Open-source LLM engineering platform for traces, evals, and prompt management
Langfuse is an open-source product for LLM application observability: it ingests traces and spans from your stack, supports datasets and prompt/version workflows, and offers optional Langfuse Cloud or self-hosted deployment. It integrates with popular Python/JS SDKs and frameworks that emit OpenTelemetry-compatible telemetry, so teams can debug agent loops, compare prompt iterations, and monitor production quality metrics without building a custom analytics pipeline from scratch.
Use cases
- Debugging tool-heavy agent runs where failures occur deep in a call chain
- Tracking latency and token usage across routes and model versions
- Building eval sets from production traces for regression testing before rollout
- Comparing prompt edits with consistent datasets rather than anecdotal chat checks
- Giving platform teams a shared view of LLM behavior in staging and production
Key features
- Trace and session views for multi-step LLM and agent workflows
- Prompt management with versioning and side-by-side comparisons
- Datasets and scoring workflows for offline evaluation and regression checks
- SDK integrations for Python and JavaScript ecosystems
- Self-hosting option alongside Langfuse Cloud for teams with data residency requirements
Who Is It For?
- ML and platform engineers operating LLM services
- Product teams shipping agentic features who need production visibility
- Developers self-hosting models or gateways who want trace storage under their control
Frequently Asked Questions
- Is Langfuse the same as a generic APM?
- It is specialized for LLM workloads: traces include prompts, completions, tool calls, and scores rather than only HTTP timings, though it can sit alongside traditional APM.
- Can I run Langfuse on my own infrastructure?
- Yes—Langfuse documents self-hosted deployment patterns in addition to its managed cloud offering.
- Does it replace automated evaluation?
- No—it helps you collect data and run eval workflows; you still define tasks, judges, or heuristics appropriate to your product.
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