@DailyDoseOfDS_: A Python decorator is all you need to trace LLM apps (open-source). Most LLM evals treat the app like an end-to-end bla…

A Python decorator is all you need to trace LLM apps (open-source). Most LLM evals treat the app like an end-to-end black box. But LLM apps need component-level evals and tracing since the issue can be anywhere inside the box, like the retriever, tool call, or the LLM itself. In DeepEval, you can do that with just 3 lines of code: - Trace individual LLM components (tools, retrievers, generators) with the "@ observe" decorator. - Attach different metrics to each part. - Get a visual breakdown of what’s working and what’s not. Done! You don't need to refactor any of your existing code. See the example below for a RAG app. Deepeval is 100% open-source with 15k+ stars, and you can easily self-host it so your data stays where you want. Find the repo in the replies!