Rename BaseTask → BaseWorker and reserve "task" for asyncio
Replaces every "task" identifier that referred to the BaseTask abstraction with "worker". Asyncio task plumbing (asyncio.Task, BaseTaskManager, TaskManager, create_task, cancel_task, etc.) stays untouched. Highlights: - Classes: BaseTask → BaseWorker, PipelineTask → PipelineWorker, LLMTask → LLMWorker, LLMContextTask → LLMContextWorker, TaskBus → WorkerBus, TaskRegistry → WorkerRegistry, TaskActivationArgs → WorkerActivationArgs, TaskReadyData → WorkerReadyData, TaskRegistryEntry → WorkerRegistryEntry, TaskObserver → WorkerObserver, all Bus*TaskMessage → Bus*WorkerMessage, BusAddTaskMessage.task field → worker, BusWorkerRegistryMessage.tasks field → workers. - Methods/decorators: activate_task → activate_worker, deactivate_task → deactivate_worker, add_task → add_worker, watch_task → watch_worker, @task_ready → @worker_ready, setup_pipeline_task hook → setup_pipeline_worker. - Params/fields: FrameProcessorSetup.pipeline_task and FunctionCallParams.pipeline_task → pipeline_worker. Parameter names like task_name → worker_name; spawn/run accept worker:. - Files: pipeline/base_task.py → base_worker.py, pipeline/task.py → worker.py (plus a re-export shim at pipeline/task.py), task_observer.py → worker_observer.py, task_ready_decorator.py → worker_ready_decorator.py, pipecat.tasks → pipecat.workers, llm_task.py → llm_worker.py, llm_context_task.py → llm_context_worker.py, examples/multi-task → examples/multi-worker. Back-compat: - PipelineTask kept as a deprecated subclass of PipelineWorker that warns on construction. - pipecat.pipeline.task re-exports PipelineWorker/PipelineTask/etc. so existing user imports keep working. - FrameProcessor.pipeline_task kept as a deprecated property that forwards to pipeline_worker. Local variables in examples that hold a worker (task = PipelineTask(...)) are renamed to worker = PipelineWorker(...). Asyncio-task locals (runner_task, etc.) are preserved.
This commit is contained in:
Aleix Conchillo Flaqué
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#
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# Copyright (c) 2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Two LLM workers with per-worker TTS voices.
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Same shape as ``local-handoff-two-agents.py``, but each child worker
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runs its own TTS with a distinct voice. The main worker has no TTS —
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audio comes from the child workers via the bus and is played by the
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main worker's transport. Tasks announce the transfer ("let me connect
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you with...") before handing off.
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Architecture::
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Main worker (no TTS):
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transport.in → STT → user_agg → BusBridge → transport.out → assistant_agg
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Child worker (with TTS):
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bridge_in → LLM → TTS → bridge_out
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Requirements:
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- OPENAI_API_KEY
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- DEEPGRAM_API_KEY
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- CARTESIA_API_KEY
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- DAILY_API_KEY (for Daily transport)
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"""
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.bus import BusBridgeProcessor
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.worker import PipelineParams, PipelineWorker
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
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load_dotenv(override=True)
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MAIN_NAME = "acme"
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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class AcmeTTSTask(LLMWorker):
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"""Child worker with its own LLM + TTS, bridged to the main worker.
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Each child wraps the standard ``Pipeline([llm])`` with an extra
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TTS processor so audio is produced locally by each child and
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shipped to the main worker over the bus.
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"""
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def __init__(self, name: str, *, llm: OpenAILLMService, voice_id: str):
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"""Initialize the child worker.
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Args:
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name: Unique worker name.
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llm: The LLM service for this child.
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voice_id: Cartesia voice id for this child's TTS.
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"""
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tts = CartesiaTTSService(
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api_key=os.environ["CARTESIA_API_KEY"],
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settings=CartesiaTTSService.Settings(voice=voice_id),
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)
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super().__init__(
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name,
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llm=llm,
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pipeline=Pipeline([llm, tts]),
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bridged=(),
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)
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@tool(cancel_on_interruption=False)
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async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
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"""Transfer the user to another agent.
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Args:
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agent (str): The agent to transfer to (e.g. 'greeter', 'support').
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reason (str): Why the user is being transferred.
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"""
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logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
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await self.activate_worker(
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agent,
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messages=[
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{
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"role": "developer",
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"content": f"Tell the user about the transfer ({reason}).",
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}
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],
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args=LLMWorkerActivationArgs(
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messages=[{"role": "developer", "content": reason}],
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),
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deactivate_self=True,
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result_callback=params.result_callback,
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)
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@tool
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async def end_conversation(self, params: FunctionCallParams, reason: str):
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"""End the conversation when the user says goodbye.
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Args:
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reason (str): Why the conversation is ending.
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"""
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logger.info(f"Task '{self.name}': ending conversation ({reason})")
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await self.end(
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reason=reason,
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messages=[{"role": "developer", "content": reason}],
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result_callback=params.result_callback,
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)
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def build_greeter() -> AcmeTTSTask:
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"""Greeter: routes the user to support when they pick a product."""
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(
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system_instruction=(
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"You are a friendly greeter for Acme Corp. The available products "
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"are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
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"Tornado Kit. Ask which one they'd like to learn more about. "
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"When the user picks a product or asks a question about one, "
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"call the transfer_to_agent tool with agent 'support'. "
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"Do not answer product questions yourself. If the user says goodbye, "
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"call the end_conversation tool. Keep responses brief — this is a "
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"voice conversation."
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),
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),
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)
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return AcmeTTSTask(
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"greeter",
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llm=llm,
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voice_id="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc", # Jacqueline
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)
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def build_support() -> AcmeTTSTask:
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"""Support: answers product questions, can hand back to the greeter."""
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(
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system_instruction=(
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"You are a support agent for Acme Corp. You know about three "
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"products: Acme Rocket Boots (jet-powered boots, $299, run up "
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"to 60 mph), Acme Invisible Paint (makes anything invisible for "
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"24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
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"generator, $199, batteries included). Answer the user's questions "
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"about these products. If the user wants to browse other products "
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"or start over, call the transfer_to_agent tool with agent "
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"'greeter'. If the user says goodbye, call the end_conversation "
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"tool. Keep responses brief — this is a voice conversation."
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),
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),
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)
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return AcmeTTSTask(
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"support",
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llm=llm,
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voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab", # Blake
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)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting two-agents-with-tts bot")
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
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context = LLMContext()
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aggregators = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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# The main worker has no TTS. Audio comes from the children over
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# the bus; the main bridge tees user context out and pushes
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# incoming audio/text frames back into the local pipeline.
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bridge = BusBridgeProcessor(
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bus=runner.bus,
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worker_name=MAIN_NAME,
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name=f"{MAIN_NAME}::BusBridge",
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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aggregators.user(),
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bridge,
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transport.output(),
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aggregators.assistant(),
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]
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)
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worker = PipelineWorker(
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pipeline,
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name=MAIN_NAME,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info("Client connected")
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await worker.activate_worker(
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"greeter",
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args=LLMWorkerActivationArgs(
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messages=[
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{
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"role": "developer",
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"content": (
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"Welcome the user to Acme Corp, mention the available products "
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"and ask how you can help."
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),
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},
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],
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),
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)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Client disconnected")
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await runner.cancel()
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await runner.add_worker(build_greeter())
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await runner.add_worker(build_support())
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await runner.add_worker(worker)
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await runner.run()
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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243
examples/multi-worker/local-handoff/local-handoff-two-agents.py
Normal file
243
examples/multi-worker/local-handoff/local-handoff-two-agents.py
Normal file
@@ -0,0 +1,243 @@
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#
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# Copyright (c) 2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Two LLM workers with a main worker bridging transport to the bus.
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Demonstrates multi-worker coordination: a main worker handles transport I/O
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(STT, TTS) and bridges frames to the bus. Two LLM workers — a greeter and
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a support worker — each run their own LLM pipeline and hand off control
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between each other.
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The user talks to one worker at a time. Hand-offs are seamless — the LLM
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decides when to transfer based on its tools.
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Requirements:
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- OPENAI_API_KEY
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- DEEPGRAM_API_KEY
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- CARTESIA_API_KEY
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- DAILY_API_KEY (for Daily transport)
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"""
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.bus import BusBridgeProcessor
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.worker import PipelineParams, PipelineWorker
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
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load_dotenv(override=True)
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MAIN_NAME = "acme"
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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class AcmeLLMTask(LLMWorker):
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"""LLM-only child worker with transfer/end tools.
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Receives user context from the main worker via the bus, runs its LLM,
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and ships generated text frames back. The main worker's TTS turns the
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text into audio.
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Passing ``bridged=()`` tells :class:`PipelineWorker` to wrap the LLM
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pipeline with bus edge processors so frames flow between this worker
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and the main worker automatically.
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"""
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@tool(cancel_on_interruption=False)
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async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
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"""Transfer the user to another agent.
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Args:
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agent (str): The agent to transfer to (e.g. 'greeter', 'support').
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reason (str): Why the user is being transferred.
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"""
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logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
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await self.activate_worker(
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agent,
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args=LLMWorkerActivationArgs(
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messages=[{"role": "developer", "content": reason}],
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),
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deactivate_self=True,
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result_callback=params.result_callback,
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)
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@tool
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async def end_conversation(self, params: FunctionCallParams, reason: str):
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"""End the conversation when the user says goodbye.
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Args:
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reason (str): Why the conversation is ending.
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"""
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logger.info(f"Task '{self.name}': ending conversation ({reason})")
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await self.end(
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reason=reason,
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messages=[{"role": "developer", "content": reason}],
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result_callback=params.result_callback,
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)
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def build_greeter() -> AcmeLLMTask:
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"""Greeter: routes the user to support when they pick a product."""
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(
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system_instruction=(
|
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"You are a friendly greeter for Acme Corp. The available products "
|
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"are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
|
||||
"Tornado Kit. Ask which one they'd like to learn more about. "
|
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"When the user picks a product or asks a question about one, "
|
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"immediately call the transfer_to_agent tool with agent 'support'. "
|
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"Do not answer product questions yourself. If the user says goodbye, "
|
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"call the end_conversation tool. Do not mention transferring — just do it "
|
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"seamlessly. Keep responses brief — this is a voice conversation."
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),
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),
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)
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return AcmeLLMTask("greeter", llm=llm, bridged=())
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def build_support() -> AcmeLLMTask:
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"""Support: answers product questions, can hand back to the greeter."""
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
|
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settings=OpenAILLMService.Settings(
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system_instruction=(
|
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"You are a support agent for Acme Corp. You know about three "
|
||||
"products: Acme Rocket Boots (jet-powered boots, $299, run up "
|
||||
"to 60 mph), Acme Invisible Paint (makes anything invisible for "
|
||||
"24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
|
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"generator, $199, batteries included). Answer the user's questions "
|
||||
"about these products. If the user wants to browse other products "
|
||||
"or start over, call the transfer_to_agent tool with agent "
|
||||
"'greeter'. If the user says goodbye, call the end_conversation "
|
||||
"tool. Do not mention transferring — just do it seamlessly. "
|
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"Keep responses brief — this is a voice conversation."
|
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),
|
||||
),
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)
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return AcmeLLMTask("support", llm=llm, bridged=())
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting two-agent bot")
|
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|
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
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|
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stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
|
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tts = CartesiaTTSService(
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api_key=os.environ["CARTESIA_API_KEY"],
|
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settings=CartesiaTTSService.Settings(
|
||||
voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc", # Jacqueline
|
||||
),
|
||||
)
|
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context = LLMContext()
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aggregators = LLMContextAggregatorPair(
|
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
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)
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# The main bridge sends user-side context downstream to the children
|
||||
# via the bus, and the children's generated text comes back here so
|
||||
# the TTS can speak it.
|
||||
bridge = BusBridgeProcessor(
|
||||
bus=runner.bus,
|
||||
worker_name=MAIN_NAME,
|
||||
name=f"{MAIN_NAME}::BusBridge",
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
aggregators.user(),
|
||||
bridge,
|
||||
tts,
|
||||
transport.output(),
|
||||
aggregators.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
worker = PipelineWorker(
|
||||
pipeline,
|
||||
name=MAIN_NAME,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
await worker.activate_worker(
|
||||
"greeter",
|
||||
args=LLMWorkerActivationArgs(
|
||||
messages=[
|
||||
{
|
||||
"role": "developer",
|
||||
"content": (
|
||||
"Welcome the user to Acme Corp, mention the available products "
|
||||
"and ask how you can help."
|
||||
),
|
||||
},
|
||||
],
|
||||
),
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await runner.cancel()
|
||||
|
||||
await runner.add_worker(build_greeter())
|
||||
await runner.add_worker(build_support())
|
||||
await runner.add_worker(worker)
|
||||
|
||||
await runner.run()
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
Reference in New Issue
Block a user