Compare · CodeRabbit
Proof vs CodeRabbit: review and requirement evidence
CodeRabbit helps review code changes using repository context and review instructions. Proof keeps requirements, evidence and known issues available as changes accumulate. Compare them on the review task your team needs to improve.
Review context and retained obligations both matter
CodeRabbit provides AI code review with configurable path instructions and retained learnings. Its custom pre-merge checks can inspect repository and PR context and return Passed, Failed or Inconclusive with reasoning.
Proof manages requirements and their links to implementation, verification evidence and known issues. Its deterministic proof review-pr command prepares a requirement-change review brief. That command does not perform AI semantic code review; a code-only change may have no changed requirements in its default brief.
The useful comparison is whether the workflow helps someone investigate the patch and retain the obligations that future patches must respect. Teams may need both. Evaluate the exact review and evidence workflow, rather than counting comments or green status icons.
Compare the work you need to do
Use the same repository change and acceptance criteria in each evaluation.
| Evaluation question | CodeRabbit | Proof |
|---|---|---|
| How is project context supplied? | Path instructions and stored learnings can guide reviews. Scope and settings determine their use. | Structured requirement records, source links, evidence and issue history provide explicit context for a change. |
| What does a check report? | Custom checks return a decision with reasoning from the available PR context. | Selected workflow and audit checks report their specific predicates. Coverage depends on inputs and configuration. |
| What about executing tests? | The documented custom-check sandbox cannot run your test suite or arbitrary repository code. This limit applies to that feature. | Proof’s tests_pass check can run the configured test command. Affected-test planning can select no tests; configure full execution when needed. Inspect the executed example and results. |
| What persists for later changes? | Review learnings can be retained and applied within their configured scope. | Requirements, trace links, recorded evidence and known issues can be reviewed as the code evolves. |
Evaluate two changes to a tenant boundary
Illustrative evaluation: a batch-update endpoint receives object IDs from a client. The rule is that every object must belong to the authenticated tenant. The first patch adds an ownership check; a later refactor routes batches through a shared helper.
Give the reviewer the rule, both code paths and the expected rejection behavior. Evaluate whether its findings identify a concrete violation and distinguish uncertain concerns from supported ones. Then add a mixed-tenant test that inspects database state as well as the HTTP response. A rejected response is insufficient if some unauthorized writes already happened.
For a Proof trial, retain that rule as a requirement, connect the relevant code and test, and inspect the evidence after the helper changes. Demonstrate a real failing and repaired execution before calling the rule verified in that scope. A retained requirement without a meaningful assertion does not close the gap.
Choose the workflow around the review problem
Choose CodeRabbit when the immediate need is automated review feedback in the code-review workflow, guided by your repository context and team instructions. Evaluate Proof when reviewers repeatedly reconstruct intended behavior, supporting evidence and unresolved obligations across changes.
Where both are used, an actionable review finding can become a recorded issue and a regression check. Establish that handoff explicitly; this page does not claim a dedicated CodeRabbit connector. For either product, repository protection and bypass rules determine whether a check result prevents merging.
Sources and comparison scope
Official CodeRabbit documentation checked October 3, 2026: path instructions, learnings and custom checks and sandbox limitations. Capabilities vary by configuration and product surface. The example above is an evaluation design, not a measured detection-rate comparison.
Evaluate Proof on your own evidence
Bring a PR whose acceptance rule is difficult to reconstruct and one later change to the same behavior. We can scope how to retain that rule, connect its checks and show reviewers the remaining gaps.