Seeing what a system
actually did.
Langfuse is an open-source observability platform for LLM applications — every prompt, every tool call, every retrieval, traced and queryable, so a production issue is something you can look up instead of something you have to guess at.
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Tracing for systems
that make decisions.
Once an LLM system involves multiple steps — retrieval, generation, tool calls, agent handoffs — "it's not working right" stops being something you can debug by reading logs. Langfuse traces each step of a request, tracks cost and latency per call, and supports running evaluations against real traffic.
We wire it in as a standard part of any production agent or RAG system, the same way we'd add monitoring to any other production service.
Observability from
day one, not after an incident.
Tracing added at launch, not bolted on after something breaks.
Every step of a multi-step request — retrieval, generation, tool calls — visible and queryable.
Per-call cost and response time tracked as a metric, feeding the same dashboards as our broader MLOps work.
Running quality checks against actual production requests, not just a static test set.
Where this fits
The observability layer underneath agent and RAG systems we ship.
The broader monitoring and reliability practice Langfuse's tracing feeds into.
Agent systems where tracing matters most — multi-step, multi-tool, hard to debug from logs alone.
Retrieval tracing to catch exactly where a RAG pipeline is pulling the wrong context.
Before you
book a call.
The questions we get asked most about Langfuse and LLM observability — answered straight, no sales pitch.
Why do LLM systems need dedicated observability instead of normal application logs?
A multi-step LLM request — retrieval, generation, tool calls — doesn't fit neatly into a single log line. Langfuse traces each step so you can see exactly where a request went wrong, not just that it returned a bad answer.
Is this only useful after something breaks?
No — we wire it in at launch specifically so issues are visible before they become incidents, and so cost and latency are tracked metrics rather than surprises.
Does this add latency to production requests?
Tracing overhead is minimal and asynchronous in most configurations — it's designed for production use, not just development debugging.
Tell us what
you're trying to monitor.
Book a 30-minute call — we'll tell you honestly whether your LLM system has the observability it needs before it ships.