Langfuse

Open-source tracing and evaluation for LLM applications.

Overview

Langfuse is an open-source AI engineering platform designed to help teams build, monitor, and improve LLM applications throughout their entire lifecycle. It provides a unified platform combining observability, prompt management, evaluation, and experimentation—all integrated to accelerate development from prototype to production.

The platform captures comprehensive traces of every LLM call, tool invocation, and retrieval step, enabling teams to understand behavior, debug issues, and optimize performance. With support for 100+ integrations and native SDKs for Python and JavaScript, Langfuse works with any stack without vendor lock-in. It's built on open standards (OpenTelemetry), fully self-hostable under the MIT license, and used by 2,300+ companies processing billions of observations monthly.

Key features

Hierarchical application tracing for LLM calls and tool invocations
Prompt management with version control and one-click deployments
LLM-as-a-judge and custom evaluation frameworks
Cost and latency monitoring dashboards
Experiment runner for systematic testing
Human annotation and collaborative review workflows
100+ framework and model provider integrations
OpenTelemetry native support

Pros

Fully open source (MIT license) with no vendor lock-in
Self-hostable at scale with multiple deployment options
Unified platform integrating tracing, prompts, evals, and experiments
Async tracing adds zero latency to applications
Large active community with 33.5k GitHub stars
Production-proven at enterprise scale (21 Fortune 500 companies)
Native SDKs for Python and JavaScript
Supports any LLM model and framework

Cons

Requires setup and configuration for self-hosting
Learning curve for teams new to observability and evaluation concepts
Free tier limited to 50k observations per month
Enterprise features require paid plan

Use this if

You need comprehensive observability for LLM applications, want to manage prompts separately from code, require evaluation and experimentation capabilities, or prefer open-source solutions with self-hosting options.

Skip this if

You only need basic logging without structured tracing, prefer fully managed SaaS without self-hosting, or are building simple chatbots without complex evaluation requirements.

Best for

Teams building and monitoring LLM applicationsAI engineers debugging production agentsPrompt optimization and experimentationCost and latency tracking for LLM systemsCollaborative AI development workflowsOrganizations requiring self-hosted solutions

Alternatives

OpenTelemetry (general-purpose tracing)Weights & Biases (ML experiment tracking)Arize (ML observability)DataDog (APM and monitoring)New Relic (observability platform)

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