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mschristensen merged 1 commit into from
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AI Transport: Add a guide for token streaming using the OpenAI SDK #3024

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9 changes: 9 additions & 0 deletions src/data/nav/aitransport.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,15 @@ export default {
},
],
},
{
name: 'Guides',
pages: [
{
name: 'OpenAI token streaming - message per token',
link: '/docs/guides/ai-transport/openai-message-per-token',
},
],
},
],
api: [],
} satisfies NavProduct;
382 changes: 382 additions & 0 deletions src/pages/docs/guides/ai-transport/openai-message-per-token.mdx
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---
title: "Guide: Stream OpenAI responses using the message-per-token pattern"
meta_description: "Stream tokens from the OpenAI Responses API over Ably in realtime."
meta_keywords: "AI, token streaming, OpenAI, Responses API, AI transport, Ably, realtime"
---

This guide shows you how to stream AI responses from OpenAI's [Responses API](https://platform.openai.com/docs/api-reference/responses) over Ably using the [message-per-token pattern](/docs/ai-transport/features/token-streaming/message-per-token). Specifically, it implements the [explicit start/stop events approach](/docs/ai-transport/features/token-streaming/message-per-token#explicit-events), which publishes each response token as an individual message, along with explicit lifecycle events to signal when responses begin and end.

Using Ably to distribute tokens from the OpenAI SDK enables you to broadcast AI responses to thousands of concurrent subscribers with reliable message delivery and ordering guarantees, ensuring that each client receives the complete response stream with all tokens delivered in order. This approach decouples your AI inference from client connections, enabling you to scale agents independently and handle reconnections gracefully.

<Aside data-type="note">
To discover other approaches to token streaming, including the [message-per-response](/docs/ai-transport/features/token-streaming/message-per-response) pattern, see the [token streaming](/docs/ai-transport/features/token-streaming) documentation.
</Aside>

## Prerequisites <a id="prerequisites"/>

To follow this guide, you need:
- Node.js 20 or higher
- An OpenAI API key
- An Ably API key

Useful links:
- [OpenAI developer quickstart](https://platform.openai.com/docs/quickstart)
- [Ably JavaScript SDK getting started](/docs/getting-started/javascript)

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

<Code>
```shell
mkdir ably-openai-example && cd ably-openai-example
npm init -y
```
</Code>

Install the required packages using NPM:

<Code>
```shell
npm install openai@^4 ably@^2
```
</Code>

<Aside data-type="note">
This guide uses version 4.x of the OpenAI SDK. Some details of interacting with the OpenAI SDK may differ from those given here if using a different major version.
</Aside>

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

<Code>
```shell
export OPENAI_API_KEY="your_api_key_here"
```
</Code>

## Step 1: Get a streamed response from OpenAI <a id="step-1"/>

Initialize an OpenAI client and use the [Responses API](https://platform.openai.com/docs/api-reference/responses) to stream model output as a series of events.

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

<Code>
```javascript
import OpenAI from 'openai';

// Initialize OpenAI client
const openai = new OpenAI();

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

// Create streaming response from OpenAI
async function streamOpenAIResponse(prompt) {
const stream = await openai.responses.create({
model: "gpt-5",
input: prompt,
stream: true,
});

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

// Usage example
streamOpenAIResponse("Tell me a short joke");
```
</Code>

### Understand OpenAI streaming events <a id="understand-streaming-events"/>

OpenAI's Responses API [streams](https://platform.openai.com/docs/guides/streaming-responses) model output as a series of events when you set `stream: true`. Each streamed event includes a `type` property which describes the [event type](https://platform.openai.com/docs/api-reference/responses-streaming). A complete text response can be constructed from the following event types:

- [`response.created`](https://platform.openai.com/docs/api-reference/responses-streaming/response/created): Signals the start of a response. Contains `response.id` to correlate subsequent events.

- [`response.output_item.added`](https://platform.openai.com/docs/api-reference/responses-streaming/response/output_item/added): Indicates a new output item. If `item.type === "message"` the item contains model response text; other types may be specified, such as `"reasoning"` for internal reasoning tokens. The `output_index` indicates the position of this item in the response's [`output`](https://platform.openai.com/docs/api-reference/responses-streaming/response/completed#responses_streaming-response-completed-response-output) array.

- [`response.content_part.added`](https://platform.openai.com/docs/api-reference/responses-streaming/response/content_part/added): Indicates a new content part within an output item. If `part.type === "output_text"` the part contains model response text; other types may be specified, such as `"reasoning_text"` for internal reasoning tokens. The `content_index` indicates the position of this item in the output items's [`content`](https://platform.openai.com/docs/api-reference/responses-streaming/response/completed#responses_streaming-response-completed-response-output-output_message-content) array.

- [`response.output_text.delta`](https://platform.openai.com/docs/api-reference/responses-streaming/response/output_text/delta): Contains a single token in the `delta` field. Use the `item_id`, `output_index`, and `content_index` to correlate tokens relating to a specific content part.

- [`response.content_part.done`](https://platform.openai.com/docs/api-reference/responses-streaming/response/content_part/done): Signals completion of a content part. Contains the complete `part` object with full text, along with `item_id`, `output_index`, and `content_index`.

- [`response.output_item.done`](https://platform.openai.com/docs/api-reference/responses-streaming/response/output_item/done): Signals completion of an output item. Contains the complete `item` object and `output_index`.

- [`response.completed`](https://platform.openai.com/docs/api-reference/responses-streaming/response/completed): Signals the end of the response. Contains the complete `response` object.

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

<Code>
```json
// 1. Response starts
{"type":"response.created","response":{"id":"resp_abc123","status":"in_progress"}}

// 2. First output item (reasoning) is added
{"type":"response.output_item.added","output_index":0,"item":{"id":"rs_456","type":"reasoning"}}
{"type":"response.output_item.done","output_index":0,"item":{"id":"rs_456","type":"reasoning"}}

// 3. Second output item (message) is added
{"type":"response.output_item.added","output_index":1,"item":{"id":"msg_789","type":"message"}}
{"type":"response.content_part.added","item_id":"msg_789","output_index":1,"content_index":0}

// 4. Text tokens stream in as delta events
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":"Why"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" don"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":"'t"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" scientists"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" trust"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" atoms"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":"?"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" Because"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" they"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" make"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" up"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":" everything"}
{"type":"response.output_text.delta","item_id":"msg_789","output_index":1,"content_index":0,"delta":"."}

// 5. Content part and output item complete
{"type":"response.content_part.done","item_id":"msg_789","output_index":1,"content_index":0,"part":{"type":"output_text","text":"Why don't scientists trust atoms? Because they make up everything."}}
{"type":"response.output_item.done","output_index":1,"item":{"id":"msg_789","type":"message","status":"completed","content":[{"type":"output_text","text":"Why don't scientists trust atoms? Because they make up everything."}]}}

// 6. Response completes
{"type":"response.completed","response":{"id":"resp_abc123","status":"completed","output":[{"id":"rs_456","type":"reasoning"},{"id":"msg_789","type":"message","status":"completed","content":[{"type":"output_text","text":"Why don't scientists trust atoms? Because they make up everything."}]}]}}
```
</Code>

<Aside data-type="note">
This is only an illustrative example for a simple "text in, text out" use case and may not reflect the exact sequence of events that you observe from the OpenAI API. It also does not describe response generation errors or refusals. For complete details on all event types and their properties, see [OpenAI Streaming events](https://platform.openai.com/docs/api-reference/responses-streaming/response).
</Aside>

## Step 2: Publish streaming events to Ably <a id="step-2"/>

Publish OpenAI streaming events to Ably to reliably and scalably distribute them to subscribers.

This implementation follows the [explicit start/stop events pattern](/docs/ai-transport/features/token-streaming/message-per-token#explicit-events), which provides clear response boundaries.

### Initialize the Ably client <a id="initialize-ably"/>

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

<Code>
```javascript
import Ably from 'ably';
import OpenAI from 'openai';

// Initialize OpenAI client
const openai = new OpenAI();

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

// Create a channel for publishing streamed AI responses
const channel = realtime.channels.get('{{RANDOM_CHANNEL_NAME}}');
```
</Code>

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.

### Map OpenAI streaming events to Ably messages <a id="map-events"/>

Choose how to map [OpenAI streaming events](#understand-streaming-events) to Ably messages. You can choose any mapping strategy that suits your application's needs. This guide uses the following pattern as an example:

- `start`: Signals the beginning of a response
- `token`: Contains the incremental text content for each delta
- `stop`: Signals the completion of a response

<Aside data-type="note">
This implementation assumes each response contains a single `message` type output item. It filters out reasoning tokens and other non-`message` output items, and processes responses sequentially. For production use cases with concurrent responses or multiple output items or content parts, consider tracking state per response ID.
</Aside>

Update your `publisher.mjs` file to initialize the Ably client and update the `processEvent()` function to publish events to Ably:

<Code>
```javascript
// Track state across events
let responseId = null;
let messageItemId = null;

// Process each streaming event and publish to Ably
function processEvent(event) {
switch (event.type) {
case 'response.created':
// Capture response ID when response starts
responseId = event.response.id;

// Publish start event
channel.publish({
name: 'start',
extras: {
headers: { responseId }
}
});
break;

case 'response.output_item.added':
// Capture message item ID when a message output item is added
if (event.item.type === 'message') {
messageItemId = event.item.id;
}
break;

case 'response.output_text.delta':
// Publish tokens from message output items only
if (event.item_id === messageItemId) {
channel.publish({
name: 'token',
data: event.delta,
extras: {
headers: { responseId }
}
});
}
break;

case 'response.completed':
// Publish stop event when response completes
channel.publish({
name: 'stop',
extras: {
headers: { responseId }
}
});
break;
}
}
```
</Code>

This implementation:

- Publishes a `start` event when the response begins
- Filters for `response.output_text.delta` events from `message` type output items and publishes them as `token` events
- Publishes a `stop` event when the response completes
- All published events include the `responseId` in message `extras` to allow the client to correlate events relating to a particular response

<Aside data-type="note">
Ably messages are published without `await` to maximize throughput. Ably maintains message ordering even without awaiting each publish. For more information, see [Publishing tokens](/docs/ai-transport/features/token-streaming/message-per-token#publishing).
</Aside>

Run the publisher to see tokens streaming to Ably:

<Code>
```shell
node publisher.mjs
```
</Code>

## Step 3: Subscribe to streaming tokens <a id="step-3"/>

Create a subscriber that receives the streaming events from Ably and reconstructs the response.

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

<Code>
```javascript
import Ably from 'ably';

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

// Get the same channel used by the publisher
const channel = realtime.channels.get('{{RANDOM_CHANNEL_NAME}}');

// Track responses by ID
const responses = new Map();

// Handle response start
await channel.subscribe('start', (message) => {
const responseId = message.extras?.headers?.responseId;
console.log('\n[Response started]', responseId);
responses.set(responseId, '');
});

// Handle tokens
await channel.subscribe('token', (message) => {
const responseId = message.extras?.headers?.responseId;
const token = message.data;

// Append token to response
const currentText = responses.get(responseId) || '';
responses.set(responseId, currentText + token);

// Display token as it arrives
process.stdout.write(token);
});

// Handle response stop
await channel.subscribe('stop', (message) => {
const responseId = message.extras?.headers?.responseId;
const finalText = responses.get(responseId);
console.log('\n[Response completed]', responseId);
});

console.log('Subscriber ready, waiting for tokens...');
```
</Code>

Run the subscriber in a separate terminal:

<Code>
```shell
node subscriber.mjs
```
</Code>

With the subscriber running, run the publisher in another terminal. The tokens stream in realtime as they are generated by the OpenAI model.

## Step 4: Stream with multiple publishers and subscribers <a id="step-4"/>

Ably's [channel-oriented sessions](/docs/ai-transport/features/sessions-identity#connection-oriented-vs-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 <a id="broadcasting"/>

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:

<Code>
```shell
# Terminal 1
node subscriber.mjs

# Terminal 2
node subscriber.mjs

# Terminal 3
node subscriber.mjs
```
</Code>

All subscribers receive the same stream of tokens in realtime.

### Publishing concurrent responses <a id="multiple-publishers"/>

The implementation uses `responseId` in message `extras` to correlate tokens with their originating response. This enables multiple publishers to stream different responses concurrently on the same channel, with each subscriber correctly tracking all responses independently.

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

<Code>
```shell
# Terminal 1
node publisher.mjs

# Terminal 2
node publisher.mjs

# Terminal 3
node publisher.mjs
```
</Code>

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

## Next steps

- Learn more about the [message-per-token pattern](/docs/ai-transport/features/token-streaming/message-per-token) used in this guide
- Learn about [client hydration strategies](/docs/ai-transport/features/token-streaming/message-per-token#hydration) for handling late joiners and reconnections
- Understand [sessions and identity](/docs/ai-transport/features/sessions-identity) in AI enabled applications
- Explore the [message-per-response pattern](/docs/ai-transport/features/token-streaming/message-per-response) for storing complete AI responses as single messages in history