​
Overview

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

Services

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

Frameworks

User RTVI for client/server communication or Pipecat Flows for building structured conversations

Utilities

Helper functions and tools for audio, filtering, and flows

​
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

​
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

​
Example Usage

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline import Pipeline
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

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

# Configure services
stt = DeepgramSTTService(api_key=KEY)
llm = OpenAILLMService(api_key=KEY, model="gpt-4o")
tts = CartesiaTTSService(api_key=KEY, voice_id=ID)

# Create context and aggregators
context = OpenAILLMContext(
    messages=[{"role": "system", "content": "You are a helpful assistant."}]
)
context_aggregator = llm.create_context_aggregator(context)

# Create pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator,assistant()
])

​
Overview

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

Services

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

Frameworks

User RTVI for client/server communication or Pipecat Flows for building structured conversations

Utilities

Helper functions and tools for audio, filtering, and flows

​
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

​
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

​
Example Usage

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline import Pipeline
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

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

# Configure services
stt = DeepgramSTTService(api_key=KEY)
llm = OpenAILLMService(api_key=KEY, model="gpt-4o")
tts = CartesiaTTSService(api_key=KEY, voice_id=ID)

# Create context and aggregators
context = OpenAILLMContext(
    messages=[{"role": "system", "content": "You are a helpful assistant."}]
)
context_aggregator = llm.create_context_aggregator(context)

# Create pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator,assistant()
])