Langfuse
ObservabilityTrace, evaluate and improve the LLM features in your apps.
Langfuse gives teams building with large language models the same visibility that error tracking gives web developers. Every model call can be traced, evaluated and compared, so improving an AI feature becomes measurable instead of guesswork.
- Trace LLM calls, retrieval, embeddings and agent actions across your app.
- Version and manage prompts centrally, and test them in a playground.
- Evaluate quality with LLM-as-a-judge, code evaluators, user feedback and datasets.
What people use it for
See what an AI feature is doing in production
Instrument the app with an SDK and follow each request through model calls, retrieval and tool use to find slow or wrong answers.
Change prompts without redeploying
Keep prompts in Langfuse, version them, and roll out an improved version after testing it in the playground.
Check quality before a release
Run a dataset of test cases through a new prompt or model and score the results with LLM-as-a-judge or code evaluators.