Session Replay in 60 seconds
Learn about Sentry's Session Replay in 60 seconds.
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Trace-connected structured logging with LogTape and Sentry
As our applications grow from simple side projects into complex distributed systems with many users, the “old way” of console.log debugging isn’t going to hold up. To build truly observable systems, we have to transition from simple text logs to structured, queryable, trace-connected events.
Another year, another $750,000 to Open Source maintainers
Bored yet? 2025 was the fifth year in a row (2024, 2023, 2022, 2021) that Sentry gave a pretty hefty chunk of change to the maintainers of the Open Source software that we rely on and love. This is our first report since we launched the Open Source Pledge, which brings together companies that share our respect for the independent maintainers in the community. Pledge members have collectively paid $4.5M to Open Source maintainers and foundations since launch. No more excuses!
Using Sentry's MCP in Vercel v0
You can use the Sentry MCP server to debug issues in applications that are built in v0. You can also use it to create projects, pull down configurations, or dig into performance issues in applications. Check it out.
Sentry Claude Code Plugin and Skills
We're looking at what building a standard set of tools to use in Claude Code looks like for Sentry - and Claude's plugin marketplace is a great way to distribute them. Take it for a spin - the plugin distributes the MCP server, a few skills for setting up core parts of Sentry, and a few commands to use.
Building a Code Review system that uses prod data to predict bugs
This post takes a closer look at how Sentry’s AI Code Review actually works. As part of Seer, Sentry’s AI debugger, it uses Sentry context to accurately predict bugs. It runs automatically or on-demand, pointing out issues and suggesting fixes before you ship. We know AI tools can be noisy, so this system focuses on finding real bugs in your actual changes—not spamming you with false positives and unhelpful style tips.
[Workshop] Building and Monitoring AI Agents and MCP servers
See how Agent Monitoring gives you a better look at all things model usage, call duration, prompting, and more Go under the hood with MCP Monitoring - and learn how to debug client connection issues, tool call performance, transports, and all things MCP When things start breaking, use Seer, Sentry's AI Debugging Agent to troubleshoot those vague issues that are crashing and get help from a team of robots using Sentry’s AI PR Review.
Monitor and reduce your mobile app size with Size Analysis (beta)
Note: This blog post was originally published for the Early Access of Size Analysis. if you're already familiar with Size Analysis in Sentry, go to the section titled What's new in the beta. If you're not familiar with Size Analysis, start at the section titled The curious case of man.jpg.
A better way to monitor your AI agents in .NET apps
We launched agent monitoring earlier this year, allowing our users to instrument LLM usage and tool calls in their applications. However, we only had Agent Monitoring support for Python and JavaScript. We’ve been working on creating an Agent Monitoring SDK for.NET — specifically for Microsoft.Extensions.AI.Abstractions.
Fixing Performance Issues Fast with Logs & Tracing
Learn how to quickly track down performance bottlenecks using Sentry Logs and Tracing. In this video, we walk through identifying a slow screen, jumping into the connected trace, and pinpointing slow backend steps, database calls, and AI/LLM operations. See how logs, issues, and traces work together to show the full picture of what happened in a single session.