code-critic

pair-review

Your AI-powered code review partner - Close the feedback loop between you and AI coding agents

GitHub Repository

Table of Contents

• What is pair-review?
• Why pair-review?
• Workflows
  • Local Review
  • Meta-Review
  • AI-Guided Review
• Quick Start
• Command Line Interface
• Configuration
  • Environment Variables
  • GitHub Token
  • AI Provider Configuration
• Features
  • Three-Level AI Analysis
  • Analysis Configuration
  • Chat
  • Customization
  • Review Feedback Export
  • Inline Comments
  • Comment Format
  • Local Mode
• Claude Code Plugins
• MCP Integration
• Development
• FAQ
• Contributing
• License

What is pair-review?

pair-review is a local web application for keeping humans in the loop with AI coding agents. Calling it an AI code review tool would be accurate but incomplete — it supports multiple workflows beyond automated review, from reviewing agent-generated code before committing, to judging AI suggestions instead of reading every line, to using AI to guide your attention during a thorough review. You pick what fits your situation.

Two Core Value Propositions

1. Tight Feedback Loop for AI Coding Agents

   • Review AI-generated code with clarity and precision
   • Provide structured feedback in markdown format
   • Copy and paste feedback directly back to your coding agent
   • Create a continuous improvement cycle where you stay in control

2. AI-Assisted Human Review Partner

   • Get AI-powered suggestions to accelerate your reviews
   • Highlight potential issues and noteworthy code patterns
   • You make the final decisions - adopt, edit, or discard AI suggestions
   • Add your own comments alongside AI insights

Why pair-review?

• Local-First: All data and processing happens on your machine - no cloud dependencies
• GitHub-Familiar UI: Interface feels instantly familiar to GitHub users
• Human-in-the-Loop: AI suggests, you decide
• Multiple AI Providers: Support for Claude, Gemini, Codex, Copilot, OpenCode, Cursor, and Pi. Use your existing subscription!
• Progressive: Start simple with manual review, add AI analysis when you need it

Workflows

There are no hard boundaries between these — mix and match as needed.

1. Local Review: Human Reviews Agent-Generated Code

When to use: You're working with a coding agent and want to review its changes before committing.

This is the core feedback loop workflow. When an agent generates code, open pair-review to review the uncommitted changes. With the GitHub-like UI, you can add comments at specific file and line locations, then copy that formatted feedback and paste it back into whatever coding agent you're using (or use MCP/skills to read comments directly into Claude Code).

Compared to giving feedback in chat, this feels like moving from a machete to a scalpel. Instead of trying to capture everything in one message, you can leave targeted comments at dozens of specific locations — and the agent addresses each one with surgical precision.

How it works:

1. Run pair-review --local to open the diff UI
2. Review changes in a familiar GitHub-like interface
3. Add comments with specific file and line locations
4. Copy formatted feedback and paste into your coding agent
5. Iterate until you're satisfied

Tips:

• Stage previous changes in git, then only review new modifications in the next round
• Local mode only shows unstaged changes and untracked files (opinionated by design)

2. Meta-Review: Judging AI Suggestions

When to use: You're not going to read every line of code. Let AI be your reader.

Instead of reviewing thousands of lines of code, you review a dozen AI suggestions. The AI reads the code; you review its recommendations. Each suggestion comes with enough context to evaluate it — even when you're not deeply familiar with the language or codebase.