Code review

AI code review tools read a pull request and leave comments before a person looks at it. A typical setup installs as an app on GitHub, GitLab or Bitbucket, posts a summary of the change, walks through the files, and adds line comments on likely bugs, missing error handling, unclear names or risky patterns. Some also answer follow-up questions in the comment thread or push a suggested fix.

Two families of tools sit in this category. AI-first reviewers such as CodeRabbit, Greptile, Graphite's reviewer and Sourcery focus on reading the change the way a colleague would. Code quality platforms such as SonarQube, DeepSource and Codacy start from static analysis rules and quality gates, and add AI to explain findings and suggest fixes. Many teams use one of each.

The value is time. Routine catches happen automatically, so human reviewers can spend their attention on design, naming and whether the change solves the right problem. The cost is noise. A reviewer that comments on everything trains people to ignore it, so tune the rules, tell it which paths to skip and give it your team's guidelines.

Check how each tool gets context. A reviewer that only sees the diff misses problems caused elsewhere in the codebase, while tools that index the repository can point to related code. Also check data handling, since the tool reads every change you open, and keep a person accountable for each approval.

Featured AI coding tools: code review

SponsoredCode review This position is open Put your tool at the top of the code review page. Paid spots are always labeled Sponsored. From $99 a month. Get featured

What a featured listing costs ยท from $99 a month

7 AI coding tools in code review

CodeRabbit

AI code reviewer that comments line by line on pull requests in GitHub, GitLab and other platforms and summarizes the changes.

  • Free plan or free version

Qodo

AI code review and test generation in the IDE and on pull requests, previously known as CodiumAI.

  • Free plan or free version
  • Works in VS Code
  • Works in JetBrains IDEs

Greptile

AI code reviewer that indexes your whole codebase so its pull request comments take related code into account.

Sourcery

AI code reviewer for pull requests and the IDE that flags bugs and suggests clearer code.

  • Free plan or free version
  • Works in VS Code

SonarQube

by Sonar

Code quality and security analysis for many languages, with AI-suggested fixes for issues it finds, in the cloud or self-managed.

  • Free plan or free version
  • Can run on your own servers or machine
  • Works in VS Code

DeepSource

Static analysis platform that reviews pull requests for bugs and security issues and suggests automatic fixes.

  • Free plan or free version

Codacy

Code quality and security platform that checks pull requests and IDE code, including code written with AI assistants.

  • Free plan or free version

Rollout works best in stages. Turn the reviewer on for a few active repositories, ask reviewers to react to its comments for a couple of weeks, and look at which kinds of comments people found useful. Then tune the configuration and expand. Teams that switch it on everywhere at once often end up turning it off.

Measure the effect on the review process, not just the number of comments. Useful signals are fewer review rounds per pull request, fewer bugs found after merge and shorter waits for a first review.

Pay attention to how a reviewer handles large pull requests and generated files. Some tools skip big diffs or lockfiles by default, which is usually what you want, while others try to comment on everything. A short exclusion list for vendored code, migrations and build output keeps reviews focused on code people wrote. If your team uses stacked pull requests or a merge queue, check that the reviewer works with that flow, since comments on outdated commits add noise instead of saving time.

Questions about code review

Can an AI reviewer replace human code review?

No. It is a useful first pass that catches routine issues, but people still need to judge design, product fit and risk. Keep human approval required on protected branches.

Which Git platforms do these reviewers support?

GitHub is supported almost everywhere. GitLab, Bitbucket and Azure DevOps support varies by tool, so check the integration list on the tool's site before you plan a rollout.

How do I stop an AI reviewer from being noisy?

Configure it. Most tools read a settings file in the repository where you can exclude paths, set the level of detail and add team guidelines. Start strict on a few repositories and widen once comments are useful.

Is static analysis still needed with an AI reviewer?

Usually yes. Rule-based analysis such as SonarQube or DeepSource is predictable and can block merges on clear criteria. AI review adds judgment on top. They complement each other.

Related categories