DEVOPSdigest

CodeRabbit is now available on the Visual Studio Code editor.

The integration brings CodeRabbit’s AI code reviews directly into Cursor, Windsurf, and VS Code at the earliest stages of software development—inside the code editor itself—at no cost to the developers.

This new support enhances CodeRabbit’s multi-layered review approach: continuing to operate within the Git platform, where all code commits come together to ensure no changes are missed, and now also adding a second layer within the IDE for real-time, in-editor reviews, that are free for individual developers (rate limits apply).

“If you look at the entire CI/CD pipeline, code review is the last remaining process that’s still manual—and it’s a costly drag on the pace of shipping software,” said Gur Singh, co-founder and COO of CodeRabbit. “By bringing CodeRabbit into VS CodeCursor, and Windsurf we’re embedding AI at the earliest stages of development, right where engineers work.”

In a world where AI is writing more code than ever before, CodeRabbit solves a critical pain point: the faster we generate code, the greater the need for consistent, high-quality review. Across nearly 5,000 paying customers and 70,000+ open-source projects, CodeRabbit cuts manual review time in half and detects twice as many bugs as manual reviews—saving teams thousands per developer and speeding up code releases where it counts.

“The average engineering organization has thousands of tickets in Jira or Linear, their own unique coding styles, and access to vast, evolving knowledge domains through LLMs. Yet they’re still stuck in the anti-pattern of using static, isolated datasets for code reviews,” Singh continued. “CodeRabbit understands your entire codebase, gathers additional context from several inputs and leverages foundational AI models to make code review smarter, faster, and far more consistent.”

CodeRabbit makes the AI code review process highly contextual—traversing code repositories in the Git platform, prior pull requests and related Jira/Linear issues, user-reinforced learnings through a chat interface, code graph analysis that understands code dependencies across files, and custom instructions using Abstract Syntax Tree (AST) patterns. In addition to applying learning models to engineering teams’ existing repositories and coding practices, CodeRabbit hydrates the code review process with dynamic data from external sources like LLMs, real-time web queries, and more.

Now, CodeRabbit brings these capabilities directly into the IDE, making the developer’s task much easier by incorporating its high-quality AI reviews into the codebase, without any cost for reviews in the IDE. Developers now get AI code reviews in two places: in the IDE when they are coding (individually for each developer) and in the Git platform before merging code into production (once across the entire team).

Ultimately, this two-pronged approach ensures even higher code quality with less manual time spent in code reviews, thus improving developer productivity.