Multiplayer, a debugging agent to run locally next to your coding agent
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We built Multiplayer because we kept running into the same problem: coding agents connected to existing observability stacks inherit all the limitations those stacks were built with. Sampled traces, aggregated metrics, context that stops at service boundaries, missing request/response content from deep within the system. The PRs they produce look plausible and fail in production (i.e. “PR slop”).Multiplayer runs locally alongside Claude Code (Codex, Copilot, and Cursor coming soon) and captures full-stack, unsampled session data across your entire system. We collect everything from frontend user actions to backend traces and logs, including request/response content and headers. It’s all the things most observability tools either sample out or don't capture at all. We only save data when something goes wrong, so you're not paying to store everything your system produces around the clock.When an issue is identified, Multiplayer deduplicates it locally before anything reaches your coding agent. The same bug appearing across a hundred sessions becomes one issue, one prompt, one PR. Your agent works from a complete, correlated picture of what actually broke rather than a partial signal from a sampled trace.We tried to make it as easy as possible to get started, so it’s just one command line to install:`npm install -g @multiplayer-app/cli && multiplayer`Happy to get into the architecture, the data model, or how we handle the local-first approach to data privacy.
HN · launched May 28, 2026 · 8 upvotes (top 51%) · 0 views
Niche: ai agent observability platforms · 220 launches, 5 in the last 7 days ▼ -64% · Explore this niche →
Category and tags
- Horizontal
- Agent / copilot
- Developer
- AI-native
- Commercial product
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More like this: all 220 launches in ai agent observability platforms →
Could you build this?
No Building an observability runtime that captures complete local network traffic, unsampled full-fidelity traces across service boundaries, and state without overhead requires deep systems programming, kernel instrumentation (e.g. eBPF/proxies), and low-level debugging hooks.
What it would actually take: The product requires local daemon proxies or eBPF hooks to intercept HTTP/gRPC traffic, environment hooks into dynamic language runtimes for zero-overhead local tracing, and integration with terminal/process monitoring. The hardest challenges are capturing complete request/response payloads without breaking SSL or performance, and structuring raw runtime telemetry into coherent LLM context. This necessitates deep systems programming, networking, and compiler/runtime instrumentation skills.
Discussion
1 comment analyzed.
Competitors
Other products that read as similar to this one : 727 launches clear the similarity bar, closest 8 shown.
Attention rank: #418 of 728 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 205 days after the earliest competitor.
- murmur · ph · 2026-11-02 · 1 upvotes · similarity 0.54
- Replicas · ph · 2026-06-03 · 241 upvotes · similarity 0.51
- Tuneloop · hn · 2026-07-30 · 5 upvotes · similarity 0.50
- AI Agent Observability · ph · 2026-10-01 · 2 upvotes · similarity 0.50
- Firetower · ph · 2026-10-01 · 79 upvotes · similarity 0.48
- Continue · hn · 2026-02-17 · 44 upvotes · similarity 0.48
- We instrumented Claude Agent SDK using a tiny Rust proxy · hn · 2025-12-04 · 6 upvotes · similarity 0.46
- Claude Code Review · ph · 2026-03-10 · 539 upvotes · similarity 0.46
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