Code reviews have always been one of the more loathsome duties in software engineering. Most developers would much rather be writing code than reviewing it. Most code reviews occur late in the development cycle, and are inconsistently applied, limited by the capacity of human coders.
For today’s developers, code review workflows are getting even harder, because developers no longer check code only inside their own repos. Engineers have to understand shifting dependencies, external APIs, version changes, and upstream logic beyond the current branch. So it’s easy to miss problems like out-of-date function usage, missing unit tests for recently updated logic, or logic drift across services or teams. And missing these types of issues during reviews leads to regressions, broken APIs, and other messy production problems.
CodeRabbit, an AI-powered code reviewer, aims to both lighten the burden of code reviews for developers and to improve their quality and consistency. CodeRabbit plugs into GitHub and other Git platforms, integrates with IDEs like Visual Studio Code, and runs real-time analysis on pull requests. Drawing on all the content of the repo for context, CodeRabbit combines code graph analysis and the power of large language models (including OpenAI’s GPT-4.5, o3, and o4-mini, and Anthropic’s Claude Opus 4 and Sonnet 4) to identify issues in code changes, suggest improvements, and generate those improvements in a new branch.
