--- name: multi-agent description: Production-ready multi-agent orchestration system for OpenClaw. Implements Coordinator Mode with real parallel worker spawning via sessions_spawn, XML task notifications, state persistence, and four-phase workflow (Research → Synthesis → Implementation → Verification). --- # Multi-Agent Skill (Phase 2.5 - Production Ready) 生产级多智能体协调系统，支持真实的并行 Worker 执行和完整的四阶段工作流。 ## Quick Start ### 1. 准备 Worker ```bash cd skills/multi-agent python3 scripts/coordinator_v2.py prepare "Your task description" --role researcher ``` 这会生成： - Worker 规格文件 `.openclaw/scratchpad/workers/{id}.json` - Worker 提示词 `.openclaw/scratchpad/prompts/prompt-{id}.txt` ### 2. 派生 Worker（真实执行） ```bash # 读取生成的 prompt 并派生 prompt=$(cat .openclaw/scratchpad/prompts/prompt-{worker-id}.txt) sessions_spawn --label "multi-agent-worker-{worker-id}" \ --task "$prompt" \ --timeout 300 \ --cleanup keep ``` ### 3. 处理完成通知当 Worker 完成时，它会输出 XML 格式的通知。收集并处理： ```bash python3 scripts/coordinator_v2.py notify {worker-id} --file notification.xml ``` ### 4. 生成规格文档 ```bash # 从已完成的 Research Workers 生成规格 python3 scripts/coordinator_v2.py spec {worker-id-1} {worker-id-2} {worker-id-3} ``` ### 5. 运行演示 ```bash # 四阶段工作流演示（模拟执行） python3 scripts/demo_workflow.py "Your task here" ``` ## Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ COORDINATOR │ │ - spawn_worker() : Prepare worker spec and prompt │ │ - process_notification() : Handle worker completion │ │ - generate_spec() : Synthesize findings from workers │ └────────────────────┬────────────────────────────────────────────┘ │ ┌────────────┼────────────┐ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ Worker 1│ │ Worker 2│ │ Worker 3│ ... (parallel) │(Research│ │(Research│ │(Research│ │ 1) │ │ 2) │ │ 3) │ └────┬────┘ └────┬────┘ └────┬────┘ │ │ │ └────────────┼────────────┘ ▼ ┌─────────────────┐ │ SYNTHESIS │ Coordinator generates spec │ (generate_spec)│ └────────┬────────┘ ▼ ┌───────────┴───────────┐ ▼ ▼ ┌─────────┐ ┌─────────┐ │Worker 4 │ │Worker 5 │ │(Impl 1) │ │(Impl 2) │ └────┬────┘ └────┬────┘ │ │ └──────────┬───────────┘ ▼ ┌─────────────────┐ │ VERIFICATION │ │ (Worker 6, 7...)│ └─────────────────┘ ``` ## File Structure ``` skills/multi-agent/ ├── SKILL.md # 本文件 ├── test-report-phase2.5.md # 测试报告 ├── scripts/ │ ├── coordinator_v2.py # ⭐ 主协调器（生产级） │ ├── demo_workflow.py # 四阶段工作流演示 │ ├── coordinator.py # Phase 1: 模拟版 │ ├── coordinator_phase2.py # Phase 2: 过渡版 │ ├── worker.py # Worker 参考实现 │ └── protocol.py # XML 协议 └── references/ └── ARCHITECTURE.md # 架构设计文档 .openclaw/scratchpad/ # 运行时生成的共享知识 ├── workers/ # Worker 状态 ├── results/ # Worker 结果 ├── specs/ # 规格文档 ├── prompts/ # Worker 提示词 └── coordinator_state.json # 协调器状态 ``` ## XML Protocol Worker 必须按以下格式返回结果： ```xml

{worker-id}

completed|failed

One-line summary

Detailed findings, changes made, or test results... Include specific file paths and code snippets. ``` ## Four-Phase Workflow ### Phase 1: Research (并行探索) - 派生 2-4 个 Researcher Worker - 每个从不同角度探索问题 - 并行执行，收集发现 ### Phase 2: Synthesis (综合) - Coordinator 读取所有 Researcher 的发现 - 生成 Implementation Specification - 定义具体的实现步骤 ### Phase 3: Implementation (实现) - 派生 1-2 个 Implementer Worker - 基于规格执行代码修改 - 可以并行处理不同模块 ### Phase 4: Verification (验证) - 派生 1-2 个 Verifier Worker - 运行测试，检查回归 - 验证实现正确性 ## Commands ### coordinator_v2.py ```bash # 准备 Worker（创建规格和提示词） python3 coordinator_v2.py prepare "Task description" --role researcher # 处理 Worker 完成通知 python3 coordinator_v2.py notify {worker-id} --file notification.xml # 列出 Workers python3 coordinator_v2.py list python3 coordinator_v2.py list --status completed # 从 Workers 生成规格 python3 coordinator_v2.py spec {id1} {id2} {id3} ``` ### demo_workflow.py ```bash # 运行完整演示（模拟执行） python3 demo_workflow.py "Your task" # 查看真实使用示例 python3 demo_workflow.py --real ``` ## Integration with OpenClaw This skill leverages OpenClaw's native capabilities: | OpenClaw Feature | Multi-Agent Usage | |-----------------|-------------------| | `sessions_spawn` | Spawn real worker agents | | `sessions_send` | Send messages to workers | | `sessions_list` | List active workers | | `sessions_history` | Collect worker results | ## State Persistence - Worker 状态自动保存到 `.openclaw/scratchpad/workers/` - Coordinator 状态保存到 `.openclaw/scratchpad/coordinator_state.json` - 支持断点续传：重启后可以恢复之前的 Workers ## Testing ```bash # 运行演示 python3 scripts/demo_workflow.py # 检查生成的文件 ls -la .openclaw/scratchpad/ cat .openclaw/scratchpad/specs/spec-*.md ``` ## Next Steps 1. **Use it**: 用真实任务测试四阶段工作流 2. **Improve prompts**: 优化 Worker 提示词模板 3. **Add features**: 实现 Agent Teams（Phase 3） 4. **Monitor**: 添加 Token 消耗和耗时统计 ## References - [Architecture Design](references/ARCHITECTURE.md) - [Test Report](test-report-phase2.5.md) - [Claude Code Coordinator Mode](https://zread.ai/instructkr/claude-code/19-coordinator-mode)

Multi-Agent Coordinator

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