AI PR Reviewer

Kit AI PR Reviewer

Kit includes a production-ready AI PR reviewer that provides professional-grade code analysis with full repository context. Choose from 10 models ranging from $0.005 to $0.91 per review with complete cost transparency.

Tip

Want to build a custom reviewer? See the Build an AI PR Reviewer tutorial to create your own using kit’s components.

🚀 Quick Start

# 1. Install kit (lightweight - no ML dependencies needed for PR review!)
pip install cased-kit

# 2. Set up configuration
kit review --init-config

# 3. Set API keys
export KIT_GITHUB_TOKEN="ghp_your_token"
export KIT_ANTHROPIC_TOKEN="sk-ant-your_key"
export KIT_OPENAI_TOKEN="sk-openai-your_key"

# 4. Review any GitHub PR
kit review https://github.com/owner/repo/pull/123

# 5. Test without posting (dry run)
kit review --dry-run https://github.com/owner/repo/pull/123

# 6. Override model for specific review
kit review --model gpt-4.1-nano https://github.com/owner/repo/pull/123

Tip

Just want the PR reviewer? The base pip install cased-kit gives you everything needed for PR reviews without heavy ML dependencies like PyTorch. If you need semantic search features later, install with pip install cased-kit[ml].

💰 Transparent Pricing

Based on real-world testing on production open source PRs:

Model Options (Large PR Example)

Model	Typical Cost	Quality	Best For
gpt-4.1-nano	$0.0015-0.004	⭐⭐⭐	High-volume, ultra-budget
gpt-4.1-mini	$0.005-0.015	⭐⭐⭐⭐	Budget-friendly, often very good for the price
gpt-4.1	$0.02-0.10	⭐⭐⭐⭐	Good value
claude-sonnet-4	0.08-$0.14	⭐⭐⭐⭐⭐	Recommended for most

In practice

Even without optimizing your model mix (see below), a team doing 500 large PRs a month will generally pay under $50 a month total (yes) for reviews with SOTA models; and likely less.

Tip

Don’t underestimate the smaller models. gpt-4.1-mini (or even nano!) delivers surprisingly useful reviews when given the right context via kit. For simple projects, you can get decent AI code reviews for less than $1/month. Here’s an example against kit itself. This review cost half a cent.

🎯 Key Features

Intelligent Analysis

• Repository Context: Full codebase understanding, not just diff analysis
• Symbol Analysis: Identifies when functions/classes are used elsewhere
• Cross-Impact Assessment: Understands how changes affect the broader system
• Multi-Language Support: Works with any language kit supports

Professional Output

• Priority-Based Issues: High/Medium/Low issue categorization
• Specific Recommendations: Concrete code suggestions with examples
• GitHub Integration: Clickable links to all referenced files
• Quality Scoring: Objective metrics for review effectiveness

Cost & Transparency

• Real-Time Cost Tracking: See exact LLM usage and costs
• Token Breakdown: Understand what drives costs
• Model Information: Know which AI provided the analysis
• No Hidden Fees: Pay only for actual LLM usage

🔧 Configuration

Model Override via CLI

Override the model for any specific review without modifying your configuration:

kit review --model gpt-4.1-nano https://github.com/owner/repo/pull/123
kit review --model gpt-4.1 https://github.com/owner/repo/pull/123

# Short flag also works
kit review -m claude-sonnet-4-20250514 https://github.com/owner/repo/pull/123

Available Models:

• OpenAI: gpt-4.1-nano, gpt-4.1-mini, gpt-4.1, gpt-4o-mini, gpt-4o
• Anthropic: claude-3-5-haiku-20241022, claude-3-5-sonnet-20241022, claude-opus-4-20250514, claude-sonnet-4-20250514

Tip

Model validation: Kit automatically validates model names and provides helpful suggestions if you mistype. Try kit review --model gpt4 to see the validation in action!

Tip

Pro tip: Use different models based on PR complexity. Save claude-opus-4 for architectural changes and use gpt-4.1-nano for documentation/minor fixes.

Setup API Keys

GitHub Token: Get from GitHub Settings → Developer settings → Personal access tokens

export KIT_GITHUB_TOKEN="ghp_your_token_here"

LLM API Keys:

# For Anthropic Claude (recommended)
export KIT_ANTHROPIC_TOKEN="sk-ant-your_key"

# For OpenAI GPT models
export KIT_OPENAI_TOKEN="sk-your_openai_key"

Configuration File

Edit ~/.kit/review-config.yaml:

github:
  token: ghp_your_token_here
  base_url: https://api.github.com

llm:
  provider: anthropic  # or "openai"
  model: claude-sonnet-4-20250514
  api_key: sk-ant-your_key_here
  max_tokens: 4000
  temperature: 0.1

review:
  post_as_comment: true
  clone_for_analysis: true
  cache_repos: true
  max_files: 50

📊 Review Examples

See real-world examples with actual costs and analysis:

• FastAPI Packaging Change ($0.034) - Architectural impact analysis
• React.dev UI Feature ($0.012) - Accessibility-focused review
• Documentation Fix ($0.006) - Proportional response
• Multi-Model Comparison - Cost vs quality analysis

🚀 CI/CD Integration

GitHub Actions

Create .github/workflows/pr-review.yml:

name: AI PR Review
on:
  pull_request:
    types: [opened, synchronize, reopened]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
      contents: read

    steps:
      - name: AI Code Review
        run: |
          pip install cased-kit
          kit review ${{ github.event.pull_request.html_url }}
        env:
          KIT_GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          KIT_ANTHROPIC_TOKEN: ${{ secrets.ANTHROPIC_API_KEY }}

Advanced Workflows

Budget-Conscious Setup (GPT-4.1-nano):

- name: Budget AI Review
  run: |
    pip install cased-kit
    # Configure for ultra-low cost
    kit review --model gpt-4.1-nano ${{ github.event.pull_request.html_url }}

Model-Based on PR Size:

- name: Smart Model Selection
  run: |
    pip install cased-kit
    # Use budget model for small PRs, premium for large ones
    FILES_CHANGED=$(gh pr view ${{ github.event.pull_request.number }} --json files --jq '.files | length')
    if [ "$FILES_CHANGED" -gt 10 ]; then
      MODEL="claude-sonnet-4-20250514"
    else
      MODEL="gpt-4o-mini"
    fi
    kit review --model "$MODEL" ${{ github.event.pull_request.html_url }}
  env:
    GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Conditional Reviews:

# Only review non-draft PRs
- name: AI Review
  if: "!github.event.pull_request.draft"

# Only review PRs with specific labels
- name: AI Review
  if: contains(github.event.pull_request.labels.*.name, 'needs-review')

# Use premium model for breaking changes
- name: Premium Review for Breaking Changes
  if: contains(github.event.pull_request.labels.*.name, 'breaking-change')
  run: |
    pip install cased-kit
    kit review --model claude-opus-4-20250514 ${{ github.event.pull_request.html_url }}
  env:
    KIT_GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
    KIT_ANTHROPIC_TOKEN: ${{ secrets.ANTHROPIC_API_KEY }}

📈 What’s Next: Roadmap

Custom Context & Learning

• Per-Organization Context: Store custom guidelines and coding standards
• Feedback Learning: Simple database to learn from review feedback
• Inline Comments: Post comments directly on specific lines
• Follow-up review awareness: Take previous reviews into account for better feedback

# Coming soon
kit profile create --name "company-standards" --file coding-guidelines.md
kit review --profile company-standards <pr-url>
kit feedback <review-id> --helpful --notes "Great catch!"

# Future features
kit review <pr-url> --consensus  # Multiple models
kit review <pr-url> --mode inline  # Line-level comments

🔍 Advanced Usage

Cache Management

# Check cache status
kit review-cache status

# Clean up old repositories
kit review-cache cleanup

# Clear all cached repositories
kit review-cache clear

Quality Validation

Every review includes objective quality scoring:

• File References: Checks if review references actual changed files
• Specificity: Measures concrete vs vague feedback
• Coverage: Assesses if major changes are addressed
• Relevance: Ensures suggestions align with actual code changes

💡 Best Practices

Cost Optimization

• Use budget models for routine changes, premium for breaking changes
• Use the --model flag to override models per PR: kit review --model gpt-4.1-nano <pr-url>
• Leverage caching - repeat reviews of same repo are 5-10x faster
• Set up profiles to avoid redundant context

Team Adoption

• Start with dry runs to build confidence
• Use budget models initially to control costs
• Create organization-specific guidelines for consistent reviews

Integration

• Add to CI/CD for all PRs or just high-impact branches
• Use conditional logic to avoid reviewing bot PRs or documentation-only changes
• Monitor costs and adjust model selection based on team needs

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The kit AI PR reviewer provides professional-grade code analysis at costs accessible to any team size, from $0.46/month for small teams to enterprise-scale deployment. With full repository context and transparent pricing, it’s designed to enhance your development workflow without breaking the budget.