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Documentation Index

Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt

Use this file to discover all available pages before exploring further.

OpenLIT hero Openlayer can act as an OpenTelemetry backend, enabling trace ingestion from any OpenTelemetry-compatible instrumentation library. This guide shows how to use the OpenLIT library to instrument an LLM framework or provider and send trace data to Openlayer for monitoring and evaluation.

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Configuration

The integration works by sending trace data to Openlayer’s OpenTelemetry endpoint.
The full code used in this guide is available here.
To set it up, you need to:
1

Set the environment variables

Set the following environment variables:
OTEL_EXPORTER_OTLP_ENDPOINT="https://api.openlayer.com/v1/otel"
OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer YOUR_OPENLAYER_API_KEY_HERE, x-bt-parent=pipeline_id:YOUR_PIPELINE_ID_HERE"
2

Initialize OpenLIT instrumentation

Initialize OpenLIT instrumentation in your application.
import openlit

openlit.init(disable_batch=True)
3

Run LLMs and workflows as usual

Once instrumentation is set up, you can run your LLM calls as usual. Trace data will be automatically captured and exported to Openlayer, where you can begin testing and analyzing it.For example:
from openai import OpenAI

client = OpenAI()
client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[{"role": "user", "content": "How are you doing today?"}],
)