Best AI Code Review Tools 2024

Let’s look at the best AI code review tools of 2024. 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. This article provides an overview of seven up-and-coming AI code review tools and examines 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:

7 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, such as GPT or Gemini, to revolutionize the way developers manage code quality.

I made the selection based on the features the tools offer, not their popularity. This means the list highlights also pretty new AI code review products and even one tool that is no longer active but serves as a nice use case for AI code reviews.

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

1. GitHub Copilot

GitHub Copilot leverages AI to provide real-time code suggestions and completions, enhancing developer productivity and ensuring code quality within the IDE. As it’s one of the most well-known tools, I’m sure it deserves its place amongst the world’s best AI code review tools. Yet, GitHub co-pilot does not natively support code reviews within the pull request lifecycle. It “only” helps developers write better code, and offers a chat feature right within your IDE.


  • Code Suggestions: Provides real-time AI-powered code completions and improvements.
  • Learning and Context Awareness: Understands the context of the code for relevant suggestions.
  • Integration: Directly integrates with Visual Studio Code and GitHub.

2. Bito AI Code Review Agent

Bito AI Code Review Agent offers comprehensive automated code review solutions, focusing on identifying bugs, code smells, and security vulnerabilities in pull requests. It automatically runs when a PR is submitted thus streamlining the review process and enhancing code quality.


  • PR Summary: Provides concise summaries of pull requests, categorizing the changes (feature, bug fix).
  • AI-based Review: Utilizes AI to detect bugs, code smells, and vulnerabilities.
  • Static Analysis: Includes tools like fbinfer and Sonar for static code analysis.
  • Security Analysis: Integrates insights from Snyk for identifying security issues.
  • Detailed Line-by-Line Review: Offers detailed, per-line suggestions and improvements.
  • Integration: Works with GitHub, GitHub Enterprise, GitLab, and self-hosted options.
  • Pricing: $15 per month, no free version available.

3. CodeRabbit

CodeRabbit provides AI-assisted code review with interactive features, including PR summaries and the ability to chat with an AI code reviewer for real-time feedback and clarification.


  • PR Summary: Generates detailed summaries for pull requests.
  • AI Chat: Allows interactive chat with the AI code reviewer.
  • Plans: Free plan available for PR summaries, detailed review plan at $12 per month.
  • Free for Open Source Projects: Fully free for open-source projects.
  • Integration: Works directly within GitHub.

4. Codium AI

Codium AI is an open-source tool offering detailed AI-powered analysis and feedback on pull requests, supporting multiple platforms and fostering community contributions.


  • PR-Agent: Provides comprehensive analysis and feedback on pull requests.
  • Support for Multiple Platforms: Compatible with many platforms such as GitHub, GitLab, BitBucket, Azure, and AWS.
  • Open Source: Fully open-source software allowing customization and community involvement.
  • Integration: Integrates seamlessly with various platforms including GitHub and GitLab.

5. AI Code Review GitHub Action

Many little integration scripts for ChatGPT for GitHub Actions have appeared over the last year. If you search the GitHub repository, you will find several different implementations, with slightly different features and workflows. One tool is called Code Review GPT, which has over 1200 GitHub stars. Another is called AI Code Review Action with around 350 stars. There is also a GitHub Action which is an old version of CoolRabbit.
What all of those tools have in common is that they use ChatGPT within GitHub Actions to automate the code review process, and sometimes offer customizable workflows to fit specific review criteria.


  • GitHub Actions Integration: Automates code reviews using ChatGPT within GitHub actions.
  • Customizable Workflows: Some can be tailored to specific review criteria and workflows.
  • Integration: Directly integrates with GitHub Actions for automated code reviews.

6. What the Diff

What the Diff simplifies the code review process by generating human-readable summaries of code changes in pull requests, making it easier for all team members to understand and review changes. This tool can update the PR summary for every new commit that is pushed to the PR. This tool does not review your code nor does it give suggestions.


  • AI Summaries: Creates easy-to-understand summaries of code changes in pull requests.
  • Ease of Use: Designed to simplify understanding of code changes for team members.
  • Integration: Integrates with GitHub to provide summaries directly within pull requests.

7. Codeball AI Code Review

Let’s start with a warning: this tool is inactive. I’m listing it because I like the use case of AI here: Codeball AI Code Review scores pull requests on a scale from 0 to 1, indicating the level of review needed. 0 means you need to carefully review this and 1 means you can merge it (according to the AI). Iff the scoring would be reliable, this tool could be a real time saver.

Pros and Cons of Using AI Code Reviewers

AI code reviewers offer significant advantages, including time savings and the ability to detect subtle issues that might be missed by human reviewers. They provide consistent and objective feedback, enhancing code quality and security. However, they also have limitations, such as a lack of knowledge sharing and contextual understanding of domain-specific requirements. Balancing AI and human reviews can help mitigate these drawbacks.


  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 and Objectivity: AI tools provide consistent and objective reviews, unaffected by human fatigue or biases. They apply the same standards across all code reviews, ensuring uniformity in feedback and adherence to coding standards.

  3. Enhanced Detection: AI reviewers excel at identifying certain types of issues that might be overlooked by human reviewers, such as subtle bugs, code duplications, and potential security threats.


  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: Sometimes, and not too less, the suggestions and code changes introduced by AI are just plain wrong,

insecure, or harmful. So, be careful who you trust to make changes to your code.

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 updating AI rules 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. Use as a Learning Tool: Leverage AI feedback as a learning opportunity by encouraging developers to understand and analyze the suggestions provided by the AI. This can enhance their coding skills and awareness of common pitfalls.
  5. Continuous Monitoring and Updating: Regularly update the AI tools with new rules and patterns based on evolving coding standards and project requirements to maintain their effectiveness and relevance over time.


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. Nowadays, new AI code review tools are popping up every day, and while it’s super fun to try them out, and evaluate them, we have to still keep in mind that AI is not fool-proof and we must be careful that they do 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 10 best code review tools of 2024.

This article first appeared on Last updated: May 09, 2024

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Written by Dr. Michaela Greiler who is obsessed with making code reviews your superpower. Learn more about her workshops.