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CrewAI Implementation

The Company Research Agent uses CrewAI to coordinate multiple AI agents in gathering and analyzing company information. This document details the implementation of the CompanyResearchCrew class.

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Class Overview

@CrewBase
class CompanyResearchCrew:
    """
    @class CompanyResearchCrew
    @description Coordinates multiple AI agents for comprehensive company research
    @param actor - Apify Actor instance for web scraping operations
    """

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Initialization

def __init__(self, actor):
    self.actor = actor
    self.llm = LLM(
        model="gemini/gemini-2.0-flash-lite",
        temperature=0.7,
        api_key=os.getenv("GOOGLE_API_KEY")
    )

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Tasks

The crew executes three main tasks:

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1. Research Company

@task
def research_company(self) -> Task:
    """
    @task Research company details using domain name
    @returns Task - Research task configuration
    """
    return Task(
        description="""Research the company using their domain name: {domain}. 
        Focus on key insights about the company's:
        1. Overview and core business
        2. Products and services
        3. Market presence and performance
        4. Key personnel and organization
        5. Financial metrics and funding
        6. Technology stack and digital presence
        7. Recent developments and news
        8. Major Competitors""",
        agent=self.researcher()
    )

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2. Analyze Data

@task
def analyze_data(self) -> Task:
    """
    @task Analyze gathered company data
    @returns Task - Analysis task configuration
    """
    return Task(
        description="""Analyze the research findings to extract key insights about:
        1. business focus,
        2. product lineup,
        3. market and demographic details,
        4. funding rounds,
        5. notable executives,
        6. social media profiles,
        7. a list of major competitors""",
        agent=self.data_analyst()
    )

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3. Compile Report

@task
def compile_report(self) -> Task:
    """
    @task Compile findings into a structured report
    @returns Task - Report compilation task configuration
    """
    return Task(
        description="""Create a comprehensive report combining all research and analysis.
        Structure the information clearly and highlight key findings.""",
        expected_output="Final structured report in JSON format",
        agent=self.content_compiler()
    )

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Crew Configuration

The crew is configured to work sequentially: 
@crew
def crew(self) -> Crew:
    """
    @returns Crew - Configured CrewAI crew
    """
    return Crew(
        agents=[
            self.researcher(),
            self.data_analyst(),
        ],
        tasks=[
            self.research_company(),
            self.analyze_data(),
        ],
        process=Process.sequential,
        verbose=True,
        max_rpm=100,
        show_tools_output=False
    )

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Usage Example

# Initialize the crew with an Apify actor instance
research_crew = CompanyResearchCrew(actor=actor)

# Start the research process
result = research_crew.crew().kickoff(
    inputs={'domain': 'example.com'}
)

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Performance Considerations

The crew implementation includes several optimizations:
  • Sequential Processing: Tasks are executed in order to ensure data consistency
  • Rate Limiting: max_rpm=100 prevents API overload
  • Output Control: show_tools_output=False reduces noise
  • Verbose Mode: Enabled for debugging and monitoring

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Error Handling

The crew handles errors at multiple levels:
  1. Task Level: Individual task failures
  2. Agent Level: Agent execution errors
  3. Tool Level: Data collection errors
The crew implementation is designed to be maintainable and extensible. New tasks or agents can be added by following the existing patterns.