llm_service

Base classes for Large Language Model services with function calling support.

class pipecat.services.llm_service.FunctionCallResultCallback(*args, **kwargs)

Bases: Protocol

Protocol for function call result callbacks.

Used for both final results and intermediate updates. Pass properties=FunctionCallResultProperties(is_final=False) to send an intermediate update (only valid for async function calls registered with cancel_on_interruption=False).

class pipecat.services.llm_service.FunctionCallParams(function_name: str, tool_call_id: str, arguments: Mapping[str, Any], llm: LLMService[Any], pipeline_worker: PipelineWorker, context: LLMContext, result_callback: FunctionCallResultCallback, app_resources: Any = None)

Bases: object

Parameters for a function call.

Parameters:

• function_name – The name of the function being called.

• tool_call_id – A unique identifier for the function call.

• arguments – The arguments for the function.

• llm – The LLMService instance being used.

• context – The LLM context.

• result_callback – Callback to deliver the result of the function call. For async function calls (cancel_on_interruption=False), call it with properties=FunctionCallResultProperties(is_final=False) to push intermediate updates before the final result.

• app_resources – The application-defined resources passed to PipelineWorker(..., app_resources=...). Same object — passed by reference, not a copy. Use it to share DB handles, clients, state, feature flags, etc. across all of a session’s tool handlers.

function_name: str

tool_call_id: str

arguments: Mapping[str, Any]

llm: LLMService[Any]

pipeline_worker: PipelineWorker

context: LLMContext

result_callback: FunctionCallResultCallback

app_resources: Any = None

property tool_resources: Any

Deprecated alias for app_resources.

Deprecated since version 1.2.0: Use app_resources instead. tool_resources. Will be removed in 2.0.0.

class pipecat.services.llm_service.FunctionCallRegistryItem(function_name: str | None, handler: Callable[[FunctionCallParams], Awaitable[None]] | DirectFunctionWrapper, cancel_on_interruption: bool, timeout_secs: float | None = None, auto_registered: bool = False)

Bases: object

Internal record of a registered function-call handler.

Created by the service when a function is registered — directly via register_function / register_direct_function, or automatically from a direct function advertised in an LLMContext / LLMSetToolsFrame. Application code doesn’t construct these.

Parameters:

• function_name – The name of the function (None for catch-all handler).

• handler – The handler for processing function call parameters.

• cancel_on_interruption – Whether to cancel the call on interruption. When False the call is treated as asynchronous: the LLM continues the conversation immediately without waiting for the result, and the result is injected later via a developer message.

• timeout_secs – Optional per-tool timeout in seconds. Overrides the global function_call_timeout_secs for this specific function.

• auto_registered – True only for a direct function that was auto-registered from an advertised tool set (listed in an LLMContext or LLMSetToolsFrame). False for every explicitly registered handler — direct or non-direct — and for the catch-all and built-in handlers.

function_name: str | None

handler: Callable[[FunctionCallParams], Awaitable[None]] | DirectFunctionWrapper

cancel_on_interruption: bool

timeout_secs: float | None = None

auto_registered: bool = False

class pipecat.services.llm_service.FunctionCallRunnerItem(registry_item: FunctionCallRegistryItem, function_name: str, tool_call_id: str, arguments: Mapping[str, Any], context: LLMContext, run_llm: bool | None = None, group_id: str | None = None)

Bases: object

Internal function call entry for the function call runner.

The runner executes function calls in order.

Parameters:

• registry_item – The registry item containing handler information.

• function_name – The name of the function.

• tool_call_id – A unique identifier for the function call.

• arguments – The arguments for the function.

• context – The LLM context.

• run_llm – Optional flag to control LLM execution after function call.

• group_id – Shared identifier for all function calls from the same LLM response batch. Used to trigger the LLM exactly once when the last call in the group completes.

registry_item: FunctionCallRegistryItem

function_name: str

tool_call_id: str

arguments: Mapping[str, Any]

context: LLMContext

run_llm: bool | None = None

group_id: str | None = None

class pipecat.services.llm_service.LLMService(run_in_parallel: bool = True, group_parallel_tools: bool = True, function_call_timeout_secs: float | None = None, enable_async_tool_cancellation: bool = False, settings: LLMSettings | None = None, **kwargs)

Bases: UserTurnCompletionLLMServiceMixin, AIService, Generic[TAdapter]

Base class for all LLM services.

Handles function calling registration and execution with support for both parallel and sequential execution modes. Provides event handlers for completion timeouts and function call lifecycle events.

The service supports the following event handlers:

• 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. 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:

@task.event_handler("on_completion_timeout")
async def on_completion_timeout(service):
    logger.warning("LLM completion timed out")

@task.event_handler("on_function_calls_started")
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")

adapter_class

alias of OpenAILLMAdapter

MISSING_FUNCTION_CALL_MESSAGE_TEMPLATE = 'The function `{function_name}` is not currently available.'

__init__(run_in_parallel: bool = True, group_parallel_tools: bool = True, function_call_timeout_secs: float | None = None, enable_async_tool_cancellation: bool = False, settings: LLMSettings | None = None, **kwargs)

Initialize the LLM service.

Parameters:

• run_in_parallel – Whether to run function calls in parallel or sequentially. Defaults to True.

• group_parallel_tools – Whether to group parallel function calls so the LLM is triggered exactly once after all calls in the batch complete. When False, each function call result triggers the LLM independently as it 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.

get_llm_adapter() → TAdapter

Get the LLM adapter instance.

Returns:

The adapter instance used for LLM communication.

create_llm_specific_message(message: Any) → LLMSpecificMessage

Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.

Parameters:

message – The message content.

Returns:

A LLMSpecificMessage instance.

async run_inference(context: LLMContext, max_tokens: int | None = None, system_instruction: str | None = None) → str | None

Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.

Must be implemented by subclasses.

Parameters:

• context – The LLM context containing conversation history.

• max_tokens – Optional maximum number of tokens to generate. If provided, overrides the service’s default max_tokens/max_completion_tokens setting.

• system_instruction – Optional system instruction to use for this inference. If provided, overrides any system instruction in the context.

Returns:

The LLM’s response as a string, or None if no response is generated.

service_metadata_frame() → LLMServiceMetadataFrame

The metadata frame this LLM service broadcasts at start.

The base returns a plain (non-realtime) frame; realtime (speech-to-speech) subclasses override this to set is_realtime_service=True and, when their turns are provider-driven, recommend ExternalUserTurnStrategies via user_turn_strategies.

Mostly here for conceptual consistency — today only realtime services need to override it — but it’s a natural placeholder for future LLM-service metadata.

async start(frame: StartFrame)

Start the LLM service.

Parameters:

frame – The start frame.

async stop(frame: EndFrame)

Stop the LLM service.

Parameters:

frame – The end frame.

async cancel(frame: CancelFrame)

Cancel the LLM service.

Parameters:

frame – The cancel frame.

async cleanup()

Release LLM service resources at teardown.

append_system_instruction(instruction: str) → None

Append durable text to the system instruction, preserving the user’s prompt.

The text is composed onto the end of the system instruction (joined with a blank line) and re-applied on every inference, so it survives context-message resets (e.g. LLMMessagesUpdateFrame(messages=[])). Intended for framework components that own an LLM and need to add standard guidance to a user-provided prompt — for example, UIWorker appends the UI wire-format guide. Appended instructions compose after the user’s base prompt and alongside the turn-completion and async-tool-cancellation instructions.

Parameters:

instruction – The instruction text to append.

async process_frame(frame: Frame, direction: FrameDirection)

Process a frame.

Parameters:

• frame – The frame to process.

• direction – The direction of frame processing.

async push_frame(frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM)

Pushes a frame.

Parameters:

• frame – The frame to push.

• direction – The direction of frame pushing.

register_function(function_name: str | None, handler: Any, *, cancel_on_interruption: bool | None = None, timeout_secs: float | None = None)

Register a function handler for LLM function calls.

Call options resolve with the precedence explicit argument > ``@tool_options`` decorator > default. None (the default) means “not provided” — the option falls back to the @tool_options value on the handler, then to the documented default.

Parameters:

• function_name – The name of the function to handle. Use None to handle all function calls with a catch-all handler.

• handler – The function handler. Should accept a single FunctionCallParams parameter.

• cancel_on_interruption – Whether to cancel this function call when an interruption occurs. When False the call is treated as asynchronous: the LLM continues the conversation immediately without waiting for the result, and the result is injected later via a developer message. Defaults to None (fall back to the @tool_options decorator value, then to True). Note: realtime LLM services deliver only the final result to the provider; intermediate streamed results (reported via FunctionCallResultProperties(is_final=False)) are dropped and an error is raised. Use a non-realtime LLM service if your tool needs to stream intermediate results.

• timeout_secs – Optional per-tool timeout in seconds, overriding the global function_call_timeout_secs. Defaults to None (fall back to the @tool_options decorator value, then to the global timeout).

register_direct_function(handler: Callable[[...], Awaitable[Any]], *, cancel_on_interruption: bool | None = None, timeout_secs: float | None = None)

Register a direct function handler for LLM function calls.

Deprecated since version 1.4.0: Direct functions are now registered automatically. List them in LLMContext(tools=[...]) for tools available at session start, or push an LLMSetToolsFrame to change tools mid-session. Will be removed in 2.0.0.

Direct functions have their metadata automatically extracted from their signature and docstring, eliminating the need for accompanying configurations (as FunctionSchemas or in provider-specific formats).

Call options resolve with the precedence explicit argument > ``@tool_options`` decorator > default. None (the default) means “not provided” — the option falls back to the decorator value, then the documented default.

Parameters:

• handler – The direct function to register. Must follow DirectFunction protocol.

• cancel_on_interruption – Whether to cancel this function call when an interruption occurs. When False the call is treated as asynchronous: the LLM continues the conversation immediately without waiting for the result, and the result is injected later via a developer message. Defaults to None (fall back to the @tool_options decorator value, then to True). Note: realtime LLM services deliver only the final result to the provider; intermediate streamed results (reported via FunctionCallResultProperties(is_final=False)) are dropped and an error is raised. Use a non-realtime LLM service if your tool needs to stream intermediate results.

• timeout_secs – Optional per-tool timeout in seconds, overriding the global function_call_timeout_secs. Defaults to None (fall back to the @tool_options decorator value, then to the global timeout).

unregister_function(function_name: str | None)

Remove a registered function handler.

Note

This removes the handler but does not stop advertising the tool to the LLM. To remove a tool cleanly, prefer an LLMSetToolsFrame with the updated tool set — that both stops advertising it and avoids the LLM trying to call a tool that’s no longer there.

Parameters:

function_name – The name of the function handler to remove.

unregister_direct_function(handler: Any)

Remove a registered direct function handler.

Deprecated since version 1.4.0: Direct-function handlers are now managed automatically. To stop advertising a tool, push an LLMSetToolsFrame with the updated tool set — the service unregisters the handler for any direct function no longer listed. Will be removed in 2.0.0.

Note

This removes the handler but does not stop advertising the tool to the LLM. To remove a tool cleanly, prefer an LLMSetToolsFrame with the updated tool set — that both stops advertising it and avoids the LLM trying to call a tool that’s no longer there.

Parameters:

handler – The direct function handler to remove.

has_function(function_name: str)

Check if a function handler is registered.

Parameters:

function_name – The name of the function to check.

Returns:

True if the function is registered or if a catch-all handler (None) is registered.

async run_function_calls(function_calls: Sequence[FunctionCallFromLLM])

Execute a sequence of function calls from the LLM.

Triggers the on_function_calls_started event and executes functions either in parallel or sequentially based on the run_in_parallel setting.

Parameters:

function_calls – The function calls to execute.

exception pipecat.services.llm_service.WebsocketReconnectedError

Bases: Exception

Raised by _ws_send/_ws_recv after a transparent reconnection.

Signals that the WebSocket connection was lost and automatically re-established. The current inference should be restarted — any connection-local state on the server (e.g. cached responses) is gone.

class pipecat.services.llm_service.WebsocketLLMService(*, reconnect_on_error: bool = True, **kwargs)

Bases: LLMService[TAdapter], WebsocketService, Generic[TAdapter]

Base class for websocket-based LLM services.

Each LLM inference is a discrete request/response exchange: send one request, receive events inline until a terminal event, then wait for the next frame to trigger an inference. This contrasts with WebsocketTTSService / WebsocketSTTService which stream data continuously via a background receive loop (_receive_task_handler). This class does not start a background receive loop.

Provides connection lifecycle management (connect on start, disconnect on stop/cancel), automatic reconnection with exponential backoff, and three helpers for running each inference:

1. _ensure_connected() — verify the websocket is alive, reconnect with exponential backoff if not.

2. _ws_send(message) — send the inference request as JSON.

3. _ws_recv() — receive and parse response events one at a time until the caller sees a terminal event.

_ws_send and _ws_recv catch ConnectionClosed transparently, auto-reconnect via _try_reconnect, and raise WebsocketReconnectedError so callers know the inference must be restarted. If reconnection fails, the original ConnectionClosed propagates.

Subclasses must implement:

_connect_websocket(): Establish the websocket connection. _disconnect_websocket(): Close the websocket and clean up.

Event handlers:

on_connection_error: Called when a websocket connection error occurs.

Example:

@llm.event_handler("on_connection_error")
async def on_connection_error(llm: LLMService, error: str):
    logger.error(f"LLM connection error: {error}")

__init__(*, reconnect_on_error: bool = True, **kwargs)

Initialize the Websocket LLM service.

Parameters:

• reconnect_on_error – Whether to automatically reconnect on websocket errors.

• **kwargs – Additional arguments passed to parent classes.

async start(frame: StartFrame)

Start the service and establish WebSocket connection.

Parameters:

frame – The start frame triggering service initialization.

async stop(frame: EndFrame)

Stop the service and close WebSocket connection.

Parameters:

frame – The end frame triggering service shutdown.

async cancel(frame: CancelFrame)

Cancel the service and close WebSocket connection.

Parameters:

frame – The cancel frame triggering service cancellation.