Introduction
LangFuse is an open-source observability and analytics platform purpose-built for LLM application monitoring, enabling teams to trace, evaluate, and optimize LLM-driven workflows with transparency and precision. LangFuse offers full visibility into prompt execution, model responses, tool usage, latency, cost, and user feedback—all in a structured and queryable format. LangFuse is a critical part of the LLMops stack, helping teams monitor performance across agents, identify prompt regressions, and drive continuous improvements in production AI applications.
Key benefits of using LangFuse include:
End-to-End Tracing: Captures structured traces for every LLM interaction, including prompt templates, function/tool calls, retries, and nested agent actions—ideal for debugging complex chains and multi-agent workflows.
Prompt and Response Analytics: Visualizes prompt usage, model completions, and token counts with detailed metrics like latency, cost, and success rates—enabling prompt optimization and ROI tracking.Custom Feedback and Evaluation: Supports human and programmatic feedback tagging (e.g., thumbs up/down, relevance scores, accuracy flags) to guide iterative improvements and fine-tuning.
Native LangChain, OpenAI, and LangGraph Support: Easily integrates into the Cake platform’s existing LLM stack with minimal overhead, supporting real-time tracing for both synchronous and streaming applications.
Team-Centric Collaboration: Dashboards, traces, and evaluations can be shared across teams for faster triage, experimentation, and incident response.
LangFuse is used to monitor production LLM applications across use cases like semantic search, chat agents, document analysis, and retrieval-augmented generation (RAG). It provides operational visibility into prompt quality, failure modes, user engagement, and the performance of different models (e.g., GPT-4o, Claude, Mistral, or vLLM-hosted LLaMA variants). By adopting LangFuse, you can ensure its LLM applications are observable, measurable, and continuously improving—empowering teams to debug faster, reduce costs, and build more reliable, user-aligned AI experiences.