Writing code got cheaper. Reading it did not. When a team adopts coding assistants, the first thing that visibly changes is the pull request queue: more PRs, bigger PRs, and the same number of humans able to review them. A new class of tools, of which CodeRabbit and Greptile are the best-known examples, exists to sit in that gap.
This piece is about the tooling landscape: what these products do, how their designs differ, and how to evaluate them without trusting a vendor’s benchmark. If you want a per-PR procedure for a human reviewer, see our checklist for reviewing agent-written code instead.
Where the bottleneck came from
The best-cited data point is from Faros AI’s AI Productivity Paradox Report 2025, which analyzed telemetry from more than 10,000 developers across 1,255 teams. It found that on teams with high AI adoption, developers merged 98% more pull requests, average PR size rose 154%, and PR review time rose 91%.
Read that carefully. It is vendor telemetry, not a controlled experiment, and the link between bigger diffs and slower review is a correlation. It cannot say whether reviewers were skimming more or simply waiting longer. But the direction matches what most engineering managers report: output at the front of the pipeline rose faster than review capacity at the back.
That is the market the review tools are selling into. They do not remove the need for review. They try to make the first pass cheap so the scarce human attention goes to the parts that need judgment.
What the tools actually do
The core loop is the same across the category. A GitHub, GitLab, Bitbucket or Azure DevOps app watches for new pull requests, runs a model over the diff plus some surrounding context, and posts comments and a summary on the PR. The differences are in how much context they gather and what they do with the findings.
CodeRabbit
CodeRabbit’s documentation describes automated, context-aware reviews on pull requests that flag bugs, enforce team standards, and learn from reviewer feedback, with one-click fixes. It lists support for GitHub, GitLab, Azure DevOps and Bitbucket. Its docs also describe workflow features beyond comments, such as ranking a PR queue by value and risk and grouping large diffs into logical layers, which speaks directly to the “too many, too big” problem above.
Greptile
Greptile’s pitch is codebase context. Its documentation says it builds a graph of your entire repository, covering functions, classes and dependencies, and reviews each PR against that graph rather than the changed files in isolation. Findings are posted as PR comments with suggested fixes, each with a button that hands the issue to a coding agent such as Claude Code, Codex, Cursor or Devin. It supports GitHub, GitLab and Bitbucket in several editions, and lists self-hosted and air-gapped deployment options.
The architectural contrast is the useful part. Diff-focused review is fast and cheap but can miss a change that breaks a caller three directories away. Graph-based review can catch that class of bug, at the cost of indexing your repo and, depending on plan, more complex pricing.
The wider field
Other entrants include Copilot’s code review, Cursor’s Bugbot and Graphite’s agent. We have not tested these for this article, and the category moves quickly enough that any ranking we published would be stale within a quarter.
Why you should distrust the leaderboards
Public comparisons of CodeRabbit and Greptile conflict with each other. Some put one ahead on overall score, some the other. At least one roundup we read credits each tool with the exact detection rate another assigns to its rival. Most come from vendors or vendor-adjacent blogs, and pricing figures for both products differ between sources, so check the vendors’ own pricing pages.
There is a real reason for the confusion. Review quality is a precision/recall trade-off, and benchmarks differ in what counts as a bug, which repositories they use, and whether a style nit counts as a true positive. A tool that comments on everything scores high recall and burns reviewer trust. A tool that comments rarely is precise and misses things. Neither number tells you how it behaves on your code.
How to evaluate one for your team
Run a bake-off on your own history instead of reading someone else’s:
- Replay real PRs. Pick 20 to 30 merged PRs that later caused a bug or revert, plus a similar number of clean ones. See what each tool flags.
- Count wasted comments. Track how many comments reviewers dismiss. A tool that generates noise trains people to ignore it, which is worse than having no tool.
- Test the cross-file case. Include a PR that changes a function signature used elsewhere. This is where the architectural differences show.
- Check platform and data fit. Confirm your Git host is supported, whether code leaves your network, and what the retention terms are.
- Model the cost at your PR volume. Per-seat and usage-based pricing behave very differently when agents open many PRs.
What these tools do not fix
An AI reviewer is a filter, not an approver. It cannot know that a feature contradicts last quarter’s product decision, that a migration needs a coordinated rollout, or that a dependency is one your security team banned. Human review stays responsible for intent and blast radius, which is the part our code review practices cover.
Treat the bot as a first reviewer whose comments must be answered, never as the sign-off. Keep the human approval rule in branch protection, measure review time and escaped defects before and after rollout, and turn the tool off for a repo if those numbers do not move.