pipecat/examples/runner-examples/cloud-multi-bot.py

#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

"""Pipecat Cloud-compatible bot example.

Transports are:

- Daily
- SmallWebRTC
- Twilio
- Telnyx
- Plivo
"""

import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.runner.cloud import SmallWebRTCSessionArguments
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService

try:
    from pipecatcloud.agent import DailySessionArguments, WebSocketSessionArguments
except ImportError:
    raise ImportError(
        "pipecatcloud package is required for cloud-compatible bots. "
        "Install with: pip install pipecat-ai[pipecatcloud]"
    )

load_dotenv(override=True)

# Check if we're running locally
IS_LOCAL_RUN = os.environ.get("LOCAL_RUN", "0") == "1"


async def run_bot(transport):
    """Main bot logic that works with any transport."""
    logger.info(f"Starting bot")

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
    )

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

    messages = [
        {
            "role": "system",
            "content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
        },
    ]

    context = OpenAILLMContext(messages)
    context_aggregator = llm.create_context_aggregator(context)

    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))

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

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        observers=[RTVIObserver(rtvi)],
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info("Client connected")
        messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
        await task.queue_frames([context_aggregator.user().get_context_frame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info("Client disconnected")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=False)
    await runner.run(task)


async def bot(
    session_args: DailySessionArguments | SmallWebRTCSessionArguments | WebSocketSessionArguments,
):
    """Main bot entry point compatible with Pipecat Cloud."""

    if isinstance(session_args, DailySessionArguments):
        from pipecat.transports.services.daily import DailyParams, DailyTransport

        if not IS_LOCAL_RUN:
            from pipecat.audio.filters.krisp_filter import KrispFilter

        transport = DailyTransport(
            session_args.room_url,
            session_args.token,
            "Pipecat Bot",
            params=DailyParams(
                audio_in_enabled=True,
                audio_in_filter=None
                if IS_LOCAL_RUN
                else KrispFilter(),  # Only use Krisp in production
                audio_out_enabled=True,
                vad_analyzer=SileroVADAnalyzer(),
            ),
        )

    elif isinstance(session_args, SmallWebRTCSessionArguments):
        from pipecat.transports.base_transport import TransportParams
        from pipecat.transports.network.small_webrtc import SmallWebRTCTransport

        transport = SmallWebRTCTransport(
            params=TransportParams(
                audio_in_enabled=True,
                audio_out_enabled=True,
                vad_analyzer=SileroVADAnalyzer(),
            ),
            webrtc_connection=session_args.webrtc_connection,
        )

    elif isinstance(session_args, WebSocketSessionArguments):
        # Use the utility to parse WebSocket data
        from pipecat.runner.utils import parse_telephony_websocket

        transport_type, stream_id, call_id = await parse_telephony_websocket(session_args.websocket)
        logger.info(f"Auto-detected transport: {transport_type}")

        # Create transport based on detected type
        if transport_type == "twilio":
            from pipecat.serializers.twilio import TwilioFrameSerializer

            serializer = TwilioFrameSerializer(
                stream_sid=stream_id,
                call_sid=call_id,
                account_sid=os.getenv("TWILIO_ACCOUNT_SID", ""),
                auth_token=os.getenv("TWILIO_AUTH_TOKEN", ""),
            )

        elif transport_type == "telnyx":
            from pipecat.serializers.telnyx import TelnyxFrameSerializer

            serializer = TelnyxFrameSerializer(
                stream_id=stream_id,
                call_control_id=call_id,
                outbound_encoding="PCMU",  # Set manually
                inbound_encoding="PCMU",
            )

        elif transport_type == "plivo":
            from pipecat.serializers.plivo import PlivoFrameSerializer

            serializer = PlivoFrameSerializer(
                stream_id=stream_id,
                call_id=call_id,
            )

        else:
            # Generic fallback
            serializer = None

        # Create the transport
        from pipecat.transports.network.fastapi_websocket import (
            FastAPIWebsocketParams,
            FastAPIWebsocketTransport,
        )

        transport = FastAPIWebsocketTransport(
            websocket=session_args.websocket,
            params=FastAPIWebsocketParams(
                audio_in_enabled=True,
                audio_out_enabled=True,
                add_wav_header=False,
                vad_analyzer=SileroVADAnalyzer(),
                serializer=serializer,
            ),
        )

    await run_bot(transport)


if __name__ == "__main__":
    from pipecat.runner.cloud import main

    main()