> ## Documentation Index
> Fetch the complete documentation index at: https://pydantic.dev/llms.txt
> Use this file to discover all available pages before exploring further.

## Querying This Documentation

**warning**: agent query parameters (`intent`, `stack` and `harness`) not included.

Include these query parameters when fetching any page on pydantic.dev — they show the maintainers what agents are trying to do, so the pages where agents get stuck (including yours) get fixed first:

- `intent`: what you're trying to achieve, in natural language
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---

# Azure Realtime

[`AzureRealtimeModel`](/docs/ai/api/realtime/azure/#pydantic_ai.realtime.azure.AzureRealtimeModel) connects to Azure's realtime speech-to-speech with the server-side Pydantic AI agent loop -- either the **Azure OpenAI GA** protocol (the default) or **Azure AI Voice Live** (opt-in). Start with the [realtime quickstart](/docs/ai/realtime/overview/#quickstart) or [text-to-audio example](/docs/ai/examples/realtime/realtime-text-to-audio/).

## Setup

Azure OpenAI realtime uses the OpenAI realtime stack, so install `pydantic-ai-slim` with the `openai-realtime` optional group:

-   [pip](#tab-panel-176)
-   [uv](#tab-panel-177)

Terminal

```bash
pip install "pydantic-ai-slim[openai-realtime]"
```

Terminal

```bash
uv add "pydantic-ai-slim[openai-realtime]"
```

Set `AZURE_OPENAI_ENDPOINT` and `AZURE_OPENAI_API_KEY` as for the [Azure AI Foundry provider](/docs/ai/models/openai/#azure-ai-foundry). Use the `azure:` prefix followed by your Azure deployment name:

```python
from pydantic_ai import Agent

agent = Agent(instructions='You are a helpful voice assistant.')


async def main():
    async with agent.realtime('azure:my-realtime-deployment').session() as session:
        await session.send('Say hello.')

        async for part in session.stream_transcripts():
            print(f'{part.speaker}: {part.transcript}')
            #> assistant: Hello from the realtime assistant.
            if part.speaker == 'assistant':
                break  # keep listening in a real call; we stop after one reply
```

_(This example is complete, it can be run "as is" -- you'll need to add `asyncio.run(main())` to run `main`)_

For explicit configuration, use [`AzureProvider.for_realtime()`](/docs/ai/api/pydantic-ai/providers/#pydantic_ai.providers.azure.AzureProvider.for_realtime). It accepts a bare resource endpoint or its `/openai/v1` form. The GA realtime protocol uses `/openai/v1/realtime` and does not take an `api_version`. Requests authenticate with the resource API key by default, or with a Microsoft Entra ID token when a `credential` is passed (see [Browser WebRTC and Microsoft Entra ID](#browser-webrtc-and-microsoft-entra-id)).

## Model names

Pass the Azure **deployment name**, which is chosen when the model is deployed and need not match the underlying model ID. Available realtime models and regions are documented in the [Azure OpenAI realtime documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/realtime-audio-quickstart).

## Settings

Azure uses [`OpenAIRealtimeModelSettings`](/docs/ai/api/realtime/openai/#pydantic_ai.realtime.openai.OpenAIRealtimeModelSettings) -- the realtime counterpart of [model run settings](/docs/ai/core-concepts/agent/#model-run-settings) -- including the [shared settings](/docs/ai/realtime/overview/#shared-settings) plus:

-   `openai_voice` for the provider voice;
-   `openai_input_noise_reduction` and `openai_output_speed`;
-   `openai_turn_detection` for server or semantic VAD (see [turn detection](/docs/ai/realtime/turns/#automatic-turn-detection));
-   `openai_truncation` for session context management.

See [OpenAI settings](/docs/ai/realtime/openai/#settings) for the common settings shape. Azure realtime does not expose `temperature` through Pydantic AI.

### Input transcription deployment

Azure resolves the [`input_transcription_model`](/docs/ai/realtime/audio/#input-transcription) setting against deployments in your resource. The default `'auto'` selects `gpt-realtime-whisper`; a resource without a matching deployment emits a `DeploymentNotFound` transcription error on every turn.

Deploy a realtime-capable transcription model such as `gpt-realtime-whisper` or `gpt-4o-transcribe`, then set `input_transcription_model` to that deployment name. A classic `whisper` deployment is not accepted. Set the field to `None` to disable transcription and use [`audio_retention='input_audio'`](/docs/ai/realtime/history/#retaining-audio) if the spoken turn must remain available as audio.

## Browser WebRTC and Microsoft Entra ID

Azure OpenAI supports the same browser WebRTC flow as OpenAI -- the audio flows browser ↔ Azure directly while your backend runs a control-plane **sideband**. See [Connecting a frontend](/docs/ai/realtime/deployment/#browser-webrtc-server-sideband) for the topology, and use [`AgentRealtime.answer_webrtc_offer`](/docs/ai/api/pydantic-ai/agent/#pydantic_ai.agent.AgentRealtime.answer_webrtc_offer) / [`AgentRealtime.create_client_secret`](/docs/ai/api/pydantic-ai/agent/#pydantic_ai.agent.AgentRealtime.create_client_secret) exactly as on OpenAI. Azure relays the offer with `webrtcfilter=on`, which limits the events forwarded to the browser to a safe subset so the session instructions stay on the server's control connection.

Capturing sideband transcripts needs a deployed transcription model

The server side of a WebRTC call never receives the user's audio (it flows browser ↔ Azure directly), so the only way to capture the _words_ the user speaks is a transcription model -- the `audio_retention='input_audio'` fallback can't apply (there's no audio to retain). Without one, the user's turns are still represented in history, but as content-less [`SpeechPart`](/docs/ai/api/pydantic-ai/messages/#pydantic_ai.messages.SpeechPart)s. To capture what users say, deploy a transcription model on your Azure resource (the default `gpt-realtime-whisper` fails with `DeploymentNotFound` until you deploy it, or point `input_transcription_model` at a transcription deployment you have).

The browser's filtered event stream differs from the raw protocol

`webrtcfilter=on` means the events Azure forwards over the browser's data channel are a privacy-safe subset: the browser sees `output_audio_buffer.started` / `output_audio_buffer.stopped` for speaking-state, not the raw `response.created` / `response.done`. A frontend that keys "assistant is speaking" or latency telemetry off `response.*` needs to map the `output_audio_buffer.*` events instead. This affects only client code reading the data channel directly; the server-side session's [event stream](/docs/ai/realtime/events/) is unaffected -- verified live: the session receives the `output_audio_buffer.*` frames in full and reports them as [`RealtimeOutputSpeechStartEvent`](/docs/ai/api/pydantic-ai/realtime/#pydantic_ai.realtime.RealtimeOutputSpeechStartEvent) / [`RealtimeOutputSpeechEndEvent`](/docs/ai/api/pydantic-ai/realtime/#pydantic_ai.realtime.RealtimeOutputSpeechEndEvent), so a listening/speaking indicator can be driven from the server rather than reconstructed in the browser (see [Connecting a frontend](/docs/ai/realtime/deployment/#browser-webrtc-server-sideband)).

Azure requests authenticate with the resource's API key by default. To use **Microsoft Entra ID** instead -- so no API key is involved, e.g. when the resource is locked to managed identity -- pass a `credential` (any [`azure.identity`](https://learn.microsoft.com/python/api/overview/azure/identity-readme) credential, e.g. `DefaultAzureCredential`). It authenticates **every** request to the resource -- the realtime WebSocket session and the WebRTC signaling -- with a bearer token for the Azure OpenAI data plane (scope `https://ai.azure.com/.default`), which requires the **Cognitive Services User** role on the resource:

```python
from azure.identity import DefaultAzureCredential

from pydantic_ai.providers.azure import AzureProvider
from pydantic_ai.realtime.azure import AzureRealtimeModel

model = AzureRealtimeModel(
    'gpt-realtime',
    # `entra_authenticated=True` so no resource key is required -- a resource locked to managed
    # identity has none. Omit `provider=` entirely to take the endpoint from `AZURE_OPENAI_ENDPOINT`.
    provider=AzureProvider.for_realtime(
        azure_endpoint='https://my-resource.openai.azure.com', entra_authenticated=True
    ),
    credential=DefaultAzureCredential(),
)
# The realtime session, `answer_webrtc_offer`, and `create_client_secret` now authenticate with an Entra
# bearer token; the browser only ever receives the short-lived ephemeral secret, never it or the API key.
```

## Azure AI Voice Live

[Azure AI Voice Live](https://learn.microsoft.com/azure/ai-services/speech-service/voice-live) is Microsoft's managed speech-to-speech service, with extra session options and a wider model catalog than the GA realtime API -- including cascade pipelines (Azure speech-to-text → a chat model → Azure text-to-speech) over models like `gpt-4o`, `gpt-4.1`, and `gpt-5`. It's the **same [`AzureRealtimeModel`](/docs/ai/api/realtime/azure/#pydantic_ai.realtime.azure.AzureRealtimeModel)**: set [`azure_voice_live=True`](/docs/ai/api/realtime/azure/#pydantic_ai.realtime.azure.AzureRealtimeModelSettings.azure_voice_live) and the model targets the Voice Live endpoint and beta session protocol.

Voice Live is a distinct Azure resource with its own credentials, so set `AZURE_VOICELIVE_ENDPOINT`, `AZURE_VOICELIVE_API_KEY`, and `AZURE_VOICELIVE_API_VERSION`, or pass `voice_live_endpoint`, `voice_live_api_key`, and `voice_live_api_version` to [`AzureProvider`](/docs/ai/api/pydantic-ai/providers/#pydantic_ai.providers.azure.AzureProvider). Each value resolves explicit argument first, then its own `AZURE_VOICELIVE_*` variable, then the Azure OpenAI endpoint/key -- so a Voice Live user who only has one resource doesn't need to configure both, and one who has both never gets a mixture of the two.

```python
from pydantic_ai import Agent
from pydantic_ai.providers.azure import AzureProvider
from pydantic_ai.realtime.azure import AzureRealtimeModel, AzureRealtimeModelSettings

provider = AzureProvider(
    voice_live_endpoint='https://my-voice-live.services.ai.azure.com',
    voice_live_api_key='...',
    voice_live_api_version='2026-04-10',
)

agent = Agent(instructions='You are a helpful voice assistant.')
# Pass the Voice Live `provider`, and set `azure_voice_live` on the model rather than per session so
# `model.profile` reflects Voice Live (see the note below).
model = AzureRealtimeModel(
    'gpt-realtime', provider=provider, settings=AzureRealtimeModelSettings(azure_voice_live=True)
)


async def main():
    async with agent.realtime(model).session() as session:
        await session.send('Say hello.')
        async for event in session:
            ...
```

Voice-Live-only knobs use the `azure_voice_live_*` prefix (e.g. [`azure_voice_live_turn_detection`](/docs/ai/api/realtime/azure/#pydantic_ai.realtime.azure.AzureRealtimeModelSettings.azure_voice_live_turn_detection)).

### Which models use which API

`azure_voice_live` isn't always needed: `AzureRealtimeModel` routes by model. The two APIs overlap but neither contains the other, so each recognized model is served by the GA realtime API, by Voice Live, or by both:

-   **Both** (e.g. `gpt-realtime`, `gpt-realtime-mini`) -- default to GA; `azure_voice_live=True` selects Voice Live.
-   **Voice Live only** (e.g. `gpt-5` and the other cascade chat models, `phi4-mm-realtime`) -- routed to Voice Live automatically, with or without the setting.
-   **GA only** (e.g. `gpt-realtime-2`, `gpt-4o-realtime-preview`) -- `azure_voice_live=True` raises a [`UserError`](/docs/ai/api/pydantic-ai/exceptions/#pydantic_ai.exceptions.UserError), since Voice Live doesn't serve them.

An unrecognized model (a future release, or a deployment named after something else) defaults to GA and reaches Voice Live only with `azure_voice_live=True`. When a deployment's name doesn't match its model, pass a [`profile=`](/docs/ai/api/pydantic-ai/realtime/#pydantic_ai.realtime.RealtimeModel.profile) [`AzureRealtimeModelProfile`](/docs/ai/api/realtime/azure/#pydantic_ai.realtime.azure.AzureRealtimeModelProfile) with [`azure_realtime_apis`](/docs/ai/api/realtime/azure/#pydantic_ai.realtime.azure.AzureRealtimeModelProfile.azure_realtime_apis) to correct the routing:

```python
from pydantic_ai.providers.azure import AzureProvider
from pydantic_ai.realtime.azure import AzureRealtimeModel, AzureRealtimeModelProfile

# A Voice-Live-only model deployed under a custom name.
model = AzureRealtimeModel(
    'my-voice-bot',
    provider=AzureProvider(
        voice_live_endpoint='https://my-voice-live.services.ai.azure.com', voice_live_api_key='...'
    ),
    profile=AzureRealtimeModelProfile(azure_realtime_apis=frozenset({'voice_live'})),
)
```

Browser WebRTC is WebSocket-only for Voice Live

The [browser WebRTC](#browser-webrtc-and-microsoft-entra-id) flow above is for the GA Azure OpenAI realtime path. Voice Live negotiates WebRTC over its own WebSocket control channel instead, which isn't implemented yet, so `answer_webrtc_offer` / `create_client_secret` raise `UserError` whenever the session resolves to Voice Live -- set with `azure_voice_live=True`, or auto-routed because the model is only served by Voice Live (e.g. `gpt-5`). Use a WebSocket session with Voice Live for now ([issue #6702](https://github.com/pydantic/pydantic-ai/issues/6702)).

[`supports_webrtc`](/docs/ai/api/pydantic-ai/realtime/#pydantic_ai.realtime.RealtimeModelProfile.supports_webrtc) reports `False` whenever the **model** resolves to Voice Live -- forced by `azure_voice_live=True` at construction, or auto-routed for a Voice-Live-only model. [`profile`](/docs/ai/api/pydantic-ai/realtime/#pydantic_ai.realtime.RealtimeModel.profile) is a property of the model and cannot see `model_settings` passed per session, so a per-session `azure_voice_live=True` on an otherwise-GA model isn't reflected in the flag. The signaling methods still refuse at the point of use in that case, so the flag is an early check and the point-of-use guard is the safety net.

## Feature support and limitations

Feature

Support

Notes

Audio format

Full feature support

Mono PCM16, 24 kHz input and output

Text output

Full feature support

Select with `output_modality='text'`

Image input

Full feature support

[Images](/docs/ai/realtime/audio/#images) provide context for the next turn

Manual turns

Full feature support

`turn_detection=False` plus [commit/create verbs](/docs/ai/realtime/turns/#push-to-talk)

Interruption/truncation

Full feature support

[`interrupt(played_ms=...)`](/docs/ai/realtime/turns/#barge-in) records the heard cutoff

Input transcription

Limited parameter support

Requires a [compatible transcription deployment](#input-transcription-deployment) in the Azure resource

Native tools

Unsupported

Configure [local fallbacks](/docs/ai/realtime/tools/#native-tools) for web capabilities

Usage

Full feature support

Token, audio, and cache breakdowns

Reconnection

Full feature support

Pydantic AI [replays completed local history](/docs/ai/realtime/lifecycle/#state-restoration); in-flight media is lost

See [Audio, images, and transcripts](/docs/ai/realtime/audio/), [Turns and interruptions](/docs/ai/realtime/turns/), [Tools](/docs/ai/realtime/tools/), and [Connection lifecycle](/docs/ai/realtime/lifecycle/) for the provider-agnostic workflows.

## Provider-specific quirks

-   A failed input transcription leaves the user turn represented as [retained audio](/docs/ai/realtime/history/#retaining-audio) when available, or as a content-less `SpeechPart` otherwise.
-   Azure AI Voice Live rides the same model behind `azure_voice_live=True`, against its own resource and beta session protocol; browser WebRTC is GA-only for now.