Now that you’ve got Pipecat installed, let’s run your first example application. We’ll use the “Say One Thing” example, which creates a simple bot that greets users when they join a room and exits when they leave.

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Getting the Example

Download the “Say One Thing” example from the pipecat repo and save it to your project directory.

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Configuring the Example

The example uses Cartesia’s text-to-speech service. Make sure you have your .env file set up with:

DAILY_SAMPLE_ROOM_URL=https://YOURDOMAIN.daily.co/YOURROOM
DAILY_API_KEY=your_daily_api_key
CARTESIA_API_KEY=your_cartesia_api_key

The example uses a pre-configured voice. You can change this by modifying the voice_id parameter in the CartesiaHttpTTSService configuration.

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Running the Example

  1. Open your Daily room URL in a browser tab
  2. Join the room
  3. Run the example:
python 01-say-one-thing.py

If all goes well, you should see another participant (named “Say One Thing”) join the room, greet you, and disconnect when you leave the room.

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Understanding How It Works

Let’s look at the key components of this example:

# Configure Daily transport for WebRTC communication
# - room_url: Where to connect
# - None: No authentication token needed
# - "Say One Thing": Bot's display name
# - Enable audio output for text-to-speech playback
transport = DailyTransport(
    room_url,
    None,
    "Say One Thing",
    DailyParams(audio_out_enabled=True)
)

# Initialize Cartesia's text-to-speech service
# Using a pre-selected British female voice
# You can find other voices at play.cartesia.ai/library
tts = CartesiaHttpTTSService(
    api_key=os.getenv("CARTESIA_API_KEY"),
    voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
)

# Create a pipeline runner to manage the processing pipeline
runner = PipelineRunner()

# Create pipeline to process text into speech and play it
# 1. tts: Converts text messages into audio
# 2. transport.output(): Sends audio to the Daily room
pipeline = Pipeline([
    tts,
    transport.output()
])

# Create PipelineTask
task = PipelineTask(pipeline)

# When the first person joins the room:
# - Extract their username from the participant info
# - Generate a greeting message
# - Queue it for text-to-speech conversion
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
    participant_name = participant.get("info", {}).get("userName", "")
    await task.queue_frame(TextFrame(f"Hello there, {participant_name}!"))

# When someone leaves the room:
# - Queue an EndFrame to gracefully shut down the application
# This ensures proper cleanup of resources
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
    await task.queue_frame(EndFrame())

# Run the pipeline task
await runner.run(task)

This example demonstrates several core Pipecat concepts:

  • Using a transport for audio output
  • Setting up a TTS service
  • Creating a simple processing pipeline
  • Handling events and generating responses
  • Managing application lifecycle

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Next Steps

Now that you’ve got your first Pipecat app running, you can:

Installation & SetupBuild your first chatbot