Langfuse: The Open-Source LLM Engineering Platform for Observability and Evaluation
Discover Langfuse, the open-source AI engineering platform combining LLM observability, evaluation, prompt management, and cost tracking in one self-hostable stack with 29,800+ GitHub stars.
What Is Langfuse?
Langfuse is the leading open-source AI engineering platform built for teams shipping LLM applications to production. With over 29,800 GitHub stars and backing from Y Combinator (W23) and ClickHouse, it unifies observability, evaluation, and prompt management into a single self-hostable stack — covering the entire LLM lifecycle from development and testing to production monitoring.
Unlike fragmented tools that handle tracing or evaluation in isolation, Langfuse combines detailed traces, cost tracking, dataset management, online evaluation dashboards, and prompt versioning in one platform. Teams get complete root-cause visibility into every model interaction without juggling multiple monitoring solutions.
Key Capabilities
LLM Observability & Tracing
Langfuse captures structured traces for every LLM call — prompts, completions, token usage, latency, and custom metadata. Traces support nested spans for complex chains, multi-agent flows, and RAG pipelines. Native OpenTelemetry support means you can drop it into existing instrumentation without vendor lock-in. Filter, search, and drill into individual sessions across thousands of traces in real time.
Evaluation & Testing
Run automated evaluations on production traces or curated datasets. Langfuse supports LLM-as-a-judge scoring, custom Python functions, model-based grading, and human annotation queues. Track metrics like correctness, faithfulness, and answer relevancy over time with configurable dashboards — catching regressions before they reach users.
Prompt Management
Manage prompts like code — create, version, tag production releases, and roll back on regressions. Every prompt version is linked to traces, letting you correlate prompt changes with quality shifts. The built-in playground lets you iterate on prompts with live model responses before promoting to production.
Quick Start with Docker
docker pull langfuse/langfuse:latest
docker run -d --name langfuse -p 3000:3000 langfuse/langfuse:latestOnce running, access the dashboard at http://localhost:3000. Configure your first project, generate an API key, and instrument your app with one of the supported SDKs (Python, Node.js, or direct OpenTelemetry).
Why Self-Host Langfuse?
Self-hosting keeps your LLM telemetry fully under your control — no data leaves your infrastructure, no per-event pricing surprises, and no third-party dependency for critical observability. Backed by ClickHouse, Langfuse handles millions of traces on modest hardware (2 CPU, 2 GB RAM is sufficient for most teams).
Conclusion
Langfuse has become the de-facto standard for open-source LLM observability. Its unified approach to tracing, evaluation, and prompt management makes it indispensable for any team shipping production LLM applications. Whether debugging a misbehaving agent, tuning prompt quality, or building compliance-ready audit trails, Langfuse delivers.