Description
Langfuse is an open-source LLM engineering platform that empowers developers to build and refine AI applications. It provides a comprehensive suite of tools for tracing, prompt management, evaluation, and analytics, streamlining the development lifecycle from prototype to production. The platform is designed to help teams debug AI applications and agents, identify issues, and continuously improve performance, cost, and quality.
Langfuse works by integrating observability, prompt management, evaluation, experiments, and human annotation into a single, connected workflow. This allows developers to gain deep visibility into their traces, track model cost and latency, and evaluate model outputs automatically. The platform supports a wide range of models, frameworks, and integrations, including OpenAI, Anthropic, LangChain, and Vercel AI SDK. It is built on open standards and data portability, ensuring that users are not locked into the platform. Langfuse offers features such as hierarchical traces, prompt management with one-click deployments and rollbacks, and collaborative human-in-the-loop workflows for review and annotation.
Langfuse's key capabilities include comprehensive observability, prompt management, evaluation, and experimentation. The platform allows users to filter traces by various criteria, manage prompts, run evaluators on production data, and compare results side-by-side. It also provides dashboards and automated alerts to monitor cost, latency, and quality. Langfuse is designed for developers, engineers, and teams building AI agents and LLM applications. It is particularly valuable for those who prioritize open-source solutions, data control, and a streamlined development process. The platform's value proposition lies in its ability to accelerate the development cycle, improve the performance of AI applications, and reduce costs.
Langfuse is used by over 2,300 companies and processes billions of observations per month. It offers a free tier with 50,000 observations per month, making it accessible for developers to get started. The platform is built for scale, with a ClickHouse backend that allows querying millions of traces in milliseconds. It also offers enterprise-grade security and compliance, including SOC 2 Type II and ISO 27001 certifications. Langfuse's open-source nature, extensive integrations, and focus on developer experience make it a compelling choice for building and improving AI applications.
Langfuse's Core Features
Hierarchical traces capture every LLM call, tool invocation, and retrieval step
Filter traces by user, session, cost, latency, or custom metadata
Run evaluators on production data or during experiments
Separate prompts from code with one-click deployments and rollbacks
Test prompts on real production inputs and compare models side-by-side
Define test cases and run experiments
Collaborative Human-in-the-Loop workflows for review and create golden datasets
Monitor cost, latency, and quality with dashboards and automated alerts
Supports any model and framework
80+ integrations
Open source (MIT license)
Scales to billions of monthly events
REST APIs for everything
Query SDK
How to use Langfuse?
Install: Install the Langfuse AI skill from github.com/langfuse/skills.
Configure: Configure the skill to add tracing to your application.
Use: Use the skill to set up evals for your application.
Optimize: Migrate prompts in your codebase to Langfuse.
Langfuse's Use Cases
- LLM Debugging
- Prompt Management
- Evaluation
- Experimentation
- Cost Monitoring
- Latency Analysis
- Human-in-the-Loop
- AI Agent Development
- Production Monitoring
