How to Build End-to-End LLM Observability in FastAPI with OpenTelemetry

**
How to Build End-to-End LLM Observability in FastAPI with OpenTelemetry
**

This article shows how to build end-to-end, code-first LLM observability in a FastAPI application using the OpenTelemetry Python SDK. Instead of relying on vendor-specific agents or opaque SDKs, we will manually design traces, spans, and semantic attributes that capture the full lifecycle of an LLM-powered request. Table of Contents Introduction Prerequisites and Technical Context Why LLM Observability Is Fundamentally Different Reference Architecture: A Traceable RAG Request Reference Architecture Explained Why This Design Is Better Than Simpler Alternatives LLM Models That Work Best for This Architecture OpenTelemetry Primer (LLM-Relevant Concepts Only) Designing LLM-Aware Spans FastAPI Example: End-to-End LLM Spans (Complete and Explained) Semantic Attributes: Best Practices for LLM Observability Evaluation Hooks Inside Traces Exporting and Visualizing Traces (Where This Fits with Vendor Tooling) Operational Patterns and Anti-Patterns Extending the System Conclusion Introduction Large Language Models (LLMs) are rapidly becoming a core component of modern software systems. Applications that once relied on deterministic APIs are now incorporating LLM-powered features such as conversational assistants, document summarization, intelligent search, and retrieval-augmented generation (RAG). While these capabilities unlock new user experiences, they also introduce operational complexity that traditional monitoring approaches were never designed to handle...

---

**[devsupporter 해설]**

이 기사는 freeCodeCamp에서 제공하는 최신 개발 동향입니다. 관련 도구나 기술에 대해 더 알아보시려면 원본 링크를 참고하세요.