270 lines
7.6 KiB
Python
270 lines
7.6 KiB
Python
#!/usr/bin/env python3
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"""
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Agent协作演示
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展示如何让不同AI agent以真实身份协作完成任务
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这个演示模拟以下场景：
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1. 架构师agent设计系统架构
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2. 开发者agent实现核心功能
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3. 运维agent配置部署
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4. 研究员agent撰写文档
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每个步骤都有真实的git提交记录
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"""
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import os
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import subprocess
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import time
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from pathlib import Path
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from agent_identity_manager import AgentIdentityManager
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class AgentCollaborationDemo:
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def __init__(self):
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self.manager = AgentIdentityManager()
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self.base_dir = Path("/home/ben/github/liurenchaxin")
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def create_demo_files(self):
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"""创建演示用的文件"""
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demo_dir = self.base_dir / "demo_feature"
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demo_dir.mkdir(exist_ok=True)
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# 架构师的设计文档
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architecture_file = demo_dir / "architecture.md"
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architecture_content = """# 新功能架构设计
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## 概述
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设计一个智能监控系统，用于跟踪AI agent的工作状态。
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## 组件设计
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- 状态收集器：收集各agent的运行状态
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- 分析引擎：分析agent行为模式
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- 告警系统：异常行为实时通知
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## 技术栈
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- Python 3.9+
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- Redis作为消息队列
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- PostgreSQL存储状态数据
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- FastAPI提供REST接口
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"""
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architecture_file.write_text(architecture_content)
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# 开发者的实现代码
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core_file = demo_dir / "monitor.py"
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core_content = """#!/usr/bin/env python3
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import asyncio
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import json
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from datetime import datetime
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from typing import Dict, Any
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class AgentMonitor:
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def __init__(self):
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self.agents_status = {}
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async def collect_status(self, agent_name: str) -> Dict[str, Any]:
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return {
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"name": agent_name,
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"timestamp": datetime.now().isoformat(),
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"status": "active",
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"tasks_completed": 0
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}
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async def run(self):
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while True:
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# 模拟状态收集
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await asyncio.sleep(1)
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if __name__ == "__main__":
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monitor = AgentMonitor()
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asyncio.run(monitor.run())
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"""
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core_file.write_text(core_content)
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# 运维的配置文件
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config_file = demo_dir / "deploy.yaml"
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config_content = """version: '3.8'
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services:
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agent-monitor:
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build: .
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ports:
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- "8000:8000"
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environment:
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- REDIS_URL=redis://redis:6379
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- DB_URL=postgresql://user:pass@postgres:5432/agentdb
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depends_on:
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- redis
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- postgres
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redis:
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image: redis:alpine
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ports:
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- "6379:6379"
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postgres:
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image: postgres:13
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environment:
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POSTGRES_DB: agentdb
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POSTGRES_USER: user
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POSTGRES_PASSWORD: pass
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"""
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config_file.write_text(config_content)
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# 研究员的文档
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docs_file = demo_dir / "usage_guide.md"
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docs_content = """# Agent监控系统使用指南
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## 快速开始
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### 1. 启动监控服务
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```bash
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docker-compose up -d
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```
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### 2. 查看agent状态
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```bash
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curl http://localhost:8000/api/agents
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```
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### 3. 配置告警
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编辑 `config/alerts.yaml` 文件设置告警规则。
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## API文档
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### GET /api/agents
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获取所有agent的当前状态
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### POST /api/agents/{name}/task
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记录agent完成的任务
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"""
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docs_file.write_text(docs_content)
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return [architecture_file, core_file, config_file, docs_file]
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def run_collaboration_demo(self):
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"""运行协作演示"""
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print("🎭 开始Agent协作演示")
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print("=" * 50)
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# 1. 架构师设计
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print("1️⃣ 架构师agent开始设计...")
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files = self.create_demo_files()
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self.manager.commit_as_agent(
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"claude-ai",
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"📐 设计智能监控系统架构 - 添加架构设计文档",
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[str(f) for f in files[:1]]
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)
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time.sleep(1)
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# 2. 开发者实现
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print("2️⃣ 开发者agent开始编码...")
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self.manager.commit_as_agent(
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"gemini-dev",
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"💻 实现监控系统核心功能 - 添加AgentMonitor类",
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[str(files[1])]
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)
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time.sleep(1)
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# 3. 运维配置
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print("3️⃣ 运维agent配置部署...")
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self.manager.commit_as_agent(
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"qwen-ops",
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"⚙️ 添加Docker部署配置 - 支持一键启动",
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[str(files[2])]
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)
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time.sleep(1)
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# 4. 研究员文档
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print("4️⃣ 研究员agent撰写文档...")
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self.manager.commit_as_agent(
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"llama-research",
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"📚 完善使用文档 - 添加API说明和快速指南",
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[str(files[3])]
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)
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time.sleep(1)
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# 5. 架构师review
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print("5️⃣ 架构师review并优化...")
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optimize_file = self.base_dir / "demo_feature" / "optimization.md"
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optimize_content = """# 架构优化建议
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基于实现代码的review，提出以下优化：
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## 性能优化
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- 使用asyncio.create_task替换直接调用
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- 添加连接池管理
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## 监控增强
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- 添加prometheus指标收集
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- 实现健康检查端点
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## 下一步计划
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1. 实现告警系统
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2. 添加Web界面
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3. 集成日志分析
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"""
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optimize_file.write_text(optimize_content)
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self.manager.commit_as_agent(
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"claude-ai",
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"🔍 架构review - 提出性能优化和监控增强建议",
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[str(optimize_file)]
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)
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print("\n✅ 协作演示完成！")
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def show_git_history(self):
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"""显示git提交历史"""
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print("\n📊 Git提交历史（按agent分组）:")
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print("=" * 50)
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for agent_name in ["claude-ai", "gemini-dev", "qwen-ops", "llama-research"]:
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stats = self.manager.get_agent_stats(agent_name)
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if stats["commits"]:
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print(f"\n👤 {agent_name}:")
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for commit in stats["commits"]:
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parts = commit.split("|", 4)
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if len(parts) >= 5:
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hash_id, name, email, date, message = parts
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print(f" {hash_id[:8]} {date} {message}")
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def cleanup_demo(self):
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"""清理演示文件"""
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demo_dir = self.base_dir / "demo_feature"
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if demo_dir.exists():
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# 保留git历史，只移除工作区文件
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subprocess.run(["git", "rm", "-rf", "demo_feature"],
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cwd=self.base_dir, capture_output=True)
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subprocess.run(["git", "commit", "-m", "🧹 清理演示文件 - 保留协作历史"],
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cwd=self.base_dir, capture_output=True)
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print("🧹 演示文件已清理，git历史保留")
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def main():
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"""主函数"""
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demo = AgentCollaborationDemo()
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print("🎭 AI Agent协作演示")
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print("=" * 50)
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print("这个演示将展示如何让不同agent以真实身份协作")
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print("每个agent都有独立的git身份和提交记录")
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print("")
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# 检查agent是否已创建
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if not demo.manager.list_agents():
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print("❌ 请先运行 ./agents/setup_agents.sh 创建agent")
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return
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# 运行演示
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demo.run_collaboration_demo()
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demo.show_git_history()
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print("\n💡 下一步:")
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print("1. 查看git log --oneline --graph 查看提交历史")
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print("2. 使用 ./agents/stats.sh 查看agent统计")
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print("3. 开始你自己的多agent协作项目！")
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# 询问是否清理
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response = input("\n是否清理演示文件？(y/N): ")
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if response.lower() == 'y':
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demo.cleanup_demo()
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if __name__ == "__main__":
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main() |