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Contributing to Entity Tracker

Thank you for considering contributing to Entity Tracker! This document provides guidelines and instructions for contributing.

Development Setup

  1. Fork and clone the repository:
git clone https://github.com/your-username/entity-tracker-langgraph.git
cd entity-tracker-langgraph
  1. Set up development environment:
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
pip install -e .  # Install in editable mode
  1. Configure environment:
cp .env.example .env
# Add your API keys for testing

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=entity_tracker --cov-report=html

# Run specific test file
pytest tests/test_agent.py

# Run integration tests only
pytest -m integration

Code Style

We follow PEP 8 style guidelines:

# Format code with black
black entity_tracker/ tests/

# Check with flake8
flake8 entity_tracker/ tests/

# Type checking with mypy
mypy entity_tracker/

Making Changes

  1. Create a feature branch:
git checkout -b feature/your-feature-name

  1. Make your changes following these guidelines:

    • Write clear, descriptive commit messages
    • Add tests for new features
    • Update documentation as needed
    • Keep changes focused and atomic

  2. Test your changes:

pytest
  1. Commit and push:
git add .
git commit -m "Add: descriptive message about your changes"
git push origin feature/your-feature-name
  1. Create a Pull Request:
    • Go to GitHub and create a PR
    • Describe your changes clearly
    • Link any related issues
    • Wait for review

Areas for Contribution

High Priority

  • Search Provider Integrations: Add support for more search providers (Exa, DuckDuckGo, Brave)
  • Database Backends: Implement PostgreSQL, MongoDB, or other database backends
  • Performance Optimization: Improve query efficiency and caching
  • Documentation: Improve examples, tutorials, and API documentation

Features

  • Entity Network Visualization: Graph visualization of entity relationships
  • Sentiment Analysis: Track sentiment about entities over time
  • Export Capabilities: PDF, JSON, CSV export formats
  • REST API: HTTP API for external integrations
  • Multi-language Support: Support for non-English entities
  • Real-time Monitoring: Webhook-based real-time updates

Testing

  • Integration Tests: More comprehensive integration test coverage
  • Performance Tests: Benchmarking and performance regression tests
  • Mock Improvements: Better mock implementations for testing

Pull Request Guidelines

Before Submitting

  • All tests pass
  • Code follows style guidelines
  • Documentation is updated
  • Commit messages are clear
  • Branch is up to date with main

PR Description Should Include

  1. What: Brief description of changes
  2. Why: Rationale for the changes
  3. How: Technical approach taken
  4. Testing: How you tested the changes
  5. Screenshots: If applicable (for UI changes)

Example PR Description

## What
Add support for Exa.ai search provider

## Why
Exa provides high-quality AI-powered search results that are particularly 
good for entity research and fact-finding.

## How
- Created `tools/exa_search.py` with EXA API integration
- Added configuration options to `configuration.py`
- Implemented search and review nodes in graph
- Added tests for Exa integration

## Testing
- Added unit tests in `tests/test_exa_search.py`
- Manually tested with various entity types
- Verified search results quality

Code Review Process

  1. Initial Review: Maintainers will review within 2-3 business days
  2. Feedback: Address any feedback or requested changes
  3. Approval: Once approved, PR will be merged
  4. Release: Changes will be included in the next release

Questions?

  • GitHub Discussions: For general questions
  • GitHub Issues: For bugs and feature requests
  • Discord: Join our community (link in README)

License

By contributing, you agree that your contributions will be licensed under the same license as the project (MIT License).

Thank you for contributing! 🙏