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Guide: Stream Vercel AI SDK responses using the message-per-response pattern

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This guide shows you how to stream AI responses from the Vercel AI SDK over Ably using the message-per-response pattern. Specifically, it appends each response token to a single Ably message, creating a complete AI response that grows incrementally while delivering tokens in realtime.

Using Ably to distribute tokens from the Vercel AI SDK enables you to broadcast AI responses to thousands of concurrent subscribers with reliable message delivery and ordering guarantees. This approach stores each complete response as a single message in channel history, making it easy to retrieve conversation history without processing thousands of individual token messages.

Prerequisites

To follow this guide, you need:

  • Node.js 20 or higher
  • A Vercel AI Gateway API key
  • An Ably API key

Useful links:

Create a new NPM package, which will contain the publisher and subscriber code:

mkdir ably-vercel-message-per-response && cd ably-vercel-message-per-response
npm init -y

Install the required packages using NPM:

npm install ai@^6 ably@^2

Export your Vercel AI Gateway API key to the environment, which will be used later in the guide by the Vercel AI SDK:

export AI_GATEWAY_API_KEY="your_api_key_here"

Step 1: Enable message appends

Message append functionality requires "Message annotations, updates, deletes and appends" to be enabled in a channel rule associated with the channel.

To enable the channel rule:

  1. Go to the Ably dashboard and select your app.
  2. Navigate to the "Configuration" > "Rules" section from the left-hand navigation bar.
  3. Choose "Add new rule".
  4. Enter a channel name or namespace pattern (e.g. ai for all channels starting with ai:).
  5. Select the "Message annotations, updates, deletes and appends" option from the list.
  6. Click "Create channel rule".

The examples in this guide use the ai: namespace prefix, which assumes you have configured the rule for ai:*.

Step 2: Get a streamed response from Vercel AI SDK

Initialize the Vercel AI SDK and use streamText to stream model output as a series of events.

Create a new file publisher.mjs with the following contents:

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import { streamText } from 'ai';

// Process each streaming event
async function processEvent(event) {
  console.log(JSON.stringify(event));
  // This function is updated in the next sections
}

// Create streaming response from Vercel AI SDK
async function streamVercelResponse(prompt) {
  const result = streamText({
    model: 'openai/gpt-4o',
    prompt: prompt,
  });

  // Iterate through streaming events using fullStream
  for await (const event of result.fullStream) {
    await processEvent(event);
  }
}

// Usage example
streamVercelResponse("Tell me a short joke");

Understand Vercel AI SDK streaming events

The Vercel AI SDK's streamText function provides a fullStream property that returns all stream events. Each event includes a type property which describes the event type. A complete text response can be constructed from the following event types:

  • text-start: Signals the start of a text response. Contains an id to correlate subsequent events.

  • text-delta: Contains a single text token in the text field. These events represent incremental text chunks as the model generates them.

  • text-end: Signals the completion of a text response.

The following example shows the event sequence received when streaming a response:

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// 1. Stream initialization
{"type":"start"}
{"type":"start-step","request":{...}}
{"type":"text-start","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","providerMetadata":{...}}

// 2. Text tokens stream in as delta events
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":"Why"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" don't"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" skeleton"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":"s"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" fight"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" each"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" other"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":"?\n\n"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":"They"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" don't"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" have"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" the"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":" guts"}
{"type":"text-delta","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","text":"!"}

// 3. Stream completion
{"type":"text-end","id":"msg_0cc4da489ab9d4d101696f97d7c9548196a04f71d10a3a4c99","providerMetadata":{...}}
{"type":"finish-step","finishReason":"stop","usage":{"inputTokens":12,"outputTokens":15,"totalTokens":27,"reasoningTokens":0,"cachedInputTokens":0},"providerMetadata":{...}}
{"type":"finish","finishReason":"stop","totalUsage":{"inputTokens":12,"outputTokens":15,"totalTokens":27,"reasoningTokens":0,"cachedInputTokens":0}}

Step 3: Publish streaming tokens to Ably

Publish Vercel AI SDK streaming events to Ably using message appends to reliably and scalably distribute them to subscribers.

Each AI response is stored as a single Ably message that grows as tokens are appended.

Initialize the Ably client

Add the Ably client initialization to your publisher.mjs file:

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import Ably from 'ably';

// Initialize Ably Realtime client
const realtime = new Ably.Realtime({
  key: 'demokey:*****',
  echoMessages: false
});

// Create a channel for publishing streamed AI responses
const channel = realtime.channels.get('ai:map-cod-cog');
API key:
DEMO ONLY

The Ably Realtime client maintains a persistent connection to the Ably service, which allows you to publish tokens at high message rates with low latency.

Publish initial message and append tokens

When a new response begins, publish an initial message to create it. Ably assigns a serial identifier to the message. Use this serial to append each token to the message as it arrives from the AI model.

Update your publisher.mjs file to publish the initial message and append tokens:

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// Track state across events
let msgSerial = null;

// Process each streaming event and publish to Ably
async function processEvent(event) {
  switch (event.type) {
    case 'text-start':
      // Publish initial empty message when response starts
      const result = await channel.publish({
        name: 'response',
        data: ''
      });

      // Capture the message serial for appending tokens
      msgSerial = result.serials[0];
      break;

    case 'text-delta':
      // Append each text token to the message
      if (msgSerial) {
        channel.appendMessage({
          serial: msgSerial,
          data: event.text
        });
      }
      break;

    case 'text-end':
      console.log('Stream completed!');
      break;
  }
}

This implementation:

  • Publishes an initial empty message when the response begins and captures the serial
  • Filters for text-delta events and appends each token to the original message
  • Logs completion when the text-end event is received

Run the publisher to see tokens streaming to Ably:

node publisher.mjs

Step 4: Subscribe to streaming tokens

Create a subscriber that receives the streaming tokens from Ably and reconstructs the response in realtime.

Create a new file subscriber.mjs with the following contents:

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import Ably from 'ably';

// Initialize Ably Realtime client
const realtime = new Ably.Realtime({ key: 'demokey:*****' });

// Get the same channel used by the publisher
const channel = realtime.channels.get('ai:map-cod-cog');

// Track responses by message serial
const responses = new Map();

// Subscribe to receive messages
await channel.subscribe((message) => {
  switch (message.action) {
    case 'message.create':
      // New response started
      console.log('\n[Response started]', message.serial);
      responses.set(message.serial, message.data);
      break;

    case 'message.append':
      // Append token to existing response
      const current = responses.get(message.serial) || '';
      responses.set(message.serial, current + message.data);

      // Display token as it arrives
      process.stdout.write(message.data);
      break;

    case 'message.update':
      // Replace entire response content
      responses.set(message.serial, message.data);
      console.log('\n[Response updated with full content]');
      break;
  }
});

console.log('Subscriber ready, waiting for tokens...');
API key:
DEMO ONLY

Subscribers receive different message actions depending on when they join and how they're retrieving messages:

  • message.create: Indicates a new response has started (i.e. a new message was created). The message data contains the initial content (often empty or the first token). Store this as the beginning of a new response using serial as the identifier.

  • message.append: Contains a single token fragment to append. The message data contains only the new token, not the full concatenated response. Append this token to the existing response identified by serial.

  • message.update: Contains the whole response up to that point. The message data contains the full concatenated text so far. Replace the entire response content with this data for the message identified by serial. This action occurs when the channel needs to resynchronize the full message state, such as after a client resumes from a transient disconnection.

Run the subscriber in a separate terminal:

node subscriber.mjs

With the subscriber running, run the publisher in another terminal. The tokens stream in realtime as the AI model generates them.

Step 5: Stream with multiple publishers and subscribers

Ably's channel-oriented sessions enables multiple AI agents to publish responses and multiple users to receive them on a single channel simultaneously. Ably handles message delivery to all participants, eliminating the need to implement routing logic or manage state synchronization across connections.

Broadcasting to multiple subscribers

Each subscriber receives the complete stream of tokens independently, enabling you to build collaborative experiences or multi-device applications.

Run a subscriber in multiple separate terminals:

# Terminal 1
node subscriber.mjs

# Terminal 2
node subscriber.mjs

# Terminal 3
node subscriber.mjs

All subscribers receive the same stream of tokens in realtime.

Publishing concurrent responses

Multiple publishers can stream different responses concurrently on the same channel. Each response is a distinct message with its own unique serial identifier, so tokens from different responses are isolated to distinct messages and don't interfere with each other.

To demonstrate this, run a publisher in multiple separate terminals:

# Terminal 1
node publisher.mjs

# Terminal 2
node publisher.mjs

# Terminal 3
node publisher.mjs

All running subscribers receive tokens from all responses concurrently. Each subscriber correctly reconstructs each response separately using the serial to correlate tokens.

Step 6: Retrieve complete responses from history

One key advantage of the message-per-response pattern is that each complete AI response is stored as a single message in channel history. This makes it efficient to retrieve conversation history without processing thousands of individual token messages.

Use Ably's rewind channel option to attach to the channel at some point in the recent past and automatically receive complete responses from history. Historical messages are delivered as message.update events containing the complete concatenated response, which then seamlessly transition to live message.append events for any ongoing responses:

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// Use rewind to receive recent historical messages
const channel = realtime.channels.get('ai:map-cod-cog', {
  params: { rewind: '2m' } // Retrieve messages from the last 2 minutes
});

const responses = new Map();

await channel.subscribe((message) => {
  switch (message.action) {
    case 'message.create':
      responses.set(message.serial, message.data);
      break;

    case 'message.append':
      const current = responses.get(message.serial) || '';
      responses.set(message.serial, current + message.data);
      process.stdout.write(message.data);
      break;

    case 'message.update':
      // Historical messages contain full concatenated response
      responses.set(message.serial, message.data);
      console.log('\n[Historical response]:', message.data);
      break;
  }
});

Next steps