Langfuse
Open-source LLM observability — trace every agent run, score outputs, and catch regressions
Langfuse is an open-source observability and evaluation platform for LLM applications. It captures traces of every agent run — which model was called, with what prompt, what tool was invoked, and what the output was — and lets you score outputs manually or automatically. It integrates natively with LangChain, LlamaIndex, and the OpenAI SDK with minimal instrumentation.
Teams that need to understand why their agent behaved a certain way, track quality regressions across prompt changes, and build evaluation datasets from production traces.
Any team shipping agents to production users. Open-source and self-hostable; Langfuse Cloud removes operational burden. Essential once agents are doing real work and failures have real consequences.
Agent Architecture Fit
Langfuse is the observability layer that wraps your entire agent blueprint. Every call your agent makes — to the model, to tools, to memory — gets recorded as a span in a trace. This gives you a complete picture of each agent run: what it decided, what it called, and what it returned. Without this layer, debugging agent failures is guesswork. In production blueprints, Langfuse is non-negotiable.
Next step
Your agent starts with a blueprint.
A blueprint tells you which tools to use, where they fit, and how they connect — before you write a line of code.
Build yours free →