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Usage Guide

Prerequisites

  1. Python 3.10 or higher
  2. API keys for:
    • GitHub Personal Access Token
    • OpenRouter API Key
    • ScrapeGraphAI API Key

Setup

1. Clone the Repository

git clone https://github.com/yourusername/github-star-to-company-agent.git
cd github-star-to-company-agent

2. Install Dependencies

pip install -r requirements.txt

3. Configure API Keys

Create a .env file in the project root with your API keys:

GITHUB_TOKEN=your_github_personal_access_token
OPENROUTER_API_KEY=your_openrouter_api_key
OPENROUTER_API_BASE=https://openrouter.ai/api/v1
SGAI_API_KEY=your_scrapegraphai_api_key

Getting API Keys:

  • GitHub Token: Go to GitHub Settings → Developer settings → Personal access tokens → Generate new token
  • OpenRouter API Key: Sign up at OpenRouter and get your API key
  • ScrapeGraphAI API Key: Sign up at ScrapeGraphAI Dashboard and get your API key

Running the Application

Start the Streamlit App

streamlit run src/app.py

The application will open in your default web browser at http://localhost:8501.

Using the Interface

  1. Enter Repository: Input a GitHub repository in the format owner/repo (e.g., ScrapeGraphAI/Scrapegraph-ai)
  2. Click Analyze: Press the "Analyze Repository" button to start the analysis
  3. View Results: The agent will:
    • Fetch stargazers from the repository
    • Trace them to their companies
    • Evaluate companies as potential sales targets
    • Display ranked results

Advanced Settings

In the sidebar, you can adjust:

  • Max companies to display: Limit the number of results shown
  • Minimum score threshold: Filter out low-scoring companies

Understanding Results

Company Scores (0-100)

  • 70-100: High Priority - Excellent fit for AI scraping infrastructure
  • 50-69: Medium Priority - Good potential, worth exploring
  • 30-49: Low Priority - Some relevance, but not ideal
  • 0-29: Not Recommended - Poor fit

Evaluation Criteria

Companies are scored based on:

  • Technology Stack: Use of AI, ML, data analytics, scraping
  • Industry: E-commerce, fintech, SaaS, analytics
  • Company Size: Larger companies score higher
  • Growth Indicators: Funding, hiring, expansion
  • Data Needs: Explicit mentions of data collection needs

Programmatic Usage

You can also use the agent programmatically:

from src.agent import run_agent

result = run_agent("facebook/react")

if result["success"]:
    for company in result["evaluations"][:10]:
        print(f"{company['company']}: Score {company['score']} - {company['recommendation']}")
else:
    print(f"Error: {result['error']}")

Testing

Run the test suite:

python -m pytest tests/

Or run specific tests:

python tests/test_agent.py

Troubleshooting

Common Issues

  1. "API key missing" errors

    • Ensure all API keys are correctly set in the .env file
    • Check that the .env file is in the project root directory

  2. Rate limiting errors

    • The agent automatically limits requests to avoid rate limiting
    • If you still hit limits, wait a few minutes before retrying

  3. No companies found

    • Some repositories may have stargazers without company information
    • Try repositories with more enterprise/professional users

  4. Scraping errors

    • Some company websites may block scraping
    • The agent will still provide evaluation based on available information

Ethical Considerations

  • Respect GitHub's rate limits and terms of service
  • Use scraped data responsibly and ethically
  • Don't use the tool for spam or unsolicited outreach
  • Respect privacy and data protection regulations

Support

For issues or questions:

  1. Check the troubleshooting section above
  2. Review the error messages in the Streamlit interface
  3. Open an issue on the GitHub repository