Friendly AI Reviewer (FAIR) Setup Guide

• Creates highly-customizable AI Reviews as PR comments.
• Installation: Just 2 files copied to your repo and a open router API Key in your secrets.
• Costs: $0.01 - $0.05 per review (depends highly on model)
• ** Example output ** LearningCircuit/local-deep-research#959 (comment)

This guide explains how to set up the automated AI PR review system using OpenRouter to analyze pull requests with your choice of AI model.

Overview

The AI Code Reviewer provides automated, comprehensive code reviews covering:

• Security 🔒 - Hardcoded secrets, SQL injection, XSS, authentication issues, input validation
• Performance ⚡ - Inefficient algorithms, N+1 queries, memory issues, blocking operations
• Code Quality 🎨 - Readability, maintainability, error handling, naming conventions
• Best Practices 📋 - Coding standards, proper patterns, type safety, dead code

The review is posted as a single comprehensive comment on your pull request.

Setup Instructions

1. Get OpenRouter API Key

1. Go to OpenRouter.ai
2. Sign up or log in
3. Navigate to API Keys section
4. Create a new API key
5. Copy the key (it starts with sk-or-v1-...)

2. Add API Key to GitHub Secrets

1. Go to your GitHub repository
2. Navigate to Settings → Secrets and variables → Actions
3. Click New repository secret
4. Name it: OPENROUTER_API_KEY
5. Paste your OpenRouter API key
6. Click Add secret

3. Configure Workflow (Optional)

The workflow is pre-configured with sensible defaults, but you can customize it by editing .github/workflows/ai-code-reviewer.yml:

• AI_MODEL: Change the AI model (see OpenRouter models)
• AI_TEMPERATURE: Adjust randomness (default: 0.1 for consistent reviews)
• AI_MAX_TOKENS: Maximum response length (default: 2000)
• MAX_DIFF_SIZE: Maximum diff size in bytes (default: 800000 / 800KB)

Usage

Triggering AI Reviews

To trigger an AI review on a PR:

1. Go to the PR page
2. Click Labels
3. Add the label: ai_code_review

The review will automatically start and post results as a comment when complete.

Re-running Reviews

To re-run the AI review after making changes:

1. Remove the ai_code_review label
2. Add the ai_code_review label again

This will generate a fresh review of the current PR state.

Review Results

The AI posts a comprehensive comment analyzing your code across all focus areas. The review is meant to assist human reviewers, not replace them.

Cost Estimation

Costs vary by model, but most code-focused models on OpenRouter are very affordable:

• Typical small PR (< 1000 lines): $0.001 - $0.01
• Large PR (1000-5000 lines): $0.01 - $0.05

Check OpenRouter pricing for specific model costs.

Customization

Changing the Review Focus

Edit ai-reviewer.sh to modify the review prompt. The current focus areas are:

• Security (secrets, injection attacks, authentication)
• Performance (algorithms, queries, memory)
• Code Quality (readability, maintainability, error handling)
• Best Practices (standards, patterns, type safety)

You can adjust these to match your team's priorities.

Troubleshooting

Reviews Not Running

• Ensure the ai_code_review label is added (not just present)
• Check that OPENROUTER_API_KEY secret is correctly configured
• Verify GitHub Actions permissions are properly set

API Errors

• Check OpenRouter API key validity
• Verify OpenRouter account has sufficient credits
• Review GitHub Actions logs for specific error messages

Diff Too Large Error

If you get a "Diff is too large" error:

• Split your PR into smaller, focused changes
• Or increase MAX_DIFF_SIZE in the workflow file
• Default limit is 800KB (~200K tokens)

Security Considerations

• API keys are stored securely in GitHub Secrets and passed via environment variables
• Reviews only run when the ai_code_review label is manually added
• All API calls are made through secure HTTPS connections
• Code diffs are sent to OpenRouter/AI provider - review their data policies
• The workflow has minimal permissions (read contents, write PR comments)

Support

For issues with:

• OpenRouter API: Check OpenRouter documentation
• GitHub Actions: Check GitHub Actions documentation
• Workflow issues: Review the GitHub Actions logs for specific error details