📰多智能体才是未来：深度解析多Agent架构带你从零到生产级部署

本文较长，建议点赞收藏，以免遗失。更多AI大模型应用开发学习视频及资料，尽在聚客AI学院。

前言："ISC.AI 2025互联网安全大会开幕式及未来峰会"于8月6日在北京举行。360集团创始人、ISC大会主席周鸿祎在演讲中表示，行业里还有很多的争论，到底是做单智能体还是多智能体。多智能体这个时代已经来了，因为单智能体的能力确实非常有限。今天我们就深入探讨多智能体系统的核心原理与工程实践。

一、为什么需要多智能体系统？

当单智能体面临复杂任务时，容易陷入​​思维混乱​​和​​工具滥用​​。多智能体系统通过角色分工解决三大核心问题：

1. 模块化：各Agent专注特定能力域（如搜索/编码/审核）
2. 错误隔离：单个Agent故障不影响整体系统
3. 解释性：每个决策节点可追溯

二、核心架构设计

系统包含三层抽象结构：

1. Tool类：能力原子化

class Tool:
    def __init__(self, name, func, params):
        self.name = name  # 工具名称
        self.func = func  # 底层函数
        self.params = params  # 参数规范
        
    def validate_input(self, inputs):
        # 类型检查与转换
        if type(inputs) != self.params["type"]:
            try:
                return self.params["type"](inputs)
            except:
                raise ValueError("Invalid input type")
    
    def execute(self, **kwargs):
        validated = {k: self.validate_input(v) for k,v in kwargs.items()}
        return self.func(**validated)

​​典型工具示例​​：

• WebSearchTool： 网络信息获取
• CodeGenerator： 代码生成
• DataAnalyzer： 结构化数据分析

2. Agent类：自主决策单元

class Agent:
    def __init__(self, role, tools, llm_backend):
        self.role = role  # 角色定义
        self.tools = {t.name:t for t in tools}  # 工具集
        self.llm = llm_backend
        self.memory = []  # 思维链存储
        
    def react_loop(self, task):
        while not self._is_task_done():
            thought = self._generate_thought(task)
            if "ACTION" in thought:
                tool_name, params = self._parse_action(thought)
                result = self.tools[tool_name].execute(**params)
                self.memory.append(f"Observation: {result}")
            else:
                return thought  # 最终输出

​​关键特性​​：

• 内置ReAct决策循环
• 动态工具选择机制
• 思维链持久化存储

3. Crew类：智能体编排引擎

class Crew:
    def __init__(self):
        self.agents = {}  # 注册的智能体
        self.dependencies = {}  # 依赖图
        
    def add_agent(self, name, agent, deps=[]):
        self.agents[name] = agent
        self.dependencies[name] = deps
        
    def execute(self, input_data):
        # 拓扑排序解决执行顺序
        ordered_agents = self._topological_sort()  
        outputs = {}
        for agent_name in ordered_agents:
            agent_input = input_data if not outputs else outputs[agent_name]
            result = self.agents[agent_name].react_loop(agent_input)
            outputs[agent_name] = result
        return outputs

​​依赖解析算法​​：

如前面所述，多智能体模式将AI工作流程结构化为一个智能体团队，这些智能体协同工作，每个智能体都有明确的角色。不是由一个代理从头到尾处理一个任务，而是有一个工作流程，其中多个代理各自负责任务的一部分，然后将结果交给下一个代理。

这种设计通常（不总是）由一个协调器来协调，以确保代理按正确的顺序运行并共享信息。

这个想法类似于装配线或接力队：代理A完成第1步，然后代理B使用A的输出完成第2步，依此类推，直到达到目标。

每个代理在其子问题上都有自己的思考/行动方式，但他们通过定义的工作流程协作，共同解决更大的问题。

从技术上讲，多智能体模式中的每个智能体仍然在内部遵循推理+行动循环 （这个我之前有写过一篇关于 ReAct 代理全流程的技术文档，粉丝朋友自行领取：《 从头构建 ReAct 代理全流程》）。

三、实践：构建多智能体新闻生成器

该应用程序将接受用户的查询，在网络上搜索，并将其转化为一篇引证良好的新闻文章！（代码放在下方，感兴趣的朋友可以实践一下）

这是此演示的技术栈：

• CrewAI 用于多代理编排。
• Cohere 的 CommandR-7B 作为 LLM。

在这个多智能体应用中，我们将有两个智能体：

1) 分析师Agent:

• 接受用户查询。
• 使用Serper网络搜索工具从互联网获取结果。
• 汇总结果。

2) 内容撰写Agent:

• 使用精心整理的结果来准备一篇 polished、出版准备好的文章。

​​实现代码​​：

安装依赖项：确保您已安装 Python 3.11 或更高版本。

pip install crewai crewai-tools

env示例：

SERPER_API_KEY=your_serper_api_key
COHERE_API_KEY=your_cohere_apikey

应用代码：

import os
import streamlit as st
from crewai import Agent, Task, Crew, LLM
from crewai_tools import SerperDevTool
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Streamlit page config
st.set_page_config(page_title="AI News Generator", page_icon="📰", layout="wide")

# Title and description
st.title("🤖 AI News Generator, powered by CrewAI and Cohere's Command R7B")
st.markdown("Generate comprehensive blog posts about any topic using AI agents.")

# Sidebar
with st.sidebar:
    st.header("Content Settings")
    
    # Make the text input take up more space
    topic = st.text_area(
        "Enter your topic",
        height=100,
        placeholder="Enter the topic you want to generate content about..."
    )
    
    # Add more sidebar controls if needed
    st.markdown("### Advanced Settings")
    temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
    
    # Add some spacing
    st.markdown("---")
    
    # Make the generate button more prominent in the sidebar
    generate_button = st.button("Generate Content", type="primary", use_container_width=True)
    
    # Add some helpful information
    with st.expander("ℹ️ How to use"):
        st.markdown("""
        1. Enter your desired topic in the text area above
        2. Adjust the temperature if needed (higher = more creative)
        3. Click 'Generate Content' to start
        4. Wait for the AI to generate your article
        5. Download the result as a markdown file
        """)

def generate_content(topic):
    llm = LLM(
        model="command-r",
        temperature=0.7
    )

    search_tool = SerperDevTool(n_results=10)

    # First Agent: Senior Research Analyst
    senior_research_analyst = Agent(
        role="Senior Research Analyst",
        goal=f"Research, analyze, and synthesize comprehensive information on {topic} from reliable web sources",
        backstory="You're an expert research analyst with advanced web research skills. "
                "You excel at finding, analyzing, and synthesizing information from "
                "across the internet using search tools. You're skilled at "
                "distinguishing reliable sources from unreliable ones, "
                "fact-checking, cross-referencing information, and "
                "identifying key patterns and insights. You provide "
                "well-organized research briefs with proper citations "
                "and source verification. Your analysis includes both "
                "raw data and interpreted insights, making complex "
                "information accessible and actionable.",
        allow_delegation=False,
        verbose=True,
        tools=[search_tool],
        llm=llm
    )

    # Second Agent: Content Writer
    content_writer = Agent(
        role="Content Writer",
        goal="Transform research findings into engaging blog posts while maintaining accuracy",
        backstory="You're a skilled content writer specialized in creating "
                "engaging, accessible content from technical research. "
                "You work closely with the Senior Research Analyst and excel at maintaining the perfect "
                "balance between informative and entertaining writing, "
                "while ensuring all facts and citations from the research "
                "are properly incorporated. You have a talent for making "
                "complex topics approachable without oversimplifying them.",
        allow_delegation=False,
        verbose=True,
        llm=llm
    )

    # Research Task
    research_task = Task(
        description=("""
            1. Conduct comprehensive research on {topic} including:
                - Recent developments and news
                - Key industry trends and innovations
                - Expert opinions and analyses
                - Statistical data and market insights
            2. Evaluate source credibility and fact-check all information
            3. Organize findings into a structured research brief
            4. Include all relevant citations and sources
        """),
        expected_output="""A detailed research report containing:
            - Executive summary of key findings
            - Comprehensive analysis of current trends and developments
            - List of verified facts and statistics
            - All citations and links to original sources
            - Clear categorization of main themes and patterns
            Please format with clear sections and bullet points for easy reference.""",
        agent=senior_research_analyst
    )

    # Writing Task
    writing_task = Task(
        description=("""
            Using the research brief provided, create an engaging blog post that:
            1. Transforms technical information into accessible content
            2. Maintains all factual accuracy and citations from the research
            3. Includes:
                - Attention-grabbing introduction
                - Well-structured body sections with clear headings
                - Compelling conclusion
            4. Preserves all source citations in [Source: URL] format
            5. Includes a References section at the end
        """),
        expected_output="""A polished blog post in markdown format that:
            - Engages readers while maintaining accuracy
            - Contains properly structured sections
            - Includes Inline citations hyperlinked to the original source url
            - Presents information in an accessible yet informative way
            - Follows proper markdown formatting, use H1 for the title and H3 for the sub-sections""",
        agent=content_writer
    )

    # Create Crew
    crew = Crew(
        agents=[senior_research_analyst, content_writer],
        tasks=[research_task, writing_task],
        verbose=True
    )

    return crew.kickoff(inputs={"topic": topic})

# Main content area
if generate_button:
    with st.spinner('Generating content... This may take a moment.'):
        try:
            result = generate_content(topic)
            st.markdown("### Generated Content")
            st.markdown(result)
            
            # Add download button
            st.download_button(
                label="Download Content",
                data=result.raw,
                file_name=f"{topic.lower().replace(' ', '_')}_article.md",
                mime="text/markdown"
            )
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")

# Footer
st.markdown("---")
st.markdown("Built with CrewAI, Streamlit and powered by Cohere's Command R7B")

四、性能优化策略

1. 短路机制：当工具返回关键错误时提前终止流程
2. 缓存层：对稳定数据（如地点信息）启用结果缓存
3. 并发控制：

# 并行执行独立Agent
with concurrent.futures.ThreadPoolExecutor() as executor:
    futures = {executor.submit(agent.react_loop): agent 
               for agent in independent_agents}

与传统框架对比

维度	原生实现	LangChain	CrewAI
启动速度	⚡️ 0.5s	🐢 3.2s	🐢 2.8s
定制灵活性	⭐️⭐️⭐️⭐️⭐️	⭐️⭐️⭐️	⭐️⭐️⭐️⭐️
内存占用	78MB	210MB	195MB
学习曲线	陡峭	平缓	中等

适用场景：需要深度定制的复杂工作流推荐原生实现，快速原型建议使用CrewAI

好了，总结一下，本次分享通过分层架构设计和模块化实现，可构建出适应复杂场景的智能协作系统。如果本次分享对你有所帮助，记得告诉身边有需要的朋友，"我们正在经历的不仅是技术迭代，而是认知革命。当人类智慧与机器智能形成共生关系，文明的火种将在新的维度延续。"在这场波澜壮阔的文明跃迁中，主动拥抱AI时代，就是掌握打开新纪元之门的密钥，让每个人都能在智能化的星辰大海中，找到属于自己的航向。