The question "what is the best AI code review tool for GitHub" gets answered with feature tables and rankings. Most of those rankings put the vendor who wrote them first. Qodo's own comparison of five GitHub review agents says plainly that "only one is built for enterprise-scale GitHub estates," and that one is Qodo. Its table gives every other tool a "No" on context depth, governance, and multi-repo awareness. There is no method and no public dataset attached. It is a sales page with a table in it.
Skip the rankings for a minute. The part that decides whether AI review works at all on GitHub is not the model or the feature list. It is the mechanism that connects the tool to your pull requests. That is something you can evaluate without trusting a vendor's self-assessment, because the constraints are engineering constraints, not marketing claims.
What the integration guide actually says
Graphite's engineering guide on integrating an AI code reviewer into GitHub, written by staff engineer Sara Verdi, walks through four distinct integration patterns and their tradeoffs. It is the most useful primary source I have found on this because it is written by an engineer, not a product marketing team.
The first is a dedicated webhook or external service. It is decoupled, scales across many repositories, and you keep full control. The cost is that you host and maintain it yourself, and network latency matters because every PR event has to round-trip to your service and back.
The second is a GitHub Action, either self-hosted or from the Marketplace. It is easy to configure, versioned along with your repository, and lives in the same repo it reviews. The constraints are Action runtime limits, per-minute cost, and the need to handle secrets securely in the workflow YAML.
The third is a GitHub App or bot with review privileges. It can comment and even approve changes automatically. That convenience comes with having to manage its permissions carefully, watch API rate limits, and keep false-positive comments low enough that reviewers keep paying attention.
The fourth is hybrid: a webhook that triggers an Action or a serverless function. It offloads the heavy work and stays flexible, but it is the most moving parts.
The guide also hands you what is effectively a permission model to think in. The tool needs write or comment scope on your repos. Its API keys have to be stored securely. And it should run incrementally on changed files rather than reanalyzing the whole repository each time.
There is a concrete shape underneath all four patterns. The pull request event fires, the review engine clones or fetches the diff along with project context, static analysis runs first, the diff goes to an LLM that generates comments and fix proposals, and then those comments are posted back through the GitHub API as line comments, status checks, or a request-changes review. Everything downstream of that event is where the four patterns differ.
The part the ranking tables leave out
Rankings compare which model sees more context, whose review catches more bugs, which vendor supports more repositories. All of that is downstream. None of it happens if the hookup is wrong, and the hookup is where the differences are concrete and non-marketing.
Consider gating. Whether the AI tool is allowed to block a merge is a property of the integration, not of the model. A GitHub App with approval authority can gate on review status. A comment-posting Action cannot. If your goal is to force every agent-generated change past a machine check before humans look, you need the integration that owns status checks and merge protection. That is decided the day you install the app and grant it scope, not by which model is smarter.
Consider who has to touch it. The cheapest tool on paper is expensive if every repo owner has to wire a webhook or maintain a runner. Vendors who install as a GitHub App remove that work, but then the permissions and rate limits live on your side of the contract. A self-hosted Action keeps it in-repo but adds to your CI bill. For a team running hundreds of repositories, installability is the strategy. For a single repo, an Action in the workflow file is the whole job.
Why the mechanism is a governance decision
The integration decision is also a policy decision, made at installation time, long before the first comment appears. This is the same point I made about whether a review policy can rely on flagging AI code: the tool's position in the pipeline determines what it can enforce. A bot that can only comment can remind, not require. An app with approval authority can become a required check that blocks an agent-generated PR until the machine is satisfied.
There is a trust axis too. Automation that posts a flood of low-value comments makes reviewers stop reading every comment, which is the same failure mode as a reviewer disengaging from a flat review queue. The false-positive budget is an integration setting. The mechanism determines whether you can tune it per repository, or whether you get one org-wide toggle that is either too loud or too quiet. Graphite's own guide flags false positives in its caveats section, which is honest: too-aggressive feedback is how AI review loses credibility, and credibility is lost faster than it is earned.
What you can verify without a ranking
You do not need to trust any vendor's "best in class" claim to evaluate the mechanism. Look at what the tool is when you install it. Is it a GitHub App that asks for review permissions, a Marketplace Action you drop into a workflow file, a webhook you point at your own service, or a layer that wraps the whole review loop on top of GitHub?
Each of those answers says something concrete about who maintains it, what merges it can block, and what happens to your runtime when a PR lands at 2am. That is more decision-relevant than a feature table where every vendor gives itself "Yes" and everyone else "No".
The ranking question is still worth asking, but invert it. Instead of "which tool is best," ask which tool uses the mechanism that fits how your team already runs review on GitHub, and what the vendor publishes about how it actually hooks in. A vendor that documents its permissions, its incremental scope, and its comment policy is giving you something checkable. A vendor that only gives you a table of "Yes" and "No" is giving you a ranking, which is not evidence. The same reading applies to vendor "tops" pages on the review benchmark, which list outcomes without a method attached. Rankings tell you who is marketing. The hookup tells you who will work.
Sources checked 2026-09-17, collected live: Graphite's integration guide, Qodo's GitHub review comparison, the GitHub Marketplace AI Code Review Action listing, and the Martian Code Review Bench results repository.
