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Overview

Pipecat’s server-side functionality is organized into three main categories:

Services

AI service integrations for speech, language, vision, and more

Base Classes

Core service implementations and interfaces

Utilities

Helper functions and tools for audio, filtering, and flows

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Service Categories

Pipecat integrates with various AI services across different categories:

Transport

WebRTC and WebSocket implementations for real-time communication

Speech Processing

Speech-to-text, text-to-speech, and speech-to-speech services

Language Models

Integration with various LLM providers

Vision & Media

Image generation, video processing, and computer vision

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Getting Started

  1. Browse our Supported Services to see available integrations
  2. Install required dependencies for your chosen services
  3. Reference individual service docs for detailed configuration options

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Example Usage

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

transport = DailyTransport(
    room_url,
    token,
    "Respond bot",
    DailyParams(
        audio_out_enabled=True,
        vad_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
        vad_audio_passthrough=True,
    ),
)

# Configure services
stt = DeepgramSTTService(api_key=KEY)
llm = OpenAILLMService(model=MODEL)
tts = CartesiaTTSService(voice_id=ID)

# Create pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    llm,
    tts,
    transport.output()
])
Reference docs