The canonical way to integrate with Openlayer is by using the SDKs, as explained in the “Instrument your code” guide. However, there are alternative paths to integrate with Openlayer monitoring mode, namely: This guide explains when you should use each of these.

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OpenTelemetry

OpenTelemetry (OTel) is an open-source framework used to collect observability data. It is widely used in industry and has been gaining popularity for GenAI systems, being nativaly supported by frameworks like Semantic Kernel, Vercel AI SDK, Spring AI, and others. Openlayer supports OTel traces. You should consider this integration path if you:
  • Already emit traces via OTel in your system.
  • Use a framework built on OTel (e.g., Semantic Kernel, PydanticAI, …).
  • Want to standardize telemetry across infra and AI.
Refer to the OpenTelemetry integration guide for more details. Or to the integration pages for the frameworks below, which leverage OTel:

OpenTelemetry

Semantic Kernel

PydanticAI

Spring AI

OpenLLMetry

OpenLIT

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REST API

Openlayer also exposes the REST API endpoint used for streaming data to the platform. You should consider this path if you:
  • Want to monitor a traditional ML system (e.g., tabular classification, tabular regression, etc.)
  • Have a custom pipeline that does not map cleanly to SDK wrappers.
  • Are using a programming language that is not supported by the SDKs.
To manually stream data to Openlayer, you can make POST requests to the /data-stream endpoint of the Openlayer REST API. Refer to the REST API reference for more details.

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Which one should you use?

  • SDKs (wrappers + @trace) -> recommended. Fastest to set up, richest traces.
  • OpenTelemetry -> best if you already instrument with OpenTelemetry or use an OTel-based framework.
  • REST -> fallback if you can’t use SDKs or OTel. More verbose, less automated.