Instructor is a Python library for working with structured outputs for LLMs built on top of Pydantic. It provides a simple way to manage schema validations, retries and streaming responses.
In this guide, we will showcase how to integrate Opik with Instructor so that all the Instructor calls are logged as traces in Opik.
Comet provides a hosted version of the Opik platform, simply create an account and grab your API Key.
You can also run the Opik platform locally, see the installation guide for more information.
First, ensure you have both opik and instructor installed:
Configure the Opik Python SDK for your deployment type. See the Python SDK Configuration guide for detailed instructions on:
opik configureopik.configure()In order to use Instructor, you will need to configure your LLM provider API keys. For this example, we’ll use OpenAI, Anthropic, and Gemini. You can find or create your API keys in these pages:
You can set them as environment variables:
Or set them programmatically:
In order to log traces from Instructor into Opik, we are going to patch the instructor library. This will log each LLM call to the Opik platform.
For all the integrations, we will first add tracking to the LLM client and then pass it to the Instructor library:
Thanks to the track_openai method, all the calls made to OpenAI will be logged to the Opik platform. This approach also works well if you are also using the opik.track decorator as it will automatically log the LLM call made with Instructor to the relevant trace.

The instructor library supports many LLM providers beyond OpenAI, including: Anthropic, AWS Bedrock, Gemini, etc. Opik supports the majority of these providers as well.
Here are the code snippets needed for the integration with different providers:
You can read more about how to use the Instructor library in their documentation.