Feature Overview

Traces allow you to track every LLM call and other relevant logic in your app.

Sessions allow to track multi-step conversations or agentic workflows.

Debug latency issues by inspecting the timeline view.

See quality, cost, and latency metrics in the dashboard to monitor your LLM application.

LLM agents can be visualized as a graph to illustrate the flow of complex agentic workflows.

Add your own userId to monitor costs and usage for each user. Optionally, create a deep link to this view in your systems.

Collaboratively version and edit prompts via UI, API, or SDKs.

Deploy prompts to production or any environment via labels - without any code changes.

Compare latency, cost, and evaluation metrics across different versions of your prompts.

Instantly test your prompts in the playground.

Link prompts with traces to understand how they perform in the context of your LLM application.

Track changes to your prompts to understand how they evolve over time.

You can reference other text prompts in your prompts using a simple tag format.

Plot evaluation results in the Langfuse Dashboard.

Collect feedback from your users. Can be captured in the frontend via our Browser SDK, server-side via the SDKs or API. Video includes example application.

Run fully managed LLM-as-a-judge evaluations on production or development traces. Can be applied to any step within your application for step-wise evaluations.

Baseline your evaluation workflow with human annotations via Annotation Queues.

Add custom evaluation results, supports numeric, boolean and categorical values.

POST /api/public/scores

Add scores via Python or JS SDK.

langfuse.score(
  trace_id="123",
  name="my_custom_evaluator",
  value=0.5,
)

Export scores in bulk via the Langfuse UI.

Collaboratively manage datasets via UI, API, or SDKs.

Run experiments on datasets. Compare metrics across experiments, and see results side-by-side.

Directly add new items from production traces to datasets to improve your datasets over time.