> ## 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. **Using an agent?** Install the [Latitude skills](/getting-started/skills) and let it handle the setup below. `latitude-setup` instruments your app or agent harness, verifies traces arrive, creates a temporary account if you don't have one yet (no signup), and ends by building your first [Artifact](/more/artifacts). ```text theme={"theme":{"light":"github-light","dark":"github-dark"}} theme={"theme":{"light":"github-light","dark":"github-dark"}} Install the `latitude-setup` skill from `github.com/latitude-dev/skills` and use it to set up Latitude tracing here. ``` ## Overview This guide shows you how to integrate **Latitude Telemetry** into an application that uses the **OpenAI** SDK. No Latitude account yet? Your agent can create a temporary one and do this whole setup with the [`latitude-setup` skill](/getting-started/skills), no signup. 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](/telemetry/frameworks/openai-agents) — agent runs use the Responses API and need a dedicated instrumentation. *** ## Requirements * A **Latitude account** and **API key**, or none yet: your agent can create a temporary account with the [`latitude-setup` skill](/getting-started/skills), no signup * A **Latitude project slug** * A project that uses the **OpenAI SDK** *** ## Steps ```bash npm theme={"theme":{"light":"github-light","dark":"github-dark"}} npm install @latitude-data/telemetry openai ``` ```bash pnpm theme={"theme":{"light":"github-light","dark":"github-dark"}} pnpm add @latitude-data/telemetry openai ``` ```bash yarn theme={"theme":{"light":"github-light","dark":"github-dark"}} yarn add @latitude-data/telemetry openai ``` ```bash bun theme={"theme":{"light":"github-light","dark":"github-dark"}} bun add @latitude-data/telemetry openai ``` ```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 ``` ```ts theme={"theme":{"light":"github-light","dark":"github-dark"}} import { createOpenAIInstrumentation } from "@latitude-data/telemetry/instrumentations/openai" 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: [createOpenAIInstrumentation(OpenAI)], }) await latitude.ready 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() ``` ```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() ``` *** ## Streaming When streaming, consume the stream inside `capture()` so the span covers the full operation: ```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() }) ``` ```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, ) 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: 1. Open your **project** in the Latitude dashboard 2. Each execution shows input/output messages, model, token usage, latency, and errors ## See what was captured Once a real run has landed, your agent builds your first [Artifact](/more/artifacts): a single HTML page, in the Latitude look, with everything the telemetry captured from that session: model calls, tool calls, tokens, cost, timing, and the conversation as the model saw it. It is the fastest way to check the integration end to end and to see what Latitude will have to work with. The `latitude-setup` skill does this as its last step from its bundled `first-artifact.html` template, filling the page with the values the `latitude` CLI returns for the trace, and adds a **Claim your workspace** button when the account is temporary. If you set things up by hand, the same template and instructions live in the [skills repo](https://github.com/latitude-dev/skills/tree/main/skills/latitude-setup). Prompt, if you need to ask for it: ```text theme={"theme":{"light":"github-light","dark":"github-dark"}} theme={"theme":{"light":"github-light","dark":"github-dark"}} Build my first Latitude Artifact from the session that just landed, using the first-artifact.html template from the latitude-setup skill (github.com/latitude-dev/skills). ```