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docs/voice/pipeline.md
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# Pipelines and workflows
[`VoicePipeline`][agents.voice.pipeline.VoicePipeline] is a class that makes it easy to turn your agentic workflows into a voice app. You pass in a workflow to run, and the pipeline takes care of transcribing input audio, detecting when the audio ends, calling your workflow at the right time, and turning the workflow output back into audio.
```mermaid
graph LR
%% Input
A["🎤 Audio Input"]
%% Voice Pipeline
subgraph Voice_Pipeline [Voice Pipeline]
direction TB
B["Transcribe (speech-to-text)"]
C["Your Code"]:::highlight
D["Text-to-speech"]
B --> C --> D
end
%% Output
E["🎧 Audio Output"]
%% Flow
A --> Voice_Pipeline
Voice_Pipeline --> E
%% Custom styling
classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700;
```
## Configuring a pipeline
When you create a pipeline, you can set a few things:
1. The [`workflow`][agents.voice.workflow.VoiceWorkflowBase], which is the code that runs each time new audio is transcribed.
2. The [`speech-to-text`][agents.voice.model.STTModel] and [`text-to-speech`][agents.voice.model.TTSModel] models used
3. The [`config`][agents.voice.pipeline_config.VoicePipelineConfig], which lets you configure things like:
- A model provider, which can map model names to models
- Tracing, including whether to disable tracing, whether audio files are uploaded, the workflow name, trace IDs etc.
- Settings on the TTS and STT models, such as the prompt, language, and data types used.
## Running a pipeline
You can run a pipeline via the [`run()`][agents.voice.pipeline.VoicePipeline.run] method, which lets you pass in audio input in two forms:
1. [`AudioInput`][agents.voice.input.AudioInput] is used when you have a complete audio input and just want to produce a result for it. This is useful in cases where you don't need to detect when a speaker is done speaking; for example, when you have pre-recorded audio or in push-to-talk apps where it's clear when the user is done speaking.
2. [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput] is used when you might need to detect when a user is done speaking. It allows you to push audio chunks as they are detected, and the voice pipeline will automatically run the agent workflow at the right time, via a process called "activity detection".
## Results
The result of a voice pipeline run is a [`StreamedAudioResult`][agents.voice.result.StreamedAudioResult]. This is an object that lets you stream events as they occur. There are a few kinds of [`VoiceStreamEvent`][agents.voice.events.VoiceStreamEvent], including:
1. [`VoiceStreamEventAudio`][agents.voice.events.VoiceStreamEventAudio], which contains a chunk of audio.
2. [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle], which informs you of lifecycle events like a turn starting or ending.
3. [`VoiceStreamEventError`][agents.voice.events.VoiceStreamEventError], which is an error event.
```python
result = await pipeline.run(input)
async for event in result.stream():
if event.type == "voice_stream_event_audio":
# play audio
pass
elif event.type == "voice_stream_event_lifecycle":
# lifecycle
pass
elif event.type == "voice_stream_event_error":
# error
pass
```
## Best practices
### Interruptions
The Agents SDK currently does not provide any built-in interruption handling for [`StreamedAudioInput`][agents.voice.input.StreamedAudioInput]. Instead, every detected turn triggers a separate run of your workflow. If you want to handle interruptions inside your application, you can listen to the [`VoiceStreamEventLifecycle`][agents.voice.events.VoiceStreamEventLifecycle] events. `turn_started` indicates that a new turn was transcribed and processing is beginning. `turn_ended` triggers after all the audio was dispatched for a respective turn. You could use these events to mute the speaker's microphone when the model starts a turn and unmute it after you flush all the related audio for a turn.
docs/voice/quickstart.md
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# Quickstart
## Prerequisites
Make sure you've followed the base [quickstart instructions](../quickstart.md) for the Agents SDK, and set up a virtual environment. Then, install the optional voice dependencies from the SDK:
```bash
pip install 'openai-agents[voice]'
```
## Concepts
The main concept to know about is a [`VoicePipeline`][agents.voice.pipeline.VoicePipeline], which is a 3 step process:
1. Run a speech-to-text model to turn audio into text.
2. Run your code, which is usually an agentic workflow, to produce a result.
3. Run a text-to-speech model to turn the result text back into audio.
```mermaid
graph LR
%% Input
A["🎤 Audio Input"]
%% Voice Pipeline
subgraph Voice_Pipeline [Voice Pipeline]
direction TB
B["Transcribe (speech-to-text)"]
C["Your Code"]:::highlight
D["Text-to-speech"]
B --> C --> D
end
%% Output
E["🎧 Audio Output"]
%% Flow
A --> Voice_Pipeline
Voice_Pipeline --> E
%% Custom styling
classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700;
```
## Agents
First, let's set up some Agents. This should feel familiar to you if you've built any agents with this SDK. We'll have a couple of Agents, a handoff, and a tool.
```python
import asyncio
import random
from agents import (
Agent,
function_tool,
)
from agents.extensions.handoff_prompt import prompt_with_handoff_instructions
@function_tool
def get_weather(city: str) -> str:
"""Get the weather for a given city."""
print(f"[debug] get_weather called with city: {city}")
choices = ["sunny", "cloudy", "rainy", "snowy"]
return f"The weather in {city} is {random.choice(choices)}."
spanish_agent = Agent(
name="Spanish",
handoff_description="A Spanish-speaking agent.",
instructions=prompt_with_handoff_instructions(
"You're speaking to a human, so be polite and concise. Speak in Spanish.",
),
model="gpt-5.6-sol",
)
agent = Agent(
name="Assistant",
instructions=prompt_with_handoff_instructions(
"You're speaking to a human, so be polite and concise. If the user speaks in Spanish, hand off to the Spanish agent.",
),
model="gpt-5.6-sol",
handoffs=[spanish_agent],
tools=[get_weather],
)
```
## Voice pipeline
We'll set up a simple voice pipeline, using [`SingleAgentVoiceWorkflow`][agents.voice.workflow.SingleAgentVoiceWorkflow] as the workflow.
```python
from agents.voice import SingleAgentVoiceWorkflow, VoicePipeline
pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent))
```
## Run the pipeline
```python
import numpy as np
import sounddevice as sd
from agents.voice import AudioInput
# For simplicity, we'll just create 3 seconds of silence
# In reality, you'd get microphone data
buffer = np.zeros(24000 * 3, dtype=np.int16)
audio_input = AudioInput(buffer=buffer)
result = await pipeline.run(audio_input)
# Create an audio player using `sounddevice`
player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
player.start()
# Play the audio stream as it comes in
async for event in result.stream():
if event.type == "voice_stream_event_audio":
player.write(event.data)
```
## Put it all together
```python
import asyncio
import random
import numpy as np
import sounddevice as sd
from agents import (
Agent,
function_tool,
set_tracing_disabled,
)
from agents.voice import (
AudioInput,
SingleAgentVoiceWorkflow,
VoicePipeline,
)
from agents.extensions.handoff_prompt import prompt_with_handoff_instructions
@function_tool
def get_weather(city: str) -> str:
"""Get the weather for a given city."""
print(f"[debug] get_weather called with city: {city}")
choices = ["sunny", "cloudy", "rainy", "snowy"]
return f"The weather in {city} is {random.choice(choices)}."
spanish_agent = Agent(
name="Spanish",
handoff_description="A Spanish-speaking agent.",
instructions=prompt_with_handoff_instructions(
"You're speaking to a human, so be polite and concise. Speak in Spanish.",
),
model="gpt-5.6-sol",
)
agent = Agent(
name="Assistant",
instructions=prompt_with_handoff_instructions(
"You're speaking to a human, so be polite and concise. If the user speaks in Spanish, hand off to the Spanish agent.",
),
model="gpt-5.6-sol",
handoffs=[spanish_agent],
tools=[get_weather],
)
async def main():
pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent))
buffer = np.zeros(24000 * 3, dtype=np.int16)
audio_input = AudioInput(buffer=buffer)
result = await pipeline.run(audio_input)
# Create an audio player using `sounddevice`
player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
player.start()
# Play the audio stream as it comes in
async for event in result.stream():
if event.type == "voice_stream_event_audio":
player.write(event.data)
if __name__ == "__main__":
asyncio.run(main())
```
If you run this example, the agent will speak to you! Check out the example in [examples/voice/static](https://github.com/openai/openai-agents-python/tree/main/examples/voice/static) to see a demo where you can speak to the agent yourself.
docs/voice/tracing.md
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# Tracing
Just like the way [agents are traced](../tracing.md), voice pipelines are also automatically traced.
You can read the tracing doc above for basic tracing information, but you can additionally configure tracing of a pipeline via [`VoicePipelineConfig`][agents.voice.pipeline_config.VoicePipelineConfig].
Key tracing related fields are:
- [`tracing_disabled`][agents.voice.pipeline_config.VoicePipelineConfig.tracing_disabled]: controls whether tracing is disabled. By default, tracing is enabled.
- [`trace_include_sensitive_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_data]: controls whether traces include potentially sensitive data, like audio transcripts. This is specifically for the voice pipeline, and not for anything that goes on inside your Workflow.
- [`trace_include_sensitive_audio_data`][agents.voice.pipeline_config.VoicePipelineConfig.trace_include_sensitive_audio_data]: controls whether traces include audio data.
- [`workflow_name`][agents.voice.pipeline_config.VoicePipelineConfig.workflow_name]: The name of the trace workflow.
- [`group_id`][agents.voice.pipeline_config.VoicePipelineConfig.group_id]: The `group_id` of the trace, which lets you link multiple traces.
- [`trace_metadata`][agents.voice.pipeline_config.VoicePipelineConfig.trace_metadata]: Additional metadata to include with the trace.