Add deterministic dev-error demo example
``examples/function-calling/function-calling-missing-handler.py`` demonstrates the missing-handler path by deliberately advertising a tool to the LLM without registering its handler — what happens when a developer forgets to call ``register_function``. Exercises the new ``logger.error`` severity end-to-end without needing to coax the LLM into hallucinating.
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Paul Kompfner
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examples/function-calling/function-calling-missing-handler.py
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187
examples/function-calling/function-calling-missing-handler.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Manual demonstration of the missing-handler (developer-error) recovery path.
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When a tool is advertised to the LLM via ``tools``/``LLMContext`` but
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the developer forgets to call ``llm.register_function(...)`` to wire up
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its handler, the LLM happily emits a tool call and then... nothing
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happens on the Pipecat side, leaving the conversation stuck.
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Pipecat's recovery path (``LLMService._missing_function_call_handler``)
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catches this case:
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- Logs a ``logger.error`` distinguishing **developer error** (tool advertised
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but no handler registered) from a hallucination (tool not advertised),
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pointing at the missing ``register_function`` call.
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- Returns a neutral terminal tool result
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(``LLMService.MISSING_FUNCTION_CALL_MESSAGE_TEMPLATE``: "The function
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`X` is not currently available.") so the call still terminates with a
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normal tool result instead of leaving the conversation stuck.
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This example is **deliberately broken**: the weather schema is in
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``tools`` but ``register_function`` is *not* called. Ask the bot about
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the weather and observe:
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1. The LLM emits a tool call for ``get_current_weather``.
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2. ``logger.error`` fires with "advertised … but has no registered handler
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— did you forget to call register_function()?"
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3. The terminal tool result is fed back to the LLM.
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4. The LLM responds in voice based on that result (typically something
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like "the weather function isn't available right now").
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Uses the OpenAI LLM service with defaults. Swap to another provider to
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validate this behavior elsewhere.
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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.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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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.task import PipelineParams, PipelineTask
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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.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.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the user's location.",
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},
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},
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required=["location", "format"],
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)
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weather_tools = ToolsSchema(standard_tools=[weather_function])
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transport_params = {
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"daily": lambda: DailyParams(audio_in_enabled=True, audio_out_enabled=True),
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"twilio": lambda: FastAPIWebsocketParams(audio_in_enabled=True, audio_out_enabled=True),
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"webrtc": lambda: TransportParams(audio_in_enabled=True, audio_out_enabled=True),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting missing-handler demo bot (no handler is registered on purpose)")
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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(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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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 helpful assistant in a voice conversation. Your responses "
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"will be spoken aloud, so avoid emojis, bullet points, or other "
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"formatting that can't be spoken. Respond briefly and naturally. "
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"Always use the get_current_weather function to answer questions "
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"about the current weather."
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),
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),
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)
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# *** DELIBERATELY OMITTED ***
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# The whole point of this example is to demonstrate the missing-handler
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# recovery path. Re-add this line to wire the tool up correctly:
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#
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# llm.register_function("get_current_weather", fetch_weather_from_api)
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context = LLMContext(tools=weather_tools)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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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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user_aggregator,
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llm,
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tts,
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transport.output(),
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
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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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logger.info(
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"=== Ask for the weather. Watch for a logger.error about the missing "
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"handler, and listen for the LLM's response based on the recovery "
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"message. ==="
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)
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context.add_message(
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{
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"role": "developer",
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"content": (
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"Please introduce yourself briefly to the user, then invite "
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"them to ask about the weather."
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),
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}
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)
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await task.queue_frames([LLMRunFrame()])
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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 task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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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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