Merge pull request #4241 from pipecat-ai/filipi/async_tools_cancellable

Enable async tool cancellation feature.

examples/function-calling/function-calling-anthropic-async-stream.py

@@ -118,6 +118,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=AnthropicLLMService.Settings(
             system_instruction=(
                 "You are a helpful assistant in a voice conversation. "
@@ -139,9 +140,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         timeout_secs=30,
     )
 
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now."))
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
 
     location_function = FunctionSchema(
         name="track_current_location",

examples/function-calling/function-calling-anthropic-async.py

@@ -77,6 +77,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=AnthropicLLMService.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.",
         ),
@@ -92,6 +93,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     weather_function = FunctionSchema(
         name="get_current_weather",
         description="Get the current weather",

examples/function-calling/function-calling-google-async-stream.py

@@ -118,6 +118,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=GoogleLLMService.Settings(
             system_instruction=(
                 "You are a helpful assistant in a voice conversation. "
@@ -143,6 +144,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now."))
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     location_function = FunctionSchema(
         name="track_current_location",
         description="Start tracking the user's current GPS location, reporting position updates until the user reaches their destination.",

examples/function-calling/function-calling-google-async.py

@@ -128,6 +128,7 @@ indicate you should use the get_image tool are:
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=GoogleLLMService.Settings(
             system_instruction=system_prompt,
         ),
@@ -140,6 +141,11 @@ indicate you should use the get_image tool are:
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     weather_function = FunctionSchema(
         name="get_weather",
         description="Get the current weather",

examples/function-calling/function-calling-openai-async-stream.py

@@ -118,6 +118,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=OpenAILLMService.Settings(
             system_instruction=(
                 "You are a helpful assistant in a voice conversation. "
@@ -143,6 +144,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now."))
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     location_function = FunctionSchema(
         name="track_current_location",
         description="Start tracking the user's current GPS location, reporting position updates until the user reaches their destination.",
@@ -181,6 +187,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     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")

examples/function-calling/function-calling-openai-async.py

@@ -87,6 +87,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
+        enable_async_tool_cancellation=True,
         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.",
         ),
@@ -106,6 +107,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     weather_function = FunctionSchema(
         name="get_current_weather",
         description="Get the current weather",
@@ -165,6 +171,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     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")

examples/function-calling/function-calling-openai-responses-async-stream.py

@@ -118,6 +118,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAIResponsesLLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=OpenAIResponsesLLMService.Settings(
             system_instruction=(
                 "You are a helpful assistant in a voice conversation. "
@@ -143,6 +144,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_function_calls_started(service, function_calls):
         await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now."))
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     location_function = FunctionSchema(
         name="track_current_location",
         description="Track the device's current GPS location during a road trip, reporting position updates as the vehicle moves through cities until it reaches the final destination.",

examples/function-calling/function-calling-openai-responses-async.py

@@ -77,6 +77,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAIResponsesLLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
+        enable_async_tool_cancellation=True,
         settings=OpenAIResponsesLLMService.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.",
         ),
@@ -104,6 +105,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # matching, forcing a full context resend.
         await tts.queue_frame(TTSSpeakFrame("Let me check on that.", append_to_context=False))
 
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
     weather_function = FunctionSchema(
         name="get_current_weather",
         description="Get the current weather",

src/pipecat/adapters/base_llm_adapter.py

@@ -10,11 +10,13 @@ This module provides the abstract base class for implementing LLM provider-speci
 adapters that handle tool format conversion and standardization.
 """
 
+import warnings
 from abc import ABC, abstractmethod
 from typing import Any, Dict, Generic, List, Optional, TypeVar
 
 from loguru import logger
 
+from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.processors.aggregators.llm_context import (
     LLMContext,
@@ -48,6 +50,21 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
     def __init__(self):
         """Initialize the adapter."""
         self._warned_system_instruction = False
+        self._builtin_tools: Dict[str, FunctionSchema] = {}
+
+    @property
+    def builtin_tools(self) -> Dict[str, FunctionSchema]:
+        """Built-in tools automatically merged into every inference request.
+
+        Keyed by tool name for O(1) lookup, insertion, and removal.  The
+        service injects tools here so they are sent transparently on every
+        inference request without the user having to add them to their
+        ``ToolsSchema``.
+
+        Returns:
+            Mutable dict mapping tool name to ``FunctionSchema``.
+        """
+        return self._builtin_tools
 
     @property
     @abstractmethod
@@ -122,6 +139,9 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
     def from_standard_tools(self, tools: Any) -> List[Any] | NotGiven:
         """Convert tools from standard format to provider format.
 
+        Built-in tools are automatically merged into the schema before conversion so that every
+        inference request receives them without the user having to declare them explicitly.
+
         Args:
             tools: Tools in standard format or provider-specific format.
 
@@ -129,8 +149,31 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
             List of tools converted to provider format, or original tools
             if not in standard format.
         """
+        if self._builtin_tools:
+            if isinstance(tools, ToolsSchema):
+                tools = ToolsSchema(
+                    standard_tools=tools.standard_tools + list(self._builtin_tools.values()),
+                    custom_tools=tools.custom_tools,
+                )
+            else:
+                # User supplied tools in a legacy/provider-specific format.
+                # Built-in tools cannot be safely merged, so they will not be injected.
+                # Migrate to ToolsSchema to enable built-in tool support; use custom_tools
+                # as an escape hatch for any provider-specific tools that don't fit the
+                # standard schema.
+                if tools is not None:
+                    warnings.warn(
+                        "Built-in tools (e.g. async tool cancellation) could not be injected "
+                        "because the supplied tools are not a ToolsSchema instance. "
+                        "Migrate to ToolsSchema to enable built-in tool support. "
+                        "Use ToolsSchema(custom_tools=...) as an escape hatch for any "
+                        "provider-specific tools that don't fit the standard schema.",
+                        DeprecationWarning,
+                        stacklevel=2,
+                    )
+                # Fall through and return the original tools unchanged.
+
         if isinstance(tools, ToolsSchema):
-            logger.debug(f"Retrieving the tools using the adapter: {type(self)}")
             return self.to_provider_tools_format(tools)
         # Fallback to return the same tools in case they are not in a standard format
         return tools

src/pipecat/services/llm_service.py

@@ -16,6 +16,7 @@ from typing import (
     Awaitable,
     Callable,
     Dict,
+    List,
     Mapping,
     Optional,
     Protocol,
@@ -60,6 +61,11 @@ from pipecat.services.ai_service import AIService
 from pipecat.services.settings import LLMSettings
 from pipecat.services.websocket_service import WebsocketService
 from pipecat.turns.user_turn_completion_mixin import UserTurnCompletionLLMServiceMixin
+from pipecat.utils.async_tool_cancellation import (
+    ASYNC_TOOL_CANCELLATION_INSTRUCTIONS,
+    CANCEL_ASYNC_TOOL_NAME,
+    CANCEL_ASYNC_TOOL_SCHEMA,
+)
 from pipecat.utils.context.llm_context_summarization import (
     DEFAULT_SUMMARIZATION_TIMEOUT,
     LLMContextSummarizationUtil,
@@ -177,7 +183,10 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
 
     - on_completion_timeout: Called when an LLM completion timeout occurs
     - on_function_calls_started: Called when function calls are received and
-      execution is about to start
+      execution is about to start. Built-in tools (e.g. ``cancel_async_tool_call``)
+      are excluded from this event.
+    - on_function_calls_cancelled: Called after one or more async tool calls are
+      cancelled.
 
     Example::
 
@@ -186,8 +195,12 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
             logger.warning("LLM completion timed out")
 
         @task.event_handler("on_function_calls_started")
-        async def on_function_calls_started(service, function_calls):
+        async def on_function_calls_started(service, function_calls: List[FunctionCallFromLLM]):
             logger.info(f"Starting {len(function_calls)} function calls")
+
+        @task.event_handler("on_function_calls_cancelled")
+        async def on_function_calls_cancelled(service, function_calls: List[FunctionCallFromLLM]):
+            logger.info(f"Cancelled {len(function_calls)} function calls")
     """
 
     _settings: LLMSettings
@@ -201,6 +214,7 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         run_in_parallel: bool = True,
         group_parallel_tools: bool = True,
         function_call_timeout_secs: Optional[float] = None,
+        enable_async_tool_cancellation: bool = False,
         settings: Optional[LLMSettings] = None,
         **kwargs,
     ):
@@ -215,6 +229,10 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
                 arrives. Defaults to True.
             function_call_timeout_secs: Optional timeout in seconds for deferred function
                 calls.
+            enable_async_tool_cancellation: When True and at least one async function
+                (``cancel_on_interruption=False``) is registered, automatically injects
+                the ``cancel_async_tool_call`` built-in tool and its system instructions
+                so the LLM can cancel stale in-progress calls. Defaults to False.
             settings: The runtime-updatable settings for the LLM service.
             **kwargs: Additional arguments passed to the parent AIService.
 
@@ -229,7 +247,9 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         self._run_in_parallel = run_in_parallel
         self._group_parallel_tools = group_parallel_tools
         self._function_call_timeout_secs = function_call_timeout_secs
+        self._enable_async_tool_cancellation: bool = enable_async_tool_cancellation
         self._filter_incomplete_user_turns: bool = False
+        self._async_tool_cancellation_enabled: bool = False
         self._base_system_instruction: Optional[str] = None
         self._adapter = self.adapter_class()
         self._functions: Dict[Optional[str], FunctionCallRegistryItem] = {}
@@ -239,6 +259,7 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         self._summary_task: Optional[asyncio.Task] = None
 
         self._register_event_handler("on_function_calls_started")
+        self._register_event_handler("on_function_calls_cancelled")
         self._register_event_handler("on_completion_timeout")
 
     def get_llm_adapter(self) -> BaseLLMAdapter:
@@ -291,6 +312,8 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         await super().start(frame)
         if not self._run_in_parallel:
             await self._create_sequential_runner_task()
+        if self._enable_async_tool_cancellation and self._has_async_tools():
+            self._setup_async_tool_cancellation()
 
     async def stop(self, frame: EndFrame):
         """Stop the LLM service.
@@ -315,17 +338,20 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         await self._cancel_summary_task()
 
     def _compose_system_instruction(self):
-        """Compose system_instruction by appending turn completion instructions.
+        """Compose system_instruction from the base and all active addon instructions.
 
         Combines the base system instruction with turn completion instructions
-        and writes the result to ``self._settings.system_instruction``.
+        (when enabled) and async tool cancellation instructions (when enabled),
+        writing the result to ``self._settings.system_instruction``.
         """
         base = self._base_system_instruction
-        completion_instructions = self._user_turn_completion_config.completion_instructions
-        if base:
-            self._settings.system_instruction = f"{base}\n\n{completion_instructions}"
-        else:
-            self._settings.system_instruction = completion_instructions
+        parts = [base] if base else []
+        if self._filter_incomplete_user_turns:
+            parts.append(self._user_turn_completion_config.completion_instructions)
+        if self._async_tool_cancellation_enabled:
+            parts.append(ASYNC_TOOL_CANCELLATION_INSTRUCTIONS)
+        composed = "\n\n".join(p for p in parts if p)
+        self._settings.system_instruction = composed or None
 
     async def _update_settings(self, delta: LLMSettings) -> dict[str, Any]:
         """Apply a settings delta, handling turn-completion fields.
@@ -361,10 +387,10 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
 
         if (
             "system_instruction" in changed
-            and self._filter_incomplete_user_turns
+            and (self._filter_incomplete_user_turns or self._async_tool_cancellation_enabled)
             and "filter_incomplete_user_turns" not in changed
         ):
-            # system_instruction changed while turn completion is active.
+            # system_instruction changed while composition is active.
             # Treat the new value as the new base and recompose.
             self._base_system_instruction = self._settings.system_instruction
             self._compose_system_instruction()
@@ -576,6 +602,11 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
                 ``function_call_timeout_secs`` for this specific function. Defaults to
                 None, which uses the global timeout.
         """
+        if function_name == CANCEL_ASYNC_TOOL_NAME:
+            raise ValueError(
+                f"'{CANCEL_ASYNC_TOOL_NAME}' is a reserved built-in tool name and cannot be "
+                "registered by user code."
+            )
         # Registering a function with the function_name set to None will run
         # that handler for all functions
         self._functions[function_name] = FunctionCallRegistryItem(
@@ -610,6 +641,11 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
                 None, which uses the global timeout.
         """
         wrapper = DirectFunctionWrapper(handler)
+        if wrapper.name == CANCEL_ASYNC_TOOL_NAME:
+            raise ValueError(
+                f"'{CANCEL_ASYNC_TOOL_NAME}' is a reserved built-in tool name and cannot be "
+                "registered by user code."
+            )
         self._functions[wrapper.name] = FunctionCallRegistryItem(
             function_name=wrapper.name,
             handler=wrapper,
@@ -624,6 +660,8 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
             function_name: The name of the function handler to remove.
         """
         del self._functions[function_name]
+        if self._async_tool_cancellation_enabled and not self._has_async_tools():
+            self._teardown_async_tool_cancellation()
 
     def unregister_direct_function(self, handler: Any):
         """Remove a registered direct function handler.
@@ -634,6 +672,8 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         wrapper = DirectFunctionWrapper(handler)
         del self._functions[wrapper.name]
         # Note: no need to remove start callback here, as direct functions don't support start callbacks.
+        if self._async_tool_cancellation_enabled and not self._has_async_tools():
+            self._teardown_async_tool_cancellation()
 
     def has_function(self, function_name: str):
         """Check if a function handler is registered.
@@ -661,9 +701,14 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         if len(function_calls) == 0:
             return
 
-        await self._call_event_handler("on_function_calls_started", function_calls)
-
-        await self.broadcast_frame(FunctionCallsStartedFrame, function_calls=function_calls)
+        # Exclude the built-in cancel tool — it's an internal mechanism and
+        # should not be surfaced to user-facing event handlers or frames.
+        user_visible_calls = [
+            fc for fc in function_calls if fc.function_name != CANCEL_ASYNC_TOOL_NAME
+        ]
+        if user_visible_calls:
+            await self._call_event_handler("on_function_calls_started", user_visible_calls)
+            await self.broadcast_frame(FunctionCallsStartedFrame, function_calls=user_visible_calls)
 
         # When group_parallel_tools is True all calls share a group_id so the
         # aggregator triggers the LLM exactly once after the last one completes.
@@ -849,8 +894,118 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
             if timeout_task and not timeout_task.done():
                 await self.cancel_task(timeout_task)
 
+    def _has_async_tools(self) -> bool:
+        """Return True if at least one non-builtin async tool is registered."""
+        return any(
+            not item.cancel_on_interruption
+            for name, item in self._functions.items()
+            if name != CANCEL_ASYNC_TOOL_NAME
+        )
+
+    def _setup_async_tool_cancellation(self):
+        """Enable async tool cancellation.
+
+        Saves the base system instruction, recomposes to include cancellation
+        instructions, registers the built-in ``cancel_async_tool_call`` handler,
+        and injects its schema into the adapter's built-in tool dict.
+        """
+        logger.debug(f"{self}: Enabling async tool cancellation")
+
+        self._async_tool_cancellation_enabled = True
+
+        if self._base_system_instruction is None:
+            self._base_system_instruction = self._settings.system_instruction
+
+        self._compose_system_instruction()
+
+        self._adapter.builtin_tools[CANCEL_ASYNC_TOOL_NAME] = CANCEL_ASYNC_TOOL_SCHEMA
+
+        if CANCEL_ASYNC_TOOL_NAME not in self._functions:
+            self._functions[CANCEL_ASYNC_TOOL_NAME] = FunctionCallRegistryItem(
+                function_name=CANCEL_ASYNC_TOOL_NAME,
+                handler=self._cancel_async_tool_call_handler,
+                cancel_on_interruption=True,
+            )
+
+    def _teardown_async_tool_cancellation(self):
+        """Disable async tool cancellation.
+
+        Removes the built-in ``cancel_async_tool_call`` handler and its schema,
+        recomposes the system instruction without cancellation instructions.
+        """
+        logger.debug(f"{self}: Disabling async tool cancellation")
+
+        self._async_tool_cancellation_enabled = False
+        self._adapter.builtin_tools.pop(CANCEL_ASYNC_TOOL_NAME, None)
+        self._functions.pop(CANCEL_ASYNC_TOOL_NAME, None)
+        self._compose_system_instruction()
+
+    async def _cancel_async_tool_call_handler(self, params: "FunctionCallParams"):
+        """Handle a ``cancel_async_tool_call`` invocation from the LLM.
+
+        Args:
+            params: Function call parameters containing ``tool_call_id`` to cancel.
+        """
+        logger.debug(f"{self}: cancel_async_tool_call invoked")
+
+        tool_call_id: Optional[str] = params.arguments.get("tool_call_id")
+        if not tool_call_id:
+            logger.warning(f"{self} cancel_async_tool_call called with no tool_call_id")
+            await params.result_callback({"cancelled": None})
+            return
+
+        await self._cancel_function_calls_by_tool_call_id(tool_call_id)
+        await params.result_callback(
+            {"cancelled": tool_call_id},
+            properties=FunctionCallResultProperties(run_llm=True),
+        )
+
+    async def _cancel_function_calls_by_tool_call_id(self, tool_call_id: str):
+        """Cancel in-progress function call tasks by their tool_call_id.
+
+        Args:
+            tool_call_id: tool_call_id to cancel.
+        """
+        cancelled_tasks = set()
+        cancelled_items = []
+        for task, runner_item in self._function_call_tasks.items():
+            if runner_item.tool_call_id == tool_call_id:
+                name = runner_item.function_name
+                tool_call_id = runner_item.tool_call_id
+
+                logger.debug(
+                    f"{self} Cancelling async function call [{name}:{tool_call_id}] "
+                    "by LLM request..."
+                )
+
+                if task:
+                    task.remove_done_callback(self._function_call_task_finished)
+                    await self.cancel_task(task)
+                    cancelled_tasks.add(task)
+
+                await self.broadcast_frame(
+                    FunctionCallCancelFrame, function_name=name, tool_call_id=tool_call_id
+                )
+
+                cancelled_items.append(
+                    FunctionCallFromLLM(
+                        function_name=runner_item.function_name,
+                        tool_call_id=runner_item.tool_call_id,
+                        arguments=runner_item.arguments,
+                        context=runner_item.context,
+                    )
+                )
+                logger.debug(f"{self} Async function call [{name}:{tool_call_id}] cancelled")
+
+        for task in cancelled_tasks:
+            self._function_call_task_finished(task)
+
+        if cancelled_items:
+            await self._call_event_handler("on_function_calls_cancelled", cancelled_items)
+
     async def _cancel_function_call(self, function_name: Optional[str]):
         cancelled_tasks = set()
+        cancelled_items = []
         for task, runner_item in self._function_call_tasks.items():
             if runner_item.registry_item.function_name == function_name:
                 name = runner_item.function_name
@@ -870,12 +1025,23 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
                     FunctionCallCancelFrame, function_name=name, tool_call_id=tool_call_id
                 )
 
+                cancelled_items.append(
+                    FunctionCallFromLLM(
+                        function_name=runner_item.function_name,
+                        tool_call_id=runner_item.tool_call_id,
+                        arguments=runner_item.arguments,
+                        context=runner_item.context,
+                    )
+                )
                 logger.debug(f"{self} Function call [{name}:{tool_call_id}] has been cancelled")
 
         # Remove all cancelled tasks from our set.
         for task in cancelled_tasks:
             self._function_call_task_finished(task)
 
+        if cancelled_items:
+            await self._call_event_handler("on_function_calls_cancelled", cancelled_items)
+
     def _function_call_task_finished(self, task: asyncio.Task):
         if task in self._function_call_tasks:
             del self._function_call_tasks[task]

src/pipecat/utils/async_tool_cancellation.py

@@ -0,0 +1,51 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Constants for the built-in async tool cancellation feature.
+
+When an ``LLMService`` has functions registered with
+``cancel_on_interruption=False`` (async tools), it automatically injects the
+``cancel_async_tool_call`` tool and the instructions below into every inference
+request so the LLM can cancel stale in-progress calls.
+"""
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+
+CANCEL_ASYNC_TOOL_NAME = "cancel_async_tool_call"
+
+ASYNC_TOOL_CANCELLATION_INSTRUCTIONS = """ASYNC TOOL CANCELLATION:
+Some tool calls run asynchronously in the background. When one starts, a tool response \
+is added to the conversation whose content is a JSON object with \
+"type": "async_tool", "status": "running", and a "tool_call_id" field containing the \
+exact ID of that call (e.g. {"type": "async_tool", "status": "running", "tool_call_id": "..."}).
+
+If the user changes topic, explicitly says they no longer need the result, or the pending \
+result would clearly be stale, call cancel_async_tool_call. \
+To find the correct tool_call_id: locate the most recent tool response in the conversation \
+whose content has "status": "running" and whose call has NOT already been cancelled, \
+then copy the "tool_call_id" value from that content exactly as-is. \
+Never invent or guess a tool_call_id."""
+
+CANCEL_ASYNC_TOOL_SCHEMA = FunctionSchema(
+    name=CANCEL_ASYNC_TOOL_NAME,
+    description=(
+        "Cancel a single async tool call whose results are no longer needed. "
+        "Use this when the user changes topic, indicates a pending result is "
+        "no longer relevant, or when processing the result would produce a "
+        "stale or confusing response. "
+        "The tool_call_id must be copied exactly from the 'tool_call_id' field "
+        "in the async tool's 'running' response visible in the conversation history."
+    ),
+    properties={
+        "tool_call_id": {
+            "type": "string",
+            "description": (
+                "The exact tool_call_id from the async tool's 'running' response to cancel."
+            ),
+        }
+    },
+    required=["tool_call_id"],
+)