Langfuse

Trace, manage, and evaluate LLM apps from prototype to production at scale

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Added Jul 2026

langfuse.com

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What is Langfuse?

Langfuse is an open-source AI engineering platform that provides observability, prompt management, evaluation, and experimentation for LLM applications and AI agents. It captures hierarchical traces of every LLM call, tool invocation, and retrieval step, enabling teams to move from prototype to production while continuously improving with real usage data. Designed for AI engineers building production-scale applications, Langfuse integrates tracing, prompt management, evaluations, and human annotation into one connected workflow.

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Key Features of Langfuse

Hierarchical trace capture for LLM calls and tool invocations
Prompt management with version control and one-click deployments
LLM-as-a-judge and heuristic evaluation functions
Playground for testing prompts on production inputs
Experiment framework with side-by-side comparison
Human annotation and collaborative workflows
Cost and latency tracking
Production data filtering by user, session, cost, and metadata
Self-hosting options (Docker, Kubernetes, AWS, GCP, Azure)
OpenAI and LangChain integration

Who Should Use Langfuse?

Monitor and optimize LLM applications in production

Manage and version control prompts across teams

Evaluate AI agent performance with automated and human review

Run experiments to compare model outputs and configurations

Track costs and latency of LLM operations

Collaborate on continuous improvement of AI products

Langfuse: Pros & Cons

✓Pros

Open-source with 31.1k GitHub stars and 300+ contributors
Used by 19 of Fortune 50 companies
Handles 10+ billion observations per month
Integrated platform combining observability, prompts, evals, and experiments
Multiple self-hosting deployment options
Active community with 1.8k Q&A threads
Production-ready and scalable

Frequently Asked Questions about Langfuse

What problem does Langfuse solve?

Langfuse helps AI engineers move LLM applications from prototype to production and continuously improve them using real production data through integrated observability, prompt management, evaluation, and experimentation tools.

How does Langfuse capture LLM activity?

Langfuse uses hierarchical traces that capture every LLM call, tool invocation, and retrieval step, allowing filtering by user, session, cost, latency, and custom metadata.

Can I self-host Langfuse?

Yes, Langfuse offers self-hosting guides for Docker Compose, Kubernetes (Helm), AWS (Terraform), GCP (Terraform), and Azure (Terraform).

What integration options are available?

Langfuse integrates with OpenAI and LangChain, with SDKs available for ingesting traces.

Is Langfuse open source?

Yes, Langfuse is an open-source platform with 31.1k GitHub stars and over 300 contributors.