Build a Complete Langfuse Observability and Evaluation Pipeline for Tracing, Prompt Management, Scoring, and Experiments

Pipeline for Tracing, Prompt Management, Scoring, and Experiments">

In this tutorial, we implement the Langfuse pipeline, an open-source LLM engineering platform, for tracing, prompt management, scoring, datasets, and experiments. We build a complete workflow that works with either a real OpenAI key or a deterministic mock LLM, allowing us to understand every major Langfuse feature without depending on paid model access. We start by setting up credentials and connecting to Langfuse.

This involves installing the required Langfuse and OpenAI packages inside the Colab environment, collecting Langfuse credentials, choosing the correct Langfuse region or self-hosted URL, and optionally accepting an OpenAI API key. We then initialize the Langfuse client, verify authentication, and confirm whether we are using OpenAI or the built-in mock LLM. The next step is to define the LLM helper that supports both real OpenAI generations and deterministic mock responses.

We ensure that even the mock path creates a proper Langfuse generation observation, making the tutorial fully traceable without an OpenAI key. We demonstrate basic decorator-based tracing by wrapping a simple story-generation pipeline with @observe. We then build a small manual RAG pipeline using a simple in-memory knowledge base for refunds, shipping, and warranty information.

We trace the retrieval step separately and use propagate_attributes to attach user ID, session ID, and tags across the full trace. We run a refund-related question and capture the trace ID so we can attach scores to it later. We create a managed Langfuse chat prompt, compile it with runtime variables, and link the prompt version to a traced generation.

We add different score types to the earlier RAG trace, including numeric, categorical, and boolean scores. We also demonstrate inline scoring by grading a capital-city answer inside the current observed span and trace. We create a Langfuse dataset for capital-city questions and add deterministic items to ensure repeated runs remain idempotent.

We define a task function that answers each item, along with item-level evaluators for accuracy and response length. We run an experiment on the dataset and print a formatted summary of item-level and aggregate results. Finally, we optionally demonstrate the LangChain integration when an OpenAI key is available, using the Langfuse callback handler to trace chain execution.

If no OpenAI key is provided, we skip this section while keeping the rest of the tutorial fully functional. We flush all buffered events to Langfuse and print where to inspect traces, prompts, scores, and dataset experiment results. In conclusion, we created a practical end-to-end Langfuse workflow that covers the most important parts of LLM observability and evaluation.