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LlamaIndex provides one-click observability 🔭 to allow you to build principled LLM applications in a production setting.

A key requirement for principled development of LLM applications over your data (RAG systems, agents) is being able to observe, debug, and evaluate your system - both as a whole and for each component.

This feature allows you to seamlessly integrate the LlamaIndex library with powerful observability/evaluation tools offered by our partners. Configure a variable once, and you'll be able to do things like the following:

  • View LLM/prompt inputs/outputs
  • Ensure that the outputs of any component (LLMs, embeddings) are performing as expected
  • View call traces for both indexing and querying

Each provider has similarities and differences. Take a look below for the full set of guides for each one!


OpenLLMetry is an open-source project based on OpenTelemetry for tracing and monitoring LLM applications. It connects to all major observability platforms and installs in minutes.

Usage Pattern

npm install @traceloop/node-server-sdk
import * as traceloop from "@traceloop/node-server-sdk";

apiKey: process.env.TRACELOOP_API_KEY,
disableBatch: true,


Enhance your observability with Langtrace, a robust open-source tool supports OpenTelemetry and is designed to trace, evaluate, and manage LLM applications seamlessly. Langtrace integrates directly with LlamaIndex, offering detailed, real-time insights into performance metrics such as accuracy, evaluations, and latency.


  • Self-host or sign-up and generate an API key using Langtrace Cloud
npm install @langtrase/typescript-sdk


import * as Langtrace from "@langtrase/typescript-sdk";
Langtrace.init({ api_key: "<YOUR_API_KEY>" });


  • OpenTelemetry compliant, ensuring broad compatibility with observability platforms.
  • Provides comprehensive logs and detailed traces of all components.
  • Real-time monitoring of accuracy, evaluations, usage, costs, and latency.
  • For more configuration options and details, visit Langtrace Docs.