Complete reference for Pipecat’s Python server APIs and services
User RTVI for client/server communication or Pipecat Flows for building structured conversations
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() ])