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Architecture Overview

The Company Research Agent is built using CrewAI, a powerful framework for orchestrating multiple AI agents to work together on complex tasks. The architecture combines specialized agents, custom tools, and the Apify platform to deliver comprehensive company research.

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High-Level Architecture

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Core Components

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1. CrewAI Crew

The CompanyResearchCrew class orchestrates three specialized agents:

@CrewBase
class CompanyResearchCrew:
    def crew(self) -> Crew:
        return Crew(
            agents=[
                self.researcher(),
                self.data_analyst(),
            ],
            tasks=[
                self.research_company(),
                self.analyze_data(),
            ],
            process=Process.sequential
        )

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2. Specialized Agents

Each agent has a specific role in the research process:

• Researcher: Gathers raw company information
• Data Analyst: Processes and analyzes the collected data
• Content Compiler: Formats the findings into structured reports

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3. Custom Tools

The system includes specialized tools for different data sources:

tools=[
    CompanyNewsSearchTool(actor=self.actor),
    ProfessionalProfilesTool(actor=self.actor),
    LinkedInScraperTool(actor=self.actor),
    CrunchbaseScraperTool(actor=self.actor),
    PitchBookScraperTool(actor=self.actor),
    GoogleSearchTool(actor=self.actor)
]

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4. Task Flow

The research process follows a sequential workflow:

1. Company research using domain name
2. Data analysis and insight extraction
3. Report compilation and formatting

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Integration Points

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Apify Platform

• Actors: Uses specialized Apify actors for web scraping
• Dataset: Stores and manages research results
• Key-Value Store: Handles intermediate data storage

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LLM Integration

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

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Data Flow

1. Input: Company domain name
2. Research Phase: Multiple tools gather data in parallel
3. Analysis Phase: Data is processed and analyzed
4. Output: Structured report in JSON format

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Error Handling

The system includes robust error handling:

• Tool-level error handling for each data source
• Agent-level task retry mechanisms
• Crew-level process management

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Performance Optimization

• Concurrent data collection where possible
• Rate limiting controls
• Memory management for large datasets