Is it safe to put my OpenAI API key in a React Native app?

No. A mobile binary can be decompiled and the key extracted, letting anyone run up your bill. Keep the key on your backend, which calls the model on the app's behalf and can enforce auth, rate limits and caching.

How do I stream AI responses in React Native?

Have your backend forward the model's token stream over Server-Sent Events or a streaming fetch, and append tokens to state in the app as they arrive so text appears word-by-word. Streaming is what makes an AI feature feel instant instead of frozen.

Which library should I use to add an LLM to React Native?

For cloud models, the Vercel AI SDK gives you provider-agnostic streaming and is a common choice; a plain fetch to your own endpoint also works. For on-device models, use react-native-executorch or Callstack's react-native-ai. Pick based on whether you need cloud capability or offline privacy.

Integrate ChatGPT & LLMs into a React Native App

Q: How do I add ChatGPT to a React Native app?

Build a small backend endpoint that holds your provider API key and calls the model, then have the React Native app call that endpoint and render the streamed response. You can start with a direct fetch or use a toolkit like the Vercel AI SDK; the key rule is that the model call goes through your server, never straight from the app.

TL;DR: Don't call the model from the app. Put your API key on a small backend proxy, call that from React Native, and stream the response token-by-token. That single pattern gives you secure keys, rate limiting, caching, provider portability, and a UI that feels alive. This is the integration layer of our wider guide to building AI-powered React Native apps.

The 30-second version

Every tutorial that has you paste an OpenAI key into a React Native file and fetch the model directly is showing you a demo, not a product. It works on your simulator and falls apart the moment real users install it. The production shape is barely more work and looks like this: the app sends a message to your server; your server adds the key, calls the model, and streams the answer back. That's it. Get this right and everything else — cost control, provider swaps, abuse protection — becomes a setting rather than a rewrite.

Rule #1: the key never lives in the app

This is the part people get wrong, so it's worth seeing both paths side by side. Toggle between them:

A mobile app ships to the user's device, and anything in it — including a "hidden" key — can be pulled out with standard tools. The proxy isn't bureaucracy; it's the thing standing between a leaked key and a five-figure surprise invoice. We unpack this and the other expensive mistakes in the pillar guide.

Stream the response, or it feels broken

A model can take several seconds to produce a full answer. Show nothing for those seconds and users assume the app froze; stream the tokens as they generate and the same wait feels instant. Streaming is the single biggest UX lever in an AI feature — here's the difference, live:

Streamed — tokens as they generate

On the backend you forward the model's stream over Server-Sent Events or a streaming fetch; in the app you append each chunk to state so the text grows in place. Add a typing indicator, handle partial responses, and time out gracefully. The model is half the experience — the other half is how honestly the UI represents waiting.

Provider and SDK choices

For cloud models the realistic shortlist is Anthropic's Claude and OpenAI's GPT family, with open-weight models as a self-hosted option. For the wiring itself you have three sensible routes:

A plain fetch to your own endpoint — the least magic, the most control, perfect for a first feature.
The Vercel AI SDK — provider-agnostic streaming and a clean message model; a common choice when you want to swap providers without rewriting client code.
A chat infrastructure SDK (e.g. Stream) — when you also need conversation history, presence and rich message rendering out of the box.

If your feature should work offline or must never send data off the device, that's a different architecture entirely — running the model on the phone with react-native-executorch or react-native-ai. We cover that in on-device AI in React Native.

Keeping the bill under control

Per-token spend is the cost that surprises teams, because it scales with context size and call volume rather than user count. The proxy you built for security is also where you control cost: right-size the model per request, cache repeat prompts and responses, trim the context you send, and set per-user rate limits. Because the app only ever talks to your endpoint, every one of those is a server change — no app-store release required.

A minimal, correct flow

// React Native (client) — talks ONLY to your backend
const res = await fetch("https://api.yourapp.com/chat", {
  method: "POST",
  headers: { "Authorization": `Bearer ${userToken}` },
  body: JSON.stringify({ message })
});
// read res.body as a stream and append chunks to state

// Your backend — holds the key, calls the model, streams back
// (auth check) -> (rate limit) -> (call LLM, stream=true) -> (pipe tokens to client)

That's the whole shape. The client never sees a provider key; the server owns capability, cost and safety. Everything fancier — retrieval, tools, voice — bolts onto this same backbone.

If your AI feature would break by open-sourcing the app, the key is in the wrong place.

Quick answers

How do I add ChatGPT to a React Native app? Build a backend endpoint that holds the key and calls the model, then call that endpoint from the app and render the streamed reply. Start with a direct fetch or the Vercel AI SDK.

Is it safe to put my API key in the app? No — it can be extracted from the binary. Keep it server-side, always.

How do I stream responses? Forward the model's token stream over SSE or a streaming fetch and append chunks to state as they arrive.

Which library? Vercel AI SDK or a plain fetch for cloud; react-native-executorch or react-native-ai for on-device.

Want this built properly the first time? We integrate LLMs into React Native apps end to end — secure proxy, streaming UI and cost controls included. Tell us what you're building.

Het Soni

Founder & Lead Engineer, Soni Consultancy Services

Integrating ChatGPT & LLMs into a React Native App