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multi-agent多智能体编排

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).

作者: admin | 来源: ClawHub
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V 0.1.0
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multi-agent

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 OpenClaws native capabilities:

OpenClaw FeatureMulti-Agent Usage
sessionsspawnSpawn real worker agents
sessionssend
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. 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

标签

skill ai

通过对话安装

该技能支持在以下平台通过对话安装:

OpenClaw WorkBuddy QClaw Kimi Claude

方式一:安装 SkillHub 和技能

帮我安装 SkillHub 和 multi-agent-1775993523 技能

方式二:设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源,然后帮我安装 multi-agent-1775993523 技能

通过命令行安装

skillhub install multi-agent-1775993523

下载

⬇ 下载 multi-agent v0.1.0(免费)

文件大小: 36.09 KB | 发布时间: 2026-4-13 11:07

v0.1.0 最新 2026-4-13 11:07
Initial release: Production-ready coordinator mode with four-phase workflow (Research → Synthesis → Implementation → Verification), real parallel worker spawning via sessions_spawn, XML task notifications, and state persistence.

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