TL;DR — Langfuse is an open-source LLM observability platform. It records prompts, completions, token counts, latencies, and user feedback so teams can debug, evaluate, and measure the cost of AI apps.
What it is
Langfuse provides tracing, scoring, prompt management, and analytics for LLM applications. It is built to sit alongside your infra observability stack but focuses on model-centric telemetry.
Why it exists
Generic metrics tell you a request was slow. Langfuse tells you which prompt, which model, how many tokens, and which downstream tools were involved. That makes it much easier to debug regressions and control spend.
How it works
Your application instruments prompts and generations with Langfuse SDKs or API calls. The platform stores traces, prompt versions, scores, and feedback so you can inspect the full chain later.
Key features
- Trace trees for multi-step agent flows.
- Prompt management with versioning.
- Token and cost analytics.
- Human feedback for eval loops.
Quick start
from langfuse import Langfuse
langfuse = Langfuse()
trace = langfuse.trace(name="chat")
span = trace.span(name="llm.call")When to use, when to skip
Use it when your AI product already has users and you need prompt-level visibility. Skip it if you only need infra metrics and no LLM-specific views.
vs / alongside
| Tool | Role | Note |
|---|---|---|
| Langfuse | LLM observability | Prompt and trace focused |
| Prometheus | Metrics store | Infra baseline |
| OpenTelemetry | Instrumentation | General telemetry |
| Grafana | Visualization | Dashboards |
References
- Langfuse — project home.
- Langfuse docs — tracing and prompt management.
- langfuse/langfuse — source.
Verified against Langfuse docs, May 2026.