We raised $143M to build the control layer for software change.Read more: We raised $143M to build the control layer for software change.
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We raised $143M to build the control layer for software change.Read more: We raised $143M to build the control layer for software change.
GetStarted in2 clicks.
More changes are flowing through pull requests than ever thanks to AI-generated code, but a human still needs to decide what ships.
CodeRabbit Security traces suspected vulnerabilities across the codebase, evaluates reachability and the conditions required for exploitation against code evidence, and proposes reviewable fixes in the pull request workflow.
CodeRabbit raised a $143 million Series C at a $1.5 billion valuation and is introducing Agentic Change Management, the control layer for software changes created by humans and agents.
CodeRabbit is committing more than $10 million of its actual direct cost to open source over the next year through cash sponsorships, free Review and Security, and broader agentic support across the software development lifecycle.
A conversation with Assistant UI founder Simon Farshid about the hidden work behind AI chat interfaces, product judgment, and his coding-agent loop with CodeRabbit.
Install the CodeRabbit plugin for Cursor and run focused code reviews from the same Agent session where you build.
More changes are flowing through pull requests than ever thanks to AI-generated code, but a human still needs to decide what ships.
CodeRabbit Security traces suspected vulnerabilities across the codebase, evaluates reachability and the conditions required for exploitation against code evidence, and proposes reviewable fixes in the pull request workflow.
CodeRabbit raised a $143 million Series C at a $1.5 billion valuation and is introducing Agentic Change Management, the control layer for software changes created by humans and agents.
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
Reviewing every agent-generated change is becoming impossible. The next bottleneck is deciding what deserves attention, rebuilding context, and keeping code safe after merge.
Code is now plentiful, but judgment about what deserves to merge is not. Better models sharpen what agents write, but they don't make that call.
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