github-code-review

You can automate AI code reviews on GitHub pull requests by orchestrating specialized agents to analyze diffs and metadata fetched via GitHub CLI. The system coordinates parallel analysis across security, performance, and architecture to post actionable feedback automatically.

Multi-agent code review deploys specialized AI agents to analyze different aspects of a pull request simultaneously. It fetches context via GitHub CLI and uses orchestration tools to run parallel checks on security, performance, style, and architecture.

Yes, GitHub CLI is used to fetch pull request context, diffs, and metadata. The data extracted using the GitHub CLI serves as the input for orchestrating parallel, agent-based automated code analysis.

Yes, automated PR review handles pull requests of varying sizes by deploying parallel analysis agents. You can configure a comprehensive depth setting to ensure large changes receive thorough security, performance, and maintainability checks.

You can run a security and performance focused code review by initializing an agent swarm with specific parameters. This deploys targeted agents that analyze your PR diffs in parallel to identify vulnerabilities and bottlenecks before posting feedback.

AI agent code reviews rely on the context fetched from GitHub CLI diffs and metadata, meaning complex architectural nuances outside the diff may be missed. Automated feedback should be treated as actionable guidance for maintainers rather than a complete replacement for human judgment.