Guide: Stream Anthropic responses using the message-per-response pattern

This guide shows you how to stream AI responses from Anthropic's Messages API 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 Anthropic 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
• An Anthropic API key
• An Ably API key

Useful links:

• Anthropic API documentation
• Ably JavaScript SDK getting started

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

mkdir ably-anthropic-example && cd ably-anthropic-example
npm init -y

Install the required packages using NPM:

npm install @anthropic-ai/sdk@^0.71 ably@^2

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

export ANTHROPIC_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 Anthropic

Initialize an Anthropic client and use the Messages API to stream model output as a series of events.

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

import Anthropic from '@anthropic-ai/sdk';

// Initialize Anthropic client
const anthropic = new Anthropic();

// 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 Anthropic
async function streamAnthropicResponse(prompt) {
  const stream = await anthropic.messages.create({
    model: "claude-sonnet-4-5",
    max_tokens: 1024,
    messages: [{ role: "user", content: prompt }],
    stream: true,
  });

  // Iterate through streaming events
  for await (const event of stream) {
    await processEvent(event);
  }
}

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

Understand Anthropic streaming events

Anthropic's Messages API streams model output as a series of events when you set stream: true. Each streamed event includes a type property which describes the event type. A complete text response can be constructed from the following event types:

• message_start: Signals the start of a response. Contains a message object with an id to correlate subsequent events.

• content_block_start: Indicates the start of a new content block. For text responses, the content_block will have type: "text"; other types may be specified, such as "thinking" for internal reasoning tokens. The index indicates the position of this item in the message's content array.

• content_block_delta: Contains a single text delta in the delta.text field. If delta.type === "text_delta" the delta contains model response text; other types may be specified, such as "thinking_delta" for internal reasoning tokens. Use the index to correlate deltas relating to a specific content block.

• content_block_stop: Signals completion of a content block. Contains the index that identifies the content block.

• message_delta: Contains additional message-level metadata that may be streamed incrementally. Includes a delta.stop_reason which indicates why the model successfully completed its response generation.

• message_stop: Signals the end of the response.

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

// 1. Message starts
{"type":"message_start","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_012zEkenyT6heaYSDvDEDdXm","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}}}

// 2. Content block starts
{"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

// 3. Text tokens stream in as delta events
{"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Why"}}
{"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" don't scientists trust atoms?\n\nBecause"}}
{"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" they make up everything!"}}

// 4. Content block completes
{"type":"content_block_stop","index":0}

// 5. Message delta (usage stats)
{"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{"input_tokens":12,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":17}}

// 6. Message completes
{"type":"message_stop"}

Step 3: Publish streaming tokens to Ably

Publish Anthropic 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:

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');

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 Anthropic model.

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

// Track state across events
let msgSerial = null;
let textBlockIndex = null;

// Process each streaming event and publish to Ably
async function processEvent(event) {
  switch (event.type) {
    case 'message_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 'content_block_start':
      // Capture text block index when a text content block is added
      if (event.content_block.type === 'text') {
        textBlockIndex = event.index;
      }
      break;

    case 'content_block_delta':
      // Append tokens from text deltas only
      if (event.index === textBlockIndex && event.delta.type === 'text_delta' && msgSerial) {
        channel.appendMessage({
          serial: msgSerial,
          data: event.delta.text
        });
      }
      break;

    case 'message_stop':
      console.log('Stream completed!');
      break;
  }
}

This implementation:

• Publishes an initial empty message when the response begins and captures the serial
• Filters for content_block_delta events with text_delta type from text content blocks
• Appends each token to the original message

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:

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...');

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 Anthropic 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:

// 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

• Learn more about the message-per-response pattern used in this guide
• Learn about client hydration strategies for handling late joiners and reconnections
• Understand sessions and identity in AI enabled applications
• Explore the message-per-token pattern for explicit control over individual token messages