List of the Top 25 AI Code Review Tools for GitLab in 2026

Introducing an advanced AI-driven code review system that enhances code quality and identifies vulnerabilities at an early stage. Effortlessly correct issues directly within your Integrated Development Environment (IDE) or through pull requests. Aikido serves as your comprehensive software security hub, covering everything from vulnerability management to penetration testing. Ensure the security of all applications you create, host, and manage. Designed for teams of all sizes, Aikido empowers organizations to deliver secure software solutions, earning the trust of notable companies such as Revolut, Deel, The Premier League, Tines, n8n, SoundCloud, and over 50,000 more. Aikido allows developers to focus on what they do best: building great products.

Qodo, which was previously known as Codium, offers an analysis of your code to detect potential bugs prior to deployment. By mapping out the behaviors inherent in your code, it effectively pinpoints edge cases and highlights any areas of concern. Subsequently, Qodo produces clear and meaningful unit tests that align with your code's functionality. This allows you to observe how your code operates and assess the impact of modifications on the surrounding codebase. With a keen focus on code coverage, it emphasizes the importance of high-quality tests that validate functionality, thereby instilling confidence in your commitment to the code. Instead of spending excessive time on dubious testing, you can redirect your efforts toward developing features that genuinely benefit your users. As you write, Qodo analyzes your code, documentation, and comments to propose tests that can be easily integrated into your suite. Committed to maintaining code integrity, Qodo not only generates tests but also aids in deepening your understanding of the code, exposing edge cases, and identifying any suspicious behaviors, ultimately enhancing the robustness of your software. By using Qodo, you can ensure a more efficient development process, allowing you to prioritize quality alongside productivity.

Bito reduces code review time by 89%, helping teams ship faster with fewer regressions. Our AI Code Reviews analyze your full codebase to deliver high-quality suggestions on every pull request. Expect improved consistency and freed up senior engineers. Bito works with GitHub, GitLab, and Bitbucket and installs in seconds. No data retained. No AI model training.

Amp is a frontier coding agent designed to redefine how developers interact with AI during software development. Built for use in terminals and modern editors, Amp allows engineers to orchestrate powerful AI agents that can reason across entire repositories, not just isolated files. It supports advanced workflows such as large-scale refactors, architecture exploration, agent-generated code reviews, and parallel course correction with forced tool usage. Amp integrates leading AI models and layers them with robust context management, subagents, and continuous tooling improvements. Developers can let agents run autonomously, trusting them to produce consistent, high-quality results across complex projects. With strong community adoption, rapid feature releases, and a focus on real engineering use cases, Amp stands out as a premium, agent-first coding platform. It empowers developers to ship faster, explore deeper, and build systems that would otherwise require significantly more time and effort.

Snyk stands at the forefront of developer security, empowering developers globally to create secure applications while also providing security teams with the tools necessary to navigate the complexities of the digital landscape. By prioritizing a developer-centric approach, we enable organizations to safeguard every vital element of their applications, spanning from code to cloud, which results in enhanced productivity for developers, increased revenue, higher customer satisfaction, reduced costs, and a stronger security framework overall. Our platform is designed to seamlessly integrate into developers' workflows and fosters collaboration between security and development teams, ensuring that security is woven into the fabric of application development. Furthermore, Snyk's commitment to innovation continually evolves to meet the changing demands of the security landscape.

ZeroPath is the AI-native SAST that finds vulnerabilities traditional tools miss. We built it because security shouldn't overwhelm developers with noise. Unlike pattern-matching tools that flood you with false positives, ZeroPath understands your code's intent and business logic. We find authentication bypasses, IDORs, broken auth, race conditions, and business logic flaws that actually get exploited and missed by traditional SAST tools. We auto-generate patches and pull requests that match your project's style. 75% fewer false positives, 200k+ scans run per month, and ~120 hours saved per team per week. Over 750 organizations use ZeroPath as their new AI-native SAST. Our research has uncovered critical vulnerabilities in widely-used projects like curl, sudo, OpenSSL, and Better Auth (CVE-2025-61928). These are the kinds of issues off-the-shelf scanners and manual reviews miss, especially in third-party dependencies. ZeroPath is an all-in-solution for your AppSec teams: 1. AI-powered SAST 2. Software Composition Analysis with reachability analysis 3. Secrets detection and validation 4. Infrastructure as Code scanning 5. Automated PR reviews 6. Automated patch generation and more...

Accelerate the speed and efficiency of software development and delivery by harnessing the power of generative AI, while maintaining strong enterprise security and privacy measures. Gemini Code Assist enhances your coding experience through its ability to complete your code in real-time and generate full code segments or functions upon request. This dynamic coding tool is compatible with a wide range of popular integrated development environments (IDEs) such as Visual Studio Code and various JetBrains IDEs, including IntelliJ, PyCharm, GoLand, and WebStorm, as well as Cloud Workstations and Cloud Shell Editor, supporting over 20 different programming languages like Java, JavaScript, Python, C, C++, Go, PHP, and SQL. With a user-friendly natural language chat interface, Gemini Code Assist allows for seamless interaction, providing answers to your programming questions or offering insights into best coding practices, and this chat feature is available across all supported IDEs. Organizations can customize Gemini Code Assist by integrating their proprietary codebases and knowledge libraries, thus enabling the tool to deliver more tailored assistance that meets unique enterprise requirements. Moreover, Gemini Code Assist is designed to facilitate substantial changes across entire codebases, thereby greatly enhancing the development workflow. This versatile approach not only increases productivity but also empowers teams to innovate at a faster pace in a secure setting, ultimately driving success in software projects. As organizations adapt to evolving technological landscapes, tools like Gemini Code Assist become essential in maintaining a competitive edge.

Amazon Q Developer is AWS’s most capable generative AI assistant for software development, engineered to enhance productivity across every phase of the build cycle. Available as a plugin for JetBrains, VS Code, Visual Studio, Eclipse, and the command line, it brings real-time code generation, debugging, and documentation directly into the developer’s workspace. Unlike simple autocomplete tools, its agentic capabilities allow it to autonomously complete complex tasks—implementing new features, performing code reviews, running tests, and upgrading legacy applications with minimal human input. It’s deeply integrated with the AWS ecosystem, offering expertise in cloud optimization, cost management, and operational diagnostics. Teams can engage Amazon Q in Slack, Microsoft Teams, GitHub, and GitLab for seamless collaboration, architecture guidance, and incident troubleshooting. The platform also supports data-driven development, helping users build analytics pipelines and machine learning workflows using natural language prompts. Its privacy-first design ensures that proprietary content is never used to train models, while AWS IAM-based access controls maintain enterprise security standards. Internal studies report up to 80% faster development speeds and a 37% acceptance rate for multi-line code suggestions—among the best in the industry. Whether upgrading Java apps, managing DevOps pipelines, or designing AI projects, Amazon Q Developer accelerates innovation while reducing friction. With flexible deployment and perpetual free access for limited use, it’s redefining the developer experience for the AI era.

ThinkReview represents a groundbreaking AI-driven solution for code review, tailored specifically for developers using GitLab and Azure DevOps, and it offers instant evaluations of merge requests and pull requests directly in the web interface. The tool simplifies the process by automatically identifying when a merge or pull request is accessed, gathering the necessary code alterations, and displaying an AI-curated review panel that includes brief summaries, security notifications, quality suggestions, and auto-generated remarks. Developers can engage conversably with the code changes, ask questions, regenerate insights from the reviews, and receive thought-provoking follow-up queries that encourage more comprehensive discussions. It supports both self-hosted and cloud environments, functions effortlessly without requiring extensive setup, and is delivered as a browser extension with features such as automatic detection of merge and pull requests, smart summaries, comment generation, and compatibility with various programming languages. By emphasizing user-friendliness and efficiency, ThinkReview seeks to improve code quality and accelerate the review process by seamlessly integrating AI into developers' workflows, which ultimately cultivates a more productive environment for coding. The ability to streamline code reviews allows teams to uphold high standards while also shortening development timelines, making it a vital asset for modern software development.

Codacy is a unified platform that brings together code quality, application security, and AI risk protection to support modern, fast-paced development environments. It provides continuous analysis across the entire software development lifecycle, from local development in IDEs to production environments. The platform performs static application security testing (SAST), dynamic testing (DAST), dependency scanning, and infrastructure-as-code analysis to detect vulnerabilities and misconfigurations early. Codacy’s AI Guardrails enhance this process by identifying and fixing issues in AI-generated code, ensuring compliance with organizational standards. Developers receive real-time feedback, automated pull request checks, and detailed insights into code complexity, duplication, and test coverage. Centralized rule management enables organizations to enforce consistent coding and security standards across all teams and repositories. The platform integrates with popular tools like GitHub, GitLab, and CI/CD pipelines, making adoption seamless. Codacy also supports automated unit test generation and advanced reporting through its MCP-powered interactions. By reducing manual effort and improving visibility, it allows developers to focus on building high-quality software. The result is faster delivery cycles, stronger security posture, and more maintainable codebases. Codacy is trusted by thousands of organizations worldwide to streamline development while minimizing risk.

DeepSource is an AI-powered platform designed to automate code reviews and help engineering teams build more secure and reliable software. It uses a hybrid analysis approach that combines deterministic static code analysis with advanced AI review agents to examine code changes. The platform integrates seamlessly with development environments such as GitHub, GitLab, Bitbucket, and Azure DevOps, enabling automatic analysis of pull requests. Each code change is scanned for bugs, security vulnerabilities, performance risks, complexity issues, and maintainability concerns. Developers receive inline comments and structured review summaries that explain problems and suggest improvements. The system includes Autofix capabilities that generate verified patches for many detected issues, allowing developers to resolve problems quickly. DeepSource also monitors dependency vulnerabilities using reachability and taint analysis to identify which open-source risks actually affect the codebase. Security tools detect exposed secrets, API keys, and credentials before they reach production environments. Infrastructure-as-code scanning helps identify configuration weaknesses in Terraform and CloudFormation files. Teams can track test coverage to ensure new code is properly tested before merging. Compliance reports map vulnerabilities to recognized security standards such as OWASP Top 10 and SANS Top 25. The platform also offers full codebase scanning to identify long-term quality and security issues across existing repositories. By combining automation, security intelligence, and actionable feedback, DeepSource enables organizations to scale development without sacrificing code quality.

Eliminate obstacles related to onboarding, knowledge barriers, or frequent context switching by leveraging Swimm, which enables you to create and update documents that are intricately connected to your code, continuously refreshed, and integrated into your workflow. Swimm features a versatile editor and innovative tools like Smart Tokens and Snippet Studio, laying the groundwork for effective modern documentation strategies. Effortlessly produce engaging documents enriched with multimedia elements that are directly linked to your codebase. Thanks to the Auto-sync algorithm, your documentation stays in sync with any code refactoring or structural alterations, thus relieving you from the burden of manually updating file names, function names, or other implementations. Swimm keeps a close watch on your documentation while your code evolves, notifying you of any inconsistencies that may develop. You can easily view documents alongside the appropriate code, which helps maintain your concentration within your integrated development environment (IDE). By clicking a link, a new tab will open, displaying the documentation beautifully rendered from Markdown, which fosters a smooth and continuous workflow. This efficient method not only bolsters teamwork but also significantly increases productivity, empowering teams to collaborate more effectively. With Swimm, the focus on documentation becomes a seamless part of your development journey, transforming how teams share knowledge and manage information.

Cody is a sophisticated AI coding assistant created by Sourcegraph to improve software development's efficiency and quality. It works effortlessly within popular Integrated Development Environments (IDEs) such as VS Code, Visual Studio, Eclipse, and various JetBrains tools, offering features like AI-enhanced chat, code autocompletion, and inline editing, all while preserving existing workflows. Tailored forenterprise teams, Cody focuses on maintaining consistency and quality throughout entire codebases by leveraging extensive context and shared prompts. Moreover, it broadens its contextual insights beyond mere code by integrating with platforms like Notion, Linear, and Prometheus, thus creating a comprehensive picture of the development landscape. By utilizing advanced Large Language Models (LLMs), including Claude Sonnet 4 and GPT-4o, Cody provides customized assistance that can be fine-tuned for various applications, striking a balance between speed and performance. Users have reported notable increases in productivity, with some indicating time savings of around 5-6 hours weekly and a doubling of their coding efficiency when utilizing Cody. As developers continue to explore its features, the potential for Cody to transform coding practices becomes increasingly evident.

Discover a privacy-focused method for evaluating pull requests that delivers comprehensive code suggestions for every line, coupled with a dynamic chat feature that evolves with use. The system effectively summarizes changes within the pull request, clarifying the intent behind each modification. Automated release notes are generated to facilitate seamless integration into your release documentation. Every code change undergoes meticulous review, offering precise and actionable feedback that can be readily applied. You can interact with the bot by posing questions directly linked to your code and providing extra context for generating tailored code snippets. As your dialogue with the bot expands, its capabilities enhance, resulting in faster review cycles and improved quality of code change recommendations. Your privacy is preserved throughout this process, allowing the system to customize the review experience to meet your specific requirements. This innovative approach continuously evolves, improving the relevance of its suggestions to better align with your unique coding style and preferences as you interact with it over time. By fostering this dynamic relationship, developers can achieve a more efficient workflow and greater satisfaction in their coding practices.

Gain prompt code evaluations from skilled engineers, enhanced by AI solutions. Every time you submit a pull request, you can effortlessly incorporate seasoned engineers into your process. Boost the speed of delivering high-quality, secure code through AI-assisted code evaluations. Regardless of whether your development team consists of 5 or 5,000 individuals, PullRequest will improve your code review framework and customize it to meet your specific needs. Our knowledgeable reviewers help detect security risks, reveal hidden bugs, and tackle performance issues before your code goes live. This entire operation is seamlessly integrated into your existing tools to ensure maximum productivity. Our experienced reviewers, supported by AI analytics, can effectively pinpoint critical security flaws. We utilize sophisticated static analysis that leverages both open-source tools and proprietary AI, offering reviewers deeper insights. Empower your senior staff to concentrate on high-level strategies while making significant progress in fixing issues and optimizing code, even as other team members continue their development work. This cutting-edge strategy enables your team to sustain productivity while guaranteeing top-notch code quality. As a result, the overall efficiency of your development process is significantly enhanced, leading to faster project turnaround times.

Metabob specializes in diagnosing, interpreting, and resolving coding problems that may stem from either human errors or artificial intelligence. Utilizing cutting-edge graph neural networks for detection alongside large language models for elucidation and resolution, Metabob effectively combines the advantages of both technologies. The graph neural networks scrutinize and classify problematic code while remaining contextually aware. This code, enriched with pertinent context, is then archived in Metabob's backend system. The stored information is later harnessed by an integrated large language model, which generates customized explanations and solutions informed by the provided context. Trained on a vast dataset comprising millions of bug fixes performed by adept developers, Metabob's AI possesses a profound understanding of coding logic and context, allowing it to pinpoint complex issues across various codebases and autonomously generate appropriate fixes. Furthermore, Metabob's AI code review feature has the capability to reveal numerous logical errors, such as race conditions and overlooked edge cases, that often elude traditional static analysis tools. This groundbreaking methodology not only boosts debugging efficiency but also significantly improves the overall quality of the codebase. As a result, developers can focus more on innovation and less on troubleshooting.

Enhance the creation of robust and secure software by leveraging AI throughout all stages of the development lifecycle. GitLab Duo acts as an AI-powered ally intricately embedded within the GitLab framework, designed to elevate productivity and improve collaboration among software developers. It delivers intelligent code suggestions, assists with code refactoring and debugging, and streamlines processes by automating repetitive tasks. Moreover, GitLab Duo facilitates natural language queries concerning project management and development analytics, providing developers with a more user-friendly approach to interact with their code repositories. With compatibility with leading IDEs like JetBrains and Neovim, GitLab Duo ensures consistent support in the environments preferred by developers. By merging AI-enhanced productivity with GitLab's extensive DevSecOps functionalities, GitLab Duo equips teams to create exceptional software more swiftly while strengthening collaborative efforts. The introduction of this groundbreaking tool fundamentally changes the methodology teams utilize in software development, paving the way for more innovative practices and efficient workflows.

Patched is a managed service designed to enhance various development processes by leveraging the open-source Patchwork framework, addressing tasks such as code reviews, bug fixes, security updates, and documentation. By utilizing advanced large language models, Patched enables developers to design and execute AI-driven workflows, referred to as "patch flows," which systematically oversee tasks post-code completion, thereby elevating code quality and accelerating development cycles. The platform boasts a user-friendly graphical interface and a visual workflow builder, making it easy to tailor patch flows without the need to manage infrastructure or LLM endpoints. For those who prefer self-hosting, Patchwork includes a command-line interface agent that seamlessly fits into current development practices. Additionally, Patched places a strong emphasis on privacy and user control, providing organizations the ability to deploy the service within their own infrastructure while using their specific LLM API keys. This amalgamation of features not only promotes process optimization but also ensures that developers can work securely and with a high degree of customization. The flexibility and security offered by Patched make it an attractive option for teams seeking to enhance their development workflows efficiently.

Korbit is a sophisticated code review tool that utilizes artificial intelligence to enhance developer productivity by providing instant, actionable feedback directly on pull requests. It seamlessly integrates with platforms such as GitHub, GitLab, and Bitbucket, allowing for swift PR evaluations that detect issues and suggest remedies, paralleling the efficiency of a human reviewer. Moreover, Korbit generates comprehensive PR descriptions that clarify the reasoning and purpose behind modifications, while also summarizing its evaluations to help teams focus on the most critical issues. A management dashboard is also provided, offering essential insights on code quality, project statuses, and developer performance, thereby enabling effective team management. The dynamic review process of Korbit capitalizes on extensive project context, individualized feedback, and customized settings to spot crucial problems and offer actionable advice on resolving them. It further improves communication by addressing questions and comments within the PR, even suggesting alternative coding approaches to assist developers in overcoming obstacles. By incorporating these functionalities, Korbit not only streamlines the development process but also cultivates a more collaborative and efficient atmosphere among team members. With its innovative approach, Korbit stands out as a pivotal tool for modern software development teams.

Matter AI acts as an intelligent code review solution that enhances the pull request process by generating detailed, context-aware summaries almost instantly, thus eliminating the need for traditional documentation methods. It bolsters code quality by identifying potential bugs, security issues, and performance problems before the code is deployed. Matter AI integrates effortlessly with numerous internal tools like Notion, JIRA, Confluence, and Linear, offering reliable summaries and evaluations of the code. The explanations generated by the AI help reviewers quickly understand complex code, which leads to faster approvals and shorter review times. With a strong emphasis on security, Matter AI holds SOC 2 Type II certification and ensures data privacy by processing code in isolated environments without storing any sensitive information. This cutting-edge tool is ideal for development teams looking to streamline their code review processes while maintaining high standards of quality and security. Furthermore, Matter AI enhances collaboration amongst team members, resulting in a more productive and unified development atmosphere. By fostering such an environment, development teams can achieve their goals more efficiently and effectively.

Entelligence AI operates as a robust engineering intelligence platform that harnesses the power of artificial intelligence to enhance development workflows, promote collaboration, and boost productivity across the software development lifecycle. By employing intelligent agents, it streamlines the code review and pull request (PR) evaluation processes, which leads to shorter review times, early detection of bugs, and improved engineering productivity. The platform's Deep Review feature conducts thorough analyses of intricate issues spanning multiple files through an extensive examination of the entire codebase, providing detailed PR summaries, insightful comments, and immediate fixes. Additionally, Entelligence AI offers essential performance metrics that assess team interactions, track sprint progress, and evaluate code quality, delivering up-to-the-minute insights into individual engineer performance, review comprehensiveness, and sprint outcomes. Moreover, its cutting-edge self-updating documentation functionality converts code into user-friendly documentation, automatically updating with each new commit to guarantee that developers can access the latest information. This extensive array of features not only streamlines the software development process but also ensures that teams can maintain high standards of clarity and efficiency in their projects. Ultimately, Entelligence AI stands out as an essential resource for contemporary software development teams striving for operational excellence and collaboration.

Sourcery functions as an AI-based automated code review tool and coding assistant dedicated to improving code quality, detecting bugs and security issues early, and maintaining consistent standards across multiple projects for developers and engineering teams. It integrates smoothly with popular development platforms such as GitHub, GitLab, and IDEs like VS Code and JetBrains, providing immediate, actionable insights on pull requests and code modifications rather than depending solely on traditional peer review methods. By combining the capabilities of large language models with static analysis techniques, Sourcery examines code differences to deliver concise summaries, detailed recommendations for individual lines, comprehensive feedback, and visual aids that clarify suggested changes, aiming to replicate the review quality of a fellow developer. Within the integrated development environment, it serves as a real-time pair programming assistant that not only highlights potential improvements but also allows for one-click implementation of suggestions and features an AI chat option for additional guidance, making it an adaptable resource for developers wanting to enhance their coding techniques. Furthermore, Sourcery's feedback in real-time cultivates a cooperative coding atmosphere, enabling teams to collaborate more effectively and streamline their workflows, ultimately leading to improved productivity and code quality. This emphasis on collaboration and efficiency makes Sourcery an invaluable asset for modern development teams.

Kodus is an innovative, collaborative platform that utilizes AI for code reviews, featuring an intelligent assistant named Kody, which integrates flawlessly with major Git services such as GitHub, GitLab, Bitbucket, and Azure DevOps, to support engineering teams in automating and improving the quality of their code evaluations. Kody conducts in-depth analyses of each pull request, considering the specific codebase, architecture, workflows, coding standards, and business rules of the team, thereby providing precise feedback that emphasizes quality, security, performance, and style, avoiding generic suggestions. Teams can customize their review parameters using natural language or opt for a selection of pre-approved rules that encourage best practices and uphold uniform standards; they also have the flexibility to implement their preferred AI models by using their own API keys. Furthermore, Kodus turns unresolved recommendations into tracked issues, helps monitor technical debt, and offers actionable insights in a way that reduces distractions, while accommodating over 30 programming languages to ensure versatility across various projects. This all-encompassing strategy not only simplifies the review process but also promotes a culture of ongoing enhancement within development teams, paving the way for more effective collaboration and higher-quality code outcomes. Ultimately, Kodus empowers teams to maintain a focused and efficient development environment while continuously refining their coding practices.

Kilo Code Reviewer represents a cutting-edge solution in the realm of code review, harnessing AI to promptly evaluate pull requests as soon as they are created or modified, while understanding the context of the changes and offering actionable feedback through inline comments, comprehensive explanations, and recommendations that aim to uncover bugs, security flaws, performance problems, style discrepancies, testing shortfalls, and absent documentation before human experts take a look. This innovative tool integrates effortlessly with platforms such as GitHub and GitLab, with plans for Bitbucket integration on the horizon, giving users the flexibility to select from various models and tailor the thoroughness and focus of reviews to fit their team's coding conventions. Additionally, it can be run locally in widely-used IDEs like VS Code or JetBrains, enabling developers to identify issues prior to committing their code. The installation process is user-friendly: just connect a repository, pick an AI model along with review parameters, and the system will automatically start tracking pull requests, ensuring consistent compliance with coding standards and providing prompt, context-aware feedback that enhances the effectiveness of human reviewers. Consequently, Kilo Code Reviewer not only optimizes the review workflow but also plays a crucial role in elevating code quality and boosting team efficiency, ultimately leading to a more robust development process. This tool represents a significant advancement in the way developers approach code quality and collaborative programming.

LaReview is a groundbreaking, open-source platform for code reviews that prioritizes local-first functionality, designed to transform pull requests and code diffs into a structured, high-quality review process that improves understanding while reducing distractions. By allowing inputs from GitHub or GitLab pull requests or raw diffs, it utilizes AI coding agents to formulate a detailed review plan that organizes changes based on workflows, potential risks, and developer intentions. This approach empowers developers to assess code thoughtfully and systematically rather than just skimming through files. LaReview employs a reviewer-focused strategy, enabling engineers to effectively strategize their evaluations before sharing feedback, and aims to produce valuable, constructive comments rather than inundating reviewers with numerous low-impact observations. The platform's AI-driven planning features analyze code similarly to a seasoned engineer, identifying potential issues and creating structured checklists, along with task-oriented review interfaces that manage tasks logically while highlighting risks with tools like file heatmaps. In this way, LaReview not only enhances the efficiency of the code review process but also cultivates a culture of meaningful and insightful feedback within development teams, ultimately leading to higher-quality code and improved collaboration. Additionally, the platform encourages continuous learning and adaptation among team members, ensuring that the review process evolves alongside coding practices and technologies.