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A complete open-source LLMOps setup you can self-host in an afternoon with Docker Compose. #
You’ve deployed your RAG pipeline, and users are testing it. Now, one user submits a bug report: “The answer is completely wrong.” You pull up the logs, only to find… nothing. You only have a record of the final response, but no record of which version of the hints was run, which data blocks were retrieved, how long the LLM calls took, or whether an upstream rejection was triggered.
This is the price every team pays for ignoring observability. For traditional software, deterministic inputs produce deterministic outputs — bugs can be reproduced simply by looking at the logs. But LLMs are inherently non-deterministic: the same input can produce different outputs under different model versions, hint tweaks, or temperature settings. Without a complete trace of every step in the pipeline, debugging is like archaeological digging.
Langfuse solves this problem perfectly. It’s an open-source LLMOps platform — licensed under the MIT license, self-hosted — designed for the entire lifecycle of LLM application development: tracing, prompt management, evaluation, and dataset-based experimentation.