What problem does it solve?
Manual code reviews are often slow, inconsistent, and prone to missing critical issues like security vulnerabilities, performance bottlenecks, or architectural flaws, especially in large codebases or fast-paced development environments.
Core Features & Use Cases
- Multi-Agent Review System: Deploy specialized AI agents (security, performance, architecture, style, accessibility) for comprehensive, parallel code analysis.
- PR-Based Swarm Management: Dynamically create and manage review swarms based on Pull Request context, labels, and complexity.
- Intelligent Comment Generation: Generate contextual, actionable review comments with code examples and suggested fixes directly on GitHub PRs.
- Automated Workflows: Trigger auto-reviews on PR creation/synchronization and enforce quality gates with configurable thresholds.
- Context-Aware Reviews: Analyze PRs with full project context, learning from historical reviews and performing cross-PR analysis.
- Use Case: Automatically initiate a comprehensive review for a critical PR, deploying security, performance, and architecture agents to identify issues, post intelligent comments, and enforce quality gates before merging.
Quick Start
Initialize a multi-agent review swarm for a Pull Request (PR 123):
gh pr view 123 --json files,diff | npx ruv-swarm github review-init --pr 123
Post an initial review status comment to the PR:
gh pr comment 123 --body "🔍 Multi-agent code review initiated"