# How Cursor uses Sentry to tackle its biggest bugs

> Learn how Cursor uses Sentry's Error Monitoring, Tracing, Session Replay, and Electron integration to find and fix bugs fast — reducing OOM crashes by 80% and attributing 30% of stability improvements to Sentry.

**URL:** https://sentry.io/customers/cursor/

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**Company:** Cursor

Cursor made a name for itself by building an AI-powered code editor that developers actually want to use. For many engineers, it was one of the first AI coding products that convinced them to change the way they work. The dev-focused approach earned them a lot of fans, fast. Just a few years after being founded, their customer list was already a who's who of tech, citing teams at companies like Figma, Ramp, Stripe, and more as users. The company's ARR went through the roof in no time flat; fittingly, SpaceX has exercised an option to acquire Cursor for a reported $60 billion. The Cursor desktop client is the kind of app that engineers leave open for hours upon hours each day. Keeping that experience stable — with lots of huge open files, multiple lengthy agent conversations, and more across multiple processes — is an engineering challenge of its own. When Cursor's client infrastructure team needed visibility into what was breaking (and why) at scale, there was no question: they'd use Sentry. Before Sentry, hunting down bugs was a very manual process — running around debugging internal bug reports in person. Sentry's Electron integration worked everywhere they needed it, giving them consistent, reliable error data regardless of where in the app something went wrong. Cursor uses errors and stack traces to find issues, breadcrumbs to reconstruct the scene right before an error occurred, and Session Replay for internal users when they need to see what was happening on screen. They also built a custom dashboard that periodically pulls in Sentry event data, giving them a real-time view of crash rates broken down by app version. More recently, Sentry has become a key component of an automated pipeline that drafts bug fixes — pulling crash stacks, feeding them to an AI agent for analysis, and proposing fixes when confident enough. They've managed to reduce out-of-memory issues by up to 80% from their peak.

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*Source: [sentry.io/customers/cursor/](https://sentry.io/customers/cursor/)*