Best AI Code Review Tools 2026

Let’s look at the best AI code review tools of 2026. AI-powered code review tools have emerged as valuable allies to developers. AI code reviewers offer automated assistance to streamline the review process, enhance code quality, and reduce the burden on human reviewers.

When I first wrote this article in 2024, seven up-and-coming tools felt like a useful overview. In 2026, new AI code reviewers seem to appear every week. I see this both in my research and in my work with clients. One reason is that AI coding agents produce more code, making review the next obvious bottleneck. Another is that it has become relatively easy to put a pull-request integration around a large language model. The difficult part is not producing review comments; it is producing high-signal comments that understand the codebase, respect team conventions, and do not exhaust developers with false positives.

This article provides an overview of 15 relevant AI code review tools. It is a snapshot rather than an exhaustive directory, and the numbering is not a ranking. I also show how to use a general-purpose LLM as a code reviewer, including when independent or multi-model review passes can help, and examine the pros and cons of using such tools. Finally, I offer best practices for integrating AI reviews into your development workflow.

Here is an outline of what you can expect:

• 15 Best AI Code Review Tools
  • Comparison Table
  • 1. GitHub Copilot
  • 2. Bito AI Code Review Agent
  • 3. CodeRabbit
  • 4. Qodo
  • 5. PR-Agent
  • 6. What The Diff
  • 7. Cursor Bugbot
  • 8. Greptile
  • 9. Graphite Agent
  • 10. GitLab Duo Code Review
  • 11. Amazon Q Developer
  • 12. CodeAnt AI
  • 13. Sourcery
  • 14. Korbit
  • 15. Kodus Kody
• Using General-Purpose LLMs for Code Review
  • Should You Use a Different Model—or Several?
• Pros and Cons of Using AI Code Reviewers
• Best Practices for Using AI Code Reviews
• Summary

15 Best AI Code Review Tools

In this article, we are not focusing on the best code review tools per se, nor will I highlight the best static and dynamic code review tools. Instead, we are in particular looking at tools that use large language models and agentic workflows to help developers understand and review code changes.

I made the selection based on the features the tools offer, their relevance in 2026, and the range of workflows they cover, not just their popularity. Some are dedicated AI reviewers, some are review features inside a larger developer platform, and one focuses more narrowly on explaining changes. I have removed inactive products from the main list.

AI Code Review Tools at a Glance

This table is a quick orientation, not a substitute for testing a tool on your own pull requests. Platform support, deployment options, and product packaging change quickly, so check the linked tool section before making a final choice.

So, let’s have a look at each one of them:

1. GitHub Copilot

GitHub Copilot code review has changed substantially since the original 2024 version of this article. Copilot can now review code in an IDE and act as a reviewer directly inside a GitHub pull request. You can request a review manually or configure automatic reviews for a repository or organization.

Copilot leaves comments and suggested changes, but it does not approve a pull request or request changes. Its review therefore does not satisfy a required human approval or block a merge.

Features:

• Native Pull Request Reviews: Add Copilot as a reviewer on GitHub or trigger it automatically with a ruleset.
• Suggested Changes: Produces a summary and comments with changes that can be applied from the pull request.
• IDE Reviews: Reviews selected code or local changes before they become a pull request.
• Custom Instructions: Can use repository-specific instructions to better match a team’s standards.

2. Bito AI Code Review Agent

Bito AI Code Review Agent offers automated, context-aware reviews for pull and merge requests. It looks for bugs, code smells, and security vulnerabilities, and posts recommendations as comments in the existing review interface.

Features:

• AI and Static Analysis: Combines LLM-generated feedback with static analysis and open-source vulnerability checks.
• Detailed Review Comments: Explains issues and proposes fixes on the relevant lines.
• Security Signals: Can include high-severity findings from third-party tools such as Snyk.
• Integration: Supports GitHub, GitLab, and Bitbucket, including self-managed GitHub and GitLab.

3. CodeRabbit

CodeRabbit has grown from a GitHub review bot into a broader code review platform. It provides context-aware pull-request feedback, severity levels, summaries, and a conversational interface for asking follow-up questions. Reviews are also available before a pull request through its IDE extensions and command-line tool.

Features:

• Context-Aware Reviews: Uses codebase and linked issue context, not only the changed lines.
• Interactive Feedback: Lets developers discuss findings and request changes in the review.
• One-Click Fixes: Turns suitable findings into directly applicable suggestions.
• Broad Integration: Supports GitHub, GitLab, Azure DevOps, and Bitbucket, plus IDE and CLI workflows.

4. Qodo

CodiumAI is now Qodo. Its original PR-Agent evolved into a hosted code review platform, and Qodo 2.0 introduced a multi-agent review architecture in 2026. It uses repository context, pull-request history, and organization-specific rules to prioritize bugs and rule violations.

Features:

• Multi-Agent Review: Specialized agents inspect a pull request from different perspectives.
• Rule System: Applies team standards learned from the codebase, review history, and explicitly defined requirements.
• Prioritized Findings: Focuses on material issues and provides remediation guidance rather than a long list of generic comments.
• Integration: Supports GitHub, GitLab, Bitbucket, and Azure DevOps, with deployment options for enterprises.

5. PR-Agent

The earlier version of this article grouped several small ChatGPT-based GitHub Actions together. A more useful 2026 option is PR-Agent, the open-source project that originally came from CodiumAI/Qodo and is now community maintained. It is separate from Qodo’s hosted product and is a good fit for teams that want to control the model, deployment, and configuration themselves.

Features:

• Open Source: Apache-2.0 licensed and deployable as a GitHub Action, CLI, webhook, or container.
• Review Workflow: Offers commands for reviewing, describing, improving, and asking questions about a pull request.
• Model Choice: Can be configured with different commercial or local model providers.
• Broad Git Support: Supports GitHub, GitLab, Bitbucket, Azure DevOps, CodeCommit, Gitea, and local repositories.

6. What The Diff

What The Diff remains the narrowest tool in this list. It primarily makes pull requests easier to understand by writing descriptions and summaries for developers and non-technical stakeholders. It now also offers changelogs, weekly progress reports, and AI-assisted refactoring of minor issues.

I would choose it for communication and change documentation, not as the main tool for finding subtle defects.

Features:

• AI Descriptions: Creates plain-language descriptions of pull-request changes.
• Stakeholder Summaries: Produces translated notifications, weekly reports, and public changelogs.
• Inline Refactoring: Helps address smaller issues during a review.
• Integration: Works with GitHub and GitLab pull-request data.

7. Cursor Bugbot

Cursor Bugbot brings Cursor into the pull-request workflow. It reviews GitHub pull requests for bugs, security issues, and code-quality problems, then links findings back to Cursor or Cursor’s web agents for fixing.

Features:

• Automatic or Manual Reviews: Runs on pull-request updates or in response to a review command.
• Repository Rules: Reads .cursor/BUGBOT.md files for project- and directory-specific guidance.
• Connected Fixes: Opens findings with their context in Cursor and supports automated fixes.
• Integration: Currently centered on GitHub and the Cursor development environment.

8. Greptile

Greptile indexes a repository as a graph before reviewing changes. This lets it reason about dependencies beyond the pull-request diff and is one of the clearest examples of the 2026 move toward codebase-aware, agentic review.

Features:

• Repository Graph: Uses functions, classes, and dependencies from the wider codebase as review context.
• Adaptive Reviews: Learns from reactions, replies, previous review comments, and custom rules.
• Fix with Your Agent: Sends a finding and its context to tools such as Claude Code, Codex, Cursor, or Devin.
• Integration and Deployment: Supports GitHub and GitLab, with cloud, self-hosted, and air-gapped options.

9. Graphite Agent

Graphite Agent is the AI reviewer inside Graphite’s GitHub-compatible code review platform. It automatically comments on pull requests, proposes fixes, and focuses on logic bugs, edge cases, security, and performance issues.

Features:

• Codebase-Aware Review: Uses repository context to evaluate pull-request changes.
• Custom Rules and Exclusions: Enforces team standards while suppressing known sources of noise.
• Review Analytics: Tracks findings, accepted suggestions, acceptance rates, and developer feedback.
• GitHub Workflow: Comments and suggested fixes appear in both Graphite and GitHub.

10. GitLab Duo Code Review

GitLab Duo code review is the most natural option for teams already working in GitLab. You assign @GitLabDuo as a reviewer. Depending on the plan and group configuration, GitLab runs either its agentic Code Review Flow or the more diff-focused GitLab Duo Code Review.

Features:

• Native Merge-Request Review: Review feedback stays inside the existing GitLab workflow.
• Agentic Option: Code Review Flow can inspect repository structure and cross-file dependencies through multi-step reasoning.
• Custom Instructions and Automation: Teams can configure guidance and automatic reviews.
• Follow-Through: GitLab Duo can help resolve review discussions by changing, committing, and pushing code.

11. Amazon Q Developer

Amazon Q Developer combines generative AI with rule-based security and quality detectors. It can review local changes, a file, or an entire project in supported IDEs. Its automated reviews inside GitHub are also noteworthy, although that integration is still marked as preview.

Features:

• Broad Security Review: Covers SAST findings, secrets, infrastructure as code, dependencies, and code quality.
• IDE and Project Reviews: Reviews a diff, file, workspace, or repository and can propose in-place fixes.
• GitHub Pull Requests: In preview, automatically reviews new pull requests and posts threaded findings and suggested fixes.
• Project Rules: Reads team standards from Markdown files in .amazonq/rules.

12. CodeAnt AI

CodeAnt AI combines AI pull-request review with a broader code-quality and security platform. It reviews changes for bugs, vulnerabilities, quality problems, and architectural concerns, then posts actionable findings directly on the pull request.

Features:

• Specialized Review Agents: Covers code quality, security, dependencies, secrets, and infrastructure configuration.
• Quality Gates: Can use findings to prevent risky changes from being merged.
• Interactive Findings: Supports re-reviews, questions, validation, and suggested fixes.
• Broad SCM Support: Works across GitHub, GitLab, Bitbucket, and Azure DevOps workflows.

13. Sourcery

Sourcery provides automated reviews in pull requests and IDEs. In addition to line-level findings, it creates a summary and a review guide with diagrams, which can help human reviewers understand larger changes.

Features:

• Broad Review Coverage: Looks for bug risks, design issues, code quality, performance, and team-standard violations.
• Reviewer Aids: Produces change summaries, inline suggestions, review guides, and diagrams.
• Customizable Feedback: Learns from reactions and can enforce a team’s own coding standards.
• Integration: Supports GitHub, GitLab, popular IDEs, and self-hosted enterprise deployments.

14. Korbit

Korbit positions itself as an interactive AI reviewer that gives feedback before the human team starts its review. It focuses on higher-impact findings, explains why they matter, and can respond to questions inside the pull-request discussion.

Features:

• Contextual Reviews: Uses project context, team feedback, and custom settings to tune findings.
• Interactive Fixes: Answers follow-up questions and can provide replacement code in the pull request.
• PR Descriptions and Summaries: Generates handoff context in addition to review findings.
• Team Insights: Extends review data into release notes, sprint summaries, and engineering reports.

15. Kodus Kody

Kodus Kody is an open-source, model-agnostic AI code reviewer. It stands out for teams that want transparency into which files and context the reviewer used and prefer to pay their model provider directly.

Features:

• Open Source and Model Agnostic: Supports different AI providers and a bring-your-own-key approach.
• Repository-Wide Context: Uses semantic analysis and code search across the repository.
• Team Rules and Learning: Enforces custom standards and adapts to developer feedback.
• Integration: Works directly with GitHub, GitLab, and Bitbucket.

Using General-Purpose LLMs for Code Review

You do not necessarily need a dedicated AI code review tool. A general-purpose LLM can review a pasted code snippet, a pull-request diff, or—if it has access to your repository—the complete change and its surrounding code. Many of the products above package this basic capability with repository access, automatic triggers, persistent rules, inline comments, and reporting.

Before pasting proprietary code into a chat interface, confirm that the provider and account are approved for that code, and check the provider’s retention and model-training policies.

The simplest possible prompt is:

Review this code.

This can be useful for a quick second look, but a more focused prompt usually produces more relevant feedback:

Review this change for security issues. Focus on authentication, authorization, input validation, injection risks, secrets, sensitive-data exposure, and unsafe dependencies. For every finding, cite the relevant code, explain the impact, and suggest a fix. Do not invent a finding when the evidence is weak.

You can change the review perspective to match the risk of the change:

Review this frontend change for usability and accessibility. Check keyboard navigation, focus management, semantic HTML, labels and accessible names, color contrast, error messages, loading states, and screen-reader behavior. Separate blocking issues from optional improvements.

For a deeper review, give the model the intended behavior rather than only the code:

Review this pull-request diff against the requirements and repository conventions below. Look for functional bugs, missing edge cases, regressions, security and privacy risks, performance problems, and missing tests. Return only actionable findings. For each one, include severity, file and line, evidence, impact, and a proposed fix. If you need context that is not available, ask for it instead of guessing.

Context matters more than a clever reviewer persona. Give the model the requirement or issue, the diff, relevant surrounding code, repository conventions, and test results when possible. In an evaluation of general-purpose LLMs as code reviewers, review performance declined when the models did not receive the problem description. Even with good context, the researchers concluded that human review remains necessary because LLMs also produce faulty assessments and fixes.

Should You Use a Different Model—or Several?

There is anecdotal evidence from practitioners, and I see this in some client workflows as well, that the model that wrote the code is not always the best model to review it. A different model may question different assumptions and notice a problem that the generating model repeats or overlooks. Research on LLMs used as judges has also found self-preference bias, although that research is broader than code review and does not prove that a different model will always review code better.

The more defensible lesson is to seek independent review passes. The 2026 SWR-Bench study found that aggregating several independent review reports—using different models or multiple runs of the same model—improved code-review F1 scores by up to 43.67%. The gains came mainly from finding more real issues; false positives remained an important limitation.

This explains why some companies run several AI reviewers on the same change, as long as they can afford the model tokens and the human attention needed to triage the results. I would use this selectively rather than adding several bots to every pull request:

1. Let one model or coding agent implement the change.
2. Give a different model a read-only review with the requirement, diff, relevant code, and test evidence.
3. For higher-risk changes, run separate focused passes for correctness, security and privacy, and—for user-facing changes—usability and accessibility.
4. Deduplicate the findings and require evidence for each one.
5. Let a human decide which findings are valid before applying changes.

More AI reviewers create more coverage, but also more cost and review noise. Agreement between several models is useful evidence, not proof of correctness. Tests, static analysis, and accountable human review still provide different and necessary layers of assurance.

Pros and Cons of Using AI Code Reviewers

AI code reviewers offer significant advantages, including fast first feedback and the ability to detect issues that a busy human reviewer might miss. They can apply configured standards consistently across many pull requests. However, they also have limitations, including false positives, incomplete domain knowledge, and no accountability for the final decision. Balancing AI and human reviews can help mitigate these drawbacks.

Pros:

1. Time Savings: AI code reviewers significantly reduce the time required for code reviews by automating the identification of bugs, code smells, and security vulnerabilities. This allows developers to focus on more complex tasks and reduces the overall time spent in the review process.
2. Consistency: AI tools can apply the same configured standards across all code reviews without getting tired. This is useful for checking recurring team conventions.
3. Additional Detection: AI reviewers provide another pass over the change and can identify bugs, duplications, missing edge cases, and potential security threats that a human reviewer might overlook.

Cons:

1. Lack of Knowledge Sharing: One significant drawback of AI code reviews is the lack of human interaction, which is essential for knowledge sharing and mentoring among developers. Code reviews conducted by humans often serve as learning opportunities where experienced developers can share insights and best practices with junior team members.
2. Limited Contextual Understanding: AI tools may lack the domain-specific and business context necessary to provide meaningful feedback. They might struggle to understand the broader implications of code changes within the specific context of a project or organization, leading to potentially less relevant or accurate suggestions.
3. Wrong Suggestions and Review Noise: AI suggestions can be incorrect, insecure, or harmful. A tool that produces too many low-value comments can also train developers to ignore all of its feedback.
4. Privacy and Governance Questions: A reviewer needs access to source code and sometimes issue, repository, or organizational context. Teams should understand what is sent to model providers, how long data is retained, and which deployment options are available.

Best Practices for Using AI Code Reviews

To maximize the benefits of AI code reviewers, it is essential to combine them with human expertise. It’s also a best practice to combine AI code reviewers that are based on large language models, with rule-based static and dynamic analysis tools. Use AI for initial reviews to catch obvious issues, followed by human reviews for contextual insights and mentoring. Customizing AI tools to align with your project’s standards, using AI feedback as a learning tool, and continuously evaluating the quality of its comments can further enhance their effectiveness.

By integrating AI code review tools thoughtfully, development teams can achieve a harmonious balance that leverages the strengths of both automated and human reviews, leading to more robust and efficient code development processes.

1. Combine AI and Human Reviews: Use AI code reviewers to handle the initial pass, identifying obvious issues and potential vulnerabilities. Follow up with human reviews to ensure contextual relevance and facilitate knowledge sharing among team members.
2. Combine with rule-based code review automation tools: Make sure to integrate static and dynamic analysis checkers within your development lifecycle. You can use them either as pre-commit or post-commit hooks, and many automated code review tools even leave comments on your reviews.
3. Customize AI Tools: Configure AI code review tools (if possible) to align with your project’s coding standards and practices. This can help improve the relevance and accuracy of the feedback provided by the AI.
4. Require Developers to Evaluate Suggestions: Developers should understand an issue and its proposed fix before applying it. Do not turn a plausible-looking AI comment into an automatic change.
5. Monitor Signal, Not Comment Volume: Pilot a tool on a few repositories and track accepted, dismissed, and incorrect findings. Tune its rules and sensitivity based on that feedback.
6. Review Data Handling: Check code retention, model-training policies, subprocessors, access permissions, and self-hosting options before connecting sensitive repositories.
7. Use Multiple Review Passes Selectively: For high-risk changes, consider independent, focused reviews with a second model or tool. Deduplicate the results and keep a human responsible for deciding which findings are valid.

Summary

By integrating some of the world’s best AI code reviewers effectively within the development workflow, teams can achieve a balance that maximizes the strengths of both automated and human reviews, leading to higher quality and more secure code. New AI code review tools are still popping up at a remarkable pace. While it is useful and often fun to evaluate them, the number of comments a tool can generate is not a measure of review quality. The important questions are whether it finds material issues, fits the team’s workflow, protects the code, and helps rather than distracts human reviewers.

AI is not foolproof, and we must be careful that it does not introduce problems and errors into our code. If you want to learn more about automation during code reviews, check out my article on static and dynamic code review tools, as well as the best code review tools.