Add local-handoff-two-agents example
Two LLM tasks (greeter and support) handing off to each other over the local `AsyncQueueBus`. The main task owns the transport pipeline (STT, TTS, transport I/O) and the child tasks each run their own LLM behind a `BusBridgeProcessor`. Each child uses `bridged=()` so `PipelineTask` auto-wraps its pipeline with the bus edge processors, and `transfer_to_agent` / `end_conversation` tools demonstrate `handoff_to(...)` and `end(...)`.
This commit is contained in:
Aleix Conchillo Flaqué
243
examples/multi-task/local-handoff/local-handoff-two-agents.py
Normal file
243
examples/multi-task/local-handoff/local-handoff-two-agents.py
Normal file
@@ -0,0 +1,243 @@
|
||||
#
|
||||
# Copyright (c) 2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Two LLM tasks with a main task bridging transport to the bus.
|
||||
|
||||
Demonstrates multi-task coordination: a main task handles transport I/O
|
||||
(STT, TTS) and bridges frames to the bus. Two LLM tasks — a greeter and
|
||||
a support task — each run their own LLM pipeline and hand off control
|
||||
between each other.
|
||||
|
||||
The user talks to one task at a time. Hand-offs are seamless — the LLM
|
||||
decides when to transfer based on its tools.
|
||||
|
||||
Requirements:
|
||||
|
||||
- OPENAI_API_KEY
|
||||
- DEEPGRAM_API_KEY
|
||||
- CARTESIA_API_KEY
|
||||
- DAILY_API_KEY (for Daily transport)
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.bus import BusBridgeProcessor
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.tasks.llm import LLMTask, LLMTaskActivationArgs, tool
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
MAIN_NAME = "acme"
|
||||
|
||||
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
class AcmeLLMTask(LLMTask):
|
||||
"""LLM-only child task with transfer/end tools.
|
||||
|
||||
Receives user context from the main task via the bus, runs its LLM,
|
||||
and ships generated text frames back. The main task's TTS turns the
|
||||
text into audio.
|
||||
|
||||
Passing ``bridged=()`` tells :class:`PipelineTask` to wrap the LLM
|
||||
pipeline with bus edge processors so frames flow between this task
|
||||
and the main task automatically.
|
||||
"""
|
||||
|
||||
@tool(cancel_on_interruption=False)
|
||||
async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
|
||||
"""Transfer the user to another agent.
|
||||
|
||||
Args:
|
||||
agent (str): The agent to transfer to (e.g. 'greeter', 'support').
|
||||
reason (str): Why the user is being transferred.
|
||||
"""
|
||||
logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
|
||||
await self.handoff_to(
|
||||
agent,
|
||||
activation_args=LLMTaskActivationArgs(
|
||||
messages=[{"role": "developer", "content": reason}],
|
||||
),
|
||||
result_callback=params.result_callback,
|
||||
)
|
||||
|
||||
@tool
|
||||
async def end_conversation(self, params: FunctionCallParams, reason: str):
|
||||
"""End the conversation when the user says goodbye.
|
||||
|
||||
Args:
|
||||
reason (str): Why the conversation is ending.
|
||||
"""
|
||||
logger.info(f"Task '{self.name}': ending conversation ({reason})")
|
||||
await self.end(
|
||||
reason=reason,
|
||||
messages=[{"role": "developer", "content": reason}],
|
||||
result_callback=params.result_callback,
|
||||
)
|
||||
|
||||
|
||||
def _build_greeter() -> AcmeLLMTask:
|
||||
"""Greeter: routes the user to support when they pick a product."""
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.environ["OPENAI_API_KEY"],
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction=(
|
||||
"You are a friendly greeter for Acme Corp. The available products "
|
||||
"are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
|
||||
"Tornado Kit. Ask which one they'd like to learn more about. "
|
||||
"When the user picks a product or asks a question about one, "
|
||||
"immediately call the transfer_to_agent tool with agent 'support'. "
|
||||
"Do not answer product questions yourself. If the user says goodbye, "
|
||||
"call the end_conversation tool. Do not mention transferring — just do it "
|
||||
"seamlessly. Keep responses brief — this is a voice conversation."
|
||||
),
|
||||
),
|
||||
)
|
||||
return AcmeLLMTask("greeter", llm=llm, bridged=())
|
||||
|
||||
|
||||
def _build_support() -> AcmeLLMTask:
|
||||
"""Support: answers product questions, can hand back to the greeter."""
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.environ["OPENAI_API_KEY"],
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction=(
|
||||
"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 "
|
||||
"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. "
|
||||
"Keep responses brief — this is a voice conversation."
|
||||
),
|
||||
),
|
||||
)
|
||||
return AcmeLLMTask("support", llm=llm, bridged=())
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting two-agent bot")
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.environ["CARTESIA_API_KEY"],
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc", # Jacqueline
|
||||
),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
aggregators = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
|
||||
# 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,
|
||||
task_name=MAIN_NAME,
|
||||
name=f"{MAIN_NAME}::BusBridge",
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
aggregators.user(),
|
||||
bridge,
|
||||
tts,
|
||||
transport.output(),
|
||||
aggregators.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
name=MAIN_NAME,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Spawn the child LLM tasks. ``bridged=()`` on each child auto-wraps
|
||||
# its pipeline with bus edges, so no extra wiring is needed here.
|
||||
await runner.spawn(_build_greeter())
|
||||
await runner.spawn(_build_support())
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
await task.activate_task(
|
||||
"greeter",
|
||||
args=LLMTaskActivationArgs(
|
||||
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 task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
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