Langfuse is an open-source observability and analytics platform tailored for LLM-based applications. It helps developers monitor, trace, and evaluate the behavior of their AI pipelines in real-time. Langfuse provides a comprehensive UI and APIs to track inputs, outputs, prompts, latency, and model performance. This insight is essential for debugging, improving response quality, and optimizing cost-efficiency when working with language models such as OpenAI, Anthropic, or local models like LLaMA or Mistral.
We integrated Langfuse into a project where fine-tuning and orchestrating LLM behavior across multiple prompts became increasingly complex. Previously, we had relied on scattered logs and manual evaluations. With Langfuse, we gained a structured view of how prompts evolved, where latency spikes occurred, and how user feedback correlated with output quality. This comes especially in handy when working with chained prompts or complex workflows, where understanding the flow of data and decisions is crucial.
It also works well alongside LangChain and LangGraph, making it ideal for LLM stack observability.
Subjective view: Langfuse feels like the missing debugging and performance layer for LLM-based systems. It’s incredibly helpful during both prototyping and production stages. We especially appreciated the seamless logging via SDK and the clean UI.