LLM-Code-Review 🤖

Supercharge your code reviews with automated expert feedback

Real-time code analysis that works alongside your team. Get detailed feedback on security risks, performance bottlenecks, and code quality - right in your pull requests. No more waiting for reviews or missing critical issues.

🎯 Key Features

• 🔄 Instant review on every PR
• 🧠 Deep code analysis with LLM
• 📊 Smart, actionable feedback
• ⚡ Performance optimization tips
• 🛡️ Built-in security checks

📸 In Action

Automated Review Summary

Detailed Recommendations

🚀 Quick Setup

1. Configure GitHub Actions

Add these secrets to your repository:

• Go to Settings → Secrets and variables → Actions
• Add OPENAI_API_KEY with your OpenAI API key

2. Enable Workflow

Create .github/workflows/code_review.yml in your repository with the required configuration for automated reviews.

3. Test It Out

1. Create a new pull request
2. Watch the automated review process in action
3. Review the detailed feedback and recommendations

⚙️ Configuration

Customize the review focus in src/config/settings.py:

@dataclass
class ReviewSettings:
    categories: List[str] = ('security', 'performance', 'style', 'conventions')
    max_file_size_kb: int = 500
    excluded_patterns: List[str] = ('*.pyc', '*.env', '__pycache__/*', '*.log')

🔧 Troubleshooting

Common issues and fixes:

1. Reviews not triggering?

   • Check Actions permissions
   • Verify OPENAI_API_KEY is set
   • Ensure workflow is enabled

2. Authentication errors?

   • Review repository permissions
   • Check token configuration
   • Verify API key validity

📖 Best Practices

1. Optimal PR Size

   • Keep changes focused
   • Submit smaller PRs
   • Clear descriptions

2. Review Process

   • Review AI suggestions
   • Test recommended changes
   • Address all categories