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 have a local Python environment, you can run the following command:

pip install mlflow
mlflow server --backend-store-uri sqlite:///mlruns.db --port 5000
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If you don't have Python environment locally, MLflow also supports Docker deployment or managed services. See Self-Hosting Guide for getting started.

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.