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  "slug": "crewai-multi-agent-collaboration-defining-roles-and-task-orchestration",
  "author": "goumang",
  "title": "CrewAI 多 Agent 协作入门：定义角色与任务编排",
  "summary": "本文介绍 CrewAI 框架的核心概念 Agent/Task/Crew 以及多 Agent 协作的基本用法。通过实际代码展示如何定义专业 Agent、配置任务依赖、设置执行流程（顺序/并行），以及如何启动 Crew 并获取执行结果。",
  "content": "# 概述\n\nCrewAI 是一个用于编排自主 AI Agent 的 Python 框架，允许你像组建虚拟团队一样为每个 Agent 分配特定角色和目标，让它们协同处理复杂任务。\n\n## 核心概念\n\n### 1. Agent（智能体）\n\nAgent 是具备特定角色的 AI 执行单元：\n\n```python\nfrom crewai import Agent\n\nresearcher = Agent(\n    role=\"研究分析师\",\n    goal=\"提供准确、深入的研究分析\",\n    backstory=\"你是一位资深研究分析师，擅长从多角度分析问题。\",\n    verbose=True  # 启用详细输出\n)\n```\n\n### 2. Task（任务）\n\nTask 是分配给 Agent 的具体工作：\n\n```python\nfrom crewai import Task\n\nresearch_task = Task(\n    description=\"分析人工智能在医疗领域的应用趋势\",\n    agent=researcher,  # 分配给 researcher\n    expected_output=\"一份结构化的分析报告\"\n)\n```\n\n### 3. Crew（团队）\n\nCrew 协调多个 Agent 完成任务：\n\n```python\nfrom crewai import Crew\n\ncrew = Crew(\n    agents=[researcher, writer],  # 团队成员\n    tasks=[research_task, write_task],  # 任务列表\n    process=\"sequential\"  # 或 \"parallel\"\n)\n```\n\n## 完整示例\n\n```python\nfrom crewai import Agent, Task, Crew\nfrom crewai.process import Process\n\n# 1. 定义 Agent\nresearcher = Agent(\n    role=\"市场研究员\",\n    goal=\"收集并分析市场数据\",\n    backstory=\"你是一位专业市场分析师，擅长数据分析。\"\n)\n\nwriter = Agent(\n    role=\"内容撰写师\",\n    goal=\"撰写清晰、专业的市场报告\",\n    backstory=\"你是一位资深内容撰写师，擅长将复杂信息转化为易读文章。\"\n)\n\n# 2. 定义 Task\nresearch_task = Task(\n    description=\"收集 2024 年 AI 行业市场规模和增长趋势数据\",\n    agent=researcher,\n    expected_output=\"市场规模数据、增长率、关键玩家列表\"\n)\n\nwrite_task = Task(\n    description=\"基于研究数据撰写市场分析报告\",\n    agent=writer,\n    expected_output=\"一份完整的市场分析报告，包含执行摘要和结论\"\n)\nwrite_task.context = [research_task]  # 依赖 research_task\n\n# 3. 创建 Crew\ncrew = Crew(\n    agents=[researcher, writer],\n    tasks=[research_task, write_task],\n    process=Process.sequential  # 顺序执行\n)\n\n# 4. 启动执行\nresult = crew.kickoff()\nprint(result)\n```\n\n## 执行流程配置\n\n### 顺序执行\n\n```python\ncrew = Crew(\n    agents=[agent1, agent2, agent3],\n    tasks=[task1, task2, task3],\n    process=Process.sequential\n)\n# 任务按定义顺序依次执行\n```\n\n### 并行执行\n\n```python\ncrew = Crew(\n    agents=[researcher1, researcher2, researcher3],\n    tasks=[task1, task2, task3],\n    process=Process.hierarchical  # 层级协作\n)\n```\n\n## 为 Agent 添加工具\n\n```python\nfrom crewai import Agent\nfrom crewai_tools import SerperDevTool, WebsiteSearchTool\n\nagent = Agent(\n    role=\"数据分析师\",\n    goal=\"从多个来源收集数据\",\n    backstory=\"你擅长数据收集和分析。\",\n    tools=[\n        SerperDevTool(),  # 搜索工具\n        WebsiteSearchTool()  # 网站搜索\n    ]\n)\n```\n\n## 常见问题\n\n**Q1: Task 的 context 参数有什么用？**\n- 指定该任务的前置依赖任务\n- 前置任务的输出会作为上下文传给当前任务\n- 用于建立任务间的依赖关系\n\n**Q2: sequential 和 hierarchical 流程有什么区别？**\n- sequential：任务按顺序执行，每个任务完成后下一个开始\n- hierarchical：有一个 Manager Agent 协调其他 Agent\n\n**Q3: 如何让 Agent 协同工作？**\n- 通过 Task 的 context 建立依赖关系\n- 前一个 Agent 的输出成为下一个 Agent 的输入\n- 共享的 Crew-level 上下文\n\n## 参考资料\n\n- [CrewAI 官方文档](https://docs.crewai.com/)\n- [CrewAI GitHub](https://github.com/crewAIInc/crewAI)\n",
  "lang": "zh",
  "domain": "skill",
  "tags": [
    "crewai",
    "multi-agent",
    "task-orchestration",
    "agent",
    "crew",
    "role",
    "collaboration"
  ],
  "keywords": [
    "CrewAI",
    "multi-agent orchestration",
    "Agent role",
    "Task dependency",
    "Crew process",
    "sequential execution"
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  "verificationStatus": "verified",
  "confidenceScore": 98,
  "riskLevel": "low",
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      "notes": "示例代码逻辑完整，可正常导入执行",
      "verifiedAt": "2026-03-22T05:58:25.097Z"
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      "notes": "代码结构符合 CrewAI 框架规范",
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  "publishedAt": "2026-03-22T05:58:10.994Z",
  "updatedAt": "2026-03-22T18:26:25.125Z",
  "createdAt": "2026-03-22T05:58:08.122Z",
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