AI GitHub Maintainer

AI GitHub Maintainer is an advanced tool that leverages artificial intelligence to automate and enhance GitHub repository maintenance. It supports multiple LLM providers and offers a wide range of features to streamline your development workflow.

Table of Contents

1. Features
2. Setup
3. Configuration
4. Usage
5. Web Interface
6. Customizing the LLM Provider
7. Plugin System
8. Contributing
9. License
10. Advanced Usage
11. Troubleshooting
12. FAQ
13. Roadmap
14. Changelog
15. Security
16. Performance Optimization
17. Integrations
18. Community and Support

Features

• Code Analysis and Improvement

  • AI-powered feature generation and implementation
  • Code optimization and complexity analysis
  • Style suggestions and code explanations
  • Code duplication detection
  • Automated refactoring suggestions
  • Dead code elimination
  • Performance hotspot identification

• Documentation and Testing

  • Automated documentation generation
  • Test case creation and expansion
  • Code coverage analysis and improvement suggestions
  • MkDocs integration for documentation sites
  • API documentation generation
  • Changelog management

• Security and Compliance

  • Security vulnerability scanning
  • License compliance checks
  • Dependency vulnerability analysis
  • SAST (Static Application Security Testing) integration
  • GDPR compliance checking
  • PII (Personally Identifiable Information) detection

• Performance Optimization

  • Performance profiling and improvement suggestions
  • Database query optimization
  • Caching strategy recommendations
  • Memory leak detection
  • CPU and memory usage analysis

• Issue and PR Management

  • AI-assisted issue triaging and prioritization
  • Automated PR reviews with detailed feedback
  • Intelligent issue creation for various analyses
  • Duplicate issue detection
  • Automated issue labeling and assignment
  • SLA (Service Level Agreement) tracking for issue resolution

• Version Control and Releases

  • Semantic versioning suggestions
  • Automated release notes generation
  • GitHub release creation and management
  • Changelog generation and updating
  • Git workflow optimization suggestions
  • Branch strategy recommendations

• Repository Insights

  • Contributor analysis and engagement metrics
  • Language usage trends and statistics
  • Commit activity analysis and visualizations
  • Code churn analysis
  • Bus factor calculation
  • Technical debt estimation

• CI/CD Integration

  • Support for external CI/CD service triggers
  • Pipeline optimization suggestions
  • Test suite execution time analysis
  • Deployment frequency and success rate tracking
  • Rollback and canary deployment support
  • Infrastructure as Code (IaC) validation
  • Continuous deployment risk assessment

• Customization and Extensibility

  • Custom LLM prompt support
  • Plugin system for extended functionality
  • Webhook integrations for external services
  • Custom reporting and dashboard creation
  • Scripting support for advanced automation
  • API for programmatic access to maintainer features

Setup

1. Clone the repository:

   git clone https://github.com/Likhithsai2580/ai-github-maintainer.git
   cd ai-github-maintainer
   

2. Set up a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

3. Move env.example to .env file with your API keys and configuration:

   GITHUB_TOKEN=your_github_token_here
   OPENAI_API_KEY=your_openai_api_key_here
   ANTHROPIC_API_KEY=your_anthropic_api_key_here
   GROQ_API_KEY=your_groq_api_key_here
   LLM_PROVIDER=openai
   LLM_MODEL=gpt-3.5-turbo
   

4. Customize the config.yaml file to suit your needs.

5. Run the application:

   python main.py
   

Configuration

The config.yaml file allows you to customize various aspects of the AI GitHub Maintainer:

• GitHub settings (organization, repositories, access tokens)
• LLM provider and model selection
• Maintenance schedule and frequency
• Feature toggles for specific maintainer capabilities
• Logging configuration and verbosity levels
• Plugin settings and activation
• Caching options for improved performance
• Custom prompts for AI interactions
• Notification settings (email, Slack, Discord)
• CI/CD integration parameters
• Security scan thresholds and policies

Refer to the comments in config.yaml for detailed explanations of each setting.

Usage

The AI GitHub Maintainer runs on a schedule defined in config.yaml. By default, it performs weekly maintenance tasks on all accessible repositories.

You can also trigger maintenance manually using the web interface or by running python main.py --repo=<repository_name>.

Advanced usage options:

• python main.py --full-scan: Perform a comprehensive analysis of all repositories
• python main.py --security-audit: Run a security-focused maintenance cycle
• python main.py --generate-report: Create a detailed report of recent maintenance activities

Web Interface

Access the web interface at http://localhost:5000. Features include:

• Manual maintenance triggering for specific repositories or organization-wide
• Real-time logs and updates on ongoing maintenance tasks
• Repository activity report generation with customizable metrics
• Plugin management interface for activating and configuring plugins
• User management for multi-user setups
• Dashboard with key metrics and insights
• Scheduling interface for customizing maintenance routines

Customizing the LLM Provider

To change the LLM provider, update the LLM_PROVIDER and LLM_MODEL variables in your .env file. Supported options:

• OpenAI: LLM_PROVIDER=openai, LLM_MODEL=gpt-3.5-turbo or gpt-4
• Anthropic: LLM_PROVIDER=anthropic, LLM_MODEL=claude-2 or claude-instant-1
• Groq: LLM_PROVIDER=groq, LLM_MODEL=llama2-70b-4096 or mixtral-8x7b-32768
• Ollama: LLM_PROVIDER=ollama, LLM_MODEL=llama2 or codellama

Custom LLM integration:

1. Create a new file in the llm_providers/ directory (e.g., custom_llm.py)
2. Implement the required interface methods (initialize, generate_response)
3. Add your custom provider to the LLM_PROVIDER options in config.yaml

Plugin System

AI GitHub Maintainer supports custom plugins to extend its functionality. For detailed information on creating and using plugins, refer to the Plugin Development Guide.

Key aspects of the plugin system:

• Hot-reloading of plugins for development
• Dependency management for plugin requirements
• Sandboxing for security isolation
• Performance profiling for plugin optimization
• Marketplace for sharing and discovering community plugins

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please ensure your code adheres to our coding standards and includes appropriate tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Advanced Usage

Custom Workflows

Create custom maintenance workflows by chaining multiple plugins:

workflows:
  security_audit:
    - security_scanner
    - dependency_checker
    - license_validator
  performance_optimization:
    - code_profiler
    - query_optimizer
    - cache_analyzer

API Integration

Integrate the AI GitHub Maintainer into your existing tools and workflows using our RESTful API:

import requests

api_url = "http://localhost:5000/api/v1"
headers = {"Authorization": "Bearer your_api_token"}

# Trigger maintenance for a specific repository
response = requests.post(f"{api_url}/maintain", json={"repo": "user/repo"}, headers=headers)
print(response.json())

Automated Reporting

Schedule automated reports to be sent to stakeholders:

reporting:
  schedule: "0 9 * * 1"  # Every Monday at 9 AM
  recipients:
    - [email protected]
    - [email protected]
  format: pdf
  sections:
    - security_summary
    - performance_metrics
    - code_quality_trends

Troubleshooting

Common issues and their solutions:

1. Rate Limiting: If you encounter GitHub API rate limits, consider using a GitHub App instead of a personal access token for higher rate limits.

2. Memory Usage: For large repositories, you may need to increase the available memory. Use the --memory-limit flag:

   python main.py --memory-limit=8G
   

3. Slow Performance: Enable caching in config.yaml to speed up repeated operations:

   caching:
     enabled: true
     backend: redis
     ttl: 3600
   

4. Plugin Errors: Check the plugin logs in logs/plugins.log for detailed error messages. Ensure all plugin dependencies are installed.

FAQ

Q: Can I use AI GitHub Maintainer with self-hosted GitHub Enterprise? A: Yes, specify your GitHub Enterprise URL in config.yaml:

github:
  api_url: https://github.example.com/api/v3

Q: How does AI GitHub Maintainer handle sensitive data? A: Sensitive data is never stored locally and is redacted from logs. All communications with LLM providers are encrypted.

Q: Can I use AI GitHub Maintainer with other version control systems? A: Currently, only GitHub is supported, but we plan to add support for GitLab and Bitbucket in future releases.

Roadmap

Upcoming features and improvements:

• Support for GitLab and Bitbucket
• Integration with popular project management tools (Jira, Trello)
• AI-powered code review suggestions
• Natural language querying of repository data
• Automated dependency updating with compatibility checks
• Machine learning models for predicting code quality issues

Changelog

v1.2.0 (2023-06-15)

• Added support for custom LLM providers
• Improved plugin sandboxing for enhanced security
• Introduced workflow automation feature
• Enhanced performance optimization capabilities

v1.1.0 (2023-05-01)

• Implemented plugin marketplace
• Added support for Groq LLM provider
• Improved documentation generation
• Enhanced security scanning features

v1.0.0 (2023-04-01)

• Initial release with core features

Security

We take security seriously. If you discover any security-related issues, please email [email protected] instead of using the issue tracker.

Security features:

• Regular dependency updates and vulnerability scanning
• Encrypted storage of sensitive configuration data
• Strict input validation and sanitization
• Regular third-party security audits

Performance Optimization

Tips for optimizing AI GitHub Maintainer performance:

1. Use the --parallel flag to run maintenance tasks concurrently:

   python main.py --parallel=4
   

2. Enable result caching to speed up repeated analyses:

   caching:
     enabled: true
     backend: redis
   

3. Use a faster LLM provider for less complex tasks:

   llm:
     provider: groq
     model: mixtral-8x7b-32768
   

4. Implement custom plugins for performance-critical tasks using compiled languages (e.g., Rust, Go) and use them via the plugin system.

Integrations

AI GitHub Maintainer integrates with various tools and services:

• CI/CD: Jenkins, GitLab CI, CircleCI, Travis CI
• Issue Tracking: Jira, Trello, Asana
• Communication: Slack, Discord, Microsoft Teams
• Monitoring: Prometheus, Grafana, DataDog
• Code Quality: SonarQube, CodeClimate, Codacy

Example Slack integration:

integrations:
  slack:
    webhook_url: https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX
    channel: #github-maintainer
    notifications:
      - security_alerts
      - performance_reports

Community and Support

• Join our Discord server for community discussions and support
• Follow us on Twitter for the latest updates and tips
• Contribute to the project on GitHub

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By leveraging the power of AI and automation, AI GitHub Maintainer helps development teams maintain high-quality, secure, and efficient code repositories with minimal manual intervention.