langfuse

Langfuse — LLM Application Observability and Monitoring Expert

Skill Overview

Langfuse is an open-source LLM observability platform that helps developers track, monitor, and optimize large language model applications in production. It supports tracing, prompt management, evaluation, and dataset management.

Use Cases

1. Monitoring LLM Applications in Production

Once your LLM application goes live, you need real-time visibility into how users are using it, whether model responses are behaving normally, and whether costs are under control. Langfuse provides comprehensive tracing and metrics. It can trace the full end-to-end lifecycle of each request and record key indicators such as token usage, response latency, and cost.

2. Prompt Versioning and A/B Testing

When you need to iterate and optimize prompts and want to compare different versions scientifically, Langfuse’s prompt management helps you version and manage prompts. Its evaluation features also allow you to quantify and compare the performance of different versions.

3. Debugging and Troubleshooting LLM Applications

When users report issues or when model outputs don’t match expectations, Langfuse’s tracing helps you replay the complete call chain. You can inspect the inputs and outputs at every step to quickly identify the root cause. It’s especially well-suited for debugging complex applications built with LangChain, Agents, and more.

Core Features

1. LLM Tracing and Observability

Automatically trace the full LLM call chain, recording the complete context for each generation (model call) and each span (operation step). Supports integrations with popular frameworks such as OpenAI, Anthropic, LangChain, and LlamaIndex. You can enable automatic tracing without modifying large amounts of code. The call chain is presented clearly using a three-level structure: trace, span, and generation.

2. Prompt Management and Version Control

Centralize management of all prompt templates with support for version control, A/B testing, and environment isolation. Edit prompts online and have changes take effect immediately—no redeployment required. With tight linkage to tracing data, you can directly see differences in outcomes between prompt versions.

3. Evaluation and Dataset Management

Includes built-in evaluation capabilities, supporting custom scoring metrics and automated evaluation workflows. You can create test datasets and run batch tests to assess the impact of prompt or model changes. Supports multiple evaluation approaches, such as user feedback collection and automated scoring (e.g., LLM-as-a-judge).

FAQ

Which LLM frameworks does Langfuse support for integration?

Langfuse provides native SDKs (Python and TypeScript) and deep integrations with popular frameworks, including drop-in replacement for the OpenAI SDK, a LangChain Callback Handler, LlamaIndex integration, and more. Whether you call the API directly or build with a framework, you can connect quickly.

Can Langfuse be deployed privately? Where does the data reside?

Langfuse is open-source and supports fully private deployment (Docker or Kubernetes), with data entirely under your control. It also offers a hosted cloud service (cloud.langfuse.com) that’s ready to use out of the box. Self-hosting requires some operational effort, but it’s ideal for enterprise scenarios with strict data security requirements.

Does using Langfuse affect application performance?

Langfuse uses asynchronous reporting and batch processing, resulting in minimal impact on application performance. By default, tracing data is sent asynchronously to the Langfuse server and won’t block the main workflow. In high-concurrency scenarios, you can further optimize performance via sampling rate, asynchronous configuration, and other settings.