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Merge pull request #4501 from pipecat-ai/aleix/fix-filter-incomplete-tool-calls

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Fix filter-incomplete + function-calling deadlock
This commit is contained in:
Aleix Conchillo Flaqué
2026-05-15 15:11:45 -07:00
committed by GitHub
parent c6ea6c6522 d39e6bf921
commit b6ecce754b
5 changed files with 247 additions and 3 deletions
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1
changelog/4501.fixed.md Normal file
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@@ -0,0 +1 @@
- Fixed bot hangs when `filter_incomplete_user_turns` was enabled and the LLM responded by calling a tool. The user turn never finalized, so the assistant aggregator gated the tool-result context push and the LLM continuation never ran. Tool calls now finalize the turn the moment they start, before the function dispatches.

2
examples/function-calling/function-calling-openai.py
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@@ -68,9 +68,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
tts = OpenAITTSService(
api_key=os.environ["OPENAI_API_KEY"],
settings=OpenAITTSService.Settings(
instructions="Please speak clearly and at a moderate pace.",
voice="ballad",
),
instructions="Please speak clearly and at a moderate pace.",
)
llm = OpenAILLMService(

201
examples/turn-management/turn-management-filter-incomplete-turns-function-calling.py Normal file
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@@ -0,0 +1,201 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Example 22: Filter Incomplete Turns
Demonstrates LLM-based turn completion detection to suppress bot responses when
the user was cut off mid-thought. The LLM outputs one of three markers:
- ✓ (complete): User finished their thought, respond normally
- ○ (incomplete short): User was cut off, wait ~5s then prompt
- ◐ (incomplete long): User needs time to think, wait ~10s then prompt
When incomplete is detected, the bot's response is suppressed. After the timeout
expires, the LLM is automatically prompted to re-engage the user.
"""
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
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 (
AssistantTurnStoppedMessage,
LLMContextAggregatorPair,
LLMUserAggregatorParams,
UserTurnStoppedMessage,
)
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.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
from pipecat.turns.user_turn_strategies import FilterIncompleteUserTurnStrategies
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
),
}
async def get_weather(params: FunctionCallParams, location: str):
"""Return the current weather for a location.
A stub that always reports the same conditions — replace with a real
weather API in production.
Args:
location (str): The city and state or country, e.g. "Paris, France".
"""
await params.result_callback(
{
"location": location,
"temperature_celsius": 22,
"conditions": "partly cloudy",
}
)
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
llm = OpenAILLMService(
api_key=os.environ["OPENAI_API_KEY"],
settings=OpenAILLMService.Settings(
system_instruction=(
"You are a helpful assistant in a voice conversation. Your "
"responses will be spoken aloud, so avoid emojis, bullet "
"points, or other formatting that can't be spoken. Respond to "
"what the user said in a creative, helpful, and brief way. "
"If the user asks about the weather, call the get_weather "
"tool and speak the result back naturally."
),
),
)
llm.register_direct_function(get_weather)
tts = CartesiaTTSService(
api_key=os.environ["CARTESIA_API_KEY"],
settings=CartesiaTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
),
)
context = LLMContext(tools=ToolsSchema(standard_tools=[get_weather]))
# `FilterIncompleteUserTurnStrategies` pairs the default detector
# chain with `LLMTurnCompletionUserTurnStopStrategy`: detectors
# trigger LLM inference but the public `on_user_turn_stopped` event
# fires only when the LLM confirms ✓. The LLM marks each response
# with one of:
# ✓ = complete (respond normally)
# ○ = incomplete short (wait 5s, then prompt)
# ◐ = incomplete long (wait 10s, then prompt)
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
vad_analyzer=SileroVADAnalyzer(),
user_turn_strategies=FilterIncompleteUserTurnStrategies(
# Optional: customize turn completion behavior
# config=UserTurnCompletionConfig(
# incomplete_short_timeout=5.0,
# incomplete_long_timeout=10.0,
# incomplete_short_prompt="Custom prompt...",
# incomplete_long_prompt="Custom prompt...",
# instructions="Custom turn completion instructions...",
# ),
),
),
)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt,
user_aggregator, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
assistant_aggregator, # Assistant spoken responses
]
)
task = PipelineTask(
pipeline,
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(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
await task.cancel()
@user_aggregator.event_handler("on_user_turn_stopped")
async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
timestamp = f"[{message.timestamp}] " if message.timestamp else ""
line = f"{timestamp}user: {message.content}"
logger.info(f"Transcript: {line}")
@assistant_aggregator.event_handler("on_assistant_turn_stopped")
async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
timestamp = f"[{message.timestamp}] " if message.timestamp else ""
line = f"{timestamp}assistant: {message.content}"
logger.info(f"Transcript: {line}")
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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()

1
scripts/evals/run-release-evals.py
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@@ -242,6 +242,7 @@ TESTS_VIDEO_AVATAR = [
TESTS_TURN_MANAGEMENT = [
("turn-management/turn-management-filter-incomplete-turns.py", EVAL_COMPLETE_TURN),
("turn-management/turn-management-filter-incomplete-turns-function-calling.py", EVAL_WEATHER),
]
TESTS_THINKING = [

45
src/pipecat/turns/user_turn_completion_mixin.py
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@@ -20,6 +20,7 @@ from loguru import logger
from pipecat.frames.frames import (
Frame,
FunctionCallsStartedFrame,
InterruptionFrame,
LLMFullResponseEndFrame,
LLMMarkerFrame,
@@ -222,6 +223,14 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor):
# ensures graceful degradation if the LLM disobeys and outputs additional text.
self._turn_suppressed = False
self._turn_complete_found = False # True when ✓ (COMPLETE) is detected
# Set when the LLM made a tool call during this turn. Informational
# only — broadcasting is idempotency-gated by
# ``_turn_completion_broadcasted``.
self._turn_had_function_call = False
# True once ``UserTurnInferenceCompletedFrame`` has been broadcast
# for this turn. Prevents double-broadcast when ✓ and a tool call
# both occur in the same turn.
self._turn_completion_broadcasted = False
# Timeout handling
self._user_turn_completion_config = UserTurnCompletionConfig()
@@ -236,6 +245,27 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor):
"""
self._user_turn_completion_config = config
async def _broadcast_turn_completion(self):
"""Broadcast ``UserTurnInferenceCompletedFrame`` at most once per turn.
Called from the two places we know the LLM has committed to a
response for the current user turn:
- the ``✓`` marker is detected in the text stream
- a ``FunctionCallsStartedFrame`` is emitted — the LLM committed
to a tool call before producing (or instead of) a marker.
Broadcasting on the tool-call path matters for races: the
downstream ``UserStoppedSpeakingFrame`` needs to propagate
before the function actually executes and a
``FunctionCallResultFrame`` flows back to the assistant
aggregator.
"""
if self._turn_completion_broadcasted:
return
self._turn_completion_broadcasted = True
await self.broadcast_frame(UserTurnInferenceCompletedFrame)
async def _start_incomplete_timeout(self, incomplete_type: Literal["short", "long"]):
"""Start a timeout task for incomplete turn handling.
@@ -325,6 +355,8 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor):
self._turn_text_buffer = ""
self._turn_suppressed = False
self._turn_complete_found = False
self._turn_had_function_call = False
self._turn_completion_broadcasted = False
async def process_frame(self, frame: Frame, direction: FrameDirection):
"""Process frames, handling turn completion state resets.
@@ -351,7 +383,14 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor):
frame: The frame to push downstream.
direction: The direction of frame flow. Defaults to downstream.
"""
if isinstance(frame, LLMFullResponseEndFrame):
if isinstance(frame, FunctionCallsStartedFrame):
self._turn_had_function_call = True
# Broadcast turn completion now, before the function dispatches
# — gives ``UserStoppedSpeakingFrame`` maximum time to propagate
# so the assistant aggregator's ``_user_speaking`` is False by
# the time a ``FunctionCallResultFrame`` arrives.
await self._broadcast_turn_completion()
elif isinstance(frame, LLMFullResponseEndFrame):
await self._turn_reset()
await super().push_frame(frame, direction)
@@ -427,7 +466,9 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor):
# LLMTurnCompletionUserTurnStopStrategy) can fire
# `on_user_turn_stopped`. Must fire before the marker so
# downstream consumers see the signal before the response.
await self.broadcast_frame(UserTurnInferenceCompletedFrame)
# Idempotent: a tool call earlier in the turn may have
# already broadcast.
await self._broadcast_turn_completion()
# Push the marker as a sideband signal that the assistant
# aggregator will prepend to the upcoming aggregated text,