CrewAI Adapter

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import asyncio
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import logging
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import os
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from dotenv import load_dotenv
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from thenvoi import Agent
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from thenvoi.adapters import CrewAIAdapter
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from thenvoi.config import load_agent_config
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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async def main():
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    load_dotenv()
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    ws_url = os.getenv("THENVOI_WS_URL")
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    rest_url = os.getenv("THENVOI_REST_URL")
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    if not ws_url:
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        raise ValueError("THENVOI_WS_URL environment variable is required")
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    if not rest_url:
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        raise ValueError("THENVOI_REST_URL environment variable is required")
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    # Load agent credentials from agent_config.yaml
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    agent_id, api_key = load_agent_config("crewai_agent")
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    # Create adapter with framework-specific settings
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    adapter = CrewAIAdapter(
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        model="gpt-4o",
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        custom_section="You are a helpful assistant. Be concise and friendly.",
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    )
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    # Create and start agent
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    agent = Agent.create(
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        adapter=adapter,
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        agent_id=agent_id,
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        api_key=api_key,
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        ws_url=ws_url,
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        rest_url=rest_url,
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    )
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    logger.info("Starting CrewAI agent...")
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    await agent.run()
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if __name__ == "__main__":
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    asyncio.run(main())

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Research Assistant",
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    goal="Help users find and analyze information",
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    backstory="Expert researcher with deep domain knowledge.",
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    custom_section="Focus on academic sources when possible.",
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    enable_execution_reporting=True,
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    verbose=True,
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    max_iter=25,
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Research Assistant",
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    goal="Help users find, analyze, and synthesize information efficiently",
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    backstory="""You are an expert research assistant with years of experience
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    in academic and business research. You excel at finding relevant information,
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    analyzing data, and presenting findings in a clear, actionable format.
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    You're known for your attention to detail and ability to connect disparate
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    pieces of information into meaningful insights.""",
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    enable_execution_reporting=True,
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    verbose=True,
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)

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from pydantic import BaseModel, Field
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class CalculatorInput(BaseModel):
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    """Perform a mathematical calculation."""
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    expression: str = Field(..., description="Mathematical expression to evaluate")
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def calculate(input: CalculatorInput) -> str:
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    try:
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        # WARNING: eval() is unsafe for production. Use a math parser instead.
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        result = eval(input.expression)
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        return f"{input.expression} = {result}"
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    except Exception as e:
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        return f"Error: {e}"
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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Math Assistant",
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    goal="Help users with calculations",
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    backstory="You are skilled at mathematics.",
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    additional_tools=[
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        (CalculatorInput, calculate),
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    ],
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)

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import httpx
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class WeatherInput(BaseModel):
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    """Get current weather for a location."""
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    location: str = Field(..., description="City name")
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async def get_weather(input: WeatherInput) -> str:
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    async with httpx.AsyncClient() as client:
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        response = await client.get(
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            f"https://api.weather.example/current?q={input.location}"
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        )
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        data = response.json()
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        return f"Weather in {input.location}: {data['temperature']}C"
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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    additional_tools=[
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        (WeatherInput, get_weather),
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    ],
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Research Assistant",
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    goal="Help users research topics",
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    backstory="Expert researcher.",
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    enable_execution_reporting=True,
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Team Coordinator",
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    goal="Orchestrate collaboration between specialized agents to accomplish complex tasks",
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    backstory="""You are an experienced project coordinator who excels at
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    breaking down complex problems into manageable tasks and delegating them
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    to the right specialists. You understand each team member's strengths
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    and know how to combine their outputs into cohesive solutions.
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    You have access to tools that let you:
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    - Look up available agents (thenvoi_lookup_peers)
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    - Add agents to the conversation (thenvoi_add_participant)
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    - Remove agents when they're no longer needed (thenvoi_remove_participant)
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    - Create new chat rooms for focused discussions (thenvoi_create_chatroom)
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    Use these tools to build the right team for each user request.""",
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    custom_section="""
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When coordinating:
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1. First understand what the user needs
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2. Identify which specialists would be helpful
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3. Use thenvoi_lookup_peers to find available agents
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4. Add relevant agents with thenvoi_add_participant
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5. Direct the conversation by mentioning specific agents
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6. Synthesize outputs from multiple agents
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7. Clean up by removing agents no longer needed
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""",
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    enable_execution_reporting=True,
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    verbose=True,
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)

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analyst_adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Research Analyst",
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    goal="Gather comprehensive information and provide well-researched insights",
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    backstory="""You are a meticulous research analyst with expertise in
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    finding reliable sources and synthesizing complex information.
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    Focus on gathering facts and data, cite sources when possible.""",
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)

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writer_adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Content Writer",
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    goal="Transform research into clear, engaging content",
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    backstory="""You are a skilled content writer who excels at taking
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    complex information and turning it into readable, engaging content.
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    Wait for the Research Analyst to provide findings before drafting.""",
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)

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editor_adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Editor",
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    goal="Ensure content quality through careful review",
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    backstory="""You are an experienced editor with a keen eye for detail.
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    Review drafts from the Content Writer, check for accuracy and clarity,
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    and provide the final polished version.""",
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o-mini",  # Use a faster, more cost-effective model
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    role="Quick Assistant",
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    goal="Provide fast, helpful responses",
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    backstory="You're optimized for quick, accurate answers.",
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    verbose=True,  # CrewAI detailed logging
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)

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import logging
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# Basic setup
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# For detailed debugging
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logging.basicConfig(level=logging.WARNING)
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logging.getLogger("thenvoi").setLevel(logging.DEBUG)

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# Good - specific and focused
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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Technical Documentation Writer",
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    goal="Create clear, accurate technical documentation",
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    backstory="""You specialize in writing documentation for APIs and SDKs.
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    You know how to explain complex technical concepts in accessible ways
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    while maintaining accuracy and completeness.""",
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)
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# Less effective - too generic
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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Helper",
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    goal="Help with stuff",
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    backstory="You help.",
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Code Reviewer",
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    goal="Ensure code quality and consistency",
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    backstory="Senior developer with expertise in code review.",
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    custom_section="""
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When reviewing code:
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1. Check for correctness and logic errors
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2. Verify adherence to coding standards
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3. Look for potential performance issues
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4. Suggest improvements with specific examples
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5. Be constructive and educational in feedback
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""",
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)

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adapter = CrewAIAdapter(
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    model="gpt-4o",
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    role="Data Analyst",
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    goal="Extract actionable insights from complex datasets",
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    backstory="""You have 10 years of experience in business intelligence.
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    You're skilled at identifying patterns, spotting anomalies, and
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    translating raw data into strategic recommendations. You communicate
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    findings clearly to both technical and non-technical audiences.""",
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)