To build an AI-powered web app with OpenAI and Node.js, send the browser’s request to a Node.js backend, call the OpenAI Responses API there using the official JavaScript SDK, then return the result to the browser. For a more dynamic interface, stream the response from your server and render text as it arrives. Keep the API key on the server; never embed it in browser code.
How do I build an AI-powered web app with OpenAI and Node.js?
The request should travel through your application server: the browser sends user input to your backend, the backend calls OpenAI, and the backend returns the generated output. This keeps the secret credential out of code delivered to users. OpenAI’s Developer quickstart identifies its official TypeScript and JavaScript SDK as suitable for server-side JavaScript environments such as Node.js, Deno, and Bun.
1. Configure the API key on the server
Create an API key in your OpenAI account and make it available to the Node.js process as an environment variable. The quickstart demonstrates exporting the key as an environment variable and using it to configure the server-side SDK client. Do not put the key in frontend JavaScript, HTML, a public repository, or a request sent directly from the browser.
2. Install the official SDK
In your Node.js project, install the package with:
npm install openaiKeep the SDK call in server-side code. A minimal Express route can accept a prompt, call the Responses API, and return the generated text:
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import express from "express";
import OpenAI from "openai";
const app = express();
app.use(express.json());
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const model = process.env.OPENAI_MODEL;
app.post("/api/generate", async (req, res) => {
const prompt = req.body?.prompt;
if (typeof prompt !== "string" || prompt.trim() === "") {
return res.status(400).json({ error: "Enter a prompt." });
}
if (!model) {
return res.status(500).json({ error: "The server model is not configured." });
}
try {
const response = await client.responses.create({
model,
input: prompt
});
return res.json({ text: response.output_text });
} catch (error) {
// Log useful operational details, but never log the API key.
console.error("OpenAI request failed", error);
return res.status(502).json({ error: "The AI request could not be completed." });
}
});
app.listen(3000);Set OPENAI_API_KEY and OPENAI_MODEL in the server’s environment. Treat the model as configuration rather than assuming a documentation example will remain available: check current model availability for your account before deployment. This example uses the Responses API’s client.responses.create(...) method; consult the current quickstart for up-to-date SDK usage.
3. Call your backend from the browser
The browser posts the user’s prompt to your application route and renders the returned text. It does not need an OpenAI credential.
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async function generate(prompt) {
const response = await fetch("/api/generate", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ prompt })
});
const result = await response.json();
if (!response.ok) {
throw new Error(result.error || "The request failed.");
}
return result.text;
}In the UI, show a pending state while the request is in progress, display the answer on success, and give the user a clear error if the backend rejects the input or the upstream request fails. Avoid returning raw server exceptions to the browser; log operational details server-side without exposing credentials.
How do I show an OpenAI response as it is generated?
A regular request returns its generated output in one response, so the UI waits for the full result before displaying it. Streaming lets the application process output while generation continues. OpenAI documents Responses API streaming over server-sent events (SSE); its streaming guide describes JavaScript events including text deltas, completion, and errors.
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Stream from the Node.js route
For a streamed interface, your backend must keep the browser connection open, forward text as it arrives, and end or report failure when the stream finishes. The following illustrates the lifecycle using the SDK’s streamed Responses API. It forwards only text deltas as newline-delimited JSON; completion and error events are handled by the server rather than shown as answer text.
app.post("/api/generate-stream", async (req, res) => {
const prompt = req.body?.prompt;
if (typeof prompt !== "string" || prompt.trim() === "") {
return res.status(400).json({ error: "Enter a prompt." });
}
if (!model) {
return res.status(500).json({ error: "The server model is not configured." });
}
res.setHeader("Content-Type", "application/x-ndjson; charset=utf-8");
res.setHeader("Cache-Control", "no-cache, no-transform");
res.setHeader("X-Accel-Buffering", "no");
try {
const stream = await client.responses.create({
model,
input: prompt,
stream: true
});
for await (const event of stream) {
if (event.type === "response.output_text.delta") {
res.write(JSON.stringify({ type: "delta", text: event.delta }) + "n");
} else if (event.type === "response.completed") {
res.write(JSON.stringify({ type: "completed" }) + "n");
} else if (event.type === "error") {
throw new Error("The OpenAI response stream reported an error.");
}
}
res.end();
} catch (error) {
console.error("OpenAI stream failed", error);
if (!res.headersSent) {
return res.status(502).json({ error: "The AI request could not be completed." });
}
res.write(JSON.stringify({ type: "error", message: "The AI request could not be completed." }) + "n");
res.end();
}
});This route uses newline-delimited JSON as its browser-facing transport; OpenAI’s upstream stream is SSE, and the server translates the events it needs into its own response format. In production, also handle client disconnects and ensure your hosting setup does not buffer the stream. If you choose to forward SSE directly instead, preserve event boundaries and parse event types rather than treating the stream as plain text.
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Render deltas and distinguish stream states
The browser can read the response body incrementally. Append each delta to the displayed answer; treat completion as a state change, not as text. If the stream fails, stop the generating state and show an error without presenting the partial answer as complete.
async function generateStream(prompt, answerElement, setStatus) {
setStatus("generating");
answerElement.textContent = "";
try {
const response = await fetch("/api/generate-stream", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ prompt })
});
if (!response.ok || !response.body) {
throw new Error("The AI request could not be started.");
}
const reader = response.body.getReader();
const decoder = new TextDecoder();
let pending = "";
let completed = false;
while (true) {
const { value, done } = await reader.read();
pending += decoder.decode(value || new Uint8Array(), { stream: !done });
const lines = pending.split("n");
pending = lines.pop() || "";
for (const line of lines) {
if (!line) continue;
const event = JSON.parse(line);
if (event.type === "delta") answerElement.textContent += event.text;
if (event.type === "completed") completed = true;
if (event.type === "error") throw new Error(event.message);
}
if (done) break;
}
if (!completed) throw new Error("The response ended before completion.");
setStatus("complete");
} catch (error) {
setStatus("error");
// Show a concise message in the UI; retain details only where appropriate.
}
}The states are an application design choice derived from the stream’s delta, completed, and error events: show “generating” before the first output, render partial text as deltas arrive, mark the answer complete only on completion, and provide an error state when the request or stream fails.
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Should I use the Responses API or the Realtime API?
Use the API surface that matches the interaction. The API overview presents Responses for direct model requests and Realtime for low-latency sessions. Streaming a Responses API output makes text arrive incrementally, but it does not by itself turn a request into a persistent interactive session.
| Need | Responses API | Realtime API |
|---|---|---|
| Typical interaction | Direct model request, with either a complete response or streamed output. | Low-latency session behavior. |
| Common fit | Text generation and tool-enabled workflows; also supports multimodal input and stateful interactions. | Interactive voice or audio experiences and other use cases requiring low-latency sessions. |
| UI pattern | Wait for a result, or render incremental output from stream events. | Build around an ongoing session rather than a single request/response exchange. |
| Implementation considerations | A basic SDK request is the simpler starting point; streaming adds event and connection lifecycle handling. | Choose when session behavior and latency requirements justify a session-oriented implementation. |
For a conventional web form, assistant panel, or tool-enabled application, start with Responses and add streaming if showing partial text improves the experience. Choose Realtime when the product needs the low-latency session interaction described in the official documentation, such as interactive audio.
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