Debugging tools compared
Bugs are inevitable. Fixing them manually is not.
Whether your team ships code with AI coding agents, human developers, or both, the debugging workflow is the same: open an APM dashboard, grep through sampled logs, and manually assemble the context needed to write a fix. Time-consuming, incomplete, and inefficient.
Multiplayer is the debugging agent that closes the bug-to-fix loop. It runs locally next to your coding agent, captures full-stack, unsampled runtime data, and manages the whole process from bug identified to PR created.
Below is how Multiplayer compares to the tools teams are already using, and why none of them were built for this job.
TL;DR
| Multiplayer Overview | |
|---|---|
| Audience | Developers working with AI tools |
| Purpose | Debugging: from bug identified to PR merged |
| Application type | Web & Mobile |
| Deployment | SaaS (hosted) & Open source (self-hosted) |
| Installation | CLI (automatic, continuous monitoring) + browser extension and in-app widget (on-demand, by developers or end-users) |
| Data captured | Full-stack, unsampled, session-based: frontend user actions, network requests, console logs, backend traces, request/response content and headers from all system components |
| Issue triage | Local deduplication, fixability scoring, and grouping before anything reaches your coding agent |
| Coding agent support | Works with Claude Code, Codex*, Copilot*, Cursor*, and any agent via MCP (VS Code, Cursor, Copilot, Claude Code, Windsurf, Zed) |
| Collaboration | Shared workspace with team visibility across all debugging agents, bugs, PRs and session recordings. Collaboration and annotation features available on the individual recordings |
| Security & compliance | SOC 2 Type 2 certified, local-first caching, self-hosting option, user inputs masked by default, configurable frontend and backend data masking |
| Telemetry standard | Built on OpenTelemetry, compatible with all backend observability platforms |
*Currently in private beta. Reach to our team if you'd like access.
Why choose Multiplayer?
Multiplayer is purpose-built for developers working with coding agents.
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Plug and play with your own agent: we run locally right next to your favorite coding agent so you can fix application bugs in production.
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Better data for coding agents and humans: APMs heavily sample and miss critical content. We capture and correlate data across your stack, from frontend screens and users actions to traces, logs and request/response content and headers for all your system components. You get deep, unsampled data across your entire system, giving your agent the complete runtime picture.
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Local-first and secure: run Multiplayer right next to your coding agent. We cache sessions locally and send data only when we identify a new issue.
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Intelligent issue triage and deduplication: we filter for high-priority bugs and group identical errors locally. You get one high-quality, merge-ready fix for critical issues, instead of an avalanche of duplicate PRs for the same bug.
vs Coding agents (Claude Code, Codex, Copilot, Cursor …)
Coding agents are great at writing code. But without runtime visibility, they can't fix what breaks.
Multiplayer connects your coding agent to production (or any other environment) giving it runtime visibility. When a bug fires, we capture the unsampled full-stack session: user interactions, network requests, console logs, backend traces, request/response content from every service. We then route it directly to your coding agent with the context it needs to write a fix worth merging.
| Multiplayer | Coding agents alone | |
|---|---|---|
| Built for … | Debugging | Generating code |
| Runtime visibility | Full-stack, unsampled | None |
| Bug detection | Automatic, continuous | Ad-hoc |
| Data to agent | Pre-correlated session data | Manually collated and correlated data |
| Output | Merge-ready PR | Depends on the prompt |
vs Error monitoring tools (Sentry …)
Error monitoring tools are good at telling you something broke. Their AI agents can take that signal and generate a PR. The problem is the data they're working from.
For example, Sentry's telemetry is sampled and missing critical information. To achieve Multiplayer’s level of unsampled, correlated, full-stack visibility, you need manual instrumentation, custom logging, third-party integrations, and higher-tier plans. A PR generated with Sentry's proprietary debugging agent (Sentry Seer) reflects those gaps: it addresses what Sentry saw, which is rarely the complete picture.
There's also the volume problem. Look at your Sentry dashboard and count the open issues. That's roughly how many PRs Seer will open. Multiplayer deduplicates issues locally before anything reaches your coding agent, scores them for fixability, and sends one prompt for one issue. A handful of high-quality PRs instead of an avalanche of noisy ones.
| Multiplayer | Error monitoring tools | |
|---|---|---|
| Built for … | Debugging | Error monitoring |
| Data quality | Unsampled, full-stack | Sampled, error-monitoring scope |
| Data volume | Session-based,local-caching | High volume, high cost |
| Request/response content | Captured | Not captured |
| Deduplication | Local, before agent prompt | Limited |
| PR volume | Targeted, deduplicated | One per Sentry issue |
| User-reported issues | Yes, with replay attached | Not supported |
vs APM tools (Datadog, New Relic …)
Observability data was designed for monitoring system health at scale: sampled traces, aggregated metrics, always-on collection that generates massive volumes and bills to match.
When you point an AI agent at that data to fix a bug, it inherits every limitation it was built with (sampled traces, missing correlation, context that stops at service boundaries) and burns tokens searching through data that wasn't collected or it can't correlate. Similarly, if you're manually investigating individual bugs, you'll spend significant time sifting through system-wide telemetry and session data to isolate what you actually need.
Multiplayer captures a short window of data around an issue: only what happened in the session that broke, pre-correlated from frontend to backend. The coding agent gets exactly what it needs, unsampled, including data APM tools don't collect such as request / response content and headers from all system components.
| Multiplayer | APM tools | |
|---|---|---|
| Built for … | Debugging | Performance monitoring |
| Data quality | Unsampled, full-stack | Sampled, broad collection |
| Data volume | Session-based, scoped to failure, lean | Always-on, high volume, high cost |
| Request/response content | Captured | Not captured |
| Deduplication | Local, before agent prompt | Limited |
| Deployment | SaaS (hosted) & Open source (self-hosted) | SaaS (hosted) |
| User-reported issues | Yes, with replay attached | Not supported |
vs SRE AI agents (Resolve.ai …)
SRE agents are built for DevOps workflows: incident response, runbook automation, infrastructure triage. They're good at what they do, and what they do is not the same as fixing application bugs in developer workflows.
The deeper problem is the same as observability-based agents: SRE agents query your existing observability stack and inherit its limitations. Too much data, or the wrong data. Either way, the coding agent working downstream doesn't have what it needs to write an accurate fix.
Multiplayer is built for developers, runs in the developer environment, and feeds data directly to the coding agent a developer is already using.
| Multiplayer | SRE tools | |
|---|---|---|
| Built for … | Developers | DevOps/SREs |
| Workflow | Bug identified to PR merged | Incident response |
| Data quality | Unsampled, full-stack, session-based | Sampled, broad collection, missing data |
| Request/response content | Captured | Not captured |
| Deduplication | Local, before agent prompt | Limited |
| Output | Merge-ready PR | Incident remediation |
| User-reported issues | Yes, with replay attached | Not supported |
vs PR review tools (CodeRabbit …)
PR review tools do static analysis on code before it merges. They catch issues that are visible in the diff: logic errors, security patterns, style violations, test coverage gaps. That's a legitimate and useful job.
Multiplayer operates at a different layer entirely. By the time a PR exists, Multiplayer has already identified the bug, captured the runtime data behind it, and prompted your coding agent to write the fix that becomes the PR. We're upstream from code review, not a replacement for it. The two tools complement each other: Multiplayer finds and fixes the bug, tools like CodeRabbit review the fix before it ships.
| Multiplayer | PR review tools | |
|---|---|---|
| Built for … | Debugging | PR reviews |
| Workflow | Bug detection and fix | Pre-merge review |
| Operates on | Unsampled, full-stack, runtime data | Sampled, broad collection, missing data |
| Output | PR with fix | Review comments |
| User-reported issues | Yes, with replay attached | Not supported |
vs Frontend session recorders (LogRocket, OpenReplay...)
Frontend session recorders give you a detailed view of what the user saw. They don't show you why it happened, and they can't fix it.
Backend data requires third-party integrations that link out to your existing APM or tracing tool. You're still dependent on whatever that tool captured, subject to its sampling rates and gaps, and missing request/response content and headers from deep within your system. Some tools in this category have added MCP servers, but an MCP server that only exposes frontend session data gives your coding agent a detailed picture of what the user experienced and a blind spot everywhere else.
When a bug needs fixing, developers are still stitching together frontend context from one tool with backend traces from another, none of it pre-correlated and ready for a coding agent to act on.
| Multiplayer | Frontend session recorders | |
|---|---|---|
| Built for … | Debugging | Replay user behavior |
| Backend data | Unsampled, full-stack, session-based, pre-correlated | Third-party integrations, sampled, incomplete |
| Request/response content | Captured | Not captured |
| Output | Merge-ready PR | Manual investigation |
| User-reported issues | Yes, with replay attached | Limited |
vs Product analytics tools (PostHog, Fullstory...)
Product analytics tools are built for understanding user behavior at scale. Teams reach for them for debugging technical issues because they're already in the stack (i.e. used for PM and UX work), not because they're the right tool for the job.
Visibility stops at the frontend: no backend traces, no request/response content, no correlation between what the user did and what broke in the system underneath. When a bug needs fixing, developers are back to manually stitching together frontend behavior from the analytics tool with backend data from somewhere else.
A coding agent working from product analytics data gets the same incomplete picture, and produces fixes that reflect it.
| Multiplayer | Product analytics tools | |
|---|---|---|
| Built for … | Debugging | Product analytics |
| Backend data | Unsampled, full-stack, session-based, pre-correlated | Third-party integrations, sampled, incomplete |
| Request/response content | Captured | Not captured |
| Output | Merge-ready PR | Manual investigation |
| User-reported issues | Yes, with replay attached | Not supported |
vs Manual debugging
Manual debugging isn't going away and it shouldn't. There are classes of problems that require human judgment: architectural decisions, ambiguous product behavior, bugs that need domain knowledge to interpret.
The problem is volume. Coding agents ship code faster than any team can debug manually, and the bugs they introduce are often subtle: they pass tests, pass static analysis, and surface only under specific production conditions. Keeping up on raw cognitive horsepower compounds the problem at team scale.
Multiplayer handles the reproducible, fixable bugs automatically (the ones where the root cause is clear and the fix is deterministic) so developers can spend their time on the problems that actually need them. Not grepping through logs for a missing request header. And Not reviewing the fifth PR for the same bug.
| Multiplayer | Manual debugging | |
|---|---|---|
| Bug detection | Continuous, automatic | Reactive |
| Data assembly | Automatic, pre-correlated | Manual, time-consuming |
| Fix loop | Automated to PR | Developer-led throughout |
| Scales with agent velocity | Yes | No |
| Best for | Reproducible, fixable bugs | Complex, judgment-dependent issues |
vs Manual screenshots and videos
Manual screen recording is the default bug reporting workflow at most companies, especially for internal tools and enterprise applications where reproducing issues requires access to sensitive systems. It's also where the most time gets wasted and the most compliance risk gets introduced.
A user records their screen, uploads the file somewhere, and writes a description of what went wrong. A support agent watches the video, asks follow-up questions because the technical context is missing, and escalates to engineering with a Slack message and a link. The developer watches the same video, still can't see what happened in the backend, and asks more questions. What should take minutes takes days.
The security problem is worse in enterprise contexts. Every manual recording requires someone to remember to redact PII, API keys, credentials, and internal URLs before sharing. There is no automated redaction, no audit trail, and no way to revoke access once a file has been forwarded. One oversight and sensitive data is in someone's email.
Multiplayer captures technical context automatically, masks user inputs by default, and keeps everything in one place from the moment an issue is reported.
| Multiplayer | Screenshots / Video / … | |
|---|---|---|
| Built for … | Debugging | Visual collaboration |
| Capture method | Automatic, with configurable masking | Manual, redaction-dependent |
| Technical context | Full-stack: frontend and backend | Visual only, no technical data |
| PII data redaction | Automated redaction, configurable | Manual, error-prone |
| Compliance | SOC 2 Type 2, audit trail, access controls | No controls once shared |
| Information storage | Centralized, searchable | Scattered across tickets, email, drives |
| Fix loop | Bug to PR, automatically | Days of back-and-forth to reproduce and serial handoffs |
Next steps
🚀 If you’re ready to trial Multiplayer with your own app, you can follow the steps in the quickstart.
📌 If you have any questions shoot us an email! 💜