---
title: Streaming responses from LLMs
description: Learn how to use the AI SDK to stream LLM responses.
url: "https://vercel.com/kb/guide/streaming-from-llm"
published: 2025-11-03
last_updated: 2026-07-29
authors: DX Team
install_vercel_plugin: npx plugins add vercel/vercel-plugin
---

AI providers can be slow when producing responses, but many make their responses available in chunks as they're processed. Streaming enables you to show users those chunks of data as they arrive rather than waiting for the full response, improving the perceived speed of AI-powered apps.

**You can use** [**Vercel's AI SDK**](https://sdk.vercel.ai/docs) **to stream responses from LLMs and AI APIs**. It reduces the boilerplate necessary for streaming responses from AI providers and allows you to change AI providers with a few lines of code, rather than rewriting your entire application.

This example demonstrates a function that sends a message to one of OpenAI's GPT models and streams the response:

## Recipe

Before you begin, ensure you're using Node.js 18 or later.

1. Install the `ai` and `@ai-sdk/openai` packages:
   

```bash
pnpm install ai @ai-sdk/openai
```

1. Copy an OpenAI API key in the `.env.local` file with name `OPENAI_API_KEY`. See the [AI SDK docs](https://sdk.vercel.ai/docs/getting-started#configure-openai-api-key) for more information on how to do this
   
2. Add the following code to your example
   

```typescript
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// This method must be named GET
export async function GET() {
  // Make a request to OpenAI's API based on
  // a placeholder prompt
  const response = streamText({
    model: openai('gpt-4o-mini'),
    messages: [{ role: 'user', content: 'Say this is a test.' }],
  });
  // Respond with the stream
  return response.toTextStreamResponse({
    headers: {
      'Content-Type': 'text/event-stream',
    },
  });
}
```
```javascript
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// This method must be named GET
export async function GET() {
  // Make a request to OpenAI's API based on
  // a placeholder prompt
  const response = streamText({
    model: openai('gpt-4o-mini'),
    messages: [{ role: 'user', content: 'Say this is a test.' }],
  });
  // Respond with the stream
  return response.toTextStreamResponse({
    headers: {
      'Content-Type': 'text/event-stream',
    },
  });
}
```
```typescript
// Streaming Functions must be defined in an
// app directory, even if the rest of your app
// is in the pages directory.
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// This method must be named GET
export async function GET() {
  // Make a request to OpenAI's API based on
  // a placeholder prompt
  const response = streamText({
    model: openai('gpt-4o-mini'),
    messages: [{ role: 'user', content: 'Say this is a test.' }],
  });
  // Respond with the stream
  return response.toTextStreamResponse({
    headers: {
      'Content-Type': 'text/event-stream',
    },
  });
}
```
```javascript
// Streaming Functions must be defined in an
// app directory, even if the rest of your app
// is in the pages directory.
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// This method must be named GET
export async function GET() {
  // Make a request to OpenAI's API based on
  // a placeholder prompt
  const response = streamText({
    model: openai('gpt-4o-mini'),
    messages: [{ role: 'user', content: 'Say this is a test.' }],
  });
  // Respond with the stream
  return response.toTextStreamResponse({
    headers: {
      'Content-Type': 'text/event-stream',
    },
  });
}
```
```typescript
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// This method must be named GET
export async function GET() {
  // Make a request to OpenAI's API based on
  // a placeholder prompt
  const response = streamText({
    model: openai('gpt-4o-mini'),
    messages: [{ role: 'user', content: 'Say this is a test.' }],
  });
  // Respond with the stream
  return response.toTextStreamResponse({
    headers: {
      'Content-Type': 'text/event-stream',
    },
  });
}
```
```javascript
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// This method must be named GET
export async function GET() {
  // Make a request to OpenAI's API based on
  // a placeholder prompt
  const response = streamText({
    model: openai('gpt-4o-mini'),
    messages: [{ role: 'user', content: 'Say this is a test.' }],
  });
  // Respond with the stream
  return response.toTextStreamResponse({
    headers: {
      'Content-Type': 'text/event-stream',
    },
  });
}
```

1. Build your app and visit `localhost:3000/api/chat-example`. You should see the text `"This is a test."` in the browser.
   

## More resources

- [Streaming on Vercel](/docs/fundamentals/what-is-streaming)
  
- [Vercel AI SDK](https://sdk.vercel.ai/docs)

## More AI SDK guides

- [Build an AI Chat Agent with Weather API Tool Calling](/kb/guide/build-ai-agent-weather-api): Build an intelligent conversational agent that fetches real-time weather data using the AI SDK, tool calling, and a backend weather API powered by Express, FastAPI or Nitro
- [Ship a GitHub code review bot with Hono and Redis](/kb/guide/ship-a-github-code-review-bot-with-hono-and-redis): This guide walks through building a GitHub bot that reviews pull requests on demand. When a user @mentions the bot on a PR, Chat SDK picks up the mention, spins up a Vercel Sandbox with the repo cloned, and uses AI SDK to analyze the diff.
- [Building community moderation agents using AI SDK](/kb/guide/building-community-moderation-agents-using-ai-sdk): Learn how to build an AI-powered community moderation agent with Next.js, the AI SDK, and AI Gateway.