deep-review

Multi-agent code review dispatches specialized AI agents to analyze bugs, security issues, conventions, and cross-file impacts across the entire repository, then validates findings through schema checks and blame analysis to minimize false positives.

Multi-agent code review reduces false positives by validating findings through a deterministic verification pipeline that includes schema checks, code checks, and blame classification, followed by an independent, blind evaluation step to confirm findings and reduce hallucinations.

Automated code review investigates not only the diff but the entire codebase for cross-file impact analysis, dispatching specialized agents to examine how changes affect other files and conventions before merging.

Deep-review works with Git repositories by leveraging GitPython for blame analysis and classification, making it suitable for high-trust PR review scenarios where identifying critical security vulnerabilities and lurking bugs before merging is essential.

The best way to verify code review findings is through a deterministic verification pipeline that validates security vulnerabilities via schema checks, code checks, and blame classification, followed by a final independent challenge step to ensure high confidence.

Use multi-agent code review for high-stakes refactors or new PRs where deep impact assessment across the entire codebase is needed, as standard diff-based analysis may miss cross-file impacts and lurking bugs.