"""Base classes and utilities for LangChain tools.""" from __future__ import annotations import functools import inspect import json import typing import warnings from abc import ABC, abstractmethod from collections.abc import Callable from inspect import signature from typing import ( TYPE_CHECKING, Annotated, Any, Literal, TypeVar, cast, get_args, get_origin, get_type_hints, ) from pydantic import ( BaseModel, ConfigDict, Field, PydanticDeprecationWarning, SkipValidation, ValidationError, validate_arguments, ) from pydantic.v1 import BaseModel as BaseModelV1 from pydantic.v1 import ValidationError as ValidationErrorV1 from pydantic.v1 import validate_arguments as validate_arguments_v1 from typing_extensions import override from langchain_core.callbacks import ( AsyncCallbackManager, CallbackManager, Callbacks, ) from langchain_core.messages.tool import ToolCall, ToolMessage, ToolOutputMixin from langchain_core.runnables import ( RunnableConfig, RunnableSerializable, ensure_config, patch_config, run_in_executor, ) from langchain_core.runnables.config import set_config_context from langchain_core.runnables.utils import coro_with_context from langchain_core.utils.function_calling import ( _parse_google_docstring, _py_38_safe_origin, ) from langchain_core.utils.pydantic import ( TypeBaseModel, _create_subset_model, get_fields, is_basemodel_subclass, is_pydantic_v1_subclass, is_pydantic_v2_subclass, ) if TYPE_CHECKING: import uuid from collections.abc import Sequence FILTERED_ARGS = ("run_manager", "callbacks") TOOL_MESSAGE_BLOCK_TYPES = ( "text", "image_url", "image", "json", "search_result", "custom_tool_call_output", "document", "file", ) class SchemaAnnotationError(TypeError): """Raised when args_schema is missing or has an incorrect type annotation.""" def _is_annotated_type(typ: type[Any]) -> bool: """Check if a type is an Annotated type. Args: typ: The type to check. Returns: `True` if the type is an Annotated type, `False` otherwise. """ return get_origin(typ) is typing.Annotated def _get_annotation_description(arg_type: type) -> str | None: """Extract description from an Annotated type. Args: arg_type: The type to extract description from. Returns: The description string if found, None otherwise. """ if _is_annotated_type(arg_type): annotated_args = get_args(arg_type) for annotation in annotated_args[1:]: if isinstance(annotation, str): return annotation return None def _get_filtered_args( inferred_model: type[BaseModel], func: Callable, *, filter_args: Sequence[str], include_injected: bool = True, ) -> dict: """Get filtered arguments from a function's signature. Args: inferred_model: The Pydantic model inferred from the function. func: The function to extract arguments from. filter_args: Arguments to exclude from the result. include_injected: Whether to include injected arguments. Returns: Dictionary of filtered arguments with their schema definitions. """ schema = inferred_model.model_json_schema()["properties"] valid_keys = signature(func).parameters return { k: schema[k] for i, (k, param) in enumerate(valid_keys.items()) if k not in filter_args and (i > 0 or param.name not in {"self", "cls"}) and (include_injected or not _is_injected_arg_type(param.annotation)) } def _parse_python_function_docstring( function: Callable, annotations: dict, *, error_on_invalid_docstring: bool = False ) -> tuple[str, dict]: """Parse function and argument descriptions from a docstring. Assumes the function docstring follows Google Python style guide. Args: function: The function to parse the docstring from. annotations: Type annotations for the function parameters. error_on_invalid_docstring: Whether to raise an error on invalid docstring. Returns: A tuple containing the function description and argument descriptions. """ docstring = inspect.getdoc(function) return _parse_google_docstring( docstring, list(annotations), error_on_invalid_docstring=error_on_invalid_docstring, ) def _validate_docstring_args_against_annotations( arg_descriptions: dict, annotations: dict ) -> None: """Validate that docstring arguments match function annotations. Args: arg_descriptions: Arguments described in the docstring. annotations: Type annotations from the function signature. Raises: ValueError: If a docstring argument is not found in function signature. """ for docstring_arg in arg_descriptions: if docstring_arg not in annotations: msg = f"Arg {docstring_arg} in docstring not found in function signature." raise ValueError(msg) def _infer_arg_descriptions( fn: Callable, *, parse_docstring: bool = False, error_on_invalid_docstring: bool = False, ) -> tuple[str, dict]: """Infer argument descriptions from function docstring and annotations. Args: fn: The function to infer descriptions from. parse_docstring: Whether to parse the docstring for descriptions. error_on_invalid_docstring: Whether to raise error on invalid docstring. Returns: A tuple containing the function description and argument descriptions. """ annotations = typing.get_type_hints(fn, include_extras=True) if parse_docstring: description, arg_descriptions = _parse_python_function_docstring( fn, annotations, error_on_invalid_docstring=error_on_invalid_docstring ) else: description = inspect.getdoc(fn) or "" arg_descriptions = {} if parse_docstring: _validate_docstring_args_against_annotations(arg_descriptions, annotations) for arg, arg_type in annotations.items(): if arg in arg_descriptions: continue if desc := _get_annotation_description(arg_type): arg_descriptions[arg] = desc return description, arg_descriptions def _is_pydantic_annotation(annotation: Any, pydantic_version: str = "v2") -> bool: """Check if a type annotation is a Pydantic model. Args: annotation: The type annotation to check. pydantic_version: The Pydantic version to check against ("v1" or "v2"). Returns: `True` if the annotation is a Pydantic model, `False` otherwise. """ base_model_class = BaseModelV1 if pydantic_version == "v1" else BaseModel try: return issubclass(annotation, base_model_class) except TypeError: return False def _function_annotations_are_pydantic_v1( signature: inspect.Signature, func: Callable ) -> bool: """Check if all Pydantic annotations in a function are from V1. Args: signature: The function signature to check. func: The function being checked. Returns: True if all Pydantic annotations are from V1, `False` otherwise. Raises: NotImplementedError: If the function contains mixed V1 and V2 annotations. """ any_v1_annotations = any( _is_pydantic_annotation(parameter.annotation, pydantic_version="v1") for parameter in signature.parameters.values() ) any_v2_annotations = any( _is_pydantic_annotation(parameter.annotation, pydantic_version="v2") for parameter in signature.parameters.values() ) if any_v1_annotations and any_v2_annotations: msg = ( f"Function {func} contains a mix of Pydantic v1 and v2 annotations. " "Only one version of Pydantic annotations per function is supported." ) raise NotImplementedError(msg) return any_v1_annotations and not any_v2_annotations class _SchemaConfig: """Configuration for Pydantic models generated from function signatures.""" extra: str = "forbid" """Whether to allow extra fields in the model.""" arbitrary_types_allowed: bool = True """Whether to allow arbitrary types in the model.""" def create_schema_from_function( model_name: str, func: Callable, *, filter_args: Sequence[str] | None = None, parse_docstring: bool = False, error_on_invalid_docstring: bool = False, include_injected: bool = True, ) -> type[BaseModel]: """Create a Pydantic schema from a function's signature. Args: model_name: Name to assign to the generated Pydantic schema. func: Function to generate the schema from. filter_args: Optional list of arguments to exclude from the schema. Defaults to `FILTERED_ARGS`. parse_docstring: Whether to parse the function's docstring for descriptions for each argument. error_on_invalid_docstring: if `parse_docstring` is provided, configure whether to raise `ValueError` on invalid Google Style docstrings. include_injected: Whether to include injected arguments in the schema. Defaults to `True`, since we want to include them in the schema when *validating* tool inputs. Returns: A Pydantic model with the same arguments as the function. """ sig = inspect.signature(func) if _function_annotations_are_pydantic_v1(sig, func): validated = validate_arguments_v1(func, config=_SchemaConfig) # type: ignore[call-overload] else: # https://docs.pydantic.dev/latest/usage/validation_decorator/ with warnings.catch_warnings(): # We are using deprecated functionality here. # This code should be re-written to simply construct a Pydantic model # using inspect.signature and create_model. warnings.simplefilter("ignore", category=PydanticDeprecationWarning) validated = validate_arguments(func, config=_SchemaConfig) # type: ignore[operator] # Let's ignore `self` and `cls` arguments for class and instance methods # If qualified name has a ".", then it likely belongs in a class namespace in_class = bool(func.__qualname__ and "." in func.__qualname__) has_args = False has_kwargs = False for param in sig.parameters.values(): if param.kind == param.VAR_POSITIONAL: has_args = True elif param.kind == param.VAR_KEYWORD: has_kwargs = True inferred_model = validated.model if filter_args: filter_args_ = filter_args else: # Handle classmethods and instance methods existing_params: list[str] = list(sig.parameters.keys()) if existing_params and existing_params[0] in {"self", "cls"} and in_class: filter_args_ = [existing_params[0], *list(FILTERED_ARGS)] else: filter_args_ = list(FILTERED_ARGS) for existing_param in existing_params: if not include_injected and _is_injected_arg_type( sig.parameters[existing_param].annotation ): filter_args_.append(existing_param) description, arg_descriptions = _infer_arg_descriptions( func, parse_docstring=parse_docstring, error_on_invalid_docstring=error_on_invalid_docstring, ) # Pydantic adds placeholder virtual fields we need to strip valid_properties = [] for field in get_fields(inferred_model): if not has_args and field == "args": continue if not has_kwargs and field == "kwargs": continue if field == "v__duplicate_kwargs": # Internal pydantic field continue if field not in filter_args_: valid_properties.append(field) return _create_subset_model( model_name, inferred_model, list(valid_properties), descriptions=arg_descriptions, fn_description=description, ) class ToolException(Exception): # noqa: N818 """Exception thrown when a tool execution error occurs. This exception allows tools to signal errors without stopping the agent. The error is handled according to the tool's handle_tool_error setting, and the result is returned as an observation to the agent. """ ArgsSchema = TypeBaseModel | dict[str, Any] _EMPTY_SET: frozenset[str] = frozenset() class BaseTool(RunnableSerializable[str | dict | ToolCall, Any]): """Base class for all LangChain tools. This abstract class defines the interface that all LangChain tools must implement. Tools are components that can be called by agents to perform specific actions. """ def __init_subclass__(cls, **kwargs: Any) -> None: """Validate the tool class definition during subclass creation. Args: **kwargs: Additional keyword arguments passed to the parent class. Raises: SchemaAnnotationError: If `args_schema` has incorrect type annotation. """ super().__init_subclass__(**kwargs) args_schema_type = cls.__annotations__.get("args_schema", None) if args_schema_type is not None and args_schema_type == BaseModel: # Throw errors for common mis-annotations. # TODO: Use get_args / get_origin and fully # specify valid annotations. typehint_mandate = """ class ChildTool(BaseTool): ... args_schema: Type[BaseModel] = SchemaClass ...""" name = cls.__name__ msg = ( f"Tool definition for {name} must include valid type annotations" f" for argument 'args_schema' to behave as expected.\n" f"Expected annotation of 'Type[BaseModel]'" f" but got '{args_schema_type}'.\n" f"Expected class looks like:\n" f"{typehint_mandate}" ) raise SchemaAnnotationError(msg) name: str """The unique name of the tool that clearly communicates its purpose.""" description: str """Used to tell the model how/when/why to use the tool. You can provide few-shot examples as a part of the description. """ args_schema: Annotated[ArgsSchema | None, SkipValidation()] = Field( default=None, description="The tool schema." ) """Pydantic model class to validate and parse the tool's input arguments. Args schema should be either: - A subclass of `pydantic.BaseModel`. - A subclass of `pydantic.v1.BaseModel` if accessing v1 namespace in pydantic 2 - A JSON schema dict """ return_direct: bool = False """Whether to return the tool's output directly. Setting this to `True` means that after the tool is called, the `AgentExecutor` will stop looping. """ verbose: bool = False """Whether to log the tool's progress.""" callbacks: Callbacks = Field(default=None, exclude=True) """Callbacks to be called during tool execution.""" tags: list[str] | None = None """Optional list of tags associated with the tool. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in `callbacks`. You can use these to, e.g., identify a specific instance of a tool with its use case. """ metadata: dict[str, Any] | None = None """Optional metadata associated with the tool. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in `callbacks`. You can use these to, e.g., identify a specific instance of a tool with its use case. """ handle_tool_error: bool | str | Callable[[ToolException], str] | None = False """Handle the content of the `ToolException` thrown.""" handle_validation_error: ( bool | str | Callable[[ValidationError | ValidationErrorV1], str] | None ) = False """Handle the content of the `ValidationError` thrown.""" response_format: Literal["content", "content_and_artifact"] = "content" """The tool response format. If `'content'` then the output of the tool is interpreted as the contents of a `ToolMessage`. If `'content_and_artifact'` then the output is expected to be a two-tuple corresponding to the `(content, artifact)` of a `ToolMessage`. """ def __init__(self, **kwargs: Any) -> None: """Initialize the tool. Raises: TypeError: If `args_schema` is not a subclass of pydantic `BaseModel` or `dict`. """ if ( "args_schema" in kwargs and kwargs["args_schema"] is not None and not is_basemodel_subclass(kwargs["args_schema"]) and not isinstance(kwargs["args_schema"], dict) ): msg = ( "args_schema must be a subclass of pydantic BaseModel or " f"a JSON schema dict. Got: {kwargs['args_schema']}." ) raise TypeError(msg) super().__init__(**kwargs) model_config = ConfigDict( arbitrary_types_allowed=True, ) @property def is_single_input(self) -> bool: """Check if the tool accepts only a single input argument. Returns: `True` if the tool has only one input argument, `False` otherwise. """ keys = {k for k in self.args if k != "kwargs"} return len(keys) == 1 @property def args(self) -> dict: """Get the tool's input arguments schema. Returns: `dict` containing the tool's argument properties. """ if isinstance(self.args_schema, dict): json_schema = self.args_schema elif self.args_schema and issubclass(self.args_schema, BaseModelV1): json_schema = self.args_schema.schema() else: input_schema = self.get_input_schema() json_schema = input_schema.model_json_schema() return json_schema["properties"] @property def tool_call_schema(self) -> ArgsSchema: """Get the schema for tool calls, excluding injected arguments. Returns: The schema that should be used for tool calls from language models. """ if isinstance(self.args_schema, dict): if self.description: return { **self.args_schema, "description": self.description, } return self.args_schema full_schema = self.get_input_schema() fields = [] for name, type_ in get_all_basemodel_annotations(full_schema).items(): if not _is_injected_arg_type(type_): fields.append(name) return _create_subset_model( self.name, full_schema, fields, fn_description=self.description ) @functools.cached_property def _injected_args_keys(self) -> frozenset[str]: # base implementation doesn't manage injected args return _EMPTY_SET # --- Runnable --- @override def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]: """The tool's input schema. Args: config: The configuration for the tool. Returns: The input schema for the tool. """ if self.args_schema is not None: if isinstance(self.args_schema, dict): return super().get_input_schema(config) return self.args_schema return create_schema_from_function(self.name, self._run) @override def invoke( self, input: str | dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any, ) -> Any: tool_input, kwargs = _prep_run_args(input, config, **kwargs) return self.run(tool_input, **kwargs) @override async def ainvoke( self, input: str | dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any, ) -> Any: tool_input, kwargs = _prep_run_args(input, config, **kwargs) return await self.arun(tool_input, **kwargs) # --- Tool --- def _parse_input( self, tool_input: str | dict, tool_call_id: str | None ) -> str | dict[str, Any]: """Parse and validate tool input using the args schema. Args: tool_input: The raw input to the tool. tool_call_id: The ID of the tool call, if available. Returns: The parsed and validated input. Raises: ValueError: If `string` input is provided with JSON schema `args_schema`. ValueError: If `InjectedToolCallId` is required but `tool_call_id` is not provided. TypeError: If `args_schema` is not a Pydantic `BaseModel` or dict. """ input_args = self.args_schema if isinstance(tool_input, str): if input_args is not None: if isinstance(input_args, dict): msg = ( "String tool inputs are not allowed when " "using tools with JSON schema args_schema." ) raise ValueError(msg) key_ = next(iter(get_fields(input_args).keys())) if issubclass(input_args, BaseModel): input_args.model_validate({key_: tool_input}) elif issubclass(input_args, BaseModelV1): input_args.parse_obj({key_: tool_input}) else: msg = f"args_schema must be a Pydantic BaseModel, got {input_args}" raise TypeError(msg) return tool_input if input_args is not None: if isinstance(input_args, dict): return tool_input if issubclass(input_args, BaseModel): # Check args_schema for InjectedToolCallId for k, v in get_all_basemodel_annotations(input_args).items(): if _is_injected_arg_type(v, injected_type=InjectedToolCallId): if tool_call_id is None: msg = ( "When tool includes an InjectedToolCallId " "argument, tool must always be invoked with a full " "model ToolCall of the form: {'args': {...}, " "'name': '...', 'type': 'tool_call', " "'tool_call_id': '...'}" ) raise ValueError(msg) tool_input[k] = tool_call_id result = input_args.model_validate(tool_input) result_dict = result.model_dump() elif issubclass(input_args, BaseModelV1): # Check args_schema for InjectedToolCallId for k, v in get_all_basemodel_annotations(input_args).items(): if _is_injected_arg_type(v, injected_type=InjectedToolCallId): if tool_call_id is None: msg = ( "When tool includes an InjectedToolCallId " "argument, tool must always be invoked with a full " "model ToolCall of the form: {'args': {...}, " "'name': '...', 'type': 'tool_call', " "'tool_call_id': '...'}" ) raise ValueError(msg) tool_input[k] = tool_call_id result = input_args.parse_obj(tool_input) result_dict = result.dict() else: msg = ( f"args_schema must be a Pydantic BaseModel, got {self.args_schema}" ) raise NotImplementedError(msg) validated_input = { k: getattr(result, k) for k in result_dict if k in tool_input } for k in self._injected_args_keys: if k == "tool_call_id": if tool_call_id is None: msg = ( "When tool includes an InjectedToolCallId " "argument, tool must always be invoked with a full " "model ToolCall of the form: {'args': {...}, " "'name': '...', 'type': 'tool_call', " "'tool_call_id': '...'}" ) raise ValueError(msg) validated_input[k] = tool_call_id if k in tool_input: injected_val = tool_input[k] validated_input[k] = injected_val return validated_input return tool_input @abstractmethod def _run(self, *args: Any, **kwargs: Any) -> Any: """Use the tool. Add `run_manager: CallbackManagerForToolRun | None = None` to child implementations to enable tracing. Returns: The result of the tool execution. """ async def _arun(self, *args: Any, **kwargs: Any) -> Any: """Use the tool asynchronously. Add `run_manager: AsyncCallbackManagerForToolRun | None = None` to child implementations to enable tracing. Returns: The result of the tool execution. """ if kwargs.get("run_manager") and signature(self._run).parameters.get( "run_manager" ): kwargs["run_manager"] = kwargs["run_manager"].get_sync() return await run_in_executor(None, self._run, *args, **kwargs) def _filter_injected_args(self, tool_input: dict) -> dict: """Filter out injected tool arguments from the input dictionary. Injected arguments are those annotated with `InjectedToolArg` or its subclasses, or arguments in `FILTERED_ARGS` like `run_manager` and callbacks. Args: tool_input: The tool input dictionary to filter. Returns: A filtered dictionary with injected arguments removed. """ # Start with filtered args from the constant filtered_keys = set[str](FILTERED_ARGS) # If we have an args_schema, use it to identify injected args if self.args_schema is not None: try: annotations = get_all_basemodel_annotations(self.args_schema) for field_name, field_type in annotations.items(): if _is_injected_arg_type(field_type): filtered_keys.add(field_name) except Exception: # noqa: S110 # If we can't get annotations, just use FILTERED_ARGS pass # Filter out the injected keys from tool_input return {k: v for k, v in tool_input.items() if k not in filtered_keys} def _to_args_and_kwargs( self, tool_input: str | dict, tool_call_id: str | None ) -> tuple[tuple, dict]: """Convert tool input to positional and keyword arguments. Args: tool_input: The input to the tool. tool_call_id: The ID of the tool call, if available. Returns: A tuple of `(positional_args, keyword_args)` for the tool. Raises: TypeError: If the tool input type is invalid. """ if ( self.args_schema is not None and isinstance(self.args_schema, type) and is_basemodel_subclass(self.args_schema) and not get_fields(self.args_schema) ): # StructuredTool with no args return (), {} tool_input = self._parse_input(tool_input, tool_call_id) # For backwards compatibility, if run_input is a string, # pass as a positional argument. if isinstance(tool_input, str): return (tool_input,), {} if isinstance(tool_input, dict): # Make a shallow copy of the input to allow downstream code # to modify the root level of the input without affecting the # original input. # This is used by the tool to inject run time information like # the callback manager. return (), tool_input.copy() # This code path is not expected to be reachable. msg = f"Invalid tool input type: {type(tool_input)}" raise TypeError(msg) def run( self, tool_input: str | dict[str, Any], verbose: bool | None = None, # noqa: FBT001 start_color: str | None = "green", color: str | None = "green", callbacks: Callbacks = None, *, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, run_name: str | None = None, run_id: uuid.UUID | None = None, config: RunnableConfig | None = None, tool_call_id: str | None = None, **kwargs: Any, ) -> Any: """Run the tool. Args: tool_input: The input to the tool. verbose: Whether to log the tool's progress. start_color: The color to use when starting the tool. color: The color to use when ending the tool. callbacks: Callbacks to be called during tool execution. tags: Optional list of tags associated with the tool. metadata: Optional metadata associated with the tool. run_name: The name of the run. run_id: The id of the run. config: The configuration for the tool. tool_call_id: The id of the tool call. **kwargs: Keyword arguments to be passed to tool callbacks (event handler) Returns: The output of the tool. Raises: ToolException: If an error occurs during tool execution. """ callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose or bool(verbose), tags, self.tags, metadata, self.metadata, ) # Filter out injected arguments from callback inputs filtered_tool_input = ( self._filter_injected_args(tool_input) if isinstance(tool_input, dict) else None ) # Use filtered inputs for the input_str parameter as well tool_input_str = ( tool_input if isinstance(tool_input, str) else str( filtered_tool_input if filtered_tool_input is not None else tool_input ) ) run_manager = callback_manager.on_tool_start( {"name": self.name, "description": self.description}, tool_input_str, color=start_color, name=run_name, run_id=run_id, inputs=filtered_tool_input, **kwargs, ) content = None artifact = None status = "success" error_to_raise: Exception | KeyboardInterrupt | None = None try: child_config = patch_config(config, callbacks=run_manager.get_child()) with set_config_context(child_config) as context: tool_args, tool_kwargs = self._to_args_and_kwargs( tool_input, tool_call_id ) if signature(self._run).parameters.get("run_manager"): tool_kwargs |= {"run_manager": run_manager} if config_param := _get_runnable_config_param(self._run): tool_kwargs |= {config_param: config} response = context.run(self._run, *tool_args, **tool_kwargs) if self.response_format == "content_and_artifact": msg = ( "Since response_format='content_and_artifact' " "a two-tuple of the message content and raw tool output is " f"expected. Instead, generated response is of type: " f"{type(response)}." ) if not isinstance(response, tuple): error_to_raise = ValueError(msg) else: try: content, artifact = response except ValueError: error_to_raise = ValueError(msg) else: content = response except (ValidationError, ValidationErrorV1) as e: if not self.handle_validation_error: error_to_raise = e else: content = _handle_validation_error(e, flag=self.handle_validation_error) status = "error" except ToolException as e: if not self.handle_tool_error: error_to_raise = e else: content = _handle_tool_error(e, flag=self.handle_tool_error) status = "error" except (Exception, KeyboardInterrupt) as e: error_to_raise = e if error_to_raise: run_manager.on_tool_error(error_to_raise) raise error_to_raise output = _format_output(content, artifact, tool_call_id, self.name, status) run_manager.on_tool_end(output, color=color, name=self.name, **kwargs) return output async def arun( self, tool_input: str | dict, verbose: bool | None = None, # noqa: FBT001 start_color: str | None = "green", color: str | None = "green", callbacks: Callbacks = None, *, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, run_name: str | None = None, run_id: uuid.UUID | None = None, config: RunnableConfig | None = None, tool_call_id: str | None = None, **kwargs: Any, ) -> Any: """Run the tool asynchronously. Args: tool_input: The input to the tool. verbose: Whether to log the tool's progress. start_color: The color to use when starting the tool. color: The color to use when ending the tool. callbacks: Callbacks to be called during tool execution. tags: Optional list of tags associated with the tool. metadata: Optional metadata associated with the tool. run_name: The name of the run. run_id: The id of the run. config: The configuration for the tool. tool_call_id: The id of the tool call. **kwargs: Keyword arguments to be passed to tool callbacks Returns: The output of the tool. Raises: ToolException: If an error occurs during tool execution. """ callback_manager = AsyncCallbackManager.configure( callbacks, self.callbacks, self.verbose or bool(verbose), tags, self.tags, metadata, self.metadata, ) # Filter out injected arguments from callback inputs filtered_tool_input = ( self._filter_injected_args(tool_input) if isinstance(tool_input, dict) else None ) # Use filtered inputs for the input_str parameter as well tool_input_str = ( tool_input if isinstance(tool_input, str) else str( filtered_tool_input if filtered_tool_input is not None else tool_input ) ) run_manager = await callback_manager.on_tool_start( {"name": self.name, "description": self.description}, tool_input_str, color=start_color, name=run_name, run_id=run_id, inputs=filtered_tool_input, **kwargs, ) content = None artifact = None status = "success" error_to_raise: Exception | KeyboardInterrupt | None = None try: tool_args, tool_kwargs = self._to_args_and_kwargs(tool_input, tool_call_id) child_config = patch_config(config, callbacks=run_manager.get_child()) with set_config_context(child_config) as context: func_to_check = ( self._run if self.__class__._arun is BaseTool._arun else self._arun # noqa: SLF001 ) if signature(func_to_check).parameters.get("run_manager"): tool_kwargs["run_manager"] = run_manager if config_param := _get_runnable_config_param(func_to_check): tool_kwargs[config_param] = config coro = self._arun(*tool_args, **tool_kwargs) response = await coro_with_context(coro, context) if self.response_format == "content_and_artifact": msg = ( "Since response_format='content_and_artifact' " "a two-tuple of the message content and raw tool output is " f"expected. Instead, generated response is of type: " f"{type(response)}." ) if not isinstance(response, tuple): error_to_raise = ValueError(msg) else: try: content, artifact = response except ValueError: error_to_raise = ValueError(msg) else: content = response except ValidationError as e: if not self.handle_validation_error: error_to_raise = e else: content = _handle_validation_error(e, flag=self.handle_validation_error) status = "error" except ToolException as e: if not self.handle_tool_error: error_to_raise = e else: content = _handle_tool_error(e, flag=self.handle_tool_error) status = "error" except (Exception, KeyboardInterrupt) as e: error_to_raise = e if error_to_raise: await run_manager.on_tool_error(error_to_raise) raise error_to_raise output = _format_output(content, artifact, tool_call_id, self.name, status) await run_manager.on_tool_end(output, color=color, name=self.name, **kwargs) return output def _is_tool_call(x: Any) -> bool: """Check if the input is a tool call dictionary. Args: x: The input to check. Returns: `True` if the input is a tool call, `False` otherwise. """ return isinstance(x, dict) and x.get("type") == "tool_call" def _handle_validation_error( e: ValidationError | ValidationErrorV1, *, flag: Literal[True] | str | Callable[[ValidationError | ValidationErrorV1], str], ) -> str: """Handle validation errors based on the configured flag. Args: e: The validation error that occurred. flag: How to handle the error (`bool`, `str`, or `Callable`). Returns: The error message to return. Raises: ValueError: If the flag type is unexpected. """ if isinstance(flag, bool): content = "Tool input validation error" elif isinstance(flag, str): content = flag elif callable(flag): content = flag(e) else: msg = ( f"Got unexpected type of `handle_validation_error`. Expected bool, " f"str or callable. Received: {flag}" ) raise ValueError(msg) # noqa: TRY004 return content def _handle_tool_error( e: ToolException, *, flag: Literal[True] | str | Callable[[ToolException], str] | None, ) -> str: """Handle tool execution errors based on the configured flag. Args: e: The tool exception that occurred. flag: How to handle the error (`bool`, `str`, or `Callable`). Returns: The error message to return. Raises: ValueError: If the flag type is unexpected. """ if isinstance(flag, bool): content = e.args[0] if e.args else "Tool execution error" elif isinstance(flag, str): content = flag elif callable(flag): content = flag(e) else: msg = ( f"Got unexpected type of `handle_tool_error`. Expected bool, str " f"or callable. Received: {flag}" ) raise ValueError(msg) # noqa: TRY004 return content def _prep_run_args( value: str | dict | ToolCall, config: RunnableConfig | None, **kwargs: Any, ) -> tuple[str | dict, dict]: """Prepare arguments for tool execution. Args: value: The input value (`str`, `dict`, or `ToolCall`). config: The runnable configuration. **kwargs: Additional keyword arguments. Returns: A tuple of `(tool_input, run_kwargs)`. """ config = ensure_config(config) if _is_tool_call(value): tool_call_id: str | None = cast("ToolCall", value)["id"] tool_input: str | dict = cast("ToolCall", value)["args"].copy() else: tool_call_id = None tool_input = cast("str | dict", value) return ( tool_input, dict( callbacks=config.get("callbacks"), tags=config.get("tags"), metadata=config.get("metadata"), run_name=config.get("run_name"), run_id=config.pop("run_id", None), config=config, tool_call_id=tool_call_id, **kwargs, ), ) def _format_output( content: Any, artifact: Any, tool_call_id: str | None, name: str, status: str, ) -> ToolOutputMixin | Any: """Format tool output as a `ToolMessage` if appropriate. Args: content: The main content of the tool output. artifact: Any artifact data from the tool. tool_call_id: The ID of the tool call. name: The name of the tool. status: The execution status. Returns: The formatted output, either as a `ToolMessage` or the original content. """ if isinstance(content, ToolOutputMixin) or tool_call_id is None: return content if not _is_message_content_type(content): content = _stringify(content) return ToolMessage( content, artifact=artifact, tool_call_id=tool_call_id, name=name, status=status, ) def _is_message_content_type(obj: Any) -> bool: """Check if object is valid message content format. Validates content for OpenAI or Anthropic format tool messages. Args: obj: The object to check. Returns: `True` if the object is valid message content, `False` otherwise. """ return isinstance(obj, str) or ( isinstance(obj, list) and all(_is_message_content_block(e) for e in obj) ) def _is_message_content_block(obj: Any) -> bool: """Check if object is a valid message content block. Validates content blocks for OpenAI or Anthropic format. Args: obj: The object to check. Returns: `True` if the object is a valid content block, `False` otherwise. """ if isinstance(obj, str): return True if isinstance(obj, dict): return obj.get("type", None) in TOOL_MESSAGE_BLOCK_TYPES return False def _stringify(content: Any) -> str: """Convert content to string, preferring JSON format. Args: content: The content to stringify. Returns: String representation of the content. """ try: return json.dumps(content, ensure_ascii=False) except Exception: return str(content) def _get_type_hints(func: Callable) -> dict[str, type] | None: """Get type hints from a function, handling partial functions. Args: func: The function to get type hints from. Returns: `dict` of type hints, or `None` if extraction fails. """ if isinstance(func, functools.partial): func = func.func try: return get_type_hints(func) except Exception: return None def _get_runnable_config_param(func: Callable) -> str | None: """Find the parameter name for `RunnableConfig` in a function. Args: func: The function to check. Returns: The parameter name for `RunnableConfig`, or `None` if not found. """ type_hints = _get_type_hints(func) if not type_hints: return None for name, type_ in type_hints.items(): if type_ is RunnableConfig: return name return None class InjectedToolArg: """Annotation for tool arguments that are injected at runtime. Tool arguments annotated with this class are not included in the tool schema sent to language models and are instead injected during execution. """ class _DirectlyInjectedToolArg: """Annotation for tool arguments that are injected at runtime. Injected via direct type annotation, rather than annotated metadata. For example, `ToolRuntime` is a directly injected argument. Note the direct annotation rather than the verbose alternative: `Annotated[ToolRuntime, InjectedRuntime]` ```python from langchain_core.tools import tool, ToolRuntime @tool def foo(x: int, runtime: ToolRuntime) -> str: # use runtime.state, runtime.context, runtime.store, etc. ... ``` """ class InjectedToolCallId(InjectedToolArg): """Annotation for injecting the tool call ID. This annotation is used to mark a tool parameter that should receive the tool call ID at runtime. ```python from typing import Annotated from langchain_core.messages import ToolMessage from langchain_core.tools import tool, InjectedToolCallId @tool def foo( x: int, tool_call_id: Annotated[str, InjectedToolCallId] ) -> ToolMessage: \"\"\"Return x.\"\"\" return ToolMessage( str(x), artifact=x, name="foo", tool_call_id=tool_call_id ) ``` """ def _is_directly_injected_arg_type(type_: Any) -> bool: """Check if a type annotation indicates a directly injected argument. This is currently only used for `ToolRuntime`. Checks if either the annotation itself is a subclass of `_DirectlyInjectedToolArg` or the origin of the annotation is a subclass of `_DirectlyInjectedToolArg`. Ex: `ToolRuntime` or `ToolRuntime[ContextT, StateT]` would both return `True`. """ return ( isinstance(type_, type) and issubclass(type_, _DirectlyInjectedToolArg) ) or ( (origin := get_origin(type_)) is not None and isinstance(origin, type) and issubclass(origin, _DirectlyInjectedToolArg) ) def _is_injected_arg_type( type_: type | TypeVar, injected_type: type[InjectedToolArg] | None = None ) -> bool: """Check if a type annotation indicates an injected argument. Args: type_: The type annotation to check. injected_type: The specific injected type to check for. Returns: `True` if the type is an injected argument, `False` otherwise. """ if injected_type is None: # if no injected type is specified, # check if the type is a directly injected argument if _is_directly_injected_arg_type(type_): return True injected_type = InjectedToolArg # if the type is an Annotated type, check if annotated metadata # is an intance or subclass of the injected type return any( isinstance(arg, injected_type) or (isinstance(arg, type) and issubclass(arg, injected_type)) for arg in get_args(type_)[1:] ) def get_all_basemodel_annotations( cls: TypeBaseModel | Any, *, default_to_bound: bool = True ) -> dict[str, type | TypeVar]: """Get all annotations from a Pydantic `BaseModel` and its parents. Args: cls: The Pydantic `BaseModel` class. default_to_bound: Whether to default to the bound of a `TypeVar` if it exists. Returns: `dict` of field names to their type annotations. """ # cls has no subscript: cls = FooBar if isinstance(cls, type): fields = get_fields(cls) alias_map = {field.alias: name for name, field in fields.items() if field.alias} annotations: dict[str, type | TypeVar] = {} for name, param in inspect.signature(cls).parameters.items(): # Exclude hidden init args added by pydantic Config. For example if # BaseModel(extra="allow") then "extra_data" will part of init sig. if fields and name not in fields and name not in alias_map: continue field_name = alias_map.get(name, name) annotations[field_name] = param.annotation orig_bases: tuple = getattr(cls, "__orig_bases__", ()) # cls has subscript: cls = FooBar[int] else: annotations = get_all_basemodel_annotations( get_origin(cls), default_to_bound=False ) orig_bases = (cls,) # Pydantic v2 automatically resolves inherited generics, Pydantic v1 does not. if not (isinstance(cls, type) and is_pydantic_v2_subclass(cls)): # if cls = FooBar inherits from Baz[str], orig_bases will contain Baz[str] # if cls = FooBar inherits from Baz, orig_bases will contain Baz # if cls = FooBar[int], orig_bases will contain FooBar[int] for parent in orig_bases: # if class = FooBar inherits from Baz, parent = Baz if isinstance(parent, type) and is_pydantic_v1_subclass(parent): annotations.update( get_all_basemodel_annotations(parent, default_to_bound=False) ) continue parent_origin = get_origin(parent) # if class = FooBar inherits from non-pydantic class if not parent_origin: continue # if class = FooBar inherits from Baz[str]: # parent = class Baz[str], # parent_origin = class Baz, # generic_type_vars = (type vars in Baz) # generic_map = {type var in Baz: str} generic_type_vars: tuple = getattr(parent_origin, "__parameters__", ()) generic_map = dict(zip(generic_type_vars, get_args(parent), strict=False)) for field in getattr(parent_origin, "__annotations__", {}): annotations[field] = _replace_type_vars( annotations[field], generic_map, default_to_bound=default_to_bound ) return { k: _replace_type_vars(v, default_to_bound=default_to_bound) for k, v in annotations.items() } def _replace_type_vars( type_: type | TypeVar, generic_map: dict[TypeVar, type] | None = None, *, default_to_bound: bool = True, ) -> type | TypeVar: """Replace `TypeVar`s in a type annotation with concrete types. Args: type_: The type annotation to process. generic_map: Mapping of `TypeVar`s to concrete types. default_to_bound: Whether to use `TypeVar` bounds as defaults. Returns: The type with `TypeVar`s replaced. """ generic_map = generic_map or {} if isinstance(type_, TypeVar): if type_ in generic_map: return generic_map[type_] if default_to_bound: return type_.__bound__ if type_.__bound__ is not None else Any return type_ if (origin := get_origin(type_)) and (args := get_args(type_)): new_args = tuple( _replace_type_vars(arg, generic_map, default_to_bound=default_to_bound) for arg in args ) return _py_38_safe_origin(origin)[new_args] # type: ignore[index] return type_ class BaseToolkit(BaseModel, ABC): """Base class for toolkits containing related tools. A toolkit is a collection of related tools that can be used together to accomplish a specific task or work with a particular system. """ @abstractmethod def get_tools(self) -> list[BaseTool]: """Get all tools in the toolkit. Returns: List of tools contained in this toolkit. """