# Sentry Sentry is an error tracking and application performance monitoring platform used by developers to diagnose, fix, and optimize the runtime behavior of their code. Originally an open-source project, it has grown into a 200-person company processing 790 billion events per month for 4 million developers across 150,000 organizations. ## What It Does Sentry captures, deduplicates, and contextualizes errors and performance issues across web, mobile, and backend stacks — turning raw stack traces into actionable issues with breadcrumbs, request data, releases, commits, and user impact. The platform spans: - **Error monitoring** — the original product - **Performance monitoring & tracing** — distributed traces, slow transactions - **Profiling** — sampled call-stack data - **Logs** - **Session Replay** — DOM-level recording of user sessions - **Cron monitoring** & **Uptime monitoring** Supports 100+ languages and integrates with GitHub, GitLab, Slack, Jira, and most CI/CD systems. ## AI-Native Features A new layer of AI products sits on top of the data Sentry already collects: - **Seer** — agentic issue analysis and auto-debugging - **Autofix** — proposes patches for tracked errors - **AI Code Review** — uses [[Warden]], Sentry's open-source agentic code review tool driven by [[AI Agent Skills]] - **MCP Server** — gives AI agents structured access to Sentry issues for terminal-based querying This positioning is notable: Sentry has the runtime error data; layering agents on top of it is a natural extension into [[DevSecOps]] and developer productivity. ## History - Started as an open-source project by founders previously at Dropbox, GitHub, and Atlassian, including [[David Cramer]] - Grown into the leading error-tracking platform - Raised $217M across 6 rounds - Offices in San Francisco, Seattle, Vienna, Toronto - Pioneered the [[Functional Source License (FSL)]], a Fair Source license now used across Sentry projects including [[Warden]] ## Why It Matters Beyond Error Tracking Sentry sits at an interesting position in the developer toolchain: it sees what actually breaks in production. That makes its data uniquely useful as input to AI agents — Autofix isn't reasoning in a vacuum, it has the real stack trace, the real release diff, and the real user context. Most AI coding tools have to *infer* what's wrong; Sentry agents *know*. ## References - https://sentry.io - https://sentry.io/about/ - https://docs.sentry.io - https://github.com/getsentry/sentry ## Related - [[David Cramer]] - [[Warden]] - [[Warden CLI]] - [[Functional Source License (FSL)]] - [[AI Observability]] - [[DevSecOps]] - [[GitHub]]