同一个团队中使用。让架构师用 Opus 4.6，开发者用 GPT-5.4，评审用本地的 Gemma 4，一次 `runTeam()` 调用全部搞定。Gemini 作为 optional peer dependency 提供：使用前需 `npm install @google/genai`。
+
+其他能力（结构化输出、任务重试、人机协同、生命周期钩子、循环检测、可观测性）在下方章节和 [`examples/`](./examples/) 里。
+
+## 哲学：我们做什么，不做什么
+
+我们的目标是做 TypeScript 生态里最简单的多智能体框架。简单不等于封闭。框架的长期价值不在于功能清单的长度，而在于它连接的网络有多大。
+
+**我们做：**
+- 一个协调者，把目标拆成任务 DAG。
+- 一个任务队列，独立任务并行执行，失败级联到下游。
+- 共享内存和消息总线，让 agent 之间能看到彼此的输出。
+- 多模型团队，每个 agent 可以用不同的 LLM provider。
+
+**我们不做：**
+- **Agent Handoffs。** 如果 agent A 需要把对话中途交接给 agent B，去用 [OpenAI Agents SDK](https://github.com/openai/openai-agents-python)。在我们的模型里，每个 agent 完整负责自己的任务，不会中途交接。
+- **状态持久化 / 检查点。** 短期内不做。加存储后端会打破 3 个依赖的承诺，而且我们的工作流执行时间是秒到分钟级，不是小时级。如果真实使用场景转向长时间工作流，我们会重新评估。
+
+**正在跟踪：**
+- **MCP 支持。** 下一个要做的，见 [#86](https://github.com/JackChen-me/open-multi-agent/issues/86)。
+- **A2A 协议。** 观望中，等生产级采纳到位再行动。
+
+完整理由见 [`DECISIONS.md`](./DECISIONS.md)。
+
+## 和 X 有什么不同？
+
+**vs. [LangGraph JS](https://github.com/langchain-ai/langgraphjs)。** LangGraph 是声明式图编排：你定义节点、边、条件路由，然后 `compile()` + `invoke()`。`open-multi-agent` 是目标驱动：你声明团队和目标，协调者在运行时把目标拆成任务 DAG。LangGraph 给你完全的拓扑控制（适合固定的生产工作流）。这个框架代码更少、迭代更快（适合探索型多智能体协作）。LangGraph 还有成熟的检查点能力，我们没有。
+
+**vs. [CrewAI](https://github.com/crewAIInc/crewAI)。** CrewAI 是成熟的 Python 选择。如果你的技术栈是 Python，用 CrewAI。`open-multi-agent` 是 TypeScript 原生：3 个运行时依赖，直接嵌入 Node.js，不需要子进程桥接。编排能力大致相当，按语言契合度选。
+
+**vs. [Vercel AI SDK](https://github.com/vercel/ai)。** AI SDK 是 LLM 调用层：统一的 TypeScript 客户端，支持 60+ provider，带流式、tool calls、结构化输出。它不做多智能体编排。`open-multi-agent` 需要多 agent 时叠在它之上。两者互补：单 agent 用 AI SDK，需要团队用这个。
+
+## 谁在用
+
+`open-multi-agent` 是一个新项目（2026-04-01 发布，MIT 许可，5,500+ stars）。生态还在成形，下面这份列表很短，但都真实：
+
+- **[temodar-agent](https://github.com/xeloxa/temodar-agent)**（约 50 stars）。WordPress 安全分析平台，作者 [Ali Sünbül](https://github.com/xeloxa)。在 Docker runtime 里直接使用我们的内置工具（`bash`、`file_*`、`grep`）。已确认生产环境使用。
+- **[rentech-quant-platform](https://github.com/rookiecoderasz/rentech-quant-platform)。** 多智能体量化交易研究平台，5 条管线 + MCP 集成，基于 `open-multi-agent` 构建。早期信号，项目非常新。
+- **家用服务器 Cybersecurity SOC。** 本地完全离线运行 Qwen 2.5 + DeepSeek Coder（通过 Ollama），在 Wazuh + Proxmox 上构建自主 SOC 流水线。早期用户，未公开。
+
+你在生产环境或 side project 里用 `open-multi-agent` 吗？[开一个 Discussion](https://github.com/JackChen-me/open-multi-agent/discussions)，我们会把你列上来。
 
 ## 快速开始
 
@@ -54,19 +88,8 @@ const architect: AgentConfig = {
   tools: ['file_write'],
 }
 
-const developer: AgentConfig = {
-  name: 'developer',
-  model: 'claude-sonnet-4-6',
-  systemPrompt: 'You implement what the architect designs.',
-  tools: ['bash', 'file_read', 'file_write', 'file_edit'],
-}
-
-const reviewer: AgentConfig = {
-  name: 'reviewer',
-  model: 'claude-sonnet-4-6',
-  systemPrompt: 'You review code for correctness and clarity.',
-  tools: ['file_read', 'grep'],
-}
+const developer: AgentConfig = { /* 同样结构，tools: ['bash', 'file_read', 'file_write', 'file_edit'] */ }
+const reviewer: AgentConfig = { /* 同样结构，tools: ['file_read', 'grep'] */ }
 
 const orchestrator = new OpenMultiAgent({
   defaultModel: 'claude-sonnet-4-6',
@@ -82,8 +105,8 @@ const team = orchestrator.createTeam('api-team', {
 // 描述一个目标——框架将其拆解为任务并编排执行
 const result = await orchestrator.runTeam(team, 'Create a REST API for a todo list in /tmp/todo-api/')
 
-console.log(`成功: ${result.success}`)
-console.log(`Token 用量: ${result.totalTokenUsage.output_tokens} output tokens`)
+console.log(`Success: ${result.success}`)
+console.log(`Tokens: ${result.totalTokenUsage.output_tokens} output tokens`)
 ```
 
 执行过程：
@@ -95,8 +118,8 @@ task_complete architect
 task_start developer
 task_start developer              // 无依赖的任务并行执行
 task_complete developer
-task_start reviewer               // 实现完成后自动解锁
 task_complete developer
+task_start reviewer               // 实现完成后自动解锁
 task_complete reviewer
 agent_complete coordinator        // 综合所有结果
 Success: true
@@ -111,29 +134,18 @@ Tokens: 12847 output tokens
 | 自动编排团队 | `runTeam()` | 给一个目标，框架自动规划和执行 |
 | 显式任务管线 | `runTasks()` | 你自己定义任务图和分配 |
 
+如果需要 MapReduce 风格的扇出而不涉及任务依赖，直接使用 `AgentPool.runParallel()`。参见[示例 07](examples/07-fan-out-aggregate.ts)。
+
 ## 示例
 
-所有示例都是可运行脚本，位于 [`examples/`](./examples/) 目录。使用 `npx tsx` 运行：
+[`examples/`](./examples/) 里有 15 个可运行脚本。推荐从这 4 个开始：
 
-```bash
-npx tsx examples/01-single-agent.ts
-```
+- [02 — 团队协作](examples/02-team-collaboration.ts)：`runTeam()` 协调者模式。
+- [06 — 本地模型](examples/06-local-model.ts)：通过 `baseURL` 把 Ollama 和 Claude 放在同一条管线。
+- [09 — 结构化输出](examples/09-structured-output.ts)：任意 agent 产出 Zod 校验过的 JSON。
+- [11 — 可观测性](examples/11-trace-observability.ts)：`onTrace` 回调，为 LLM 调用、工具、任务发出结构化 span。
 
-| 示例 | 展示内容 |
-|------|----------|
-| [01 — 单智能体](examples/01-single-agent.ts) | `runAgent()` 单次调用、`stream()` 流式输出、`prompt()` 多轮对话 |
-| [02 — 团队协作](examples/02-team-collaboration.ts) | `runTeam()` 自动编排 + 协调者模式 |
-| [03 — 任务流水线](examples/03-task-pipeline.ts) | `runTasks()` 显式依赖图（设计 → 实现 → 测试 + 评审） |
-| [04 — 多模型团队](examples/04-multi-model-team.ts) | `defineTool()` 自定义工具、Anthropic + OpenAI 混合、`AgentPool` |
-| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot 作为 LLM 提供者 |
-| [06 — 本地模型](examples/06-local-model.ts) | Ollama + Claude 混合流水线，通过 `baseURL` 接入（兼容 vLLM、LM Studio 等） |
-| [07 — 扇出聚合](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 个分析师并行，然后综合 |
-| [08 — Gemma 4 本地](examples/08-gemma4-local.ts) | `runTasks()` + `runTeam()` 本地 Gemma 4 via Ollama — 零 API 费用 |
-| [09 — 结构化输出](examples/09-structured-output.ts) | `outputSchema`（Zod）— 校验 JSON 输出，通过 `result.structured` 获取 |
-| [10 — 任务重试](examples/10-task-retry.ts) | `maxRetries` / `retryDelayMs` / `retryBackoff` + `task_retry` 进度事件 |
-| [11 — 可观测性](examples/11-trace-observability.ts) | `onTrace` 回调 — LLM 调用、工具、任务、智能体的结构化 span 事件 |
-| [12 — Grok](examples/12-grok.ts) | 同示例 02（`runTeam()` 团队协作），使用 Grok（`XAI_API_KEY`） |
-| [13 — Gemini](examples/13-gemini.ts) | Gemini 适配器测试，使用 `gemini-2.5-flash`（`GEMINI_API_KEY`） |
+用 `npx tsx examples/02-team-collaboration.ts` 运行任意一个。
 
 ## 架构
 
@@ -188,6 +200,54 @@ npx tsx examples/01-single-agent.ts
 | `file_edit` | 通过精确字符串匹配编辑文件。 |
 | `grep` | 使用正则表达式搜索文件内容。优先使用 ripgrep，回退到 Node.js 实现。 |
 
+## 工具配置
+
+可以通过预设、白名单和黑名单对 agent 的工具访问进行精细控制。
+
+### 工具预设
+
+为常见场景预定义的工具组合：
+
+```typescript
+const readonlyAgent: AgentConfig = {
+  name: 'reader',
+  model: 'claude-sonnet-4-6',
+  toolPreset: 'readonly',  // file_read, grep, glob
+}
+
+const readwriteAgent: AgentConfig = {
+  name: 'editor',
+  model: 'claude-sonnet-4-6',
+  toolPreset: 'readwrite',  // file_read, file_write, file_edit, grep, glob
+}
+
+const fullAgent: AgentConfig = {
+  name: 'executor',
+  model: 'claude-sonnet-4-6',
+  toolPreset: 'full',  // file_read, file_write, file_edit, grep, glob, bash
+}
+```
+
+### 高级过滤
+
+将预设与白名单、黑名单组合，实现精确控制：
+
+```typescript
+const customAgent: AgentConfig = {
+  name: 'custom',
+  model: 'claude-sonnet-4-6',
+  toolPreset: 'readwrite',        // 起点：file_read, file_write, file_edit, grep, glob
+  tools: ['file_read', 'grep'],   // 白名单：与预设取交集 = file_read, grep
+  disallowedTools: ['grep'],      // 黑名单：再减去 = 只剩 file_read
+}
+```
+
+**解析顺序：** preset → allowlist → denylist → 框架安全护栏。
+
+### 自定义工具
+
+通过 `agent.addTool()` 添加的工具始终可用，不受过滤规则影响。
+
 ## 支持的 Provider
 
 | Provider | 配置 | 环境变量 | 状态 |
@@ -200,6 +260,8 @@ npx tsx examples/01-single-agent.ts
 | Ollama / vLLM / LM Studio | `provider: 'openai'` + `baseURL` | — | 已验证 |
 | llama.cpp server | `provider: 'openai'` + `baseURL` | — | 已验证 |
 
+Gemini 需要 `npm install @google/genai`（optional peer dependency）。
+
 已验证支持 tool-calling 的本地模型：**Gemma 4**（见[示例 08](examples/08-gemma4-local.ts)）。
 
 任何 OpenAI 兼容 API 均可通过 `provider: 'openai'` + `baseURL` 接入（DeepSeek、Groq、Mistral、Qwen、MiniMax 等）。**Grok 现已原生支持**，使用 `provider: 'grok'`。
@@ -248,27 +310,22 @@ const grokAgent: AgentConfig = {
 
 欢迎提 Issue、功能需求和 PR。以下方向的贡献尤其有价值：
 
-- **Provider 集成** — 验证并文档化 OpenAI 兼容 Provider（DeepSeek、Groq、Qwen、MiniMax 等）通过 `baseURL` 接入。详见 [#25](https://github.com/JackChen-me/open-multi-agent/issues/25)。对于非 OpenAI 兼容的 Provider，欢迎贡献新的 `LLMAdapter` 实现——接口只需两个方法：`chat()` 和 `stream()`。
 - **示例** — 真实场景的工作流和用例。
 - **文档** — 指南、教程和 API 文档。
 
-## 作者
-
-> JackChen — 前 WPS 产品经理，现独立创业者。关注小红书[「杰克西｜硅基杠杆」](https://www.xiaohongshu.com/user/profile/5a1bdc1e4eacab4aa39ea6d6)，持续获取我的 AI Agent 观点和思考。
-
 ## 贡献者
 
 <a href="https://github.com/JackChen-me/open-multi-agent/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent&v=20260408" />
+  <img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent&max=20&v=20260411" />
 </a>
 
 ## Star 趋势
 
 <a href="https://star-history.com/#JackChen-me/open-multi-agent&Date">
  <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&theme=dark&v=20260408" />
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-   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&v=20260408" />
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date&theme=dark" />
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+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=JackChen-me/open-multi-agent&type=Date" />
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package.json

@@ -1,6 +1,6 @@
 {
   "name": "@jackchen_me/open-multi-agent",
-  "version": "1.0.1",
+  "version": "1.1.0",
   "description": "TypeScript multi-agent framework — one runTeam() call from goal to result. Auto task decomposition, parallel execution. 3 dependencies, deploys anywhere Node.js runs.",
   "files": [
     "dist",