协作

概览

CrewAI 中的协作让 agents 能够通过委派任务和提问来协同工作，并利用彼此的专长。只要设置 allow_delegation=True，agents 就会自动获得强大的协作工具。

快速开始：启用协作

from crewai import Agent, Crew, Task

# 为 agents 启用协作
researcher = Agent(
    role="Research Specialist",
    goal="Conduct thorough research on any topic",
    backstory="Expert researcher with access to various sources",
    allow_delegation=True,  # 🔑 Key setting for collaboration
    verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Create engaging content based on research",
    backstory="Skilled writer who transforms research into compelling content",
    allow_delegation=True,  # 🔑 Enables asking questions to other agents
    verbose=True
)

# Agents 现在可以自动协作
crew = Crew(
    agents=[researcher, writer],
    tasks=[...],
    verbose=True
)

Agent 协作如何运作

当 allow_delegation=True 时，CrewAI 会自动为 agents 提供两个强大的工具：

1. Delegate Work Tool

允许 agents 将任务分派给具有特定专长的队友。

# Agent 会自动获得这个工具：
# Delegate work to coworker(task: str, context: str, coworker: str)

2. Ask Question Tool

让 agents 能够提出具体问题，从同事那里获取信息。

# Agent 会自动获得这个工具：
# Ask question to coworker(question: str, context: str, coworker: str)

协作实战

下面是一个完整示例，展示 agents 如何在内容创建任务中协作：

from crewai import Agent, Crew, Task, Process

# 创建协作型 agents
researcher = Agent(
    role="Research Specialist",
    goal="Find accurate, up-to-date information on any topic",
    backstory="""You're a meticulous researcher with expertise in finding
    reliable sources and fact-checking information across various domains.""",
    allow_delegation=True,
    verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Create engaging, well-structured content",
    backstory="""You're a skilled content writer who excels at transforming
    research into compelling, readable content for different audiences.""",
    allow_delegation=True,
    verbose=True
)

editor = Agent(
    role="Content Editor",
    goal="Ensure content quality and consistency",
    backstory="""You're an experienced editor with an eye for detail,
    ensuring content meets high standards for clarity and accuracy.""",
    allow_delegation=True,
    verbose=True
)

# 创建一个鼓励协作的任务
article_task = Task(
    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.

    The article should include:
    - Current AI applications in healthcare
    - Emerging trends and technologies
    - Potential challenges and ethical considerations
    - Expert predictions for the next 5 years

    Collaborate with your teammates to ensure accuracy and quality.""",
    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
    agent=writer  # Writer leads, but can delegate research to researcher
)

# 创建协作型 crew
crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[article_task],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff()

协作模式

模式 1：研究 → 写作 → 编辑

research_task = Task(
    description="Research the latest developments in quantum computing",
    expected_output="Comprehensive research summary with key findings and sources",
    agent=researcher
)

writing_task = Task(
    description="Write an article based on the research findings",
    expected_output="Engaging 800-word article about quantum computing",
    agent=writer,
    context=[research_task]  # Gets research output as context
)

editing_task = Task(
    description="Edit and polish the article for publication",
    expected_output="Publication-ready article with improved clarity and flow",
    agent=editor,
    context=[writing_task]  # Gets article draft as context
)

模式 2：协作式单任务

collaborative_task = Task(
    description="""Create a marketing strategy for a new AI product.

    Writer: Focus on messaging and content strategy
    Researcher: Provide market analysis and competitor insights

    Work together to create a comprehensive strategy.""",
    expected_output="Complete marketing strategy with research backing",
    agent=writer  # Lead agent, but can delegate to researcher
)

层级式协作

对于复杂项目，可以使用带有 manager agent 的层级流程：

from crewai import Agent, Crew, Task, Process

# Manager agent 协调整个团队
manager = Agent(
    role="Project Manager",
    goal="Coordinate team efforts and ensure project success",
    backstory="Experienced project manager skilled at delegation and quality control",
    allow_delegation=True,
    verbose=True
)

# Specialist agents
researcher = Agent(
    role="Researcher",
    goal="Provide accurate research and analysis",
    backstory="Expert researcher with deep analytical skills",
    allow_delegation=False,  # Specialists focus on their expertise
    verbose=True
)

writer = Agent(
    role="Writer",
    goal="Create compelling content",
    backstory="Skilled writer who creates engaging content",
    allow_delegation=False,
    verbose=True
)

# Manager-led task
project_task = Task(
    description="Create a comprehensive market analysis report with recommendations",
    expected_output="Executive summary, detailed analysis, and strategic recommendations",
    agent=manager  # Manager will delegate to specialists
)

# Hierarchical crew
crew = Crew(
    agents=[manager, researcher, writer],
    tasks=[project_task],
    process=Process.hierarchical,  # Manager coordinates everything
    manager_llm="gpt-4o",  # Specify LLM for manager
    verbose=True
)

协作最佳实践

1. 清晰定义角色

# ✅ Good: Specific, complementary roles
researcher = Agent(role="Market Research Analyst", ...)
writer = Agent(role="Technical Content Writer", ...)

# ❌ Avoid: Overlapping or vague roles
agent1 = Agent(role="General Assistant", ...)
agent2 = Agent(role="Helper", ...)

2. 有策略地启用委派

# ✅ Enable delegation for coordinators and generalists
lead_agent = Agent(
    role="Content Lead",
    allow_delegation=True,  # Can delegate to specialists
    ...
)

# ✅ Disable for focused specialists (optional)
specialist_agent = Agent(
    role="Data Analyst",
    allow_delegation=False,  # Focuses on core expertise
    ...
)

3. 共享上下文

# ✅ Use context parameter for task dependencies
writing_task = Task(
    description="Write article based on research",
    agent=writer,
    context=[research_task],  # Shares research results
    ...
)

4. 清晰的任务描述

# ✅ Specific, actionable descriptions
Task(
    description="""Research competitors in the AI chatbot space.
    Focus on: pricing models, key features, target markets.
    Provide data in a structured format.""",
    ...
)

# ❌ Vague descriptions that don't guide collaboration
Task(description="Do some research about chatbots", ...)

协作排障

问题：Agents 没有协作

症状： Agents 各自独立工作，没有发生委派

# ✅ Solution: Ensure delegation is enabled
agent = Agent(
    role="...",
    allow_delegation=True,  # This is required!
    ...
)

问题：来回沟通过多

症状： Agents 反复提问，进度变慢

# ✅ Solution: Provide better context and specific roles
Task(
    description="""Write a technical blog post about machine learning.

    Context: Target audience is software developers with basic ML knowledge.
    Length: 1200 words
    Include: code examples, practical applications, best practices

    If you need specific technical details, delegate research to the researcher.""",
    ...
)

问题：委派循环

症状： Agents 之间无限相互委派

# ✅ Solution: Clear hierarchy and responsibilities
manager = Agent(role="Manager", allow_delegation=True)
specialist1 = Agent(role="Specialist A", allow_delegation=False)  # No re-delegation
specialist2 = Agent(role="Specialist B", allow_delegation=False)

高级协作功能

自定义协作规则

# Set specific collaboration guidelines in agent backstory
agent = Agent(
    role="Senior Developer",
    backstory="""You lead development projects and coordinate with team members.

    Collaboration guidelines:
    - Delegate research tasks to the Research Analyst
    - Ask the Designer for UI/UX guidance
    - Consult the QA Engineer for testing strategies
    - Only escalate blocking issues to the Project Manager""",
    allow_delegation=True
)

监控协作

def track_collaboration(output):
    """Track collaboration patterns"""
    if "Delegate work to coworker" in output.raw:
        print("🤝 Delegation occurred")
    if "Ask question to coworker" in output.raw:
        print("❓ Question asked")

crew = Crew(
    agents=[...],
    tasks=[...],
    step_callback=track_collaboration,  # Monitor collaboration
    verbose=True
)

记忆与学习

启用 agents 记住过去的协作：

agent = Agent(
    role="Content Lead",
    memory=True,  # Remembers past interactions
    allow_delegation=True,
    verbose=True
)

启用 memory 后，agents 会从之前的协作中学习，并随着时间推移改进委派决策。

下一步

• 试试这些示例：先从基础协作示例开始
• 尝试不同角色：测试不同的 agent 角色组合
• 监控交互：使用 verbose=True 查看协作过程
• 优化任务描述：清晰的任务会带来更好的协作效果
• 逐步扩展：复杂项目可以尝试层级流程

协作会把单个 AI agents 变成强大的团队，让它们能够一起应对复杂、多面的挑战。