MLflow Typescript SDK - OpenAI

Seamlessly integrate MLflow Tracing with OpenAI to automatically trace your OpenAI API calls.

Package	NPM	Description
mlflow-openai		Auto-instrumentation integration for OpenAI.

Installation

npm install mlflow-openai
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The package includes the mlflow-tracing package and openai package as peer dependencies. Depending on your package manager, you may need to install these two packages separately.

Quickstart

Start MLflow Tracking Server if you don't have one already:

pip install mlflow
mlflow server --backend-store-uri sqlite:///mlruns.db --port 5000
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Self-hosting MLflow server requires Python 3.10 or higher. If you don't have one, you can also use managed MLflow service for free to get started quickly.

Instantiate MLflow SDK in your application:

import * as mlflow from 'mlflow-tracing';

mlflow.init({
  trackingUri: 'http://localhost:5000',
  experimentId: '<experiment-id>'
});
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Create a trace:

import { OpenAI } from 'openai';
import { tracedOpenAI } from 'mlflow-openai';

// Wrap the OpenAI client with the tracedOpenAI function
const client = tracedOpenAI(new OpenAI());

// Invoke the client as usual
const response = await client.chat.completions.create({
  model: 'o4-mini',
  messages: [
    { role: 'system', content: 'You are a helpful weather assistant.' },
    { role: 'user', content: "What's the weather like in Seattle?" }
  ]
});
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View traces in MLflow UI:

Documentation 📘

Official documentation for MLflow Typescript SDK can be found here.

License

This project is licensed under the Apache License 2.0.