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Stream Protocols

AI SDK UI functions such as useChat and useCompletion support both text streams and data streams. The stream protocol defines how the data is streamed to the frontend on top of the HTTP protocol. This page describes both protocols and how to use them in the backend and frontend. You can use this information to develop custom backends and frontends for your use case, e.g., to provide compatible API endpoints that are implemented in a different language such as Python. For instance, here’s an example using FastAPI as a backend.

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Text Stream Protocol

A text stream contains chunks in plain text, that are streamed to the frontend. Each chunk is then appended together to form a full text response. Text streams are supported by useChat, useCompletion, and useObject. When you use useChat or useCompletion, you need to enable text streaming by setting the streamProtocol options to text. You can generate text streams with streamText in the backend. When you call toTextStreamResponse() on the result object, a streaming HTTP response is returned.
Text streams only support basic text data. If you need to stream other types of data such as tool calls, use data streams.

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Text Stream Example

Here is a Next.js example that uses the text stream protocol: 
'use client';

import { useChat } from '@ai-sdk/react';
import { TextStreamChatTransport } from 'ai';
import { useState } from 'react';

export default function Chat() {
  const [input, setInput] = useState('');
  const { messages, sendMessage } = useChat({
    transport: new TextStreamChatTransport({ api: '/api/chat' }),
  });

  return (
    <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
      {messages.map(message => (
        <div key={message.id} className="whitespace-pre-wrap">
          {message.role === 'user' ? 'User: ' : 'AI: '}
          {message.parts.map((part, i) => {
            switch (part.type) {
              case 'text':
                return <div key={`${message.id}-${i}`}>{part.text}</div>;
            }
          })}
        </div>
      ))}

      <form
        onSubmit={e => {
          e.preventDefault();
          sendMessage({ text: input });
          setInput('');
        }}
      >
        <input
          className="fixed dark:bg-zinc-900 bottom-0 w-full max-w-md p-2 mb-8 border border-zinc-300 dark:border-zinc-800 rounded shadow-xl"
          value={input}
          placeholder="Say something..."
          onChange={e => setInput(e.currentTarget.value)}
        />
      </form>
    </div>
  );
}

import { streamText, UIMessage, convertToModelMessages } from 'ai';
import { openai } from '@ai-sdk/openai';

// Allow streaming responses up to 30 seconds
export const maxDuration = 30;

export async function POST(req: Request) {
  const { messages }: { messages: UIMessage[] } = await req.json();

  const result = streamText({
    model: openai('gpt-4-turbo'),
    messages: await convertToModelMessages(messages),
  });

  return result.toTextStreamResponse();
}

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Data Stream Protocol

A data stream follows a special protocol that the AI SDK provides to send information to the frontend. The data stream protocol uses Server-Sent Events (SSE) format for improved standardization, keep-alive through ping, reconnect capabilities, and better cache handling.
When you provide data streams from a custom backend, you need to set the x-vercel-ai-ui-message-stream header to v1.
The following stream parts are currently supported:

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Message Start Part

Indicates the beginning of a new message with metadata. Format: Server-Sent Event with JSON object Example: 
data: {"type":"start","messageId":"..."}

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Text Parts

Text content is streamed using a start/delta/end pattern with unique IDs for each text block.

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Text Start Part

Indicates the beginning of a text block. Format: Server-Sent Event with JSON object Example: 
data: {"type":"text-start","id":"msg_68679a454370819ca74c8eb3d04379630dd1afb72306ca5d"}

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Text Delta Part

Contains incremental text content for the text block. Format: Server-Sent Event with JSON object Example: 
data: {"type":"text-delta","id":"msg_68679a454370819ca74c8eb3d04379630dd1afb72306ca5d","delta":"Hello"}

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Text End Part

Indicates the completion of a text block. Format: Server-Sent Event with JSON object Example: 
data: {"type":"text-end","id":"msg_68679a454370819ca74c8eb3d04379630dd1afb72306ca5d"}

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Reasoning Parts

Reasoning content is streamed using a start/delta/end pattern with unique IDs for each reasoning block.

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Reasoning Start Part

Indicates the beginning of a reasoning block. Format: Server-Sent Event with JSON object Example: 
data: {"type":"reasoning-start","id":"reasoning_123"}

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Reasoning Delta Part

Contains incremental reasoning content for the reasoning block. Format: Server-Sent Event with JSON object Example: 
data: {"type":"reasoning-delta","id":"reasoning_123","delta":"This is some reasoning"}

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Reasoning End Part

Indicates the completion of a reasoning block. Format: Server-Sent Event with JSON object Example: 
data: {"type":"reasoning-end","id":"reasoning_123"}

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Source Parts

Source parts provide references to external content sources.

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Source URL Part

References to external URLs. Format: Server-Sent Event with JSON object Example: 
data: {"type":"source-url","sourceId":"https://example.com","url":"https://example.com"}

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Source Document Part

References to documents or files. Format: Server-Sent Event with JSON object Example: 
data: {"type":"source-document","sourceId":"https://example.com","mediaType":"file","title":"Title"}

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File Part

The file parts contain references to files with their media type. Format: Server-Sent Event with JSON object Example: 
data: {"type":"file","url":"https://example.com/file.png","mediaType":"image/png"}

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Data Parts

Custom data parts allow streaming of arbitrary structured data with type-specific handling. Format: Server-Sent Event with JSON object where the type includes a custom suffix Example: 
data: {"type":"data-weather","data":{"location":"SF","temperature":100}}
The data-* type pattern allows you to define custom data types that your frontend can handle specifically.

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Error Part

The error parts are appended to the message as they are received. Format: Server-Sent Event with JSON object Example: 
data: {"type":"error","errorText":"error message"}

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Tool Input Start Part

Indicates the beginning of tool input streaming. Format: Server-Sent Event with JSON object Example: 
data: {"type":"tool-input-start","toolCallId":"call_fJdQDqnXeGxTmr4E3YPSR7Ar","toolName":"getWeatherInformation"}

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Tool Input Delta Part

Incremental chunks of tool input as it’s being generated. Format: Server-Sent Event with JSON object Example: 
data: {"type":"tool-input-delta","toolCallId":"call_fJdQDqnXeGxTmr4E3YPSR7Ar","inputTextDelta":"San Francisco"}

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Tool Input Available Part

Indicates that tool input is complete and ready for execution. Format: Server-Sent Event with JSON object Example: 
data: {"type":"tool-input-available","toolCallId":"call_fJdQDqnXeGxTmr4E3YPSR7Ar","toolName":"getWeatherInformation","input":{"city":"San Francisco"}}

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Tool Output Available Part

Contains the result of tool execution. Format: Server-Sent Event with JSON object Example: 
data: {"type":"tool-output-available","toolCallId":"call_fJdQDqnXeGxTmr4E3YPSR7Ar","output":{"city":"San Francisco","weather":"sunny"}}

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Start Step Part

A part indicating the start of a step. Format: Server-Sent Event with JSON object Example: 
data: {"type":"start-step"}

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Finish Step Part

A part indicating that a step (i.e., one LLM API call in the backend) has been completed. This part is necessary to correctly process multiple stitched assistant calls, e.g. when calling tools in the backend, and using steps in useChat at the same time. Format: Server-Sent Event with JSON object Example: 
data: {"type":"finish-step"}

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Finish Message Part

A part indicating the completion of a message. Format: Server-Sent Event with JSON object Example: 
data: {"type":"finish"}

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Abort Part

Indicates the stream was aborted. Format: Server-Sent Event with JSON object Example: 
data: {"type":"abort","reason":"user cancelled"}

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Stream Termination

The stream ends with a special [DONE] marker. Format: Server-Sent Event with literal [DONE] Example: 
data: [DONE]
The data stream protocol is supported by useChat and useCompletion on the frontend and used by default. useCompletion only supports the text and data stream parts. On the backend, you can use toUIMessageStreamResponse() from the streamText result object to return a streaming HTTP response.

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UI Message Stream Example

Here is a Next.js example that uses the UI message stream protocol: 
'use client';

import { useChat } from '@ai-sdk/react';
import { useState } from 'react';

export default function Chat() {
  const [input, setInput] = useState('');
  const { messages, sendMessage } = useChat();

  return (
    <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
      {messages.map(message => (
        <div key={message.id} className="whitespace-pre-wrap">
          {message.role === 'user' ? 'User: ' : 'AI: '}
          {message.parts.map((part, i) => {
            switch (part.type) {
              case 'text':
                return <div key={`${message.id}-${i}`}>{part.text}</div>;
            }
          })}
        </div>
      ))}

      <form
        onSubmit={e => {
          e.preventDefault();
          sendMessage({ text: input });
          setInput('');
        }}
      >
        <input
          className="fixed dark:bg-zinc-900 bottom-0 w-full max-w-md p-2 mb-8 border border-zinc-300 dark:border-zinc-800 rounded shadow-xl"
          value={input}
          placeholder="Say something..."
          onChange={e => setInput(e.currentTarget.value)}
        />
      </form>
    </div>
  );
}

import { streamText, UIMessage, convertToModelMessages } from 'ai';
import { openai } from '@ai-sdk/openai';

// Allow streaming responses up to 30 seconds
export const maxDuration = 30;

export async function POST(req: Request) {
  const { messages }: { messages: UIMessage[] } = await req.json();

  const result = streamText({
    model: openai('gpt-4-turbo'),
    messages: await convertToModelMessages(messages),
  });

  return result.toUIMessageStreamResponse();
}