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To build an app with the OpenAI API, make the API request from trusted server-side code, keep your API key out of the browser, and return only the result your app needs. Start with one narrow feature: define its input and output, create a key, use an official SDK to make a Responses API request, and then connect that request to your app.
1. Define one task for your app
Before writing code, decide what the feature should do and what it needs to receive and return. For example, a support app might accept a customer’s question and return a draft answer. A first prototype should handle one such task rather than trying to build a general-purpose assistant.
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- Input: What will the user provide, and in what form?
- Output: What should the app display or do with the model’s response?
- Boundary: What should the feature not attempt to do?
This scope helps you choose whether a plain text request is enough or whether the feature needs images, files, tools, or streaming.
2. Create and protect an API key
Create an API key in the OpenAI dashboard, following the official quickstart. Treat the key like a password: OpenAI’s API reference says it should be securely loaded from an environment variable or key-management service on the server. Do not put it in browser JavaScript, a mobile app bundle, a public repository, or other client-side code.
A browser or app distributed to users cannot keep a secret embedded in its code. Instead, have the client call your server, and have that server authenticate to the OpenAI API. Keep the key in the server environment or an appropriate key-management system, and do not send it back to the client.
3. Install the official JavaScript SDK and make a first request
The JavaScript quickstart demonstrates a server-side integration with the official openai package and the Responses API. Install the package in your server project and set the API key in that server’s environment as described in the quickstart. The basic request pattern is:
import OpenAI from "openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "gpt-5",
input: "Write a one-sentence welcome message for a new user."
});
console.log(response.output_text);This illustrates the request shape, not a complete application or deployment setup. The model name shown is the one used in the quickstart example; check the current quickstart for the model name available and appropriate when implementing your app.
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In this example, the SDK client reads the API key from the server environment. The request supplies a model and input, and the example prints the response’s text. In an app, use the returned value in the feature’s own flow rather than exposing credentials or returning unnecessary data.
4. Connect the request to your app
Put the API call in a server-side route, controller, or other trusted part of your application. Your frontend sends the task input to your server; the server validates it, makes the OpenAI request, and returns the result the interface needs. The official quickstart provides a server-side JavaScript example, but it does not prescribe a particular frontend framework, hosting provider, or complete production architecture.
- Receive the app’s input. Accept only the fields the feature needs.
- Validate it on the server. Apply your app’s rules before sending user input to the API.
- Make the API request. Use the server-side SDK and secret key.
- Return a useful result. Send the relevant output to the client, not the key or unrelated response data.
- Handle failures. Ensure the interface can respond sensibly if the request fails; design error handling for your application rather than assuming every request succeeds.
The exact route, validation rules, and error responses depend on your framework and product. The quickstart supports the server-side request pattern and key-protection requirement, not a framework-specific recipe.
5. Add capabilities that fit the feature
A basic text request is only one option. The quickstart also demonstrates image and file inputs, tools such as web search and function calling, streaming events, and agent workflows. Choose a capability because the task requires it, not because it is available.
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- Text: Use for a feature whose input and desired response are text.
- Images or files: Consider these when users need to provide visual or document content; account for the format and handling requirements of the feature.
- Tools: Use web search or function calling when the app needs a supported tool workflow, rather than treating a model response as if it had performed an external action on its own.
- Streaming: Consider streaming when showing output as it arrives improves the interaction. A standard completed response may suit a feature that can wait for the full result.
- Agent workflows: Use these only when the feature genuinely needs a multi-step workflow; they are not a prerequisite for a first API call.
6. Check data handling before sending user information
Review OpenAI’s data-controls documentation alongside your app’s own data requirements. That page says Responses API application state is retained for 30 days by default or when store is true, while endpoint features and approved controls affect behavior. This is not a claim that all API requests are stored identically or that every account can use every exception or control.
Before sending personal, sensitive, or otherwise restricted information, check the current documentation for the specific endpoint and features you intend to use, the applicable retention behavior, and whether any controls you need are available to your account. Make the app’s data handling fit the requirements you have promised users.
7. Prepare the feature for real users
Once the narrow request works, extend it deliberately. These are general engineering considerations, not a platform-specific production checklist:
- Validate inputs: Reject missing, malformed, or out-of-scope input before making a request.
- Design the user experience: Show progress when a request takes time, explain failures clearly, and give users a way to correct or retry input where appropriate.
- Monitor behavior: Track application-level failures and latency without logging secrets or collecting more user data than necessary.
- Control usage: Set limits appropriate to your app and monitor API usage so unexpected traffic does not go unnoticed.
- Deploy securely: Configure the key and other secrets in the server environment or key-management service used by your deployment; never bundle them with client assets.
There is no single frontend framework or hosting provider prescribed by the cited quickstart. Choose a stack that fits your app, while preserving the server-side boundary for the API key.
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