Developer quickstart

import { Assistant, Camera, ChatTripleDots, Code, Bolt, Speed, SquarePlus, } from "@components/react/oai/platform/ui/Icon.react";

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.

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"

Export an environment variable in PowerShell

setx 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

<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.

Access dashboard features 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. StatsigClient.logEvent( "quickstart_add_credits_agent_builder_click", null, null ) } > 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.

Use a file URL as input

curl "https://api.openai.com/v1/responses" \\
    -H "Content-Type: application/json" \\
    -H "Authorization: Bearer $OPENAI_API_KEY" \\
    -d '{
        "model": "gpt-5",
        "input": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_text",
                        "text": "Analyze the letter and provide a summary of the key points."
                    },
                    {
                        "type": "input_file",
                        "file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf"
                    }
                ]
            }
        ]
    }'

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

const response = await client.responses.create({
    model: "gpt-5",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_text",
                    text: "Analyze the letter and provide a summary of the key points.",
                },
                {
                    type: "input_file",
                    file_url: "https://www.berkshirehathaway.com/letters/2024ltr.pdf",
                },
            ],
        },
    ],
});

console.log(response.output_text);

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "Analyze the letter and provide a summary of the key points.",
                },
                {
                    "type": "input_file",
                    "file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf",
                },
            ],
        },
    ]
)

print(response.output_text)

using OpenAI.Files;
using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);

using HttpClient http = new();
using Stream stream = await http.GetStreamAsync("https://www.berkshirehathaway.com/letters/2024ltr.pdf");
OpenAIFileClient files = new(key);
OpenAIFile file = files.UploadFile(stream, "2024ltr.pdf", FileUploadPurpose.UserData);

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("Analyze the letter and provide a summary of the key points."),
        ResponseContentPart.CreateInputFilePart(file.Id),
    ]),
]);

Console.WriteLine(response.GetOutputText());

Upload a file and use it as input

curl https://api.openai.com/v1/files \\
    -H "Authorization: Bearer $OPENAI_API_KEY" \\
    -F purpose="user_data" \\
    -F file="@draconomicon.pdf"

curl "https://api.openai.com/v1/responses" \\
    -H "Content-Type: application/json" \\
    -H "Authorization: Bearer $OPENAI_API_KEY" \\
    -d '{
        "model": "gpt-5",
        "input": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_file",
                        "file_id": "file-6F2ksmvXxt4VdoqmHRw6kL"
                    },
                    {
                        "type": "input_text",
                        "text": "What is the first dragon in the book?"
                    }
                ]
            }
        ]
    }'

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

const file = await client.files.create({
    file: fs.createReadStream("draconomicon.pdf"),
    purpose: "user_data",
});

const response = await client.responses.create({
    model: "gpt-5",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_file",
                    file_id: file.id,
                },
                {
                    type: "input_text",
                    text: "What is the first dragon in the book?",
                },
            ],
        },
    ],
});

console.log(response.output_text);

from openai import OpenAI
client = OpenAI()

file = client.files.create(
    file=open("draconomicon.pdf", "rb"),
    purpose="user_data"
)

response = client.responses.create(
    model="gpt-5",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_file",
                    "file_id": file.id,
                },
                {
                    "type": "input_text",
                    "text": "What is the first dragon in the book?",
                },
            ]
        }
    ]
)

print(response.output_text)

using OpenAI.Files;
using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);

OpenAIFileClient files = new(key);
OpenAIFile file = files.UploadFile("draconomicon.pdf", FileUploadPurpose.UserData);

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputFilePart(file.Id),
        ResponseContentPart.CreateInputTextPart("What is the first dragon in the book?"),
    ]),
]);

Console.WriteLine(response.GetOutputText());

[

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.

Search your files in a response

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-4.1",
    input="What is deep research by OpenAI?",
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["<vector_store_id>"]
    }]
)
print(response)

import OpenAI from "openai";
const openai = new OpenAI();

const response = await openai.responses.create({
    model: "gpt-4.1",
    input: "What is deep research by OpenAI?",
    tools: [
        {
            type: "file_search",
            vector_store_ids: ["<vector_store_id>"],
        },
    ],
});
console.log(response);

using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateFileSearchTool(["<vector_store_id>"]));

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("What is deep research by OpenAI?"),
    ]),
], options);

Console.WriteLine(response.GetOutputText());

Call a remote MCP server

curl https://api.openai.com/v1/responses \\ 
-H "Content-Type: application/json" \\ 
-H "Authorization: Bearer $OPENAI_API_KEY" \\ 
-d '{
  "model": "gpt-5",
    "tools": [
      {
        "type": "mcp",
        "server_label": "dmcp",
        "server_description": "A Dungeons and Dragons MCP server to assist with dice rolling.",
        "server_url": "https://dmcp-server.deno.dev/sse",
        "require_approval": "never"
      }
    ],
    "input": "Roll 2d4+1"
  }'

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

const resp = await client.responses.create({
  model: "gpt-5",
  tools: [
    {
      type: "mcp",
      server_label: "dmcp",
      server_description: "A Dungeons and Dragons MCP server to assist with dice rolling.",
      server_url: "https://dmcp-server.deno.dev/sse",
      require_approval: "never",
    },
  ],
  input: "Roll 2d4+1",
});

console.log(resp.output_text);

from openai import OpenAI

client = OpenAI()

resp = client.responses.create(
    model="gpt-5",
    tools=[
        {
            "type": "mcp",
            "server_label": "dmcp",
            "server_description": "A Dungeons and Dragons MCP server to assist with dice rolling.",
            "server_url": "https://dmcp-server.deno.dev/sse",
            "require_approval": "never",
        },
    ],
    input="Roll 2d4+1",
)

print(resp.output_text)

using OpenAI.Responses;

string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateMcpTool(
    serverLabel: "dmcp",
    serverUri: new Uri("https://dmcp-server.deno.dev/sse"),
    toolCallApprovalPolicy: new McpToolCallApprovalPolicy(GlobalMcpToolCallApprovalPolicy.NeverRequireApproval)
));

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("Roll 2d4+1")
    ])
], options);

Console.WriteLine(response.GetOutputText());

[

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 realtime apps

Use server‑sent streaming events to show results as they’re generated, or the Realtime API for interactive voice and multimodal apps.

[

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 the backend.

[

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

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

quickstart.md 2026-04-07 05:51 UTC to 2026-04-08 05:51 UTC