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title: How to Build End-to-End LLM Observability in FastAPI...
description: A practical guide to implementing end-to-end LLM observability in FastAPI using the OpenTelemetry Python SDK without relying on vendor-specific agents. Covers...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

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

**[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 24 min read · 3 upvotes · 0 comments

## Summary

A practical guide to implementing end-to-end LLM observability in FastAPI using the OpenTelemetry Python SDK without relying on vendor-specific agents. Covers designing a span hierarchy for a RAG pipeline with spans for retrieval, LLM invocation, and post-processing. Explains how to capture semantic attributes including prompt metadata (hashed for privacy), token usage, estimated cost, and model configuration. Also covers evaluation hooks for attaching quality signals to traces, exporting to backends like Jaeger, Grafana Tempo, or Arize Phoenix, and best practices around sampling, privacy, and anti-patterns.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.freecodecamp.org/news/build-end-to-end-llm-observability-in-fastapi-with-opentelemetry/>

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---

Tags: [#devops](https://daily.dev/tags/devops), [#fastapi](https://daily.dev/tags/fastapi), [#llm-observability](https://daily.dev/tags/llm-observability), [#opentelemetry](https://daily.dev/tags/opentelemetry), [#rag](https://daily.dev/tags/rag)

[View this post on daily.dev](https://daily.dev/posts/how-to-build-end-to-end-llm-observability-in-fastapi-with-opentelemetry-n6rfzz43o)

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