LLMs function as black boxes, making it difficult to determine their behavior. Observability is crucial for opening this black box and understanding how LLM applications operate in production. Our teams have had positive experiences with for observing, monitoring and evaluating LLM-based applications. Its tracing, analytics and evaluation capabilities allow us to analyze completion performance and accuracy, manage costs and latency and understand production usage patterns, thus facilitating continuous, data-driven improvements. Instrumentation data provides complete traceability of the request-response flow and intermediate steps, which can be used as test data to validate the application before deploying new changes. We've utilized Langfuse with RAG (retrieval-augmented generation), among other LLM architectures, and LLM-powered autonomous agents. In a RAG-based application, for example, analyzing low-scoring conversation traces helps identify which parts of the architecture — pre-retrieval, retrieval or generation — need refinement. Another option worth considering in this space is .
is an engineering platform for observability, testing and monitoring large language model (LLM) applications. Its SDKs support Python, JavaScript and TypeScript, OpenAI, LangChain and LiteLLM among other languages and frameworks. You can self-host the open-source version or use it as a paid cloud service. Our teams have had a positive experience, particularly in debugging complex LLM chains, analyzing completions and monitoring key metrics such as cost and latency across users, sessions, geographies, features and model versions. If you’re looking to build data-driven LLM applications, Langfuse is a good option to consider.
