Langfuse is an open source observability and evaluation platform for LLM applications, agents, prompts, traces, and production usage.

Who should use Langfuse?

Langfuse is best for developers and AI teams that need to debug, monitor, and evaluate LLM applications in production.

Can Langfuse track costs?

Yes. Langfuse can help teams track model usage, latency, traces, and costs depending on how it is integrated.

Is Langfuse open source?

Yes. Langfuse has an open source option, with managed and enterprise options available for teams that prefer hosted infrastructure.

Does Langfuse replace evals?

No. It supports observability and evaluation workflows, but teams still need to define what good outputs look like.

Is Langfuse useful for agents?

Yes. It can help inspect multi step agent traces, tool calls, prompts, and outputs when debugging agent behavior.

Langfuse

Name: Langfuse
Brand: Langfuse

Open-source LLM observability platform for tracing, prompts, evals, and metrics.

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What is Langfuse?

TL;DR

Langfuse is an open-source LLM observability platform for tracing, prompts, evaluations, analytics, costs, and debugging across AI applications and production agent workflows with cloud and self-hosted options.

Trace and debug LLM application behavior
Manage prompts and run evaluations
Monitor cost, latency, quality, and usage

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About

Langfuse is an open-source LLM observability platform for teams building AI applications. It helps developers trace model calls, inspect prompts, measure latency and cost, evaluate outputs, and understand how real users interact with AI features in production. As AI applications move from prototype to production, teams need visibility into what the model is doing, where failures happen, how much each workflow costs, and whether outputs are improving over time. Langfuse gives developers tools for tracing, prompt management, dataset creation, evaluations, analytics, and debugging across LLM-powered products. The product is especially useful for SaaS teams, AI startups, and technical founders building chatbots, agents, RAG systems, support copilots, research tools, or internal automation workflows. Instead of guessing why an AI workflow failed, teams can inspect the full chain of prompts, responses, metadata, scores, and user feedback. Langfuse can be used as a hosted cloud product or self-hosted for more control. That makes it relevant for both indie builders who want fast observability and teams with stricter data requirements. For FutureStack, Langfuse belongs in Developer Tools because it supports the invisible but critical part of AI product development: monitoring quality, cost, reliability, and prompt behavior after launch.

Use Cases

LLM Observability

Trace model calls, prompts, tools, retrieval, and user sessions to understand production AI behavior.

AI Evaluation

Create datasets and evaluation workflows to measure output quality and detect regressions over time.

Prompt Management

Track and manage prompts while connecting changes to observed production performance and costs.

Agent Debugging

Inspect multi-step traces and tool calls when AI agents fail or behave unexpectedly in production.

Key Features

Trace and debug LLM application behavior
Manage prompts and run evaluations
Monitor cost, latency, quality, and usage
Open-source with cloud and enterprise options

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