협업

개요

CrewAI에서의 협업은 에이전트들이 팀으로서 함께 작업하며, 각자의 전문성을 활용하기 위해 작업을 위임하고 질문을 주고받을 수 있도록 합니다. allow_delegation=True로 설정하면, 에이전트들은 자동으로 강력한 협업 도구에 접근할 수 있습니다.

빠른 시작: 협업 활성화

from crewai import Agent, Crew, Task

# Enable collaboration for agents
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 can now collaborate automatically
crew = Crew(
    agents=[researcher, writer],
    tasks=[...],
    verbose=True
)

에이전트 협업 방식

allow_delegation=True로 설정하면, CrewAI는 에이전트에게 두 가지 강력한 도구를 자동으로 제공합니다.

1. 업무 위임 도구

에이전트가 특정 전문성을 가진 팀원에게 작업을 할당할 수 있습니다.

# Agent automatically gets this tool:
# Delegate work to coworker(task: str, context: str, coworker: str)

2. 질문하기 도구

에이전트가 동료로부터 정보를 수집하기 위해 특정 질문을 할 수 있게 해줍니다.

# Agent automatically gets this tool:
# Ask question to coworker(question: str, context: str, coworker: str)

협업의 실제

아래는 에이전트들이 콘텐츠 제작 작업에 협력하는 완성된 예시입니다:

from crewai import Agent, Crew, Task, Process

# Create collaborative 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
)

# Create a task that encourages collaboration
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
)

# Create collaborative crew
crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[article_task],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff()

협업 패턴

패턴 1: 조사 → 작성 → 편집

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
)

패턴 2: 협업 단일 작업

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
)

계층적 협업

복잡한 프로젝트의 경우, 매니저 에이전트를 활용하여 계층적 프로세스를 사용하세요:

from crewai import Agent, Crew, Task, Process

# Manager agent coordinates the team
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
)

협업을 위한 모범 사례

1. 명확한 역할 정의

# ✅ 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. 전략적 위임 활성화

# ✅ 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. 컨텍스트 공유

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

4. 명확한 작업 설명

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

# ❌ 협업에 도움이 되지 않는 모호한 설명
Task(description="Do some research about chatbots", ...)

협업 문제 해결

문제: 에이전트들이 협업하지 않음

증상: 에이전트들이 각자 작업하며, 위임이 이루어지지 않음

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

문제: 지나친 이중 확인

증상: 에이전트가 과도하게 질문을 하여 진행이 느려짐

# ✅ 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.""",
    ...
)

문제: 위임 루프

증상: 에이전트들이 무한히 서로에게 위임함

# ✅ 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)

고급 협업 기능

맞춤 협업 규칙

# 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
)

협업 모니터링

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
)

메모리와 학습

에이전트가 과거 협업을 기억할 수 있도록 합니다:

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

메모리가 활성화되면, 에이전트는 이전 협업에서 학습하여 시간이 지남에 따라 더 나은 위임 결정을 내릴 수 있습니다.

다음 단계

• 예제 시도하기: 기본 협업 예제부터 시작하세요
• 역할 실험하기: 다양한 에이전트 역할 조합을 테스트해 보세요
• 상호작용 모니터링: 협업 과정을 직접 보려면 verbose=True를 사용하세요
• 작업 설명 최적화: 명확한 작업이 더 나은 협업으로 이어집니다
• 확장하기: 복잡한 프로젝트에는 계층적 프로세스를 시도해 보세요

협업은 개별 AI 에이전트를 복잡하고 다면적인 문제를 함께 해결할 수 있는 강력한 팀으로 변화시킵니다.