Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations

May 11, 2026 · View on GitHub

Learn how to use langfuse/langfuse to trace, evaluate, and improve production LLM systems with structured observability workflows.

Why This Track Matters

Teams shipping LLM features need visibility into quality, latency, and cost. Langfuse provides the feedback loop for prompt and pipeline improvement.

This track focuses on:

end-to-end tracing for LLM chains/agents
prompt lifecycle and versioning discipline
evaluation workflows (LLM-as-judge + human)
production analytics and deployment operations

Current Snapshot (auto-updated)

repository: langfuse/langfuse
stars: about 27k
latest release: v3.173.0 (published 2026-05-08)

Mental Model

flowchart LR
    A[LLM App] --> B[Langfuse Instrumentation]
    B --> C[Trace and Event Data]
    C --> D[Prompt and Eval Layer]
    D --> E[Analytics and Insights]
    E --> F[Iterative Quality Improvement]

Chapter Guide

Chapter	Key Question	Outcome
01 - Getting Started	How do I install and capture first traces?	Working Langfuse baseline
02 - Tracing Fundamentals	How should traces be structured for debugging?	Reliable observability model
03 - Prompt Management	How do I version and ship prompts safely?	Prompt ops playbook
04 - Evaluation	How do I measure quality systematically?	Repeatable eval framework
05 - Analytics and Metrics	How do I track cost, latency, and usage?	Production monitoring baseline
06 - Datasets and Testing	How do I build regression datasets from real traffic?	Better offline/online testing loops
07 - Integrations	How does Langfuse fit existing stacks?	Framework and SDK integration patterns
08 - Production Deployment	How do I run Langfuse reliably in production?	Deployment and scaling guidance

What You Will Learn

how to instrument LLM workflows for high-signal debugging
how to connect traces to prompt and evaluation loops
how to monitor quality/cost/latency with actionable metrics
how to operate Langfuse in production environments

Source References

Start with Chapter 1: Getting Started.

Full Chapter Map

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