OpenLIT uses OpenTelemetry Auto-Instrumentation to help you monitor AI applications built using the Replicate API. This includes tracking performance, model usage, and how users interact with your application. Auto-instrumentation means you don’t have to set up monitoring manually for different models or providers. By simply adding OpenLIT in your application, all the necessary monitoring configurations are automatically set up. The integration is compatible withDocumentation Index
Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
Use this file to discover all available pages before exploring further.
- Replicate JavaScript/TypeScript SDK client
Get started
Initialize OpenLIT in your Application
- Python
- Typescript
- Zero Code Instrumentation
- One-Line Instrumentation
Perfect for existing applications - no code modifications needed:
- Via CLI Arguments
- Via Environment Variables
Perfect for: Legacy applications, production systems where code changes need approval, quick testing, or when you want to add observability without touching existing code.
YOUR_OTEL_ENDPOINT with the URL of your OpenTelemetry backend, such as http://127.0.0.1:4318 if you are using OpenLIT and a local OTel Collector.To send metrics and traces to other Observability tools, refer to the supported destinations.For more advanced configurations and application use cases, visit the OpenLIT Python repository or OpenLIT Typescript repository.Quickstart: LLM Observability
Production-ready AI monitoring setup in 2 simple steps with zero code changes
Configuration
Configure the OpenLIT SDK according to you requirements.
Destinations
Send telemetry to Datadog, Grafana, New Relic, and other observability stacks
Running in Kubernetes? Try the OpenLIT Operator
Automatically inject instrumentation into existing workloads without modifying pod specs, container images, or application code.

