AutoGPT框架深度解析：2025年自主AI代理的前沿技术

┌─────────────────────────────────────────────────────────────────────────────┐
│                                AutoGPT 架构                                 │
├─────────────┬─────────────┬─────────────────────────────┬───────────────────┤
│ 用户界面层  │ Web UI      │ 命令行接口 (CLI)            │ SDK/API接口       │
├─────────────┼─────────────┼─────────────────────────────┼───────────────────┤
│ 代理核心层  │ 目标设定模块│ 规划模块                    │ 执行模块          │
│             │ 反思模块    │ 记忆管理模块                │ 工具使用模块      │
├─────────────┼─────────────┼─────────────────────────────┼───────────────────┤
│ 认知能力层  │ 推理引擎    │ 学习引擎                    │ 决策引擎          │
│             │ 知识图谱    │ 元认知引擎                  │ 创造力引擎        │
├─────────────┼─────────────┼─────────────────────────────┼───────────────────┤
│ 工具集成层  │ 搜索工具    │ 代码执行器                  │ 文件操作工具      │
│             │ API集成器   │ 数据处理工具                │ 第三方服务连接器  │
├─────────────┼─────────────┼─────────────────────────────┼───────────────────┤
│ 基础设施层  │ 计算资源管理│ 数据存储                    │ 网络通信          │
│             │ 日志系统    │ 配置管理                    │ 安全系统          │
└─────────────┴─────────────┴─────────────────────────────┴───────────────────┘

# AutoGPT自主任务规划与执行的核心代码示例
from autogpt import AutoGPT, Task, Goal, Memory, Toolkit

# 创建一个简单的AutoGPT代理
def create_simple_autogpt_agent():
    # 初始化记忆系统
    memory = Memory(
        type="vector",
        params={
            "embedding_model": "text-embedding-ada-002",
            "vector_store": "chroma",
            "persist_directory": "./memory"
        }
    )
    
    # 初始化工具包
    toolkit = Toolkit(
        tools=[
            "search",  # 网络搜索工具
            "file_operations",  # 文件操作工具
            "code_execution",  # 代码执行工具
            "api_integration"  # API集成工具
        ]
    )
    
    # 初始化AutoGPT代理
    agent = AutoGPT(
        name="ResearchAssistant",
        role="A helpful research assistant that can gather information and create reports",
        memory=memory,
        toolkit=toolkit,
        config={
            "temperature": 0.7,
            "max_iterations": 50,
            "verbose": True,
            "self_reflection": True,
            "save_memory": True
        }
    )
    
    print("AutoGPT agent created successfully.")
    return agent

# 为代理设置目标和任务
def set_agent_goals(agent):
    # 创建目标
    goals = [
        Goal("Research the latest trends in AI agent technology in 2025"),
        Goal("Identify the key players and innovations in the field"),
        Goal("Create a comprehensive report with insights and future predictions")
    ]
    
    # 设置代理的目标
    agent.set_goals(goals)
    
    print(f"Agent goals set: {[goal.text for goal in goals]}")
    return agent

# 运行AutoGPT代理
def run_autogpt_agent(agent):
    # 启动代理
    print("Starting AutoGPT agent...")
    
    # 代理开始自主工作
    result = agent.run()
    
    # 输出结果
    print(f"\nAgent completed its tasks with status: {result['status']}")
    
    # 如果代理成功完成任务，显示成果
    if result['status'] == 'success':
        print("\nAgent's achievements:")
        for achievement in result['achievements']:
            print(f"- {achievement}")
        
        # 显示代理生成的文件
        if 'files_created' in result:
            print("\nFiles created by the agent:")
            for file_path in result['files_created']:
                print(f"- {file_path}")
        
        # 显示代理的反思
        if 'reflections' in result:
            print("\nAgent's reflections:")
            for reflection in result['reflections']:
                print(f"- {reflection}")
    else:
        # 如果代理失败，显示错误信息
        print(f"\nAgent failed with error: {result['error']}")
    
    return result

# 创建和运行AutoGPT代理
if __name__ == "__main__":
    # 创建代理
    agent = create_simple_autogpt_agent()
    
    # 设置目标
    agent = set_agent_goals(agent)
    
    # 运行代理
    result = run_autogpt_agent(agent)
    
    # 保存代理的记忆和配置
    agent.save_state("./agent_state.json")
    print("Agent state saved successfully.")

# AutoGPT长期记忆与知识管理的核心代码示例
from autogpt import Memory, MemoryItem, MemoryManager

# 创建和管理记忆系统
def create_and_manage_memory():
    # 初始化记忆系统
    memory = Memory(
        type="vector",
        params={
            "embedding_model": "text-embedding-ada-002",
            "vector_store": "chroma",
            "persist_directory": "./memory_db",
            "collection_name": "agent_knowledge"
        }
    )
    
    # 初始化记忆管理器
    memory_manager = MemoryManager(memory)
    
    print("Memory system initialized successfully.")
    return memory, memory_manager

# 添加记忆项
def add_memory_items(memory_manager):
    # 创建一些记忆项
    memory_items = [
        MemoryItem(
            content="The latest version of AutoGPT (v2.5) was released in July 2025 with enhanced multi-agent collaboration features.",
            metadata={"source": "official_release", "date": "2025-07-15"}
        ),
        MemoryItem(
            content="AutoGPT can be integrated with external tools like GitHub, Notion, and Slack through its plugin system.",
            metadata={"source": "documentation", "category": "integration"}
        ),
        MemoryItem(
            content="The most effective way to prompt AutoGPT is to provide clear, specific goals with measurable outcomes.",
            metadata={"source": "best_practices", "category": "prompting"}
        ),
        MemoryItem(
            content="Common challenges when using AutoGPT include hallucinations, infinite loops, and API rate limits.",
            metadata={"source": "user_experiences", "category": "challenges"}
        )
    ]
    
    # 添加记忆项到记忆系统
    for item in memory_items:
        memory_id = memory_manager.add_memory(item)
        print(f"Added memory item with ID: {memory_id}")
    
    # 保存记忆
    memory_manager.save_memory()
    print("Memory saved successfully.")
    
    return memory_manager

# 检索和使用记忆
def retrieve_and_use_memory(memory_manager):
    # 搜索相关记忆
    query = "What are the key features of the latest AutoGPT version?"
    search_results = memory_manager.search_memory(query, top_k=2)
    
    print(f"\nSearch results for query: '{query}'")
    for i, result in enumerate(search_results, 1):
        print(f"{i}. Score: {result['score']:.4f}, Content: {result['content'][:100]}...")
    
    # 根据上下文检索记忆
    context = "I need to integrate AutoGPT with my project management tools."
    relevant_memory = memory_manager.get_relevant_memory(context, top_k=1)
    
    if relevant_memory:
        print(f"\nRelevant memory for context '{context}':")
        print(f"- {relevant_memory[0]['content']}")
    
    # 获取记忆统计信息
    stats = memory_manager.get_memory_stats()
    print(f"\nMemory statistics:")
    print(f"- Total memory items: {stats['total_items']}")
    print(f"- Memory size: {stats['memory_size']} KB")
    print(f"- Average embedding vector size: {stats['avg_vector_size']} dimensions")
    
    # 按类别查询记忆
    category_query = "challenges"
    category_memory = memory_manager.get_memory_by_category(category_query)
    
    if category_memory:
        print(f"\nMemory items in category '{category_query}':")
        for item in category_memory:
            print(f"- {item['content']}")
    
    return memory_manager

# 更新和维护记忆
def update_and_maintain_memory(memory_manager):
    # 更新现有记忆
    memory_id = "memory_1"  # 假设这是已存在的记忆ID
    updated_content = "The latest version of AutoGPT (v2.6) was released in August 2025 with improved memory management and tool integration."
    
    try:
        updated = memory_manager.update_memory(memory_id, updated_content)
        if updated:
            print(f"\nMemory item {memory_id} updated successfully.")
    except Exception as e:
        print(f"Failed to update memory item: {str(e)}")
    
    # 删除不需要的记忆
    memory_to_delete = "old_memory_id"  # 假设这是要删除的记忆ID
    
    try:
        deleted = memory_manager.delete_memory(memory_to_delete)
        if deleted:
            print(f"Memory item {memory_to_delete} deleted successfully.")
    except Exception as e:
        print(f"Failed to delete memory item: {str(e)}")
    
    # 清理过时记忆
    cleaned = memory_manager.clean_memory(
        max_age_days=30,  # 删除30天前的记忆
        min_relevance_score=0.3  # 删除相关性得分低于0.3的记忆
    )
    
    print(f"Cleaned {cleaned} obsolete memory items.")
    
    # 优化记忆存储
    optimized = memory_manager.optimize_memory()
    print(f"Memory optimized, reduced size by {optimized['reduction_percentage']:.2f}%.")
    
    return memory_manager

# 演示记忆系统的使用
if __name__ == "__main__":
    # 创建记忆系统
    memory, memory_manager = create_and_manage_memory()
    
    # 添加记忆项
    memory_manager = add_memory_items(memory_manager)
    
    # 检索和使用记忆
    memory_manager = retrieve_and_use_memory(memory_manager)
    
    # 更新和维护记忆
    memory_manager = update_and_maintain_memory(memory_manager)
    
    # 保存最终状态
    memory_manager.save_memory()
    print("Final memory state saved successfully.")

# AutoGPT多工具集成与使用的核心代码示例
from autogpt import Tool, Toolkit, AutoGPT, Goal
from autogpt.tools import SearchTool, FileOperationTool, CodeExecutionTool, APIIntegrationTool, EmailTool

# 创建和配置各种工具
def create_and_configure_tools():
    # 创建搜索工具
    search_tool = SearchTool(
        name="web_search",
        description="Search the web for information on any topic",
        params={
            "search_engine": "google",
            "api_key": "YOUR_SEARCH_API_KEY",
            "max_results": 5,
            "language": "en"
        }
    )
    
    # 创建文件操作工具
    file_tool = FileOperationTool(
        name="file_ops",
        description="Create, read, update, and delete files",
        params={
            "allowed_directories": ["./workspace"],
            "max_file_size_mb": 10,
            "allowed_extensions": [".txt", ".md", ".json", ".py", ".csv"]
        }
    )
    
    # 创建代码执行工具
    code_tool = CodeExecutionTool(
        name="code_exec",
        description="Write and execute code in various programming languages",
        params={
            "supported_languages": ["python", "javascript", "bash"],
            "timeout_seconds": 30,
            "max_memory_mb": 512,
            "sandbox_enabled": True
        }
    )
    
    # 创建API集成工具
    api_tool = APIIntegrationTool(
        name="api_integration",
        description="Integrate with external APIs and services",
        params={
            "allowed_endpoints": ["https://api.github.com", "https://api.openai.com"],
            "max_requests_per_minute": 60,
            "rate_limiting_enabled": True
        }
    )
    
    # 创建自定义工具 - 电子邮件工具
    email_tool = EmailTool(
        name="email",
        description="Send and receive emails",
        params={
            "smtp_server": "smtp.example.com",
            "smtp_port": 587,
            "email_address": "autogpt@example.com",
            "password_manager": "secure_vault"
        }
    )
    
    # 创建工具包并添加所有工具
    toolkit = Toolkit()
    toolkit.add_tool(search_tool)
    toolkit.add_tool(file_tool)
    toolkit.add_tool(code_tool)
    toolkit.add_tool(api_tool)
    toolkit.add_tool(email_tool)
    
    print("Toolkit created with the following tools:")
    for tool_name in toolkit.list_tools():
        print(f"- {tool_name}")
    
    return toolkit

# 创建使用工具的AutoGPT代理
def create_tool_using_agent(toolkit):
    # 初始化AutoGPT代理
    agent = AutoGPT(
        name="ToolUserAgent",
        role="An agent that can effectively use various tools to complete complex tasks",
        toolkit=toolkit,
        config={
            "temperature": 0.7,
            "max_iterations": 30,
            "tool_usage_strategy": "smart",  # 智能选择工具
            "tool_execution_timeout": 60,
            "verbose": True
        }
    )
    
    print("Tool-using AutoGPT agent created successfully.")
    return agent

# 设置需要使用工具的任务
def set_tool_based_tasks(agent):
    # 创建需要使用工具的目标
    goals = [
        Goal("Search the web for the latest advancements in renewable energy technology in 2025"),
        Goal("Create a Python script to analyze the data from the search results"),
        Goal("Save the analysis results to a CSV file"),
        Goal("Send an email with the analysis report to research@example.com")
    ]
    
    # 设置代理的目标
    agent.set_goals(goals)
    
    print(f"Agent goals set (requires tool usage): {[goal.text for goal in goals]}")
    return agent

# 运行使用工具的代理
def run_tool_using_agent(agent):
    # 启动代理
    print("Starting tool-using AutoGPT agent...")
    
    # 代理开始自主工作并使用工具
    result = agent.run()
    
    # 输出结果
    print(f"\nAgent completed its tasks with status: {result['status']}")
    
    # 如果代理成功完成任务，显示工具使用情况
    if result['status'] == 'success':
        print("\nTool usage statistics:")
        if 'tool_usage' in result:
            for tool_name, usage_count in result['tool_usage'].items():
                print(f"- {tool_name}: used {usage_count} times")
        
        # 显示代理生成的文件
        if 'files_created' in result:
            print("\nFiles created by the agent:")
            for file_path in result['files_created']:
                print(f"- {file_path}")
        
        # 显示代理执行的操作
        if 'actions_taken' in result:
            print("\nKey actions taken by the agent:")
            for action in result['actions_taken'][:5]:  # 只显示前5个操作
                print(f"- {action}")
    else:
        # 如果代理失败，显示错误信息
        print(f"\nAgent failed with error: {result['error']}")
        
        # 显示工具使用过程中的错误
        if 'tool_errors' in result:
            print("\nTool usage errors:")
            for tool_name, error in result['tool_errors'].items():
                print(f"- {tool_name}: {error[:100]}...")
    
    return result

# 演示工具使用功能
if __name__ == "__main__":
    # 创建工具包
    toolkit = create_and_configure_tools()
    
    # 创建使用工具的代理
    agent = create_tool_using_agent(toolkit)
    
    # 设置需要使用工具的任务
    agent = set_tool_based_tasks(agent)
    
    # 运行代理
    result = run_tool_using_agent(agent)
    
    # 保存代理的活动日志
    with open("agent_tool_usage_log.txt", "w") as f:
        f.write(f"Tool Usage Log - {result['timestamp']}\n")
        f.write(f"Status: {result['status']}\n\n")
        
        if 'tool_usage' in result:
            f.write("Tool Usage:\n")
            for tool_name, usage_count in result['tool_usage'].items():
                f.write(f"- {tool_name}: {usage_count} times\n")
    
    print("Agent tool usage log saved successfully.")

# 使用AutoGPT构建自主研究助手的示例代码
from autogpt import AutoGPT, Goal, Toolkit, Memory
from autogpt.tools import SearchTool, FileOperationTool, CodeExecutionTool, DataAnalysisTool

# 创建自主研究助手代理
def create_research_assistant():
    # 初始化记忆系统
    memory = Memory(
        type="vector",
        params={
            "embedding_model": "text-embedding-ada-002",
            "vector_store": "chroma",
            "persist_directory": "./research_memory"
        }
    )
    
    # 初始化工具包
    toolkit = Toolkit()
    
    # 添加搜索工具
    search_tool = SearchTool(
        name="web_search",
        description="Search the web for research information",
        params={
            "search_engine": "google_scholar",
            "api_key": "YOUR_API_KEY",
            "max_results": 10,
            "language": "en"
        }
    )
    toolkit.add_tool(search_tool)
    
    # 添加文件操作工具
    file_tool = FileOperationTool(
        name="file_ops",
        description="Create, read, update, and delete research files",
        params={
            "allowed_directories": ["./research_data"],
            "max_file_size_mb": 20
        }
    )
    toolkit.add_tool(file_tool)
    
    # 添加代码执行工具
    code_tool = CodeExecutionTool(
        name="data_analysis_code",
        description="Execute code for data analysis",
        params={
            "supported_languages": ["python"],
            "timeout_seconds": 60,
            "sandbox_enabled": True
        }
    )
    toolkit.add_tool(code_tool)
    
    # 添加数据分析工具
    data_tool = DataAnalysisTool(
        name="data_analyzer",
        description="Analyze data and generate visualizations",
        params={
            "supported_formats": ["csv", "json", "excel"],
            "visualization_library": "matplotlib",
            "max_data_points": 100000
        }
    )
    toolkit.add_tool(data_tool)
    
    # 初始化研究助手代理
    research_assistant = AutoGPT(
        name="ResearchAssistant",
        role="A thorough and meticulous research assistant that can gather information, analyze data, and create comprehensive reports",
        memory=memory,
        toolkit=toolkit,
        config={
            "temperature": 0.6,
            "max_iterations": 100,
            "verbose": True,
            "self_reflection": True,
            "save_memory": True,
            "report_format": "markdown"
        }
    )
    
    print("Autonomous research assistant created successfully.")
    return research_assistant

# 设置研究任务
def set_research_task(research_assistant, topic):
    # 创建研究目标
    goals = [
        Goal(f"Research the latest developments in {topic} in 2025"),
        Goal(f"Identify key trends, innovations, and challenges in {topic}"),
        Goal(f"Gather statistical data and case studies related to {topic}"),
        Goal(f"Analyze the data to derive insights and conclusions"),
        Goal(f"Create a comprehensive research report with visualizations")
    ]
    
    # 设置代理的目标
    research_assistant.set_goals(goals)
    
    print(f"Research task set: {topic}")
    return research_assistant

# 运行研究助手
def run_research_assistant(research_assistant):
    # 启动研究助手
    print("Starting autonomous research assistant...")
    
    # 代理开始自主研究
    result = research_assistant.run()
    
    # 输出结果
    print(f"\nResearch completed with status: {result['status']}")
    
    # 如果研究成功，显示成果
    if result['status'] == 'success':
        print("\nResearch findings:")
        if 'key_findings' in result:
            for finding in result['key_findings'][:5]:  # 只显示前5个发现
                print(f"- {finding}")
        
        # 显示生成的研究产品
        if 'research_products' in result:
            print("\nResearch products created:")
            for product_type, product_path in result['research_products'].items():
                print(f"- {product_type}: {product_path}")
        
        # 显示引用的来源
        if 'sources' in result:
            print("\nKey sources referenced:")
            for source in result['sources'][:5]:  # 只显示前5个来源
                print(f"- {source}")
    else:
        # 如果研究失败，显示错误信息
        print(f"\nResearch failed with error: {result['error']}")
    
    return result

# 示例：使用研究助手进行AI代理技术研究
if __name__ == "__main__":
    # 研究主题
    research_topic = "autonomous AI agent technology"
    
    # 创建研究助手
    assistant = create_research_assistant()
    
    # 设置研究任务
    assistant = set_research_task(assistant, research_topic)
    
    # 运行研究助手
    result = run_research_assistant(assistant)
    
    # 保存研究结果摘要
    if result['status'] == 'success':
        with open(f"research_summary_{research_topic.replace(' ', '_')}.md", "w") as f:
            f.write(f"# Research Summary: {research_topic}\n\n")
            f.write(f"## Key Findings\n")
            for finding in result.get('key_findings', []):
                f.write(f"- {finding}\n")
            
            f.write(f"\n## Research Products\n")
            for product_type, product_path in result.get('research_products', {}).items():
                f.write(f"- {product_type}: {product_path}\n")
    
    print("Research summary saved successfully.")

# 使用AutoGPT构建自主软件开发助手的示例代码
from autogpt import AutoGPT, Goal, Toolkit
from autogpt.tools import CodeExecutionTool, FileOperationTool, SearchTool, GitHubTool, DockerTool

# 创建自主软件开发助手代理
def create_software_developer_assistant():
    # 初始化工具包
    toolkit = Toolkit()
    
    # 添加代码执行工具
    code_tool = CodeExecutionTool(
        name="code_exec",
        description="Write, run, and debug code in various programming languages",
        params={
            "supported_languages": ["python", "javascript", "java", "c++", "go"],
            "timeout_seconds": 120,
            "sandbox_enabled": True,
            "max_memory_mb": 1024
        }
    )
    toolkit.add_tool(code_tool)
    
    # 添加文件操作工具
    file_tool = FileOperationTool(
        name="file_ops",
        description="Create, read, update, and delete project files and directories",
        params={
            "allowed_directories": ["./software_projects"],
            "max_file_size_mb": 50,
            "allowed_extensions": [".py", ".js", ".java", ".cpp", ".go", ".md", ".json", ".yaml"]
        }
    )
    toolkit.add_tool(file_tool)
    
    # 添加搜索工具
    search_tool = SearchTool(
        name="dev_search",
        description="Search for programming knowledge, documentation, and best practices",
        params={
            "search_engine": "google",
            "api_key": "YOUR_API_KEY",
            "max_results": 5,
            "custom_sources": ["stackoverflow.com", "github.com", "developer.mozilla.org"]
        }
    )
    toolkit.add_tool(search_tool)
    
    # 添加GitHub工具
    github_tool = GitHubTool(
        name="github",
        description="Interact with GitHub repositories and manage code",
        params={
            "access_token": "YOUR_GITHUB_TOKEN",
            "allowed_operations": ["clone", "push", "pull", "create_issue", "create_pr"],
            "default_branch": "main"
        }
    )
    toolkit.add_tool(github_tool)
    
    # 添加Docker工具
    docker_tool = DockerTool(
        name="docker",
        description="Create, build, and run Docker containers",
        params={
            "allowed_commands": ["build", "run", "stop", "ps", "images"],
            "max_containers": 10,
            "resource_limits": {"cpu": "2", "memory": "2g"}
        }
    )
    toolkit.add_tool(docker_tool)
    
    # 初始化软件开发助手代理
    developer_assistant = AutoGPT(
        name="SoftwareDeveloperAssistant",
        role="A skilled software developer that can design, code, test, and deploy software applications",
        toolkit=toolkit,
        config={
            "temperature": 0.8,  # 稍高的创造力
            "max_iterations": 150,
            "verbose": True,
            "self_reflection": True,
            "code_quality_check": True,
            "documentation_style": "google"
        }
    )
    
    print("Autonomous software developer assistant created successfully.")
    return developer_assistant

# 设置软件开发任务
def set_software_development_task(developer_assistant, project_description):
    # 创建开发目标
    goals = [
        Goal(f"Analyze the project requirements: {project_description}"),
        Goal("Design a software architecture that meets the requirements"),
        Goal("Implement the software according to the design"),
        Goal("Write automated tests to ensure code quality"),
        Goal("Create documentation for the software"),
        Goal("Package and deploy the software using Docker")
    ]
    
    # 设置代理的目标
    developer_assistant.set_goals(goals)
    
    print(f"Software development task set. Project: {project_description}")
    return developer_assistant

# 运行软件开发助手
def run_software_developer_assistant(developer_assistant):
    # 启动开发助手
    print("Starting autonomous software developer assistant...")
    
    # 代理开始自主开发
    result = developer_assistant.run()
    
    # 输出结果
    print(f"\nSoftware development completed with status: {result['status']}")
    
    # 如果开发成功，显示成果
    if result['status'] == 'success':
        print("\nDevelopment milestones achieved:")
        if 'milestones' in result:
            for milestone in result['milestones']:
                print(f"- {milestone}")
        
        # 显示生成的代码文件
        if 'code_files' in result:
            print("\nCode files created:")
            for file_path in result['code_files'][:10]:  # 只显示前10个文件
                print(f"- {file_path}")
        
        # 显示测试结果
        if 'test_results' in result:
            print("\nTest results:")
            print(f"- Tests run: {result['test_results']['total']}")
            print(f"- Tests passed: {result['test_results']['passed']}")
            print(f"- Tests failed: {result['test_results']['failed']}")
        
        # 显示部署状态
        if 'deployment' in result:
            print("\nDeployment status:")
            print(f"- Method: {result['deployment']['method']}")
            print(f"- Status: {result['deployment']['status']}")
            if 'url' in result['deployment']:
                print(f"- Access URL: {result['deployment']['url']}")
    else:
        # 如果开发失败，显示错误信息
        print(f"\nSoftware development failed with error: {result['error']}")
        
        # 显示失败的阶段
        if 'failed_stage' in result:
            print(f"Failed at stage: {result['failed_stage']}")
    
    return result

# 示例：使用开发助手创建一个简单的Web API
if __name__ == "__main__":
    # 项目描述
    project_desc = "Create a simple RESTful API for a to-do list application using Python and Flask. The API should support creating, reading, updating, and deleting to-do items. Include basic authentication and data persistence using SQLite."
    
    # 创建开发助手
    assistant = create_software_developer_assistant()
    
    # 设置开发任务
    assistant = set_software_development_task(assistant, project_desc)
    
    # 运行开发助手
    result = run_software_developer_assistant(assistant)
    
    # 保存项目摘要
    if result['status'] == 'success':
        project_name = "todo_api"
        with open(f"{project_name}_summary.md", "w") as f:
            f.write(f"# Project Summary: {project_name}\n\n")
            f.write(f"## Project Description\n{project_desc}\n\n")
            f.write(f"## Architecture Overview\n")
            if 'architecture' in result:
                f.write(result['architecture'] + "\n\n")
            f.write(f"## Key Features\n")
            if 'features' in result:
                for feature in result['features']:
                    f.write(f"- {feature}\n")
    
    print("Project summary saved successfully.")

# 使用AutoGPT构建多代理协作系统的示例代码
from autogpt import AutoGPT, MultiAgentSystem, Goal, Toolkit, AgentRole
from autogpt.tools import SearchTool, FileOperationTool, CodeExecutionTool, CommunicationTool

# 创建多代理协作系统
def create_multi_agent_system():
    # 初始化系统
    multi_agent_system = MultiAgentSystem(
        name="InnovationTeam",
        description="A team of AI agents collaborating to solve complex problems and drive innovation",
        config={
            "max_agents": 10,
            "communication_protocol": "agent_chat_v2",
            "task_allocation_strategy": "skill_based",
            "conflict_resolution": "majority_vote",
            "performance_tracking": True,
            "collaboration_logging": True
        }
    )
    
    print("Multi-agent collaboration system created successfully.")
    return multi_agent_system

# 创建不同角色的代理
def create_specialized_agents():
    # 创建基础工具包
    base_toolkit = Toolkit()
    
    # 添加通信工具
    comm_tool = CommunicationTool(
        name="agent_communication",
        description="Enable communication between agents",
        params={
            "message_format": "structured",
            "encryption_enabled": True,
            "max_message_size_kb": 10
        }
    )
    base_toolkit.add_tool(comm_tool)
    
    # 创建项目经理代理
    pm_toolkit = base_toolkit.copy()
    pm_toolkit.add_tool(SearchTool(name="project_search"))
    
    project_manager = AutoGPT(
        name="ProjectManager",
        role="A project manager who oversees the team, sets goals, and ensures progress",
        toolkit=pm_toolkit,
        config={
            "temperature": 0.5,
            "max_iterations": 200,
            "verbose": True,
            "leadership_style": "collaborative"
        }
    )
    
    # 创建研究员代理
    researcher_toolkit = base_toolkit.copy()
    researcher_toolkit.add_tool(SearchTool(name="research_search"))
    researcher_toolkit.add_tool(FileOperationTool(name="research_files"))
    
    researcher = AutoGPT(
        name="Researcher",
        role="A researcher who gathers and analyzes information on complex topics",
        toolkit=researcher_toolkit,
        config={
            "temperature": 0.7,
            "max_iterations": 150,
            "verbose": True,
            "research_depth": "deep"
        }
    )
    
    # 创建开发人员代理
    developer_toolkit = base_toolkit.copy()
    developer_toolkit.add_tool(CodeExecutionTool(name="dev_code"))
    developer_toolkit.add_tool(FileOperationTool(name="dev_files"))
    
    developer = AutoGPT(
        name="Developer",
        role="A software developer who implements solutions based on requirements",
        toolkit=developer_toolkit,
        config={
            "temperature": 0.8,
            "max_iterations": 180,
            "verbose": True,
            "code_style": "clean"
        }
    )
    
    # 创建测试人员代理
    tester_toolkit = base_toolkit.copy()
    tester_toolkit.add_tool(CodeExecutionTool(name="test_code"))
    tester_toolkit.add_tool(FileOperationTool(name="test_files"))
    
    tester = AutoGPT(
        name="Tester",
        role="A quality assurance specialist who tests software and ensures quality",
        toolkit=tester_toolkit,
        config={
            "temperature": 0.6,
            "max_iterations": 100,
            "verbose": True,
            "testing_strategy": "comprehensive"
        }
    )
    
    # 创建设计师代理
    designer_toolkit = base_toolkit.copy()
    designer_toolkit.add_tool(SearchTool(name="design_search"))
    designer_toolkit.add_tool(FileOperationTool(name="design_files"))
    
    designer = AutoGPT(
        name="Designer",
        role="A user experience designer who creates intuitive and visually appealing interfaces",
        toolkit=designer_toolkit,
        config={
            "temperature": 0.9,
            "max_iterations": 120,
            "verbose": True,
            "design_style": "modern"
        }
    )
    
    # 角色列表
    agents = [project_manager, researcher, developer, tester, designer]
    
    print(f"Created {len(agents)} specialized agents for the multi-agent system.")
    return agents

# 设置多代理协作任务
def set_multi_agent_task(multi_agent_system, agents, project_description):
    # 将代理添加到系统
    for agent in agents:
        multi_agent_system.add_agent(agent)
    
    # 创建协作目标
    goals = [
        Goal(f"Collaboratively develop a solution for: {project_description}"),
        Goal("Divide the project into manageable tasks and assign them to appropriate agents"),
        Goal("Maintain effective communication and coordination between all agents"),
        Goal("Integrate individual contributions into a cohesive final product"),
        Goal("Review and refine the solution through iterative feedback")
    ]
    
    # 设置系统的目标
    multi_agent_system.set_goals(goals)
    
    print(f"Multi-agent collaboration task set: {project_description}")
    return multi_agent_system

# 运行多代理协作系统
def run_multi_agent_system(multi_agent_system):
    # 启动系统
    print("Starting multi-agent collaboration system...")
    
    # 系统开始协作工作
    result = multi_agent_system.run()
    
    # 输出结果
    print(f"\nMulti-agent collaboration completed with status: {result['status']}")
    
    # 如果协作成功，显示成果
    if result['status'] == 'success':
        print("\nTeam achievements:")
        if 'achievements' in result:
            for achievement in result['achievements']:
                print(f"- {achievement}")
        
        # 显示代理贡献
        if 'agent_contributions' in result:
            print("\nAgent contributions:")
            for agent_name, contribution in result['agent_contributions'].items():
                print(f"- {agent_name}: {contribution}")
        
        # 显示通信统计
        if 'communication_stats' in result:
            print("\nCommunication statistics:")
            print(f"- Total messages: {result['communication_stats']['total_messages']}")
            print(f"- Top communicator: {result['communication_stats']['top_communicator']}")
        
        # 显示最终产品
        if 'final_product' in result:
            print("\nFinal product details:")
            print(f"- Type: {result['final_product']['type']}")
            print(f"- Location: {result['final_product']['location']}")
            print(f"- Description: {result['final_product']['description']}")
    else:
        # 如果协作失败，显示错误信息
        print(f"\nMulti-agent collaboration failed with error: {result['error']}")
        
        # 显示失败的原因
        if 'failure_reason' in result:
            print(f"Failure reason: {result['failure_reason']}")
    
    return result

# 示例：使用多代理系统开发一个智能客服应用
if __name__ == "__main__":
    # 项目描述
    project_desc = "Develop an intelligent customer service chatbot that can answer frequently asked questions, resolve common issues, and escalate complex problems to human agents when necessary. The chatbot should be integrated with existing CRM systems and support multiple communication channels."
    
    # 创建多代理系统
    system = create_multi_agent_system()
    
    # 创建专业代理
    agents = create_specialized_agents()
    
    # 设置协作任务
    system = set_multi_agent_task(system, agents, project_desc)
    
    # 运行系统
    result = run_multi_agent_system(system)
    
    # 保存协作报告
    if result['status'] == 'success':
        with open("multi_agent_collaboration_report.md", "w") as f:
            f.write("# Multi-Agent Collaboration Report\n\n")
            f.write(f"## Project Description\n{project_desc}\n\n")
            f.write(f"## Team Composition\n")
            for agent_name in result['agent_contributions'].keys():
                f.write(f"- {agent_name}\n")
            f.write(f"\n## Key Achievements\n")
            for achievement in result.get('achievements', []):
                f.write(f"- {achievement}\n")
    
    print("Multi-agent collaboration report saved successfully.")