Business Researcher

An AI-powered business research tool that automates the process of gathering comprehensive information about professionals and companies using web search and LLM-based analysis.

Features

Research Capabilities

Person Research:

• LinkedIn profile, role, and work email
• Current location and company
• Work history and years of experience
• Contact information and career trajectory

Company Research:

• Official details (website, LinkedIn, Crunchbase)
• Leadership team and organizational structure
• Products, services, and company overview
• Funding information (rounds, amounts, dates)
• Distinguishing features from similar companies

Architecture

Built using LangGraph as a state-based workflow orchestrator with a multi-step research pipeline:

1. Query Writer - Generates optimized search queries based on input
2. Web Search - Executes searches using Tavily API integration
3. Note Taker - Extracts structured information from search results
4. Note Reviewer - Validates and refines extracted data with iterative improvement

Technical Stack

• LangGraph: Workflow orchestration and state management
• LangChain: LLM integrations and model abstraction
• Tavily: Web search API for real-time information
• Pydantic: Data validation and structured outputs
• Rich: Enhanced console output formatting

Supported LLM Providers

• Groq: Llama-3.3-70b-versatile, DeepSeek-R1-distill
• OpenAI: GPT-4.1, O3 reasoning model
• vLLM: Custom model endpoints
• Ollama: Local model deployment

Installation

# Clone the repository
git clone <repository-url>
cd business-researcher

# Install dependencies using uv
uv sync

Configuration

Set up your API keys in the configuration:

• GROQ_API_KEY - For Groq LLM access
• OPENAI_API_KEY - For OpenAI models
• TAVILY_API_KEY - For web search functionality

Usage

from business_researcher import BusinessResearcher, SearchType
from ai_common import LlmServers

# Initialize the researcher
researcher = BusinessResearcher(
    llm_server=LlmServers.GROQ,
    llm_config=llm_config,
    web_search_api_key=tavily_api_key
)

# Research a person
person_input = {
    "name": "John Doe",
    "company": "Tech Corp",
    "search_type": SearchType.PERSON
}

# Research a company
company_input = {
    "name": "Techy Tech Studios",
    "search_type": SearchType.COMPANY
}

# Get results
results_for_person = researcher.get_response(person_input)
results_for_company = researcher.get_response(company_input)

Project Structure

src/
├── business_researcher/
│   ├── components/          # Individual processing components
│   │   ├── query_writer.py  # Search query generation
│   │   ├── note_taker.py    # Information extraction
│   │   ├── note_reviewer.py # Quality validation
│   │   └── routing.py       # Workflow routing logic
│   ├── researcher.py        # Main orchestrator class
│   ├── schema.py           # Data models and validation
│   ├── state.py            # Workflow state management
│   └── enums.py            # Type definitions
└── main_dev.py             # Development entry point

License

This project is licensed under the MIT License.