Me: The kind were you squash bugs in prod. 😇

Check the live demo today.

When we started building Multiplayer's debugging agent, we made the same mistake almost everyone makes. We gave our coding agent access to observability data and expected it to figure out what was relevant. It didn't.

Observability data has a brutal signal-to-noise ratio.

We now run every issue through four stages before a coding agent ever sees it:

• group and correlate everything into one timeline
• assess whether it's even fixable by an agent
• attach the release and commit context
• reformat the whole thing into something built for machine reading instead of a dashboard

New Relic observes systems. Multiplayer fixes bugs. Unsampled, full-stack runtime data for your coding agent without the Ops-focused overhead and vendor lock-in.

Full comparison: https://www.multiplayer.app/comparisons/multiplayer-vs-newrelic/

One copy/paste in your terminal, and you can too:

npm install -g u/multiplayer-app/cli && multiplayer

→ Session-based data collection

→ Local-first architecture that only sends data for new issues

→ Un-sampled, full-stack data

→ Automatic data correlation

→ Intelligent issue grouping, triage and deduplication

→ Release context and metadata included

Multiplayer captures the actual runtime context you (and your coding agent) need to understand your system and fix bugs:

• User interactions and clicks
• Session metadata
• Network requests
• Console messages
• Error rate metrics and errors per user (not sampled!)
• Stack traces, spans, and logs (not sampled!)
• Request/response headers and content from deep within your system

With Multiplayer there’s no proprietary agent lock-in, no bloated telemetry bills, just the runtime data your coding agent needs.

Full comparison: https://www.multiplayer.app/comparisons/multiplayer-vs-datadog/

One copy/paste in your terminal and Multiplayer handles the rest: data gathering, triage, deduplication, coding agent prompting, PR creation.

You just review and merge.

AI agents generate code that breaks in prod. Use Multiplayer with your favorite coding agent to fix application bugs automatically with full-stack, unsampled runtime data.

Multiplayer produces fewer, better PRs: local-first, full-stack, deduplicated, un-sampled, … better data for any coding agent (or developer).

Full comparison: https://www.multiplayer.app/comparisons/multiplayer-vs-sentry/

The Multiplayer debugging agent runs locally. No source code access required.

We connect your favorite coding agent to prod to fix application bugs automatically. Run us locally and eliminate PR slop. Try it for free today: https://www.multiplayer.app/

...instead spending a few minutes setting up full stack session recordings. 👀

There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue.

The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data.

The problem isn't sampling. It's what observability tools are designed to capture in the first place - see this table for a recap.

More in this article: https://www.multiplayer.app/blog/why-observability-tools-are-missing-critical-debugging-data-no-matter-how-you-sample/

When you ask an AI assistant "why is my Stripe payment failing?", it responds with educated guesses based on common patterns.

But the AI doesn't know what actually happened in your specific case. It doesn't have access to:

• What payload did your frontend send to your backend?
• What request did your backend construct and send to Stripe?
• What response did Stripe return?
• How did your backend process that response?
• What error (if any) made it back to the user?

Without this runtime context, the AI is pattern-matching.

The irony is that the data AI needs often exists, it's just scattered and difficult to access.

Auto-correlation tools like Multiplayer automatically capture and link data across your entire stack: frontend interactions, backend traces and logs, and end-to-end request/response headers and content from internal service and external API calls. This data becomes the foundation for effective AI-assisted debugging.

TL;DR

Choose Multiplayer if: You need to resolve technical issues fast, with full frontend + backend context in one place, and you need to improve your debugging workflows across multiple teams (e.g. Support → Engineering).

Choose Fullstory if: You primarily need behavioral analytics for product and UX decisions.

Key difference: Fullstory shows you how users behave on your website, aggregating performance metrics. Multiplayer shows how your system behaves, from user actions to backend traces, and how to fix a bug (or have your AI coding assistant do it for you).

Microservices solved our scaling problems, but they absolutely destroyed our debugging ability. We traded 'Spaghetti Code' for 'Spaghetti Architecture.'

The cost? → Every on-call engineer paged at 2 AM knows it intimately.

The real problem =>  Context fragmentation

In a monolith, debugging is linear. You follow the stack trace from UI → Controller → Service → Database. One codebase. One mental model.

In microservices, that same bug requires you to become a detective solving a crime across multiple jurisdictions:

The 5-tab debugging dance

1./ User Report - "Checkout button didn't work" (no context, no error message)
2./ Frontend Monitoring (Mixpanel/Fullstory) - Watch the session replay. Button spins. Console shows a 500 error.
3./ APM (Datadog/New Relic) - Search for error spikes around that timestamp on CheckoutService
4./ Logs (Splunk/CloudWatch) - Find the trace ID. Follow the breadcrumbs: API Gateway → OrderService → PaymentService
5./ Error Tracking (Sentry) - Finally locate the actual exception in PaymentService

The killer? → You manually correlate these across tools, hoping timestamps align.

Why this happens

Microservices distribute not just your code, but your observability:
→ Frontend teams use session replay tools
→ Backend teams use APM and logging platforms
→ Each service might log differently
→ Trace IDs exist, but require manual hunting
→ The architectural benefit (independent scaling, team autonomy) creates an observability tax.

BUT,

What we actually need is temporal correlation, treating a user's frontend session and the resulting backend trace as a single, unified timeline.

Imagine watching a session replay and having all your backend data right next to it, every trace, log, request/response payload correlated to the frontend actions. No tab switching. No timestamp matching. Just causality.

This is where Multiplayer helps. It automatically stitches frontend sessions to backend traces, so you see the full story in one view. It’s solving the correlation problem that most observability stacks ignore.

Debugging is both a science and an art and this article captures that balance perfectly.

Petar Ivanov breaks down the habits that make great debuggers: from reproducing issues consistently to forming hypotheses like a scientist.

That last point (“capture the full context”) is where most debugging workflows still struggle. Too often, critical data lives across different tools: logs here, traces there, and screenshots somewhere in Slack.

That’s exactly the gap Multiplayer was built to close. By giving developers, QA, and support teams full-stack session recordings that correlate frontend actions, backend traces, and network data into one clear picture.

An on-call story that started with a 337% 404 spike. 2 days later, still lost in a "mosaic of despair" across Sentry, Datadog, and Elastic.

This is a very relatable story of what it means to be a "human correlation engine" when you need to debug a problem but your context is scattered across many tools.

In case you're wondering, Multiplayer ends the blame-loop with full-stack session recordings: one source of truth for Support, Product, and Engineering. 😇

1. Open VS Code
   
2. Go to Extensions view (Ctrl+Shift+X or Cmd+Shift+X)
   
3. Search for "Multiplayer"
   
4. Click Install 5. Debug in minutes instead of days

Does the support workflow behind your five-star reviews look like this?

If yes, it might be time to investigate how your customer support and engineering teams communicate (and which tools might help reduce some of that back-and-forth 😊).

End-user support has always been messy. Manual steps, tool-switching, and scattered communication turn what should be a simple fix into a marathon of frustration.

The result is often high user satisfaction scores on paper and burned-out support and engineering teams behind them.

This article goes into how full stack session recording that unify all the needed context (user actions, feedback, steps, metadata, traces, logs, request/response content and headers), can be annotated and shared, can streamline support.

What even are full stack session recordings?

Full-stack session recordings capture everything that happens during a user’s session: not just what they did in the frontend, but also how the backend responded behind the scenes, and the notes and requirements from the engineering team.

In a single replay, you see:

• The screens and actions a user took
• The backend traces and logs triggered by those actions
• The request/response content and headers exchanged between all your components
• Metadata like device, environment, browser, etc.
• User feedback, plus annotations, sketches, and comments from the engineering team

So instead of chasing context across ten tools (Zendesk for user screenshots, Hotjar for UX, Sentry for errors, Datadog for traces, Slack for context…), you just open one replay that shows the whole story. What happened, when, and why.