How to Fix AI SDK Streaming in React Native: Complete Guide 2026

Want to build a ChatGPT-like experience in your React Native app? You're in the right place!

This guide will help you add real-time AI chat streaming to your React Native app. If you've tried using the Vercel AI SDK's useChat hook in React Native, you've probably seen this error:

Error: The response body is empty.

What's happening? Your backend is sending data correctly, but React Native can't read it because it doesn't support streaming the same way web browsers do. Don't worry - we'll fix this together!

What you'll learn:

No prior experience needed! We'll explain everything step-by-step. Click any item above to jump directly to that section! 🎯

Why React Native Streaming Fails (Simple Explanation)

The Problem: In web browsers, when you make a request, you can receive data piece by piece as it arrives (this is called "streaming"). React Native's fetch function doesn't support this - it waits for ALL the data to arrive before giving it to you.

What this means: Instead of seeing AI responses appear word-by-word like ChatGPT, you'd have to wait for the entire response to finish before seeing anything.

The Solution: React Native has an older technology called XMLHttpRequest (often shortened to "XHR") that CAN receive data as it arrives. We'll use that instead!

Think of it like this:

• fetch = Waiting for a full letter to arrive before reading it
• XMLHttpRequest = Reading the letter as it's being written, word by word

Here's what happens when you try to use fetch in React Native:

const response = await fetch(url, options);
console.log(response.body); // null in React Native! ❌
// In web browsers, this would be a stream, but React Native returns null

The good news? XMLHttpRequest works perfectly for streaming in React Native! We'll use it to create a solution that works just like ChatGPT.

Latest Versions (Updated January 2026)

This guide has been tested with the following versions:

• React Native: 0.83.0 (stable, December 2025)
• React: 19.2.0 (bundled with React Native 0.83)
• Node.js: 20.x LTS or higher (required for React Native 0.81+)
• AI SDK (ai): ^6.0.40
• @ai-sdk/react: ^3.0.42
• web-streams-polyfill: ^4.2.0
• @stardazed/streams-text-encoding: ^1.0.2

Note: These versions are current as of January 2026. Always check the official npm pages for the most up-to-date versions before installing:

• ai package
• @ai-sdk/react
• web-streams-polyfill
• @stardazed/streams-text-encoding

Prerequisites (What You Need)

Before we start, make sure you have:

1. A React Native project - If you don't have one yet, create it with:

   npx react-native@latest init MyApp

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   We tested with React Native 0.83.0, but 0.81+ should work.

2. Node.js installed - Version 20 or higher. Check your version:

   node --version

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   If you need to update, download from nodejs.org

3. Basic React knowledge - You should know how to:

   • Create components
   • Use hooks like useState
   • Import and use functions

4. A backend API - Your server needs to send data in "Server-Sent Events" (SSE) format. Don't worry - we'll show you how to set this up too!

Don't have a backend yet? That's okay! We'll show you how to create one in the "Backend Setup" section below.

Solution Overview (What We're Building)

In simple terms: We're creating a "bridge" that connects React Native to the AI SDK. This bridge uses XMLHttpRequest (which works in React Native) instead of fetch (which doesn't).

Here's what we'll do:

1. Install packages - Get the tools we need
2. Add polyfills - Give React Native the ability to handle streams (think of it as adding missing features)
3. Create a custom transport - This is our "bridge" that makes everything work
4. Use it in your app - Connect it to your chat screen

What is a "transport"? It's just a fancy word for "how data gets from your app to the server and back." We're replacing the default transport (which doesn't work) with one that does!

Don't worry if this sounds complex - we'll walk through each step with code examples. You can copy and paste most of it!

Step 1: Install Required Packages

What we're doing: Installing the tools we need. Think of this like getting ingredients before cooking.

Open your terminal in your project folder and run:

npm install ai@latest @ai-sdk/react@latest web-streams-polyfill@latest @stardazed/streams-text-encoding@latest

What each package does (in simple terms):

• ai - The main AI SDK that talks to AI models (like GPT-4)
• @ai-sdk/react - React hooks that make it easy to use AI in your components (like useChat)
• web-streams-polyfill - Adds missing "stream" features to React Native (a "polyfill" adds features that are missing)
• @stardazed/streams-text-encoding - Helps handle text encoding for streams

What's a polyfill? It's code that adds features your environment doesn't have. Like adding a missing tool to your toolbox!

💡 Tip: If you get any errors, make sure you're in your project's root folder (where package.json is located).

Step 2: Set Up Stream Polyfills

What we're doing: We're adding the missing "stream" features to React Native. This file needs to run FIRST, before anything else in your app.

Why? React Native doesn't have built-in support for streams (the technology that lets us receive data piece by piece). We're adding it ourselves!

Create a new file called polyfills.js in your project root (same folder as package.json):

// This file adds streaming support to React Native
import { Platform } from 'react-native';
import {
  TransformStream,
  ReadableStream,
  WritableStream,
} from 'web-streams-polyfill';
 
// Only run this on mobile (not on web, where it's already supported)
if (Platform.OS !== 'web') {
  const setupPolyfills = async () => {
    // Get React Native's function for adding global features
    const { polyfillGlobal } = await import(
      'react-native/Libraries/Utilities/PolyfillFunctions'
    );
 
    // Get text encoding tools
    const { TextEncoderStream, TextDecoderStream } = await import(
      '@stardazed/streams-text-encoding'
    );
 
    // Add ReadableStream if it doesn't exist
    if (!('ReadableStream' in global)) {
      polyfillGlobal('ReadableStream', () => ReadableStream);
    }
 
    // Add WritableStream if it doesn't exist
    if (!('WritableStream' in global)) {
      polyfillGlobal('WritableStream', () => WritableStream);
    }
 
    // Add TransformStream if it doesn't exist
    if (!('TransformStream' in global)) {
      polyfillGlobal('TransformStream', () => TransformStream);
    }
 
    // Add text encoding streams
    polyfillGlobal('TextEncoderStream', () => TextEncoderStream);
    polyfillGlobal('TextDecoderStream', () => TextDecoderStream);
  };
 
  // Run the setup
  setupPolyfills();
}
 
export {};

What this code does:

• Checks if we're on mobile (not web)
• Adds missing stream features to React Native
• Makes them available globally so the AI SDK can use them

Important: Import this file at the VERY TOP of your index.js (or index.tsx). It MUST be the first import!

Open your index.js file and make sure it looks like this:

// ⚠️ THIS MUST BE FIRST! Don't put anything above this line
import './polyfills';
 
// Now your other imports
import { AppRegistry } from 'react-native';
import App from './App';
import { name as appName } from './app.json';
 
AppRegistry.registerComponent(appName, () => App);

Why first? The polyfills need to be loaded before any other code tries to use streams. Think of it like setting up the foundation before building a house!

Step 3: Create the Custom Transport (The Magic Bridge!)

What we're doing: Creating a "transport" class that uses XMLHttpRequest instead of fetch. This is the core of our solution - it's what makes streaming work!

Don't worry about understanding every line - you can copy this code and it will work. We'll explain the important parts.

Create a new file: src/api/ReactNativeChatTransport.ts (create the src/api folder if it doesn't exist)

import { Platform } from 'react-native';
import type { 
  ChatTransport, 
  UIMessage, 
  UIMessageChunk, 
  ChatRequestOptions 
} from 'ai';
 
interface TransportOptions {
  api: string;
  headers?: () => Promise<Record<string, string>>;
  prepareBody?: (options: {
    messages: UIMessage[];
    trigger: 'submit-message' | 'regenerate-message';
    messageId?: string;
  }) => object;
}
 
// Helper function: Parse Server-Sent Events (SSE) format
// SSE format looks like: "data: {json data here}"
function parseSSELine(line: string): UIMessageChunk | null {
  // Check if this line starts with "data: "
  if (!line.startsWith('data: ')) return null;
  
  // Extract the JSON part (everything after "data: ")
  const data = line.slice(6).trim();
  
  // If we see "[DONE]", the stream is finished
  if (data === '[DONE]') return null;
  
  // Try to parse the JSON data
  try {
    return JSON.parse(data);
  } catch {
    // If parsing fails, ignore this line
    return null;
  }
}
 
// This function creates a stream using XMLHttpRequest
// This is the KEY function that makes streaming work in React Native!
function createXHRStream(
  url: string,                    // Your API endpoint
  headers: Record<string, string>, // Request headers (like auth tokens)
  body: string,                   // Request body (the messages)
): ReadableStream<UIMessageChunk> {
  let xhr: XMLHttpRequest | null = null;
  let lastIndex = 0;  // Track where we last read from
  let buffer = '';    // Store incomplete lines
 
  return new ReadableStream({
    start(controller) {
      // Create a new XMLHttpRequest
      const xhrInstance = new XMLHttpRequest();
      xhr = xhrInstance;
      
      // Open a POST request to the URL
      xhrInstance.open('POST', url);
 
      // Add all the headers (like Content-Type, Authorization, etc.)
      Object.entries(headers).forEach(([key, value]) => {
        xhrInstance.setRequestHeader(key, value);
      });
 
      // 🎯 THIS IS THE MAGIC! onprogress fires as data arrives
      // Unlike fetch, this gives us data piece by piece!
      xhrInstance.onprogress = () => {
        // Get only the NEW data since last time
        const newData = xhrInstance.responseText.slice(lastIndex);
        lastIndex = xhrInstance.responseText.length;
        
        // Add new data to our buffer
        buffer += newData;
 
        // Split into lines (SSE format uses newlines)
        const lines = buffer.split('\n');
        // Keep the last incomplete line in the buffer
        buffer = lines.pop() || '';
 
        // Process each complete line
        for (const line of lines) {
          const chunk = parseSSELine(line.trim());
          if (chunk) {
            // Send this chunk to the AI SDK
            controller.enqueue(chunk);
          }
        }
      };
 
      // When the request finishes
      xhrInstance.onload = () => {
        // Process any remaining data in the buffer
        if (buffer.trim()) {
          const chunk = parseSSELine(buffer.trim());
          if (chunk) controller.enqueue(chunk);
        }
        // Close the stream
        controller.close();
      };
 
      // Handle errors
      xhrInstance.onerror = () => controller.error(new Error('Network error'));
      xhrInstance.onabort = () => controller.close();
      
      // Actually send the request
      xhrInstance.send(body);
    },
    // Allow canceling the request
    cancel() {
      xhr?.abort();
    },
  });
}
 
export class ReactNativeChatTransport implements ChatTransport<UIMessage> {
  private api: string;
  private getHeaders?: () => Promise<Record<string, string>>;
  private prepareBody?: TransportOptions['prepareBody'];
 
  constructor(options: TransportOptions) {
    this.api = options.api;
    this.getHeaders = options.headers;
    this.prepareBody = options.prepareBody;
  }
 
  async sendMessages(options: {
    trigger: 'submit-message' | 'regenerate-message';
    chatId: string;
    messageId?: string;
    messages: UIMessage[];
    abortSignal?: AbortSignal;
  } & ChatRequestOptions): Promise<ReadableStream<UIMessageChunk>> {
    
    // Get custom headers
    const customHeaders = this.getHeaders ? await this.getHeaders() : {};
 
    const headers: Record<string, string> = {
      'Content-Type': 'application/json',
      'Accept': 'text/event-stream',
      'X-Chat-Id': options.chatId,
      ...customHeaders,
    };
 
    // Prepare request body
    const body = this.prepareBody
      ? this.prepareBody({
          messages: options.messages,
          trigger: options.trigger,
          messageId: options.messageId,
        })
      : {
          messages: options.messages,
          trigger: options.trigger,
        };
 
    // Web uses fetch, React Native uses XHR
    if (Platform.OS === 'web') {
      return this.fetchStream(headers, body);
    }
    
    return createXHRStream(this.api, headers, JSON.stringify(body));
  }
 
  private async fetchStream(
    headers: Record<string, string>,
    body: object,
  ): Promise<ReadableStream<UIMessageChunk>> {
    const response = await fetch(this.api, {
      method: 'POST',
      headers,
      body: JSON.stringify(body),
      credentials: 'include',
    });
 
    if (!response.ok || !response.body) {
      throw new Error('Failed to fetch');
    }
 
    const reader = response.body.getReader();
    const decoder = new TextDecoder();
    let buffer = '';
 
    return new ReadableStream({
      async pull(controller) {
        const { done, value } = await reader.read();
        if (done) {
          controller.close();
          return;
        }
 
        buffer += decoder.decode(value, { stream: true });
        const lines = buffer.split('\n');
        buffer = lines.pop() || '';
 
        for (const line of lines) {
          const chunk = parseSSELine(line.trim());
          if (chunk) controller.enqueue(chunk);
        }
      },
    });
  }
 
  async reconnectToStream(): Promise<ReadableStream<UIMessageChunk> | null> {
    return null;
  }
}

Step 4: Use It in Your Chat Screen

What we're doing: Connecting our custom transport to a React component so you can actually use it in your app!

This is a complete example of a chat screen. You can copy this and customize it to match your app's design:

import React, { useMemo, useState } from 'react';
import { View, Text, TextInput, TouchableOpacity, FlatList } from 'react-native';
import { useChat } from '@ai-sdk/react';
import { ReactNativeChatTransport } from '../api/ReactNativeChatTransport';
 
export function ChatScreen() {
  // Store what the user is typing
  const [input, setInput] = useState('');
 
  // Create the transport (this connects to your API)
  // useMemo ensures we only create it once
  const transport = useMemo(
    () =>
      new ReactNativeChatTransport({
        // ⚠️ Replace this with YOUR API URL!
        api: 'https://your-api.com/api/chat',
        headers: async () => {
          // If you need authentication, add it here
          // For now, we'll leave it empty (remove this if you don't need auth)
          const token = await getAuthToken(); // You'll need to implement this
          return {
            Authorization: `Bearer ${token}`,
          };
        },
      }),
    [], // Empty array = only create once
  );
 
  // The useChat hook handles all the chat logic!
  const { messages, sendMessage, status, error } = useChat({
    transport,        // Use our custom transport
    messages: [],      // Start with no messages
    onError: (err) => console.error('Chat error:', err),
  });
 
  // Handle when user presses send
  const handleSend = () => {
    if (!input.trim()) return; // Don't send empty messages
    sendMessage({ text: input });
    setInput(''); // Clear the input
  };
 
  return (
    <View style={{ flex: 1 }}>
      {/* List of messages */}
      <FlatList
        data={messages}
        keyExtractor={(item) => item.id}
        renderItem={({ item }) => (
          <View style={{ padding: 10 }}>
            <Text style={{ fontWeight: 'bold' }}>
              {item.role === 'user' ? 'You' : 'AI'}
            </Text>
            {/* Display each part of the message */}
            {item.parts.map((part, i) =>
              part.type === 'text' ? <Text key={i}>{part.text}</Text> : null
            )}
          </View>
        )}
      />
      
      {/* Show "AI is typing..." when streaming */}
      {status === 'streaming' && <Text>AI is typing...</Text>}
      
      {/* Input area */}
      <View style={{ flexDirection: 'row', padding: 10 }}>
        <TextInput
          style={{ flex: 1, borderWidth: 1, padding: 10, borderRadius: 8 }}
          value={input}
          onChangeText={setInput}
          placeholder="Type a message..."
        />
        <TouchableOpacity
          style={{ padding: 10, backgroundColor: '#007AFF', borderRadius: 8, marginLeft: 8 }}
          onPress={handleSend}
        >
          <Text style={{ color: 'white' }}>Send</Text>
        </TouchableOpacity>
      </View>
    </View>
  );
}

Key things to customize:

1. Replace the API URL - Change 'https://your-api.com/api/chat' to your actual backend URL
2. Add authentication - If your API needs auth tokens, implement getAuthToken() or remove the headers function if you don't need auth
3. Style it - Customize the colors, fonts, and layout to match your app!

Adding Authentication (Optional)

When you need this: If your backend requires authentication (like API keys or user tokens), you'll need to add headers to your requests.

How it works: The transport accepts a function that returns headers. This function runs every time you send a message, so you can get fresh tokens.

Here's how to add authentication:

new ReactNativeChatTransport({
  api: 'https://api.example.com/chat',
  headers: async () => {
    const token = await AsyncStorage.getItem('auth_token');
    const cookies = await getCookiesFromStorage();
    
    return {
      'Authorization': `Bearer ${token}`,
      'Cookie': cookies,
      'x-rn-origin': 'myapp://',
    };
  },
});

Backend Setup: Creating Your API

Don't have a backend yet? No problem! Here's how to create one.

What is SSE? Server-Sent Events (SSE) is a format for sending data from server to client. It's perfect for streaming because data arrives piece by piece.

Your backend needs to:

1. Accept POST requests at /api/chat
2. Send responses in SSE format
3. Stream the AI's response word by word

Here's a complete example using Node.js/Express with the Vercel AI SDK:

// Install these on your backend:
// npm install ai @ai-sdk/openai express
 
import express from 'express';
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';
 
const app = express();
app.use(express.json()); // Parse JSON request bodies
 
// Your chat endpoint
app.post('/api/chat', async (req, res) => {
  const { messages } = req.body;
 
  // Create a streaming response
  const result = streamText({
    model: openai('gpt-4o'), // Change to 'gpt-4o-mini' for cheaper, or 'gpt-3.5-turbo'
    messages, // The conversation history
  });
 
  // Convert to SSE format and send it
  return result.toUIMessageStreamResponse();
});
 
app.listen(3000, () => {
  console.log('Server running on http://localhost:3000');
});

What this does:

• Receives messages from your React Native app
• Sends them to OpenAI (or another AI provider)
• Streams the response back in SSE format
• Your React Native app receives it word by word!

Setting up OpenAI:

1. Get an API key from platform.openai.com
2. Set it as an environment variable: OPENAI_API_KEY=your-key-here
3. The @ai-sdk/openai package will automatically use it

What the SSE format looks like:

data: {"type":"text-delta","delta":"Hello"}

data: {"type":"text-delta","delta":" World"}

data: {"type":"finish","finishReason":"stop"}

data: [DONE]

How It Works (Simple Explanation)

The Journey of a Message:

1. You type a message → Your React Native app sends it to the backend
2. Backend sends to AI → Your server forwards it to OpenAI (or another AI)
3. AI starts responding → The AI begins generating a response
4. Data arrives piece by piece → XMLHttpRequest's onprogress event fires each time new data arrives
5. We parse it → We extract the text from each chunk (SSE format: data: {...})
6. UI updates in real-time → The useChat hook automatically updates your screen as each word arrives

The Magic: Instead of waiting for the entire response, we process it word-by-word as it arrives. This is why you see text appearing in real-time, just like ChatGPT!

Think of it like:

• ❌ Without streaming: Wait 10 seconds, then see the whole response
• ✅ With streaming: See words appear one by one over 10 seconds (much better UX!)

Common Problems and How to Fix Them

Problem: "Response body is empty" Error

What it means: React Native can't read the streaming data.

Common causes:

1. Polyfills not imported first - This is the #1 mistake! They MUST be the first import.
2. Wrong transport - Make sure you're using ReactNativeChatTransport, not the default one.
3. Backend issue - Your backend might not be sending the right headers.

Fix #1: Check your imports

// ✅ CORRECT - polyfills are first!
import './polyfills';
import { AppRegistry } from 'react-native';
 
// ❌ WRONG - polyfills are after other imports
import { AppRegistry } from 'react-native';
import './polyfills'; // Too late! This won't work

Fix #2: Verify your transport Make sure you're using ReactNativeChatTransport in your component, not the default transport.

Problem: Text Appears All at Once (Not Streaming)

What it means: Instead of seeing words appear one by one, the entire response shows up at once.

Common causes:

1. Backend is buffering - Your server might be waiting to send everything at once
2. CDN/Proxy buffering - Services like Cloudflare might be caching/buffering
3. Testing on web - Make sure you're testing on iOS/Android, not in a web browser

How to test your backend: Open your terminal and run:

curl -N -H "Accept: text/event-stream" https://your-api.com/api/chat

What to look for:

• ✅ Good: You see text appearing line by line over time
• ❌ Bad: Nothing appears, then everything appears at once

If it's bad: The problem is on your backend. Make sure:

• Your backend is using streamText from the AI SDK
• You're not using compression middleware that buffers responses
• You're returning result.toUIMessageStreamResponse() (not waiting for the full response)

Problem: Network Errors

What to check:

1. CORS (Cross-Origin Resource Sharing) - If you see CORS errors, your backend needs to allow requests from your app. Add this to your backend:

   app.use(cors({
     origin: '*', // In production, use your app's specific origin
   }));

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2. Wrong API URL - Double-check the URL in your ReactNativeChatTransport. Make sure it's:

   • The correct address
   • Accessible from your device/emulator (use your computer's IP address, not localhost)

3. Authentication - If your API requires auth, make sure you're sending the token correctly in the headers function.

Problem: App Feels Slow or Freezes

Possible causes:

• Too many re-renders - The useChat hook should handle this, but check React DevTools
• Large responses - Very long AI responses might use a lot of memory
• Network issues - Slow internet can make streaming feel laggy

Quick fixes:

• Test on a faster network
• Check if the issue happens with shorter messages
• Make sure you're not doing heavy work in the render function

Frequently Asked Questions (FAQ)

Can I use this with Expo?

Yes! This solution works with both Expo and React Native CLI. Just make sure to import the polyfills in your index.js or App.js entry point.

Does this work with other AI providers?

Absolutely. This solution works with any AI provider that supports Server-Sent Events streaming, including:

• OpenAI (GPT-4o, GPT-4o-mini, GPT-4-turbo, GPT-3.5-turbo) - via @ai-sdk/openai
• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus) - via @ai-sdk/anthropic
• Google (Gemini Pro, Gemini Flash) - via @ai-sdk/google
• Mistral - via @ai-sdk/mistral
• Cohere - via @ai-sdk/cohere
• Any provider compatible with the Vercel AI SDK

Note: Always check the Vercel AI SDK documentation and respective provider npm packages for the latest supported models and versions.

Can I use this for non-chat streaming?

Yes! The same pattern works for any streaming use case. You can adapt the ReactNativeChatTransport for:

• Real-time data visualization
• Live transcription
• Progressive file uploads
• Any streaming API endpoint

Is there a performance impact?

The XHR approach is actually more efficient for streaming in React Native than trying to use fetch polyfills. You'll see:

• Lower memory usage (data processed incrementally)
• Better user experience (real-time updates)
• More reliable connection handling

Can I use this with TypeScript?

Yes, the code examples are already in TypeScript. The custom transport is fully typed and works seamlessly with TypeScript projects.

Summary: What We Built

The Problem: React Native's fetch doesn't support streaming, so AI responses couldn't appear in real-time.

The Solution: We created a custom transport using XMLHttpRequest (which DOES support streaming) and connected it to the AI SDK.

What You Can Do Now:

• ✅ Build ChatGPT-like experiences in React Native
• ✅ See AI responses appear word-by-word in real-time
• ✅ Use authentication and custom headers
• ✅ Works on both iOS and Android

Remember:

• Polyfills must be imported FIRST in index.js
• Use ReactNativeChatTransport (not the default)
• Your backend needs to send SSE format responses

You're all set! 🎉 You now have everything you need to build amazing AI chat features in React Native.

Next Steps

Now that you have streaming working, consider:

• Adding error retry logic for failed requests
• Implementing message persistence with AsyncStorage
• Adding typing indicators and loading states
• Optimizing for large conversations with message pagination
• Adding support for file attachments and multimodal inputs

Keeping Dependencies Updated

To ensure you're using the latest versions and security patches:

1. Check for updates regularly:

   npm outdated

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2. Update packages safely:

   npm update ai @ai-sdk/react web-streams-polyfill @stardazed/streams-text-encoding

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3. Check official sources:

   • Vercel AI SDK Documentation - Latest features and breaking changes
   • React Native Releases - React Native version history
   • Node.js Releases - Node.js LTS schedule

4. Read changelogs before major version updates to check for breaking changes