OpenAI

Overview

This guide shows you how to integrate Latitude Telemetry into an application that uses the OpenAI SDK.

You’ll keep calling OpenAI exactly as you do today. Telemetry simply observes and enriches those calls.

Using the OpenAI Agents SDK (@openai/agents)? See OpenAI Agents SDK — agent runs use the Responses API and need a dedicated instrumentation.

Requirements

A Latitude account and API key
A Latitude project slug
A project that uses the OpenAI SDK

Steps

Install

TypeScript
Python

npm install @latitude-data/telemetry

pnpm add @latitude-data/telemetry

yarn add @latitude-data/telemetry

bun add @latitude-data/telemetry

pip install latitude-telemetry

uv add latitude-telemetry

poetry add latitude-telemetry

Initialize and use

TypeScript
Python

import { Latitude, capture } from "@latitude-data/telemetry"
import OpenAI from "openai"

const latitude = new Latitude({
  apiKey: process.env.LATITUDE_API_KEY!,
  project: process.env.LATITUDE_PROJECT_SLUG!,
  instrumentations: { openai: OpenAI },
})


const openai = new OpenAI()

await capture("generate-support-reply", async () => {
  const completion = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: "Hello" }],
  })
  return completion.choices[0].message.content
})

await latitude.shutdown()

import openai
from openai import OpenAI

from latitude_telemetry import Latitude, capture

latitude = Latitude(
    api_key="your-api-key",
    project="your-project-slug",
    instrumentations={"openai": openai},
)

client = OpenAI()

def generate_support_reply():
    completion = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}],
    )
    return completion.choices[0].message.content

capture("generate-support-reply", generate_support_reply)

latitude.shutdown()

Streaming

When streaming, consume the stream inside capture() so the span covers the full operation:

TypeScript
Python

await capture("stream-reply", async () => {
  const stream = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: input }],
    stream: true,
  })

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content
    if (content) res.write(content)
  }
  res.end()
})

def stream_reply():
    stream = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": input}],
        stream=True,
    )
    chunks = []
    for chunk in stream:
        content = chunk.choices[0].delta.content
        if content:
            chunks.append(content)
    return "".join(chunks)

capture("stream-reply", stream_reply)

Seeing Your Traces

Once connected, traces appear automatically in Latitude:

Open your project in the Latitude dashboard
Each execution shows input/output messages, model, token usage, latency, and errors

Overview

Getting Started

Observe

Understand

Refine

Security and Compliance

Deployment

Development

More

Overview

Requirements

Steps

Streaming

Seeing Your Traces

​Overview

​Requirements

​Steps

​Streaming

​Seeing Your Traces

Overview

Requirements

Steps

Streaming

Seeing Your Traces