This content originally appeared on DEV Community and was authored by Panto AI
Top GitLab Code Review Tools to Supercharge Your Workflow in 2025
GitLab Code Review Tools 2025
Looking for the best GitLab code review tools in 2025? This guide breaks down the leading solutions that can improve code quality, accelerate pull requests, and streamline your DevOps workflow.
Why GitLab Code Review Tools Matter
Code review isn’t just a step in the pipeline — it’s the heartbeat of software quality. GitLab’s native merge request features are solid: inline comments, approval settings, pipelines, and discussions all help teams ship.
But for fast-scaling projects, native tools alone often fall short. Teams today need:
AI-driven insights
Automated checks at scale
Deeper compliance enforcement
Context across massive codebases
That’s where third-party GitLab code review tools come in. By reducing grunt work and surfacing high-signal insights, they cut review times, reduce merge friction, and help teams move faster without compromising security.
Leading GitLab Code Review Tools in 2025
1. Panto AI
Panto AI adds context-driven AI to GitLab reviews — bridging business requirements, security, and engineering insights. Features include:
Automated PR summaries and conversational Q&A
Integration with Jira, Confluence, and more for business context
30,000+ security rules and 30+ language support
Cloud and on-prem options with zero code retention
Teams report reduced cognitive load, faster shipping, and more confidence. Panto AI transforms GitLab’s native flow into a context-rich, high-signal review process.
2. Greptile
Greptile provides full codebase-aware reviews, not just diff checks. Ideal for enterprises, it supports monorepos, microservices, and customizable rule sets. SOC 2 compliance and enterprise-grade security make it a strong pick for compliance-heavy teams.
3. CodeAnt AI
CodeAnt AI speeds up reviews with:
AI-powered summaries in plain English
Static analysis and custom rule enforcement
Security scans across languages and frameworks
Notifications via IDE, Slack, and email
It’s designed to halve review time for large GitLab projects.
4. SonarQube
SonarQube integrates directly into GitLab CI, enforcing “quality gates” before merges. It checks for bugs, vulnerabilities, and code smells, while dashboards help teams track and prioritize remediation. Popular in regulated industries and enterprises.
5. Codacy
Codacy reviews for style, complexity, duplication, test coverage, and vulnerabilities. Its dashboards give a clear health overview across repos, and its rule sets are highly customizable.
6. Snyk (DeepCode)
Snyk’s DeepCode engine specializes in real-time security scanning. It runs dependency checks, flags vulnerabilities, and integrates with GitLab CI/CD. A must-have for teams prioritizing supply chain security.
7. Elipsis AI Reviewer
With @ellipsis-dev mentions in GitLab, teams get instant AI-powered bug detection and fixes. SOC 2 certified, Elipsis focuses on speed and automation — great for startups and mid-tier orgs.
8. CodeRabbit
A lightweight AI reviewer that provides GPT-powered comments. It’s simple, fast, and easy to deploy, making it ideal for small teams, though larger orgs may outgrow its limited context.
9. Crucible
Atlassian’s Crucible offers enterprise-ready peer reviews with deep metrics, workflows, and integrations. Its power comes with a learning curve, making it better suited to larger orgs.
10. Review Board
An open-source option with flexible review workflows and static analysis integration. Strong for distributed teams, though setup and UI can feel dated.
11. CodiumAI
CodiumAI generates automated tests and validates logic pre-merge, boosting TDD practices. It doesn’t replace GitLab reviews but adds value earlier in the dev cycle.
How to Choose the Right Tool
Each tool integrates with GitLab, but your ideal choice depends on priorities:
Speed & AI summaries: CodeAnt AI, Elipsis, CodeRabbit
Security focus: Snyk (DeepCode)
Open-source flexibility: Review Board
Test-generation support: CodiumAI
Enterprise workflows: Crucible
The Future of GitLab Code Review
As teams and codebases grow, code review must evolve from a bottleneck into a strategic enabler. The next wave of tools focuses on:
Automation to eliminate repetitive checks
Context that links code to business goals
Security baked into every merge request
Platforms like Panto AI lead this shift by blending AI, compliance, and business context into GitLab reviews.
The result: faster merges, cleaner code, and teams that ship with confidence.
Ready to upgrade your GitLab workflow? Explore these tools and see how Panto AI can help your team bridge the gap between code and business context.
This content originally appeared on DEV Community and was authored by Panto AI