> ## Documentation Index
> Fetch the complete documentation index at: https://docs.latitude.so/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI

> Connect your OpenAI-powered application to Latitude for observability.

## Overview

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

<Check>
  You'll keep calling OpenAI exactly as you do today. Telemetry simply
  observes and enriches those calls.
</Check>

<Note>
  Using the **OpenAI Agents SDK** (`@openai/agents`)? See [OpenAI Agents SDK](/telemetry/frameworks/openai-agents) — agent runs use the Responses API and need a dedicated instrumentation.
</Note>

***

## Requirements

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

***

## Steps

<Steps>
  <Step title="Install">
    <Tabs>
      <Tab title="TypeScript">
        <CodeGroup>
          ```bash npm theme={"theme":{"light":"github-light","dark":"github-dark"}}
          npm install @latitude-data/telemetry
          ```

          ```bash pnpm theme={"theme":{"light":"github-light","dark":"github-dark"}}
          pnpm add @latitude-data/telemetry
          ```

          ```bash yarn theme={"theme":{"light":"github-light","dark":"github-dark"}}
          yarn add @latitude-data/telemetry
          ```

          ```bash bun theme={"theme":{"light":"github-light","dark":"github-dark"}}
          bun add @latitude-data/telemetry
          ```
        </CodeGroup>
      </Tab>

      <Tab title="Python">
        <CodeGroup>
          ```bash pip theme={"theme":{"light":"github-light","dark":"github-dark"}}
          pip install latitude-telemetry
          ```

          ```bash uv theme={"theme":{"light":"github-light","dark":"github-dark"}}
          uv add latitude-telemetry
          ```

          ```bash poetry theme={"theme":{"light":"github-light","dark":"github-dark"}}
          poetry add latitude-telemetry
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Initialize and use">
    <Tabs>
      <Tab title="TypeScript">
        ```ts theme={"theme":{"light":"github-light","dark":"github-dark"}}
        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()
        ```
      </Tab>

      <Tab title="Python">
        ```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
        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()
        ```
      </Tab>
    </Tabs>
  </Step>
</Steps>

***

## Streaming

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

<Tabs>
  <Tab title="TypeScript">
    ```ts theme={"theme":{"light":"github-light","dark":"github-dark"}}
    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()
    })
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
    def stream_reply():
        stream = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": input}],
            stream=True,
        )
        for chunk in stream:
            if chunk.choices[0].delta.content:
                yield chunk.choices[0].delta.content

    capture("stream-reply", stream_reply)
    ```
  </Tab>
</Tabs>

***

## Seeing Your Traces

Once connected, traces appear automatically in Latitude:

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