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Developer mode
Developer quickstart
Take your first steps with the OpenAI API.
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The OpenAI API provides a simple interface to state-of-the-art AI models for text generation, natural language processing, computer vision, and more. Get started by creating an API Key and running your first API call. Discover how to generate text, analyze images, build agents, and more.
Build with the OpenAI API in Codex
The OpenAI Developers plugin enables Codex to connect to the OpenAI Platform, follow OpenAI API setup guidance, and create project API keys when your app needs one.
Create and export an API key
Before you begin, create an API key in the dashboard, which you’ll use to securely access the API. Store the key in a safe location, like a .zshrc file or another text file on your computer. Once you’ve generated an API key, export it as an environment variable in your terminal.
macOS / Linux
Windows
Export an environment variable on macOS or Linux systems
export OPENAI_API_KEY="your_api_key_here"
OpenAI SDKs are configured to automatically read your API key from the system environment.
Install the OpenAI SDK and Run an API Call
JavaScript
Python
.NET
Java
Go
To use the OpenAI API in server-side JavaScript environments like Node.js, Deno, or Bun, you can use the official OpenAI SDK for TypeScript and JavaScript. Get started by installing the SDK using npm or your preferred package manager:
Install the OpenAI SDK with npm
npm install openai
With the OpenAI SDK installed, create a file called example.mjs and copy the example code into it:
Test a basic API request
import OpenAI from "openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "gpt-5.5",
input: "Write a one-sentence bedtime story about a unicorn."
});
console.log(response.output_text);
Execute the code with node example.mjs (or the equivalent command for Deno or Bun). In a few moments, you should see the output of your API request.
Learn more on GitHub
Discover more SDK capabilities and options on the library’s GitHub README.
Responses starter app
Start building with the Responses API.
Text generation and prompting
Learn more about prompting, message roles, and building conversational apps.
Add credits to keep building
Congrats on running a free test API request! Start building real applications with higher limits and use our models to generate text, audio, images, videos and more.
Access dashboard features designed to help you ship faster:
Chat Playground
Build & test conversational prompts and embed them in your app.
Agent Builder
Build, deploy, and optimize agent workflows.
Analyze images and files
Send image URLs, uploaded files, or PDF documents directly to the model to extract text, classify content, or detect visual elements.
Image URL
File URL
Upload file
Analyze the content of an image
import OpenAI from "openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "gpt-5.5",
input: [
{
role: "user",
content: [
{
type: "input_text",
text: "What is in this image?",
},
{
type: "input_image",
image_url: "https://public.bnbstatic.com/static/content/square/images/b601711066c348f58b95dabca72d6e71.png",
},
],
},
],
});
console.log(response.output_text);
Image inputs guide
Learn to use image inputs to the model and extract meaning from images.
File inputs guide
Learn to use file inputs to the model and extract meaning from documents.
Extend the model with tools
Give the model access to external data and functions by attaching tools. Use built-in tools like web search or file search, or define your own for calling APIs, running code, or integrating with third-party systems.
Web search
File search
Function calling
Remote MCP
Use web search in a response
import OpenAI from "openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "gpt-5.5",
tools: [
{ type: "web_search" },
],
input: "What was a positive news story from today?",
});
console.log(response.output_text);
Use built-in tools
Learn about powerful built-in tools like web search and file search.
Function calling guide
Learn to enable the model to call your own custom code.
Stream responses and build realtime apps
Use server‑sent streaming events to show results as they’re generated, or the Realtime API for interactive voice and multimodal apps.
Stream server-sent events from the API
import { OpenAI } from "openai";
const client = new OpenAI();
const stream = await client.responses.create({
model: "gpt-5.5",
input: [
{
role: "user",
content: "Say 'double bubble bath' ten times fast.",
},
],
stream: true,
});
for await (const event of stream) {
console.log(event);
}
Use streaming events
Use server-sent events to stream model responses to users fast.
Get started with the Realtime API
Use WebRTC or WebSockets for super fast speech-to-speech AI apps.
Build agents
Use the OpenAI platform to build agents capable of taking action—like controlling computers—on behalf of your users. Use the Agents SDK to create orchestration logic on the backend.
Build a language triage agent
import { Agent, run } from '@openai/agents';
const spanishAgent = new Agent({
name: 'Spanish agent',
instructions: 'You only speak Spanish.',
});
const englishAgent = new Agent({
name: 'English agent',
instructions: 'You only speak English',
});
const triageAgent = new Agent({
name: 'Triage agent',
instructions:
'Handoff to the appropriate agent based on the language of the request.',
handoffs: [spanishAgent, englishAgent],
});
const result = await run(triageAgent, 'Hola, ¿cómo estás?');
console.log(result.finalOutput);
Build agents that can take action
Learn how to use the OpenAI platform to build powerful, capable AI agents."
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