According to Virtue Market Research, demand for AI code review has surged by 35% in the last three years. The rise of DevOps, faster release cycles, and agile development has made slow, manual code reviews harder to justify.
If you have ever waited days for someone to review your pull request or rewritten the same fixes three times, you already know why.
Traditional reviews are time-consuming, inconsistent, and often miss what actually matters. AI code review is solving that.
The AI-powered code review tools use machine learning to detect bugs, enforce best practices, and improve code quality without slowing you down. These tools sit inside your workflow and give you feedback that is fast, contextual, and actionable.
In this blog, I will walk you through everything you need to know. What AI code review means, how it works, and which tools are leading the space.
What is AI code review?
AI code review is the process of using artificial intelligence to evaluate code for issues like bugs, logic errors, security risks, and inconsistencies.
It uses models trained on large datasets of code to understand patterns, intent, and structure. The AI looks at the code the way a developer would — not just for syntax, but for what the code is trying to do and how safely or efficiently it does it.
Instead of relying on manual reading or static rules, AI code review adds context, learns from feedback, and improves the accuracy of suggestions over time.
The goal is to support faster, higher quality reviews by offering feedback that is timely, intelligent, and rooted in real-world development patterns.
Why AI code review matters?
Modern development teams work inside continuous integration pipelines and fast-moving pull request workflows. With frequent deploys and tight release cycles, code review is often the last thing standing between a bug and production.
This is where AI code review becomes essential.
It increases review coverage across the entire codebase without adding more manual overhead. It ensures that no function, variable, or logic branch goes unchecked, even when human reviewers are busy or unavailable.
For developers, this means less time stuck waiting for approvals and more confidence in the code they ship. For teams, it means better consistency, reduced risk, and higher overall productivity.
Manual vs Automated code review
When I first heard about automated code reviews, my first instinct was the same as you: to compare manual vs automated code review. Let’s talk about it quickly:
Manual code review involves developers reading through each other’s pull requests and leaving comments. While it plays a crucial role in sharing knowledge and catching high-level issues, it has its limits.
Some of the common problems with manual reviews include:
- Missed errors due to time pressure or review fatigue
- Repetitive feedback on code style or formatting
- Inconsistent standards across different reviewers
- Long delays between submission and approval
In short, painfully slow and error prone!
Automated code review tackles these challenges directly.
- It scales code review coverage without requiring more people.
- It provides real-time suggestions right inside the development environment.
- It flags problems as they happen, not days later.
Key components of AI code review
AI code review combines multiple techniques to analyze, understand, and comment on code like a human reviewer would. Each component contributes to a specific part of the review process:
- Static code analysis
- Dynamic code analysis
- Rule-based systems
- Natural language processing (NLP)
- Large language models (LLMs)
1. Static code analysis
Static code analysis is the backbone of any AI review tool. It inspects your code without executing it, helping catch syntax errors, undefined variables, and unreachable code before runtime.
This process improves code safety by flagging issues early. It also ensures compliance with language-specific standards like PEP8 for Python or Google Java Style Guide.
Many compare AI code reviews with static analysis, which is not wrong. But it is important to understand that every AI code reviewer starts here before adding smarter insights on top.
2. Dynamic code analysis
While static analysis looks at code in theory, dynamic analysis observes how it behaves during execution. This often happens during testing, where AI can simulate inputs and trace code paths.
Dynamic checks help reveal issues like memory leaks, unhandled exceptions, or performance bottlenecks that static scans might miss. They also validate how different modules interact at runtime.
For AI code reviewers, dynamic analysis adds context that makes suggestions more precise.
3. Rule-based systems
Rule-based systems use predefined logic to evaluate your code. They are the part of the engine responsible for enforcing consistent formatting, naming conventions, and other team-level standards.
This is where automated style enforcement comes in. For example, checking for snake_case in Python or semicolon use in JavaScript.
Though limited in flexibility, these systems ensure a baseline of quality across all reviews.
4. Natural language processing (NLP) models
NLP is what lets AI code reviewers speak and understand human intent. These models process documentation, commit messages, and code comments to provide more natural feedback.
They help the AI generate suggestions in plain language. Instead of cryptic warnings, you get understandable, relevant advice.
NLP also powers reverse communication – letting developers ask questions and get responses directly inside the pull request.
5. Large language models (LLMs)
LLMs are what bring real intelligence into AI code reviews. Trained on massive datasets of code and natural language, they can understand context, purpose, and logic in a way older tools cannot.
LLMs do more than just detecting issues. They rewrite code, explain alternatives, and adapt to your project’s architecture. They make review feedback smarter and more flexible over time.
What makes a good AI code review tool?
If you’re evaluating AI code review tools, these are the capabilities that matter. Each one contributes directly to reliability, adoption, and long-term use in real development workflows.
If you’re also reviewing code manually or pairing AI with human input, we’ve written a full 24-point code review checklist to help you assess what actually matters in every pull request.
- Accuracy and relevance of suggestions: The tool should surface issues that affect logic, structure, or maintainability. Cosmetic feedback should be secondary, not the default.
- Context awareness and adaptability: It must understand how changes relate to the surrounding codebase. Reviewing a single line without context often leads to false positives.
- Ease of integration with existing workflows: The tool should plug into your version control system and review pull requests without needing additional steps or switching tools.
- Customizability for team conventions: Review output should reflect your team’s standards. It should allow configuration for naming, formatting, and architectural preferences.
- Speed and responsiveness: Review comments should be generated quickly enough to be useful during the review cycle. Latency leads to dropped adoption.
- Transparency and explainability: Each suggestion should include a reason. Developers need to understand what is flagged and why it matters in that context.
- Support for multiple languages and frameworks: If your stack includes multiple languages or environments, the tool should handle them with consistent reliability.
- Feedback learning and rule refinement: If your team disagrees with a type of suggestion, the tool should adapt. Static behavior leads to repetitive and ignored feedback.
In the next section, we’ll look at tools that offer these capabilities and how they compare in real use.
Quick comparison between top 10 AI code review tools
Below is a side-by-side comparison of various automated AI code review tools. The comparison highlights that Bito’s AI Code Review Agent outperforms other AI-powered tools in code review quality. Bito fully understands your entire codebase, enabling it to review code just like a senior developer on your team.
Integration and Review Process Features
| Feature | Bito’s AI Code Review Agent | CodeRabbit | CodiumAI’s PR-Agent | Codacy | Code Climate | Amazon CodeGuru Reviewer | CodeScene | PullRequest | Reviewable | Snyk |
|---|---|---|---|---|---|---|---|---|---|---|
| Pull Request Summary | Yes | Yes | No | No | Yes | No | No | No | No | No |
| Git Provider Integration | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Review Effort Estimation | Yes | No | No | No | No | No | No | No | No | No |
| Continuous/Incremental Reviews | Yes | Yes | No | No | No | No | No | No | No | No |
Code Analysis and Security Features
| Feature | Bito’s AI Code Review Agent | CodeRabbit | CodiumAI’s PR-Agent | Codacy | Code Climate | Amazon CodeGuru Reviewer | CodeScene | PullRequest | Reviewable | Snyk |
|---|---|---|---|---|---|---|---|---|---|---|
| Real-Time Code Analysis | Yes | No | No | Yes | No | No | No | No | No | Yes |
| Static Code Analysis | Yes | No | No | Yes | No | No | No | No | No | No |
| Security Vulnerability Check | Yes | No | No | No | No | Yes | No | No | No | Yes |
Advanced AI and Customization Features
| Feature | Bito’s AI Code Review Agent | CodeRabbit | CodiumAI’s PR-Agent | Codacy | Code Climate | Amazon CodeGuru Reviewer | CodeScene | PullRequest | Reviewable | Snyk |
|---|---|---|---|---|---|---|---|---|---|---|
| AI-Powered Code Review | Yes | Yes | Yes | Yes | No | Yes | No | Yes | No | No |
| Auto Description/Code Suggestions | Yes | No | Yes | No | No | No | No | No | No | No |
| Customizable Reviews | Yes | Yes | Yes | No | No | No | No | Yes | No | No |
| Machine Learning Models | Yes | Yes | No | No | No | Yes | No | No | No | Yes |
| Feedback Mechanism | Yes | No | Yes | No | Yes | Yes | Yes | No | No | No |
Compliance and Language Support Features
| Feature | Bito’s AI Code Review Agent | CodeRabbit | CodiumAI’s PR-Agent | Codacy | Code Climate | Amazon CodeGuru Reviewer | CodeScene | PullRequest | Reviewable | Snyk |
|---|---|---|---|---|---|---|---|---|---|---|
| Issue Validation | Yes | Yes | No | No | No | No | No | No | No | No |
| Data Privacy Compliance | Yes | Yes | No | No | No | No | No | No | No | No |
| Comprehensive Language Support | Yes | No | No | Yes | No | Yes | No | Yes | No | Yes |
Pricing Details
| Pricing | Bito’s AI Code Review Agent | CodeRabbit | CodiumAI’s PR-Agent | Codacy | Code Climate | Amazon CodeGuru Reviewer | CodeScene | PullRequest | Reviewable | Snyk |
|---|---|---|---|---|---|---|---|---|---|---|
| Free Plan Available? | Yes! Get a 14-day FREE trial. | Yes | Yes | Yes | Yes | Free Trial | Yes | Yes (Limited) | Yes | Yes |
| Paid Plan | $15 per user per month (billed monthly) | $12 per month (billed annually) or $15 monthly per contributing developer | $19 per user per month | $15 per user per month (billed annually) or $18 billed monthly | $16.67 per user per month (billed annually) or $20 billed monthly | $10 for first 100K lines of code. $30 per each additional 100k lines of code. | €27 Per active user per month (billed annually) | $129 per user per month | $16 per user per month | Starting at $25 per month per product |
1. Bito’s AI Code Review Agent
Bito’s AI Code Review Agent conducts a human-like code review using the latest Generative AI models (such as Claude Sonnet 3.5 and best in class AI models). This tool is expert at examining every Pull/Merge Request in a repository, swiftly identifying issues such as errors, code smells, and security vulnerabilities.
It integrates smoothly with GitHub, GitLab, and Bitbucket, so it fits right into your existing workflow.
Bito focuses on deeply understanding your codebase to improve the relevance of its suggestions. It combines multiple techniques for this:
- RAG (Retrieval Augmented Generation): Used to enhance AI understanding by pulling relevant context dynamically.
- Dynamic symbol search engine: Finds all relevant symbols in the code being changed, such as function or class names. This lookup completes in just a few milliseconds.
- Abstract Syntax Trees (ASTs): Bito builds language-specific ASTs to crawl the code tree and understand structural relationships. For example, it can trace a function back to the class it belongs to.
This deep context is what helps the AI generate more accurate and meaningful code review feedback.
Here’s some more info on Bito’s approach: How does Bito’s “AI that understands your code” work?
What sets Bito’s AI Code Review Agent apart is its understanding of code, availability in the IDE to provide suggestions as developer write code, enterprise reporting, and customization options. It’s designed to align with diverse team workflows and specific needs.
Bito also lets you create custom AI agents using its open framework. These agents can:
- Understand your code and team context
- Interact with your internal tools and data
- Be shared across teams or organizations
This flexibility makes Bito a highly adaptable solution in the evolving space of code review and developer productivity.
Features
- Deep Code Understanding: Deep understanding of your code including libraries, frameworks, functionality to improve code review.
- Free AI-generated PR Summary: Quick, comprehensive overviews of pull requests.
- Review Effort Estimation: Evaluates PR complexity for better scheduling.
- AI Code Review: Assesses security, performance, scalability, optimization, impact on existing features, code structure, and coding standards.
- Tailored Code Suggestions: Precise, line-specific improvement suggestions.
- Static Code Analysis: In-depth analysis with tools like fbinfer and Sonar.
- Security Vulnerability Check: Uses tools like OWASP Dependency-Check for detecting severe security flaws.
- Feedback Integration: Directly posts comments in PRs/MRs.
- Real-Time Feedback: (Upcoming) Instant code feedback in IDE.
Pricing Details
Free plan: Yes! Get a 14-day FREE trial.
Paid plan: $15 per user per month (billed monthly)
2. CodeRabbit
CodeRabbit is an AI-driven platform that revolutionizes the way we conduct code reviews. It employs advanced AI models like gpt-3.5-turbo and gpt-4 to provide instant, line-by-line insights on pull requests, saving time and ensuring early issue detection. The platform’s collaborative chat feature and continuous, incremental reviews further enhance efficiency and align with individual coding preferences.
CodeRabbit prioritizes data privacy, adhering to SOC2 Type II, GDPR, and HIPAA standards.
It stands out from traditional tools by providing human-like review feedback, addressing issues beyond linting and static code analysis.
User testimonials underscore its impact, reducing review time, improving code quality, and offering unique features like issue validation and customizable reviews.
Overall, CodeRabbit is an innovative, user-friendly AI application transforming code reviews for developers, ensuring a more efficient and insightful coding experience.
Features
- Context-Aware Feedback: Provides intelligent, rapid feedback, detecting issues missed by other tools.
- Chat Interaction: Enables in-code conversations with the bot for context, queries, and code generation.
- Continuous Reviews: Offers ongoing, incremental reviews through GitHub and GitLab integration.
- Issue Validation: Checks code changes against related issues to foresee impacts.
- PR Summarization: Creates concise technical and non-technical summaries of pull requests.
- Deep Code Insights: Offers thorough codebase verification and in-depth understanding.
- Customizable Reviews: Allows feedback customization to meet specific coding requirements.
- Data Privacy: Ensures data confidentiality complying with SOC2 Type II, GDPR, and HIPAA, with opt-out options.
- Smart Review Skipping: Omits detailed reviews for minor changes, concentrating on crucial elements.
- Incremental Large PR Reviews: Efficiently handles large pull requests with up to 100 files, ensuring accuracy and time efficiency.
Pricing Details
Free plan: Yes
Paid plan: $12 per month (billed annually) or $15 monthly per contributing developer
3. CodiumAI’s PR-Agent (now Qodo)
Qodo is an open-source AI tool specifically designed to enhance Pull Request (PR) analysis and feedback. By automating the review process, this tool aims to make PRs less time-consuming and more efficient.
Whether for small personal projects or large enterprise solutions, PR-Agent caters to all with customizable plans.
Hosted options are available for GitHub, GitLab, and Bitbucket, providing flexibility for either self-hosting for greater control or a fully managed service for ease of use. It allows developers to seamlessly interact with PR-Agent within their usual work environment, streamlining the PR approval process.
PR-Agent stands out as a highly intelligent, AI-powered assistant that aids in automating the analysis of PRs. It offers a range of feedback options, such as auto-description, PR review, and code suggestions.
This tool is not just about identifying problems; it also provides solutions and improvements, significantly reducing the time developers spend on PRs.
Features
- Auto Description: Generates a comprehensive PR description, including title, type, summary, and a code walkthrough.
- Auto Review: Provides adjustable feedback on various aspects of the PR, including theme, type, tests, and security issues.
- Question Answering: Answers free-text questions about specific details of the PR.
- Code Suggestions: Offers actionable, committable code suggestions to refine and improve the PR.
- Update Changelog: Automatically updates the CHANGELOG.md file with details from the PR.
- Find Similar Issue: Identifies and presents issues similar to the one being addressed in the PR.
- Add Documentation: Automatically adds necessary documentation to modified methods, functions, or classes.
- Generate Custom Labels: Suggests custom labels for the PR based on the code changes.
- Analyze: Performs a thorough analysis of the PR, presenting a detailed walkthrough for each component.
- Custom Suggestions: Generates tailored suggestions for PR code improvements, based on user-defined guidelines.
- Generate Tests: Automatically creates unit tests for selected components, aligning with the PR’s code changes.
Pricing Details
Free plan: Yes
Paid plan: $19 per user per month
4. Codacy
Codacy is an automated code quality and coverage platform that analyzes your source code in real-time, providing valuable insights and suggestions for improvement. It supports over 40 programming languages, including popular ones like JavaScript, Python, Java, C#, and PHP.
By seamlessly integrating with your Git provider, it tracks your team’s progress, identifies issues, and suggests fixes, ensuring clean, secure code effortlessly.
Features
- Real-time Code Analysis: Instant source code analysis for continuous quality assurance.
- Support for 40+ Languages: Covers a wide array of languages like JavaScript, Python, Java, C#, PHP.
- Git Integration: Tracks progress, analyzes commits, and protects codebase in Git.
- Automated Issue Detection: Detects code format, standard violations, and security risks.
- AI-driven Suggestions: Provides AI-based fixes within Git for code complexity and performance issues.
- Unified Security Tools: Comprehensive security suite including SAST, SCA, and more.
- Code Coverage Monitoring: Tracks test coverage with insights into line, diff, and variation coverage.
- Enforced Coding Standards on Pull Requests: Applies coding standards consistently to pull requests.
- Data-Driven Insights with DORA Metrics: Offers insights using DORA metrics for performance tracking.
- Comprehensive Performance Benchmarking: Sets benchmarking goals for coding practices and project improvement.
Pricing Details
Free plan: Yes
Paid plan: $15 per user per month (billed annually) or $18 billed monthly
5. Code Climate
Code Climate offers an automated solution for enhancing code quality and efficiency. It streamlines the review process by providing automated comments on pull requests and a detailed 10-point technical debt assessment.
This tool delivers real-time feedback, enabling developers to focus on critical aspects of their code. It’s designed to ensure quality and maintainability in code development, promoting confidence in code merging.
The platform stands out for its comprehensive approach to code management. It offers line-by-line test coverage within diffs, ensuring that no code is merged without sufficient testing. Its maintainability alerts help developers tackle technical debt by highlighting files with coverage and maintainability issues. Additionally, Code Climate aids in prioritizing code by correlating quality information with areas of high churn, enabling teams to concentrate on the most impactful issues.
Features
- Pull Request Statuses: Offers coverage and maintainability statuses for each pull request, providing detailed insights.
- Initial Review Analysis: Analyzes initial pull request responses to assess review quality.
- Coverage: Tracks the percentage of files commented on to measure code review thoroughness.
- Speed: Measures ‘Time to Review’ from pull request opening to initial review, indicating review responsiveness.
- Influence: Assesses the impact of review comments on code changes and responses.
- Reviewers Analysis: Provides metrics on reviewer involvement to highlight bottlenecks and ensure balanced participation.
- Browser Extension: Enhances GitHub accessibility and integration via a convenient browser extension.
- Full REST API: Allows for extensive customizations and integrations through a complete REST API.
- Shared Configurations: Enables organization-wide configurations for team consistency.
- Local IDE Analytics: Supports immediate feedback in development with IDE/editor integrations and command-line interfaces.
- 3rd-Party Integrations: Integrates with Jira, Trello, and GitHub Issues, enhancing workflow management.
- Two-Factor Authentication: Ensures robust security with GitHub OAuth, two-factor authentication, and enterprise-grade protocols.
- GitHub Repos Support: Offers seamless integration for GitHub.com repositories.
- Secure Data Handling: Ensures data security with encryption and fine-grained permissions.
Pricing Details
Free plan: Yes
Paid plan: $16.67 per user per month (billed annually) or $20 billed monthly
6. Amazon CodeGuru Reviewer
Amazon CodeGuru Reviewer leverages machine learning and program analysis to identify and suggest improvements for complex defects in Java and Python code. It stands out for its ability to pinpoint issues not typically caught during standard code reviews, thereby enhancing code quality and maintainability.
This service, now widely available, integrates seamlessly into the development workflow, providing vital insights during the code review phase.
CodeGuru Reviewer focuses on high-level recommendations rather than basic syntactic errors. It excels in identifying intricate problems, offering solutions for resource leaks, security issues, and more.
This tool is particularly useful for developers working with AWS, as it includes integration with AWS Secrets Manager for detecting unprotected secrets in code. Its compatibility with various source providers like AWS CodeCommit, Bitbucket, GitHub, and Amazon S3 (via GitHub Actions) adds to its versatility.
Features
- Advanced Program Analysis: Detects complex code issues for higher code quality.
- Machine Learning Models: Trained on extensive code data for accurate, relevant recommendations.
- Support for Java and Python: Tailored for these languages, catering to many developers.
- Integration with Source Providers: Compatible with AWS CodeCommit, Bitbucket, GitHub, etc., for easy workflow integration.
- AWS Secrets Manager Integration: Identifies unprotected secrets via a secrets detector.
- Actionable Recommendations: Offers practical advice for resource management and security.
- Low False Positive Rate: Provides relevant suggestions with minimal false positives.
- Incremental and Full Repository Analysis: Supports both types of code reviews for flexibility.
- Customizable Analysis: Allows exclusion of specific files/directories using ‘aws-codeguru-reviewer.yml’.
- Feedback Mechanism: Developer feedback enhances tool’s accuracy and effectiveness.
Pricing Details
Free plan: Free trial for 90 days for up to 100K lines of code
Paid plan: $10 for first 100K lines of code. $30 per each additional 100k lines of code.
7. CodeScene
CodeScene, an innovative tool designed to automate code reviews, significantly enhances the software development process. It seamlessly integrates with pull requests, offering a dual advantage: it acts as a quality gate and provides an early feedback loop.
This integration is crucial in modern software development, where maintaining and improving code quality amidst complex systems is a challenging task. CodeScene steps in to ensure that the code doesn’t deteriorate over time, making it easier to read, understand, and maintain.
The solution offered by CodeScene is both practical and timely.
By automating code reviews within pull requests, it not only detects code quality issues but also guides developers towards improving the codebase. This approach is beneficial for keeping the code aligned with set standards and goals.
It acts as a quality controller, giving instant feedback and recommendations, which are vital for steering the code in the desired direction.
Features
- Instant Feedback: CodeScene provides quick feedback on code health declines in hotspots with detailed explanations and suggestions.
- Code Quality Protection: Teams can set and maintain code quality standards, with alerts for deviations and hotspot health declines.
- Pull Request Supervision: Offers immediate code improvement recommendations during pull requests to enhance code health.
- Health Trend Monitoring: CodeScene monitors code health trends over time for a dynamic view of system evolution.
- Trend-Based Targets: Allows setting targets and goals based on code development trend measures.
- Feedback for Organization and Developers: Delivers crucial feedback for organizational decisions and developer improvement.
- Automated Code Reviews: Streamlines the review process for time efficiency and consistent quality checks.
- Git Hosting Integration: Supports automated integration with major Git platforms for versatile development environments.
- Build Pipeline Compatibility: Compatible with Jenkins and other build pipelines for CI/CD workflows.
- Open REST API: Offers an open REST API for custom project-specific integrations.
Pricing Details
Free plan: Yes
Paid plan: €27 Per active user per month (billed annually)
8. PullRequest
PullRequest utilize both expert human engineers and AI technology to enhance the code review process of pull requests. This service is designed to cater to development teams of varying sizes, ranging from small groups to large corporations.
It supports a wide array of programming languages and frameworks, ensuring comprehensive coverage for diverse coding projects.
PullRequest’s primary goal is to help teams deploy better quality code more quickly, while also identifying and rectifying security vulnerabilities and performance issues before the code reaches production. This integration happens within the existing tools and workflows of the development team, providing a seamless and efficient experience.
The platform has a notable feature where AI-powered tools assist human reviewers in identifying high-risk areas in code, thus focusing on crucial aspects that require attention.
PullRequest’s network of reviewers is impressive, comprising senior engineers with experience in top tech organizations. These reviewers not only provide quality code reviews but also share valuable knowledge, helping to elevate the skills and standards of the client’s development team.
The service is designed to save time for senior staff and improve overall code quality, ensuring that the final product is secure and performs well. This approach to code review is both innovative and efficient, aiming to transform the traditional code review process into a more advanced, effective practice.
Features
- AI-Powered Selection: AI targets key code sections for high-security risks, streamlining reviews.
- Expert Review: Senior developers from top firms review high-risk changes for thorough analysis.
- Speedy Reviews: Reviews mostly done within 90 minutes, accelerating secure code deployment.
- Custom Workflows: Tailored workflows emphasize high-risk changes and general code checks.
- Vetted Experts: Access to top engineering talent for superior feedback and learning.
- Quality Focus: Enhances code by spotting security flaws, bugs, and advising on architecture and readability.
- Quick Cycles: Reduces review bottlenecks, offering fast review turnaround.
- Data Insights: Offers actionable metrics via dashboards and weekly summaries to refine development processes.
Pricing Details
Free plan: Yes, only access to metrics and benchmarks.
Paid plan: $129 per user per month
9. Reviewable
Reviewable is a specialized code review platform that exclusively supports GitHub repositories. It stands out for its thoroughness, ensuring every discussion is resolved before considering the review complete. The tool is designed with a customizable framework, allowing teams to set their own criteria for review completion.
Reviewable also excels in efficiency, adeptly handling changes in code—even if commits are rebased or amended. It simplifies the review process by displaying net changes since the last review.
Its focus is solely on GitHub and GitHub Enterprise, which enables a smooth integration without the clutter of unnecessary features. This tool is ideal for teams seeking efficient and comprehensive code reviews within the GitHub ecosystem.
The tool boasts a range of features to streamline code reviews.
Instant diff viewing between any two file revisions is possible in both unified and side-by-side formats. Reviewable allows hiding of minor changes, such as whitespace alterations and merge/rebase deltas, to focus on significant code changes.
Its intelligent line mapping ensures comments remain linked to the relevant lines across file revisions until issues are fully resolved.
Tracking who reviewed which file revision ensures comprehensive coverage and avoids overlooking changes. Users can choose to review combined commits or each commit individually. Personalization options like adjustable line length margins, fonts, colors, and keyboard shortcuts enhance the user experience.
Direct navigation from comments to the corresponding location in one’s preferred editor, a modern user interface, comprehensive contextual help, and quick support all contribute to a seamless review process.
Integration is further simplified as users sign in with their GitHub account, avoiding the hassle of managing separate accounts. With Reviewable, code remains on GitHub’s servers, maintaining data security and integrity.
Features
- Instant Diff Viewing: Compare any two revisions of a file easily.
- Hide Minor Changes: Focus on significant code changes by hiding minor edits.
- Intelligent Line Mapping: Keep track of comments across revisions until fully resolved.
- Revision Tracking: Monitor who reviewed which part of the code.
- Flexible Review Options: Choose between reviewing combined commits or individual ones.
- Customizable Interface: Tailor line length, fonts, colors, and shortcuts to your preference.
- Editor Integration: Jump directly from comments to the corresponding code in your favorite editor.
- Modern UI: Enjoy a clean and user-friendly interface.
- Contextual Help and Support: Access comprehensive help and prompt support.
- GitHub Integration: Sign in with GitHub account for a seamless experience.
Pricing Details
Free plan: Yes
Paid plan: $16 per user per month
10. Snyk
Snyk offers a developer-focused approach to Static Application Security Testing (SAST), ensuring code security in real-time during the development process. It integrates seamlessly into the developer’s workflow, providing security intelligence and remediation advice without causing disruptions.
This tool is designed to assist teams in identifying and rectifying vulnerabilities swiftly and efficiently. Snyk Code’s emphasis on a developer-friendly experience is evident in its ability to deliver fast, accurate, and actionable results directly within the integrated development environment (IDE), enhancing both security and productivity.
The platform boasts numerous features that cater to the modern development landscape.
Its real-time scanning capability allows for immediate vulnerability detection and fixing, eliminating the wait for traditional SAST reports. Snyk supports a wide range of languages and tools, and continuously expands its coverage.
The backbone of Snyk Code is its revolutionary knowledge base, powered by a machine learning engine that analyzes millions of open-source libraries.
This AI-augmented system prioritizes top code risks and integrates security into various stages of the software development lifecycle (SDLC), including IDE, continuous integration/continuous deployment (CI/CD), and in-workflow testing. Snyk’s AI, built with DeepCode technology, is tailored specifically for secure development, boasting unmatched scanning accuracy and developer-friendly fix recommendations.
Features
- Developer-Friendly Interface: Offers guidance for fixing issues without disrupting the development process.
- Real-Time Scanning and Fixing: Enables immediate scanning and remediation of vulnerabilities in the code.
- Comprehensive Language and Tool Coverage: Supports a wide array of programming languages, IDEs, and CI/CD tools.
- Advanced Knowledge Base: Utilizes a machine learning engine to constantly enhance security tooling based on global open-source contributions.
- Risk Prioritization: Helps in identifying and focusing on the most critical code risks.
- Integrated Security in Daily Workflow: Seamlessly integrates security testing into the regular development process.
- AI-Augmented Developer Experience: Employs DeepCode AI for rapid and accurate vulnerability detection and fixing.
- CI/CD Security Gate: Integrates vulnerability scans into the build process for enhanced application security.
- Customizable Testing and Rules: Allows for the creation of custom queries and rules using DeepCode AI Search.
Pricing Details
Free plan: Yes
Paid plan: Starting at $25 per month per product
Automated PR reviews with Bito: How to get started
Most AI code review tools work by integrating directly with your Git provider. Once connected, they scan your pull requests and leave feedback just like a human reviewer would. You can then respond, accept, or ignore those suggestions depending on your team’s workflow.
If you want to try it for yourself, Bito offers two ways to run AI code reviews: through Bito Cloud or as a self-hosted service.
Use Bito Cloud (Recommended for most teams)
Bito Cloud is the simplest way to enable AI code reviews. It does not require any local installation or infrastructure setup. All you need is access to your repository.
Here is how to set it up:
- Log in to Bito Cloud and select a workspace.
- Go to the Code Review section and click Repositories.
- Select your Git provider from GitHub, GitLab, or Bitbucket.
- Install the Bito app when prompted and authorize access to your repositories.
- Choose whether to enable all repositories or select specific ones.
- Use the toggle in the dashboard to turn the AI Code Review Agent on or off for each repository.
- Create a pull request. Bito will automatically review it and leave comments inside the PR.
You can also manually trigger a review by typing /review in the pull request comment.
By default, Bito only reviews pull requests that merge into the default branch. To include additional branches, use the “Include Source/Target Branches” filter.
Self-host the AI Code Review Agent
If your team prefers to manage infrastructure internally, you can deploy the AI Code Review Agent as a self-hosted service.
Why teams self-host:
- Full control over infrastructure and data
- Option to integrate deeply with internal systems
- Better alignment with strict privacy or compliance policies
Things to consider:
- You will handle updates, scaling, and maintenance
- Setup is more involved and requires devops support
For teams with security requirements or custom environments, this setup offers the most flexibility.
Final thoughts
If you’ve made it this far, you already know how painful code reviews can get. Especially when the same low-effort fixes keep showing up in every pull request.
AI code reviews help with that. They take care of the repetitive comments, surface real issues early, and give you back the time to focus on more important decisions.
You are still in control. The tool just helps you move faster, with fewer distractions and cleaner code from the start.
Try automated code reviews with Bito. See how it fits into your workflow. You might not go back.
FAQs
What is the best AI tool for code review?
Bito is one of the best AI tools for code review. It helps you catch bugs, spot vulnerabilities, and improve your code instantly, right inside your favorite editor.
What is the AI tool to check code quality?
Bito makes it super easy to check code quality with just one click. It automatically reviews your code for errors, security issues, and best practices.
Can code review be automated?
Yes, absolutely! With AI tools like Bito, you can automate code reviews, saving time and catching mistakes faster than doing it manually.
How do I automate code review in GitHub?
You can integrate Bito into your workflow to automate code reviews on GitHub. It reviews your pull requests in real-time and gives you instant, smart feedback.
Is SonarQube an automation tool?
Yes, SonarQube is an automation tool, but if you want an AI-powered option, Bito offers a much smarter and faster experience for code reviews.
by the Bito team. 
