8 Best AI Code Review Tools

AI code review tools can point out a risky change, explain a diff, or help teams find issues before a pull request is merged. They may help independent developers and busy engineering teams, but they do not replace tests or the reviewer accountable for the decision.

Some products focus on security findings, while others comment on code changes or answer questions about a repository. Start with the type of review you need and check whether the tool fits your source-control workflow. A small team may prefer one clear, low-friction check, while a larger organisation may need controls for access and reporting. None can take responsibility for whether a release is ready.

Quick picks

• Best GitHub workflow: GitHub Copilot code review — comments within an established platform.
• Best for automated review comments: CodeRabbit — pull-request feedback with conversational follow-up.
• Best for security scanning: Snyk Code — static analysis aimed at code security issues.
• Best for large repositories: Sourcegraph Cody — codebase context for investigations and review.

How we picked

We included tools with meaningfully different review approaches, from pull-request feedback to security analysis and repository questions. AI findings can be incomplete or noisy, so we favour products that fit into a review process with human checks.

• Coverage: whether it reviews diffs, repositories, or security concerns.
• Workflow: how naturally it fits code hosting and team habits.
• Actionability: whether findings give reviewers a useful starting point.
• Control: access, configuration, and data handling matter for private code.
• Limitations: false positives and missed defects remain possible.

Pull request companions

1. GitHub Copilot code review

GitHub Copilot can provide AI feedback on pull requests in GitHub-oriented workflows. Teams already using the platform may find it convenient to ask for an additional perspective without sending reviewers to a separate dashboard. Its comments can overlook project-specific rules or flag something harmless, so treat each note as a prompt to investigate. Access depends on the current plan and feature availability.

Best for: GitHub teams wanting an extra pass on proposed changes.

2. CodeRabbit

CodeRabbit focuses on AI reviews of pull requests and supports follow-up discussion around findings. That conversational style can help a contributor understand why a change deserves attention, rather than receiving only a terse warning. Review quality depends on context, and too many low-value comments can distract a team. It offers free and paid options; check current repository and plan limits.

Best for: teams that want detailed, interactive feedback on pull requests.

3. Qodo

Qodo provides AI coding tools that include pull-request review and code-quality workflows. It can help reviewers inspect a change with generated feedback, especially when a team wants assistance integrated into its development process. No automated reviewer knows every business rule, and generated comments need to be validated before they become required checks. Plans and capabilities vary; consult Qodo's current product details.

Best for: engineering groups looking to add AI feedback to review routines.

4. Graphite Reviewer

Graphite builds tools around pull requests and stacked changes, with AI review features in that workflow. Teams already managing dependent changes may appreciate keeping feedback near the relevant code instead of switching between systems. It may be more tooling than a small project needs, and automated suggestions still need a maintainer's judgement. Graphite offers free and paid options; verify the latest feature access.

Best for: teams that organise work through stacked pull requests.

Security and codebase analysis

5. Snyk Code

Snyk Code performs static analysis to identify potential security issues in source code. Its security focus makes it a useful complement to ordinary style and correctness review, particularly when teams need findings tied to remediation. Static analysis can produce false positives and cannot establish that an application is secure by itself. Snyk offers free access and paid plans; check current usage and feature terms.

Best for: developers adding security-focused checks to their review process.

6. Semgrep

Semgrep is a code analysis platform built around rules that detect patterns, including security issues and code-quality concerns. Teams can use established rules or develop checks that reflect their own conventions, making it more adaptable than a general chat review. Rule configuration takes care, and results still need triage. Semgrep offers open-source and paid options; confirm which capabilities match your repository needs.

Best for: teams that want configurable code-pattern and security checks.

7. Sourcegraph Cody

Sourcegraph Cody helps developers ask questions with codebase context, which can support investigation while reviewing a change. It is useful when a reviewer needs to trace a function's callers or understand how a repository handles a particular pattern. Context setup affects results, and a response should be checked directly against the code. Check Sourcegraph's current availability and terms.

Best for: reviewers tracing changes through a large or unfamiliar codebase.

8. Amazon Q Developer

AWS offers Amazon Q Developer for code assistance and AWS-related development workflows. Its ecosystem knowledge can help when a change touches cloud configuration or services, although it is less suited to reviews with no AWS connection. As with any generated suggestion, a maintainer should verify the impact and test the code. It has a free plan with paid upgrades; confirm present feature access.

Best for: teams reviewing code that integrates with AWS services.

How to choose the right one

First decide what you want the tool to catch. Pull-request assistants comment on changes; security scanners look for known risky patterns; codebase question tools help a reviewer follow behaviour across files. They are complements, not interchangeable guarantees. Run a trial on a representative repository and compare useful findings with irrelevant ones before making it part of a required workflow.

Protect private source code by reviewing data handling, permissions, and retention settings with the people responsible for your repository. Avoid granting write access unless it is genuinely needed. Keep existing tests, static checks, and human approval in place, and do not accept a finding just because it sounds authoritative. An AI review is another input, not a security certification.

Stockio's JSON formatter can make a JSON payload easier to inspect when investigating a reported issue; it does not validate the application's logic. Explore the AI coding assistant directory, and see our guides to reviewing AI-generated code and security risks in AI-written code for useful review habits.

Key takeaways

• Match a product to pull-request feedback, codebase investigation, or security analysis.
• Test review tools on real changes and measure noise as well as useful findings.
• Read repository permissions and data policies before connecting private code.
• Keep tests and accountable human review as part of the release process.
• Treat every AI comment as something to investigate, not an instruction to obey.