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SDK Configuration Options

OpenLIT offers automatic instrumentation with OpenTelemetry for various LLM providers, frameworks, and VectorDBs, enabling you to gain valuable insights into the behavior and performance of your AI applications through metrics.

Quickstart: LLM Observablity

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Integrations

60+ AI integrations with automatic instrumentation and performance tracking

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Disable Metrics

You have the option to disable the collection of metrics if needed. By default, metrics collection is enabled. Example: 
# Disable metrics collection
openlit.init(disable_metrics=True)

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Using an existing OTel Metrics instance

You have the flexibility to integrate your existing OpenTelemetry (OTel) Metrics instance configuration with OpenLIT. If you already have an OTel Metrics instance instantiated in your application, you can pass it directly to openlit.init(meter=meter). This integration ensures that OpenLIT utilizes your custom OTel metrics instance settings, allowing for a unified metrics setup across your application. Example: 
# Instantiate an OpenTelemetry Metrics meter
meter = ...

# Pass the meter to OpenLIT
openlit.init(meter=meter)

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Add custom resource attributes

The OTEL_RESOURCE_ATTRIBUTES environment variable allows you to provide additional OpenTelemetry resource attributes when starting your application with OpenLIT. OpenLIT already includes some default resource attributes:
  • telemetry.sdk.name: openlit
  • service.name: YOUR_SERVICE_NAME
  • deployment.environment: YOUR_ENVIRONMENT_NAME
You can enhance these default resource attributes by adding your own using the OTEL_RESOURCE_ATTRIBUTES variable. Your custom attributes will be added on top of the existing OpenLIT attributes, providing additional context to your telemetry data. Simply format your attributes as key1=value1,key2=value2. For example: 
export OTEL_RESOURCE_ATTRIBUTES="service.instance.id=YOUR_SERVICE_ID,k8s.pod.name=K8S_POD_NAME,k8s.namespace.name=K8S_NAMESPACE,k8s.node.name=K8S_NODE_NAME"

Deploy OpenLIT

Deployment options for scalable LLM monitoring infrastructure

Integrations

60+ AI integrations with automatic instrumentation and performance tracking

Destinations

Send elemetry to Datadog, Grafana, New Relic, and other observability stacks
Kubernetes

Running in Kubernetes? Try the OpenLIT Operator

Automatically inject instrumentation into existing workloads without modifying pod specs, container images, or application code.