Langfuse

What is Langfuse?

Langfuse is an open-source platform designed to help developers build, monitor, and improve large language model (LLM) applications. It provides comprehensive observability by tracing API calls, tracking token costs, and measuring latency across complex AI workflows. Teams can manage their prompts centrally, evaluate LLM outputs manually or automatically, and analyze user feedback. By offering a unified workflow from development to production, Langfuse makes LLM engineering more robust, cost-effective, and transparent.

How to use Langfuse?

To use Langfuse, developers begin by integrating the Langfuse SDK (available for Python and JS/TS) or API into their application code to capture traces, metrics, and generation data. Once the application is instrumented, users can log into the Langfuse dashboard to visually monitor live executions, debug complex LLM chains, and track latency and token costs. Teams can also utilize the platform's dashboard to centrally version prompts, configure automated model evaluations, and monitor user feedback to continuously iterate and improve their AI features.

Langfuse's Core Features

• Captures detailed, step-by-step traces of complex LLM calls, chains, and agents for easy debugging.

• Provides comprehensive cost and token tracking across a wide variety of supported LLM providers.

• Includes a central prompt management system to securely version, test, and deploy prompts dynamically.

• Enables automated, model-driven evaluations of system outputs using customizable LLM-as-a-judge frameworks.

• Supports manual annotation and the collection of end-user feedback directly within the platform.

• Offers robust analytics dashboards to aggregate and monitor latency, quality, and usage metrics over time.

• Provides native SDKs for Python and JS/TS, alongside seamless drop-in integrations with frameworks like LangChain and LlamaIndex.

• Allows for flexible deployment options, including a fully managed cloud version and self-hosted open-source environments.

Langfuse's Use Cases

• #1

  Tracing complex LLM chains and autonomous agents to debug errors and timeouts.

• #2

  Tracking API costs and token usage across different LLM providers like OpenAI and Anthropic.

• #3

  Managing and versioning prompts centrally as a CMS outside of the main codebase.

• #4

  Evaluating model output quality automatically using LLM-as-a-judge workflows.

• #5

  Collecting and analyzing user feedback (e.g., thumbs up/down) directly on AI responses.

• #6

  Monitoring application latency to optimize performance and user experience.