github-code-review

Automate GitHub pull request code review by orchestrating AI agents in a swarm to analyze security, performance, architecture, style, and accessibility. The RUV Swarm framework coordinates parallel agent workloads, generates inline comments, and enforces quality gates across diverse repositories.

Multi-agent swarm code review works by spawning dedicated AI agents that analyze pull request diffs in parallel across security, performance, style, architecture, and accessibility domains. Each agent surfaces issues and delivers actionable feedback through inline comments on the pull request.

Yes, running AI-powered PR reviews requires GitHub CLI, Node.js, and the RUV Swarm framework. These dependencies enable the system to fetch pull request data, orchestrate agent workloads, generate inline comments, and enforce quality gates within your repositories.

You can enforce quality gates on GitHub pull requests by integrating policy-based checks through GitHub actions. The swarm review system applies automated checks across multiple domains and blocks merges until security, performance, and architecture standards are met.

Reviewing pull requests across multiple repositories at scale is best handled by a multi-agent swarm that applies parallel analysis to diverse repositories. The RUV Swarm framework coordinates dedicated agents for each review domain, enabling consistent quality enforcement regardless of repository count.

Initialize a code review swarm by fetching the pull request diff via GitHub CLI and spawning agents with the RUV Swarm review-init command. Specify the PR number and desired agent domains such as security, performance, style, architecture, and accessibility to begin parallel analysis.