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See which open-source projects ran the most CodeRabbit reviews in September and how we’re backing maintainers through a commitment of more than $10 million to open source.
Bring the people who know each part of a coding task into one shared Slack Code channel, where they can shape the spec and build with CodeRabbit Agent for Slack.
CodeRabbit Security led three other systems in our vulnerability-detection benchmark. Here’s what we tested and how we scored it.
Independent AI code review gives teams evidence to trust proposed changes, supported by CodeRabbit’s governance and control layer for agentic software development.
Connect GitHits MCP to CodeRabbit so reviews can draw on open-source code and package data.
TypeSafe's Allie Laabs explains how to find Jev-shaped problems in your code, why you should ask every question up front, and why coding agents still need help getting it right.
OpenAI customers can apply their existing OpenAI spend commitment towards CodeRabbit through the OpenAI Marketplace, simplifying procurement.
We benchmarked Claude Sonnet 5.5 for AI code review against Sonnet 5 and Opus 5.5: which bugs it catches, how fast it reviews, and what a review costs.
Cua AI co-founder Francesco Bonacci explains why developers need to check whether AI agents finish the job, even when the tools they use work correctly.
Agents can open a pull request that spans an API, a migration, and tests in minutes. Change Stack connects the change’s intent, behavior, and dependencies to the code so reviewers can verify the explanation before they merge.
We tested Opus 5.5 against our production reviewer to see which bugs it catches, which it misses, and whether higher reasoning effort actually improves the results.
CodeRabbit Triage brings priority, risk, ownership, and next-action context into the pull request queue so reviewers know where their attention is needed.
CodeRabbit Triage brings priority, risk, ownership, and next-action context into the pull request queue so reviewers know where their attention is needed.
Agents can write, test, and revise code across much of the delivery process. But teams still need a reliable way to decide which changes can move automatically and which require accountable human review.
Engineering taste develops through experience, exploring alternatives, and learning from use. How can teams preserve those opportunities in an agentic SDLC?
See CodeRabbit’s GPT-6 Astra evaluation: cross-file bug detection, review findings, customer data protection, and the trade-offs in API cost.
Kent C. Dodds explains why product judgment, system understanding, and ownership become more valuable as AI agents take on more implementation work.
Read CodeRabbit’s Fable 5.1 review, including coding tasks, code review results, latency, and the trade-offs to test before switching models.
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