# LlamaIndex Observability & Monitoring with OpenTelemetry > Source: > Published: 2026-06-09 00:00:00+00:00 Overview This guide walks you through enabling observability and monitoring for your Python-based [LlamaIndex](https://www.llamaindex.ai/) application and streaming telemetry data to SigNoz Cloud using OpenTelemetry. By the end of this setup, you'll be able to monitor AI-specific operations such as document ingestion, document retrieval, user querying, text generation, and user feedback within LlamaIndex, with detailed spans capturing request durations, node and query inputs, model outputs, retrieval scores, metadata, and intermediate steps throughout the pipeline. Instrumenting your RAG workflows with telemetry enables full observability across the retrieval and generation pipeline. This is especially valuable when building production-grade developer-facing tools, where insight into model behavior, latency bottlenecks, and retrieval accuracy is essential. With SigNoz, you can trace each user question end-to-end, from prompt to response, and continuously improve performance and reliability. To get started, check out our example LlamaIndex RAG Q&A bot, complete with OpenTelemetry-based monitoring (via OpenInference). View the full repository [here](https://github.com/SigNoz/llamaindex-rag-opentelemetry-demo). Prerequisites - A Python application using **Python 3.8+** - LlamaIndex integrated into your app, with document ingestion and query interfaces set up - Basic understanding of RAG (Retrieval-Augmented Generation) workflows - SigNoz setup (choose one): [SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key- Self-hosted SigNoz instance `pip` installed for managing Python packages- Internet access to send telemetry data to SigNoz Cloud *(Optional but recommended)*A Python virtual environment to isolate dependencies Instrument your LlamaIndex application To capture detailed telemetry from LlamaIndex without modifying your core application logic, we use [OpenInference](https://arize.com/docs/ax/learn/tracing-concepts/what-is-openinference), a community-driven standard that provides pre-built instrumentation for popular AI frameworks like LlamaIndex, built on top of OpenTelemetry. This allows you to trace your LlamaIndex application with minimal configuration. Check out detailed instructions on how to set up OpenInference instrumentation in your LlamaIndex application over [here](https://pypi.org/project/openinference-instrumentation-llama-index/). No-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries. **Step 1:** Install the necessary packages in your Python environment. ``` pip install \ opentelemetry-distro \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ llama-index \ openinference-instrumentation-llama-index ``` **Step 2:** Add Automatic Instrumentation ``` opentelemetry-bootstrap --action=install ``` **Step 3:** Configure logging level To ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher: ``` python import logging logging.getLogger().setLevel(logging.INFO) ``` This sets the minimum log level for the root logger to INFO, which ensures that `logger.info()` calls and higher severity logs (WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz. **Step 4:** Run an example ``` python from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-4o") response = llm.complete("Hello, world!") print(response) ``` 📌 Note: Ensure that the `OPENAI_API_KEY` environment variable is properly defined with your API key before running the code. **Step 5:** Run your application with auto-instrumentation ``` OTEL_RESOURCE_ATTRIBUTES="service.name=" \ OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest..signoz.cloud:443" \ OTEL_EXPORTER_OTLP_HEADERS="signoz-ingestion-key=" \ OTEL_EXPORTER_OTLP_PROTOCOL=grpc \ OTEL_TRACES_EXPORTER=otlp \ OTEL_METRICS_EXPORTER=otlp \ OTEL_LOGS_EXPORTER=otlp \ OTEL_PYTHON_LOG_CORRELATION=true \ OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \ opentelemetry-instrument ``` is the name of your service`` `` : Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`` : Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace `` with the actual command you would use to run your application. For example:`python main.py` Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted). Code-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure. **Step 1:** Install OpenInference and OpenTelemetry related packages ``` pip install openinference-instrumentation-llama-index \ opentelemetry-exporter-otlp \ opentelemetry-sdk \ llama-index ``` **Step 2:** Import the necessary modules in your Python application ``` python from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.sdk.resources import Resource from openinference.instrumentation.llama_index import LlamaIndexInstrumentor ``` **Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud ``` resource = Resource.create({"service.name": ""}) provider = TracerProvider(resource=resource) span_exporter = OTLPSpanExporter( endpoint="https://ingest..signoz.cloud:443/v1/traces", headers={"signoz-ingestion-key": ""}, ) provider.add_span_processor(BatchSpanProcessor(span_exporter)) ``` is the name of your service`` : Your SigNoz Cloud`` **region**: Your SigNoz`` **ingestion key** Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted). **Step 4:** Instrument LlamaIndex using OpenInference and the configured Tracer Provider Use the `LlamaIndexInstrumentor` from OpenInference to automatically trace LlamaIndex operations with your OpenTelemetry setup: ``` LlamaIndexInstrumentor().instrument(tracer_provider=provider) ``` 📌 Important: Place this code at the start of your application logic — before any LlamaIndex functions are called or used — to ensure telemetry is correctly captured. **Step 5:** Run an example ``` python from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-4o") response = llm.complete("Hello, world!") print(response) ``` 📌 Note: Ensure that the `OPENAI_API_KEY` environment variable is properly defined with your API key before running the code. Your LlamaIndex commands should now automatically emit traces, spans, and attributes. Finally, you should be able to view this data in Signoz Cloud under the traces tab: When you click on a trace ID in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes. Additional Resources - Read [Observing LlamaIndex Apps with OpenTelemetry + SigNoz](https://signoz.io/blog/opentelemetry-llamaindex/) - Set up [alerts](https://signoz.io/docs/alerts/)for high latency or error rates - Learn more about [querying traces](https://signoz.io/docs/userguide/traces/)