개요
CrewAI에서의 협업은 에이전트들이 팀으로서 함께 작업하며, 각자의 전문성을 활용하기 위해 작업을 위임하고 질문을 주고받을 수 있도록 합니다.allow_delegation=True
로 설정하면, 에이전트들은 자동으로 강력한 협업 도구에 접근할 수 있습니다.
빠른 시작: 협업 활성화
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Ask AI
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=True
로 설정하면, CrewAI는 에이전트에게 두 가지 강력한 도구를 자동으로 제공합니다.
1. 업무 위임 도구
에이전트가 특정 전문성을 가진 팀원에게 작업을 할당할 수 있습니다.Copy
Ask AI
# 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.Copy
Ask AI
# 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:Copy
Ask AI
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
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Ask AI
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
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Ask AI
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:Copy
Ask AI
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
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Ask AI
# ✅ 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
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Ask AI
# ✅ 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
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Ask AI
# ✅ 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
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Ask AI
# ✅ 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 않음Copy
Ask AI
# ✅ Solution: Ensure delegation is enabled
agent = Agent(
role="...",
allow_delegation=True, # This is required!
...
)
문제: 지나친 이중 확인
증상: 에이전트가 과도하게 질문을 하여 진행이 느려짐Copy
Ask AI
# ✅ 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 위임함Copy
Ask AI
# ✅ 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
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Ask AI
# 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
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Ask AI
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:Copy
Ask AI
agent = Agent(
role="Content Lead",
memory=True, # Remembers past interactions
allow_delegation=True,
verbose=True
)
다음 단계
- 예제 시도하기: 기본 협업 예제부터 시작하세요
- 역할 실험하기: 다양한 에이전트 역할 조합을 테스트해 보세요
- 상호작용 모니터링: 협업 과정을 직접 보려면
verbose=True
를 사용하세요 - 작업 설명 최적화: 명확한 작업이 더 나은 협업으로 이어집니다
- 확장하기: 복잡한 프로젝트에는 계층적 프로세스를 시도해 보세요