Linear DiffsReview and ship code without ever leaving your issue tracker

Linear Diffs

Linear Diffs transforms code review from a bottleneck into a seamless workflow by bringing native diff viewing directly into Linear. Engineering teams can now review pull requests, iterate with AI agents, and ship code—all without leaving their project management environment. Every review syncs bidirectionally with GitHub, ensuring complete visibility across tools.

Product Highlights

• Guided Reviews (Beta): Navigate large PRs efficiently with AI-powered explanations that surface core changes first, separating critical logic from glue code
• Review Inbox: Centralize all review notifications alongside your regular work, with smart sorting that prioritizes PRs closest to shipping
• Agent-Powered Iteration: Request changes directly from the diff surface and watch AI agents update code in real-time without local checkout
• Performance-Optimized Interface: Review massive PRs smoothly with unified/split views, structural highlighting, and customizable code themes
• Native GitHub Sync: All review states, approvals, and comments synchronize bidirectionally between Linear and GitHub

Use Cases

• AI-Assisted Development: Manage agent-generated code changes at scale while maintaining human accountability for every merge
• High-Velocity Teams: Reduce context switching by consolidating code review into the same tool used for issue tracking and sprint planning
• Large-Scale Refactoring: Tackle substantial PRs confidently with guided reviews that break complex changes into digestible, explained sections
• Distributed Engineering: Keep remote teams aligned with a single source of truth for both task management and code review status

Target Audience

Linear Diffs is designed for engineering teams and tech leads at fast-growing startups and enterprise companies who use Linear for project management and want to eliminate friction between task tracking and code review. Ideal for organizations adopting AI coding tools that need to maintain quality and accountability at scale.