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# Introduction
As teams use AI coding agents and assistants like Copilot, Cursor, and Claude Code, developers are generating code faster than ever. But the review process hasn’t quite kept pace. Pull requests often sit idle for days or weeks, context gets lost, and subtle bugs often slip through manual inspection.
A more effective approach is to improve the review process with AI tools. Unlike traditional linters, modern AI tools analyze code in context, recognize architectural patterns, identify subtle logic flaws, and provide meaningful recommendations within seconds. This article covers five AI code review tools that offer real value across different team needs like:
- Comprehensive workflow platforms
- Deep codebase understanding
- Test generation and quality analysis
- Standalone review automation
- Automated fix implementation
This article is not an exhaustive list, but rather an overview of the top tools in the space, presented in no particular ranked order.
# 1. Rethinking Workflows with Graphite
Most AI review tools are just bots that leave comments on existing pull requests. Graphite is a complete review platform that rethinks the entire code review workflow. It combines stacked pull requests (PR) with AI-powered analysis for faster, higher-quality reviews.
Here are the features that make the Graphite agent useful for development teams:
- Enables stacked pull requests that break large features into atomic, reviewable chunks that AI can analyze more effectively
- Provides an interactive AI companion directly in your PR interface where you can ask questions and get instant context-aware answers
- Generates test plans and summaries automatically
- Delivers reviews through a cleaner, faster interface than GitHub’s native UI
The Graphite guides page has several practical guides categorized by the use case. Graphite + AI Agents: Testing Stacked Diffs is a good walkthrough as well.
# 2. Indexing Codebases with Greptile
While most tools only analyze changed lines in a PR, Greptile builds a comprehensive knowledge graph of your entire repository. This facilitates deep context analysis that traces how changes ripple through your entire system.
What makes Greptile worth considering:
- Creates a full-repository index that understands every function, dependency, and historical change across your codebase
- Performs cross-module dependency analysis to identify potential breaking changes and architectural impacts automatically
- Useful for answering complex questions like “What services depend on this API?” or “How does this affect downstream systems?”
The 5-Minute Quickstart on Greptile’s documentation includes setup guides for different repository sizes. The Greptile in Action | Real Examples page features several examples that show how Graphite is used in large open-source repositories.
# 3. Improving Quality with Qodo
Qodo takes a behavior-focused approach to code review by automatically generating comprehensive test suites and analyzing code quality. This helps teams catch bugs before they reach production.
Here’s what makes Qodo useful for code quality:
- Generates unit tests automatically based on your code changes, including edge cases and boundary conditions you might miss
- Provides behavioral analysis that examines function inputs, outputs, and potential failure modes
- Offers code quality suggestions focused on maintainability, readability, and best practices
- Integrates directly into your IDE and PR workflow with support for multiple programming languages
Check out Qodo’s Getting Started Guide for installation and setup. You can refer to the documentation for more details on how to use Qodo in CLI, IDE, and Git interface.
# 4. Automating Reviews with CodeRabbit
CodeRabbit is a popular third-party bot that connects to GitHub, GitLab, or Bitbucket. It provides comprehensive AI-powered reviews through detailed PR comments and an interactive chat interface.
The features that make CodeRabbit worth exploring:
- Generates detailed walkthrough summaries automatically when you open a pull request, explaining what changed and why
- Runs different code analyzers combining large language models with traditional linters for comprehensive feedback
- Provides a chat interface in PR comments where you can ask follow-up questions and request clarification
- Offers highly configurable rules that let you tune feedback levels and train the AI based on your team’s preferences
The CodeRabbit Quickstart Guide covers setup and configuration options. Their integration guides show how to connect with different Git platforms and customize feedback levels.
# 5. Bridging the Gap with Ellipsis
Ellipsis bridges the gap between code review and implementation by automatically generating fixes for reviewer comments. This helps reduce the back-and-forth cycles that slow down development.
What makes Ellipsis useful for reducing review cycles:
- Reads reviewer comments and automatically implements requested changes
- Generates commits with fixes after running tests to verify nothing breaks
- Maintains understanding of your coding standards and replicates consistent patterns across your codebase
- Works with GitHub and supports multiple programming languages
The installation guide includes setup instructions. The code review guide explains how to use Ellipsis for code reviews, which types of changes work best with automated implementation, and more.
# Wrapping Up
AI-powered code review tools have moved from experimental add-ons to essential components of modern development workflows. As code generation accelerates through AI assistants, intelligent review automation becomes necessary rather than optional for maintaining quality and velocity.
The right tool, however, depends on your specific challenges. And the key is matching the tool to your bottleneck.
Don’t just add AI code review tools to a broken process; instead, choose tools that address the root causes of slow reviews in your workflow. Start with one tool, measure the impact on review time and code quality, and expand from there. Happy exploring!
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
