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quickstart.md 2026-07-14 21:58 UTC to 2026-07-15 16:58 UTC

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Developer quickstart

The OpenAI API provides a consistent 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.

Create and export an API key

StatsigClient.logEvent("quickstart_create_api_key_click", null, null) }

Create an API Key

Before you begin, create an API key in the dashboard, which you'll use to securely [access the API](https://developers.openai.com/api/docs/api-reference/authentication). Store the key in a safe location, like a [`.zshrc` file](https://www.freecodecamp.org/news/how-do-zsh-configuration-files-work/) or another text file on your computer. Once you've generated an API key, export it as an [environment variable](https://en.wikipedia.org/wiki/Environment_variable) in your terminal.
Export an environment variable on macOS or Linux systems 
export OPENAI_API_KEY="your_api_key_here"

Each OpenAI SDK automatically reads your API key from the system environment.

Install the OpenAI SDK and Run an API Call

<a href="https://github.com/openai/openai-responses-starter-app" target="_blank" rel="noreferrer"

Start building with the Responses API.

[

Learn more about prompting, message roles, and building conversational apps.

](https://developers.openai.com/api/docs/guides/text)

Add credits to keep building

StatsigClient.logEvent("quickstart_add_credits_billing_click", null, null) }

Go to billing

{/* prettier-ignore */}

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.
Explore tools and docs designed to help you ship faster:
StatsigClient.logEvent( "quickstart_add_credits_chat_playground_click", null, null ) } > Build & test conversational prompts and embed them in your app. [ Use the Agents SDK to build, run, and observe agent workflows.

](https://developers.openai.com/api/docs/guides/agents)

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.

Analyze the content of an image 
import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
    model: "gpt-5.6",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_text",
                    text: "What is in this image?",
                },
                {
                    type: "input_image",
                    image_url: "https://openai-documentation.vercel.app/images/cat_and_otter.png",
                },
            ],
        },
    ],
});

console.log(response.output_text);

curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.6",
        "input": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_text",
                        "text": "What is in this image?"
                    },
                    {
                        "type": "input_image",
                        "image_url": "https://openai-documentation.vercel.app/images/cat_and_otter.png"
                    }
                ]
            }
        ]
}'

openai responses create \
  --model gpt-5.6 \
  --raw-output \
  --transform 'output.#(type=="message").content.0.text' <<'YAML'
input:
  - role: user
    content:
      - type: input_text
        text: What is in this image?
      - type: input_image
        image_url: https://openai-documentation.vercel.app/images/cat_and_otter.png
YAML

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.6",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "What teams are playing in this image?",
                },
                {
                    "type": "input_image",
                    "image_url": "https://api.nga.gov/iiif/a2e6da57-3cd1-4235-b20e-95dcaefed6c8/full/!800,800/0/default.jpg"
                }
            ]
        }
    ]
)

print(response.output_text)

using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.6", apiKey: key);

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("What is in this image?"),
        ResponseContentPart.CreateInputImagePart(new Uri("https://openai-documentation.vercel.app/images/cat_and_otter.png")),
    ]),
]);

Console.WriteLine(response.GetOutputText());

require "openai"

openai = OpenAI::Client.new

response = openai.responses.create(
  model: "gpt-5.6",
  input: [
    {
      role: "user",
      content: [
        {
          type: "input_text",
          text: "What teams are playing in this image?"
        },
        {
          type: "input_image",
          image_url: "https://api.nga.gov/iiif/a2e6da57-3cd1-4235-b20e-95dcaefed6c8/full/!800,800/0/default.jpg"
        }
      ]
    }
  ]
)

puts(response.output_text)

[

Learn to use image inputs to the model and extract meaning from images.

](https://developers.openai.com/api/docs/guides/images)

[

Learn to use file inputs to the model and extract meaning from documents.

](https://developers.openai.com/api/docs/guides/file-inputs)

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.

Use web search in a response 
import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
    model: "gpt-5.6",
    tools: [
        { type: "web_search" },
    ],
    input: "What was a positive news story from today?",
});

console.log(response.output_text);

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.6",
    tools=[{"type": "web_search"}],
    input="What was a positive news story from today?"
)

print(response.output_text)

curl "https://api.openai.com/v1/responses" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
        "model": "gpt-5.6",
        "tools": [{"type": "web_search"}],
        "input": "what was a positive news story from today?"
}'

openai responses create \
  --model gpt-5.6 \
  --raw-output \
  --transform 'output.#(type=="message").content.0.text' <<'YAML'
tools:
  - type: web_search
input: What was a positive news story from today?
YAML

using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.6", apiKey: key);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateWebSearchTool());

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("What was a positive news story from today?"),
    ]),
], options);

Console.WriteLine(response.GetOutputText());

require "openai"

openai = OpenAI::Client.new

response = openai.responses.create(
  model: "gpt-5.6",
  tools: [{type: "web_search"}],
  input: "What was a positive news story from today?"
)

puts(response.output_text)

[

Learn about powerful built-in tools like web search and file search.

](https://developers.openai.com/api/docs/guides/tools)

[

Learn to enable the model to call your own custom code.

](https://developers.openai.com/api/docs/guides/function-calling)

Stream responses and build real-time apps

Use server‑sent streaming events to show results as they’re generated, or use the Realtime API for interactive voice apps and apps with text, audio, and image inputs.

Stream server-sent events from the API

import { OpenAI } from "openai";
const client = new OpenAI();

const stream = await client.responses.create({
    model: "gpt-5.6",
    input: [
        {
            role: "user",
            content: "Say 'double bubble bath' ten times fast.",
        },
    ],
    stream: true,
});

for await (const event of stream) {
    console.log(event);
}

from openai import OpenAI
client = OpenAI()

stream = client.responses.create(
    model="gpt-5.6",
    input=[
        {
            "role": "user",
            "content": "Say 'double bubble bath' ten times fast.",
        },
    ],
    stream=True,
)

for event in stream:
    print(event)

using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5.6", apiKey: key);

var responses = client.CreateResponseStreamingAsync([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("Say 'double bubble bath' ten times fast."),
    ]),
]);

await foreach (var response in responses)
{
    if (response is StreamingResponseOutputTextDeltaUpdate delta)
    {
        Console.Write(delta.Delta);
    }
}

require "openai"

openai = OpenAI::Client.new

stream = openai.responses.stream(
  model: "gpt-5.6",
  input: [
    {
      role: "user",
      content: "Say 'double bubble bath' ten times fast."
    }
  ]
)

stream.each do |event|
  puts(event)
end

[

Use server-sent events to stream model responses to users fast.

](https://developers.openai.com/api/docs/guides/streaming-responses)

[

Use WebRTC or WebSockets for super fast speech-to-speech AI apps.

](https://developers.openai.com/api/docs/guides/realtime)

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 your server.

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);

from agents import Agent, Runner
import asyncio

spanish_agent = Agent(
    name="Spanish agent",
    instructions="You only speak Spanish.",
)

english_agent = Agent(
    name="English agent",
    instructions="You only speak English",
)

triage_agent = Agent(
    name="Triage agent",
    instructions="Handoff to the appropriate agent based on the language of the request.",
    handoffs=[spanish_agent, english_agent],
)


async def main():
    result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
    print(result.final_output)


if __name__ == "__main__":
    asyncio.run(main())

[

Learn how to use the OpenAI platform to build powerful, capable AI agents.

](https://developers.openai.com/api/docs/guides/agents)