에이전트

# src/latest_ai_development/config/agents.yaml
researcher:
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering the latest
    developments in {topic}. Known for your ability to find the most relevant
    information and present it in a clear and concise manner.

reporting_analyst:
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} data analysis and research findings
  backstory: >
    You're a meticulous analyst with a keen eye for detail. You're known for
    your ability to turn complex data into clear and concise reports, making
    it easy for others to understand and act on the information you provide.

# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process
from crewai.project import CrewBase, agent, crew
from crewai_tools import SerperDevTool

@CrewBase
class LatestAiDevelopmentCrew():
  """LatestAiDevelopment crew"""

  agents_config = "config/agents.yaml"

  @agent
  def researcher(self) -> Agent:
    return Agent(
      config=self.agents_config['researcher'], # type: ignore[index]
      verbose=True,
      tools=[SerperDevTool()]
    )

  @agent
  def reporting_analyst(self) -> Agent:
    return Agent(
      config=self.agents_config['reporting_analyst'], # type: ignore[index]
      verbose=True
    )

from crewai import Agent
from crewai_tools import SerperDevTool

# 사용 가능한 모든 파라미터로 agent 생성
agent = Agent(
    role="Senior Data Scientist",
    goal="Analyze and interpret complex datasets to provide actionable insights",
    backstory="With over 10 years of experience in data science and machine learning, "
              "you excel at finding patterns in complex datasets.",
    llm="gpt-4",  # 기본값: OPENAI_MODEL_NAME 또는 "gpt-4"
    function_calling_llm=None,  # 옵션: 도구 호출을 위한 별도의 LLM
    verbose=False,  # 기본값: False
    allow_delegation=False,  # 기본값: False
    max_iter=20,  # 기본값: 20번 반복
    max_rpm=None,  # 옵션: API 호출에 대한 속도 제한
    max_execution_time=None,  # 옵션: 최대 실행 시간(초 단위)
    max_retry_limit=2,  # 기본값: 오류 시 2번 재시도
    allow_code_execution=False,  # 기본값: False
    code_execution_mode="safe",  # 기본값: "safe" (옵션: "safe", "unsafe")
    respect_context_window=True,  # 기본값: True
    use_system_prompt=True,  # 기본값: True
    multimodal=False,  # 기본값: False
    inject_date=False,  # 기본값: False
    date_format="%Y-%m-%d",  # 기본값: ISO 형식
    reasoning=False,  # 기본값: False
    max_reasoning_attempts=None,  # 기본값: None
    tools=[SerperDevTool()],  # 옵션: 도구 리스트
    knowledge_sources=None,  # 옵션: 지식 소스 리스트
    embedder=None,  # 옵션: 커스텀 임베더 구성
    system_template=None,  # 옵션: 커스텀 시스템 프롬프트 템플릿
    prompt_template=None,  # 옵션: 커스텀 프롬프트 템플릿
    response_template=None,  # 옵션: 커스텀 응답 템플릿
    step_callback=None,  # 옵션: 모니터링용 콜백 함수
)

research_agent = Agent(
    role="Research Analyst",
    goal="Find and summarize information about specific topics",
    backstory="You are an experienced researcher with attention to detail",
    tools=[SerperDevTool()],
    verbose=True  # Enable logging for debugging
)

dev_agent = Agent(
    role="Senior Python Developer",
    goal="Write and debug Python code",
    backstory="Expert Python developer with 10 years of experience",
    allow_code_execution=True,
    code_execution_mode="safe",  # Uses Docker for safety
    max_execution_time=300,  # 5-minute timeout
    max_retry_limit=3  # More retries for complex code tasks
)

analysis_agent = Agent(
    role="Data Analyst",
    goal="Perform deep analysis of large datasets",
    backstory="Specialized in big data analysis and pattern recognition",
    memory=True,
    respect_context_window=True,
    max_rpm=10,  # Limit API calls
    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
)

custom_agent = Agent(
    role="Customer Service Representative",
    goal="Assist customers with their inquiries",
    backstory="Experienced in customer support with a focus on satisfaction",
    system_template="""<|start_header_id|>system<|end_header_id|>
                        {{ .System }}<|eot_id|>""",
    prompt_template="""<|start_header_id|>user<|end_header_id|>
                        {{ .Prompt }}<|eot_id|>""",
    response_template="""<|start_header_id|>assistant<|end_header_id|>
                        {{ .Response }}<|eot_id|>""",
)

strategic_agent = Agent(
    role="Market Analyst",
    goal="Track market movements with precise date references and strategic planning",
    backstory="Expert in time-sensitive financial analysis and strategic reporting",
    inject_date=True,  # Automatically inject current date into tasks
    date_format="%B %d, %Y",  # Format as "May 21, 2025"
    reasoning=True,  # Enable strategic planning
    max_reasoning_attempts=2,  # Limit planning iterations
    verbose=True
)

reasoning_agent = Agent(
    role="Strategic Planner",
    goal="Analyze complex problems and create detailed execution plans",
    backstory="Expert strategic planner who methodically breaks down complex challenges",
    reasoning=True,  # Enable reasoning and planning
    max_reasoning_attempts=3,  # Limit reasoning attempts
    max_iter=30,  # Allow more iterations for complex planning
    verbose=True
)

multimodal_agent = Agent(
    role="Visual Content Analyst",
    goal="Analyze and process both text and visual content",
    backstory="Specialized in multimodal analysis combining text and image understanding",
    multimodal=True,  # Enable multimodal capabilities
    verbose=True
)

from crewai import Agent
from crewai_tools import SerperDevTool, WikipediaTools

# 도구 생성
search_tool = SerperDevTool()
wiki_tool = WikipediaTools()

# 에이전트에 도구 추가
researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[search_tool, wiki_tool],
    verbose=True
)

from crewai import Agent

analyst = Agent(
    role="Data Analyst",
    goal="Analyze and remember complex data patterns",
    memory=True,  # Enable memory
    verbose=True
)

# Agent with automatic context management (default)
smart_agent = Agent(
    role="Research Analyst",
    goal="Analyze large documents and datasets",
    backstory="Expert at processing extensive information",
    respect_context_window=True,  # 🔑 Default: auto-handle context limits
    verbose=True
)

# Agent with strict context limits
strict_agent = Agent(
    role="Legal Document Reviewer",
    goal="Provide precise legal analysis without information loss",
    backstory="Legal expert requiring complete context for accurate analysis",
    respect_context_window=False,  # ❌ Stop execution on context limit
    verbose=True
)

# Perfect for document processing
document_processor = Agent(
    role="Document Analyst",
    goal="Extract insights from large research papers",
    backstory="Expert at analyzing extensive documentation",
    respect_context_window=True,  # Handle large documents gracefully
    max_iter=50,  # Allow more iterations for complex analysis
    verbose=True
)

# Perfect for precision tasks
precision_agent = Agent(
    role="Code Security Auditor",
    goal="Identify security vulnerabilities in code",
    backstory="Security expert requiring complete code context",
    respect_context_window=False,  # Prefer failure over incomplete analysis
    max_retry_limit=1,  # Fail fast on context issues
    verbose=True
)

from crewai_tools import RagTool

# Create RAG tool for large document processing
rag_tool = RagTool()

rag_agent = Agent(
    role="Research Assistant",
    goal="Query large knowledge bases efficiently",
    backstory="Expert at using RAG tools for information retrieval",
    tools=[rag_tool],  # Use RAG instead of large context windows
    respect_context_window=True,
    verbose=True
)

# Use knowledge sources instead of large prompts
knowledge_agent = Agent(
    role="Knowledge Expert",
    goal="Answer questions using curated knowledge",
    backstory="Expert at leveraging structured knowledge sources",
    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
    respect_context_window=True,
    verbose=True
)

# 빠른 해결책: 자동 처리 활성화
agent.respect_context_window = True

# 더 나은 솔루션: 대용량 데이터에는 RAG 도구 사용
from crewai_tools import RagTool
agent.tools = [RagTool()]

# 대안: 작업을 더 작은 단위로 나누기
# 또는 대용량 프롬프트 대신 knowledge 소스 사용

# 자동 요약 비활성화 후 RAG 사용
agent = Agent(
    role="Detailed Analyst",
    goal="Maintain complete information accuracy",
    backstory="Expert requiring full context",
    respect_context_window=False,  # 요약 안 함
    tools=[RagTool()],  # 대용량 데이터에는 RAG 사용
    verbose=True
)

from crewai import Agent
from crewai_tools import SerperDevTool

# Create an agent
researcher = Agent(
    role="AI Technology Researcher",
    goal="Research the latest AI developments",
    tools=[SerperDevTool()],
    verbose=True
)

# Use kickoff() to interact directly with the agent
result = researcher.kickoff("What are the latest developments in language models?")

# Access the raw response
print(result.raw)

from pydantic import BaseModel
from typing import List

class ResearchFindings(BaseModel):
    main_points: List[str]
    key_technologies: List[str]
    future_predictions: str

# Get structured output
result = researcher.kickoff(
    "Summarize the latest developments in AI for 2025",
    response_format=ResearchFindings
)

# Access structured data
print(result.pydantic.main_points)
print(result.pydantic.future_predictions)

messages = [
    {"role": "user", "content": "I need information about large language models"},
    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
    {"role": "user", "content": "What are the latest developments in 2025?"}
]

result = researcher.kickoff(messages)

import asyncio

async def main():
    result = await researcher.kickoff_async("What are the latest developments in AI?")
    print(result.raw)

asyncio.run(main())