Building an LLM-powered application is only the beginning — understanding how it performs in production, identifying issues, and iteratively improving quality are ongoing challenges. Langfuse is an open-source observability and analytics platform purpose-built for LLM applications, providing the visibility developers need to build reliable AI systems.
Langfuse offers tracing, prompt management, evaluation, and analytics capabilities that help teams understand what their LLM applications are doing, how well they are performing, and where improvements are needed. It integrates with popular frameworks like LangChain, LlamaIndex, and OpenAI SDKs, making it straightforward to add observability to existing applications.
Key Features of Langfuse
LLM Tracing: Langfuse captures detailed traces of LLM application execution, including individual LLM calls, retrieval steps, tool usage, and custom events. This gives developers a complete picture of what happens during each request.
Prompt Management: Langfuse provides a centralized prompt management system that allows teams to version, deploy, and A/B test prompts without code changes, streamlining the prompt engineering workflow.
Evaluation and Scoring: Langfuse supports both automated evaluations (using LLM-as-a-judge or custom scoring functions) and manual human annotations, enabling systematic quality assessment of LLM outputs.
Cost and Latency Tracking: Every trace includes detailed cost and latency breakdowns, helping teams understand the economics of their LLM usage and identify performance bottlenecks.
Analytics Dashboards: Langfuse provides built-in dashboards for monitoring key metrics over time, including quality scores, costs, latency distributions, and usage patterns.
Open Source and Self-Hostable: Langfuse is fully open source (MIT license) and can be self-hosted for organizations that need to keep data on their own infrastructure, while also offering a managed cloud version.
Use Cases for Langfuse
Langfuse is used by AI teams throughout the development and production lifecycle:
- Debugging and Root Cause Analysis: Trace through individual requests to understand why an LLM application produced an unexpected or incorrect response.
- Quality Monitoring: Track evaluation scores and user feedback over time to detect regressions and measure the impact of changes to prompts, models, or retrieval strategies.
- Cost Optimization: Analyze token usage and costs across different models and features to optimize spending and identify opportunities to use smaller or cheaper models.
- Prompt Iteration: Use Langfuse's prompt management and evaluation tools to systematically test and improve prompts, comparing performance across versions.
- Compliance and Auditing: Maintain detailed logs of all LLM interactions for compliance requirements, with the ability to self-host for data sovereignty.
Langfuse in the AI Development Stack
Langfuse complements frameworks like LangChain, LangGraph, and LlamaIndex by adding the observability layer that is essential for production AI applications. While these frameworks handle the building and orchestration of LLM workflows, Langfuse provides the monitoring, evaluation, and analytics needed to operate them reliably at scale.