에이전트 저장소

crewai org switch <org_id>

from crewai import Agent

# Create an agent by loading it from a repository
# The agent is loaded with all its predefined configurations
researcher = Agent(
    from_repository="market-research-agent"
)

researcher = Agent(
    from_repository="market-research-agent",
    goal="Research the latest trends in AI development",  # Override the repository goal
    verbose=True  # Add a setting not in the repository
)

from crewai import Crew, Agent, Task

# Load agents from repositories
researcher = Agent(
    from_repository="market-research-agent"
)

writer = Agent(
    from_repository="content-writer-agent"
)

# Create tasks
research_task = Task(
    description="Research the latest trends in AI",
    agent=researcher
)

writing_task = Task(
    description="Write a comprehensive report based on the research",
    agent=writer
)

# Create the crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)

# Run the crew
result = crew.kickoff()

from crewai import Agent
from pydantic import BaseModel
from typing import List

# 구조화된 출력 형식 정의
class MarketAnalysis(BaseModel):
    key_trends: List[str]
    opportunities: List[str]
    recommendation: str

# 저장소에서 agent 불러오기
analyst = Agent(
    from_repository="market-analyst-agent",
    verbose=True
)

# 자유 형식 응답 받기
result = analyst.kickoff("Analyze the AI market in 2025")
print(result.raw)  # 원시 응답 접근

# 구조화된 출력 받기
structured_result = analyst.kickoff(
    "Provide a structured analysis of the AI market in 2025",
    response_format=MarketAnalysis
)

# 구조화된 데이터 접근
print(f"Key Trends: {structured_result.pydantic.key_trends}")
print(f"Recommendation: {structured_result.pydantic.recommendation}")

# 현재 조직 보기
crewai org current

# 다른 조직으로 전환
crewai org switch <org_id>

# 사용 가능한 모든 조직 목록 확인
crewai org list