"""Base classes and utilities for LangChain tools."""

from __future__ import annotations

import functools
import inspect
import json
import logging
import typing
import warnings
from abc import ABC, abstractmethod
from collections.abc import Callable, Mapping, Sequence
from inspect import signature
from typing import (
    TYPE_CHECKING,
    Annotated,
    Any,
    Literal,
    TypeVar,
    cast,
    get_args,
    get_origin,
    get_type_hints,
)

import typing_extensions
from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
    PrivateAttr,
    PydanticDeprecationWarning,
    SkipValidation,
    ValidationError,
    validate_arguments,
)
from pydantic.fields import FieldInfo
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 Self, 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,
    model_json_schema,
)

if TYPE_CHECKING:
    import uuid

FILTERED_ARGS = ("run_manager", "callbacks")
TOOL_MESSAGE_BLOCK_TYPES = (
    "text",
    "image_url",
    "image",
    "json",
    "search_result",
    "custom_tool_call_output",
    "document",
    "file",
)

_logger = logging.getLogger(__name__)


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) in {typing.Annotated, typing_extensions.Annotated}


def _get_annotation_description(arg_type: type) -> str | None:
    """Extract description from an `Annotated` type.

    Checks for string annotations and `FieldInfo` objects with descriptions.

    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
            if isinstance(annotation, FieldInfo) and annotation.description:
                return annotation.description
    return None


def _parse_python_function_docstring(
    function: Callable[..., Any],
    annotations: dict[str, Any],
    *,
    error_on_invalid_docstring: bool = False,
) -> tuple[str, dict[str, str]]:
    """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[str, str], annotations: dict[str, Any]
) -> 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[..., Any],
    *,
    parse_docstring: bool = False,
    error_on_invalid_docstring: bool = False,
) -> tuple[str, dict[str, str]]:
    """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[..., Any]
) -> 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[..., Any],
    *,
    filter_args: Sequence[str] | None = None,
    parse_docstring: bool = False,
    error_on_invalid_docstring: bool = False,
    include_injected: bool = True,
) -> TypeBaseModel:
    """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:
        # Copy to avoid mutating the caller's sequence below.
        filter_args_ = list(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)

    # Exclude injected args from the schema regardless of whether `filter_args`
    # was provided. Injected args (e.g. `InjectedToolArg`, `ToolRuntime`) are
    # supplied at runtime rather than by the model, so they should not appear in
    # the generated schema.
    if not include_injected:
        for existing_param in sig.parameters:
            if (
                _is_injected_arg_type(sig.parameters[existing_param].annotation)
                and existing_param not in filter_args_
            ):
                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]
MessageContentBlock = str | dict[str, Any]
"""A single message content block: plain text or a structured block.

A dict block is only considered valid at runtime when its `type` key is one of
`TOOL_MESSAGE_BLOCK_TYPES` (see `_is_message_content_block`); the static type
intentionally stays broad because block payloads vary by provider format.
"""
ToolExceptionHandlerOutput = str | Sequence[MessageContentBlock]
"""Content returned by a `handle_tool_error` callable.

Error handlers may return plain text or a sequence of structured message
content blocks. When the original tool call includes a `tool_call_id`, this
content is normalized to the content of a `ToolMessage` with `status="error"`.
"""

_EMPTY_SET: frozenset[str] = frozenset()


_TOOL_CALL_SCHEMA_FIELDS = frozenset({"name", "description", "args_schema"})
"""Fields the memoized `tool_call_schema` is built from; reassignment clears it."""


def _patch_json_schema_cache(model_cls: type) -> None:
    """Patch `model_json_schema` (or `schema` for pydantic v1) to cache.

    Pydantic regenerates the full JSON-schema dict on every
    `model_json_schema()` call — there is no per-class cache.  When the
    model class is stable (memoized on a `BaseTool` instance), this patch
    caches the dict on the class so repeated calls return instantly.

    Only calls with all-default arguments are cached; any explicit arguments
    bypass the cache and delegate to the original method.
    """
    method_name = (
        "model_json_schema" if hasattr(model_cls, "model_json_schema") else "schema"
    )
    orig = getattr(model_cls, method_name)

    def _cached_json_schema(cls: type, *args: Any, **kwargs: Any) -> dict[str, Any]:
        if not args and not kwargs:
            cached = cls.__dict__.get("_json_schema_cache")
            if cached is not None:
                return cast("dict[str, Any]", cached)
        result = orig(*args, **kwargs)
        if not args and not kwargs:
            cls._json_schema_cache = result  # type: ignore[attr-defined]
        return cast("dict[str, Any]", result)

    setattr(model_cls, method_name, classmethod(_cached_json_schema))


class BaseTool(RunnableSerializable[str | dict[str, Any] | 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 usecase.
    """

    handle_tool_error: (
        bool | str | Callable[[ToolException], ToolExceptionHandlerOutput] | None
    ) = False
    """Handle `ToolException` raised by tool execution.

    If `False`, the exception is re-raised. If `True`, the exception message is
    returned as tool output. If a string is passed, that string is returned
    as tool output. If a callable is passed, it receives the exception and
    its return value is used as the tool output.

    Callable handlers may return either a string or a list of message
    content blocks. If the tool was invoked with a `tool_call_id`, the handled
    content is wrapped in a `ToolMessage` with `status="error"`.
    """

    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`.
    """

    extras: dict[str, Any] | None = None
    """Optional provider-specific extra fields for the tool.

    This is used to pass provider-specific configuration that doesn't fit into
    standard tool fields.

    Example:
        Anthropic-specific fields like [`cache_control`](https://docs.langchain.com/oss/python/integrations/chat/anthropic#prompt-caching),
        [`defer_loading`](https://docs.langchain.com/oss/python/integrations/chat/anthropic#tool-search),
        or `input_examples`.

        ```python
        @tool(extras={"defer_loading": True, "cache_control": {"type": "ephemeral"}})
        def my_tool(x: str) -> str:
            return x
        ```
    """

    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[str, Any]:
        """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
        else:
            input_schema = self.tool_call_schema
            if isinstance(input_schema, dict):
                json_schema = input_schema
            else:
                json_schema = model_json_schema(input_schema)
        return cast("dict[str, Any]", json_schema["properties"])

    _tool_call_schema_memo: ArgsSchema | None = PrivateAttr(default=None)
    """Memoized `tool_call_schema` result.

    Building the subset model is expensive, and pydantic does not cache
    `model_json_schema()` per class, so agent loops would otherwise pay full
    schema generation for every tool on every model call. The subset model
    class is memoized here and its `model_json_schema`/`schema` method is
    patched to cache the generated dict, so both costs are paid only once per
    tool instance.
    Cleared whenever `name`, `description`, or `args_schema` is reassigned (see
    `__setattr__` and `model_copy`).
    """

    @override
    def __setattr__(self, name: str, value: Any) -> None:
        """Clear the tool-call schema memo when an input to it is reassigned."""
        super().__setattr__(name, value)
        if name in _TOOL_CALL_SCHEMA_FIELDS and self.__pydantic_private__ is not None:
            self._tool_call_schema_memo = None

    @override
    def model_copy(
        self, *, update: Mapping[str, Any] | None = None, deep: bool = False
    ) -> Self:
        """Copy the tool, clearing the schema memo if `update` affects it.

        `model_copy` writes `update` directly to the copy's `__dict__` without
        going through `__setattr__`, and private attributes (including the
        memo) carry over to the copy, so the memo is cleared here when the
        update touches one of the fields the schema is built from.
        """
        copied = super().model_copy(update=update, deep=deep)
        if update and not _TOOL_CALL_SCHEMA_FIELDS.isdisjoint(update):
            copied._tool_call_schema_memo = None  # noqa: SLF001
        return copied

    def __getstate__(self) -> dict[Any, Any]:
        """Drop the tool-call schema memo when pickling.

        The memoized subset model is a dynamically created class that cannot be
        pickled by reference; it is rebuilt lazily on next access.
        """
        state = super().__getstate__()
        private = state.get("__pydantic_private__")
        if private and private.get("_tool_call_schema_memo") is not None:
            state = dict(state)
            state["__pydantic_private__"] = {
                **private,
                "_tool_call_schema_memo": None,
            }
        return state

    @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.

            The returned model class is memoized per tool instance (invalidated
            when `name`, `description`, or `args_schema` is reassigned) so
            repeated access does not regenerate the class. The class's
            `model_json_schema` method is also patched to cache the generated
            schema dict, since pydantic does not cache it per class.
        """
        if isinstance(self.args_schema, dict):
            if self.description:
                return {
                    **self.args_schema,
                    "description": self.description,
                }

            return self.args_schema

        if (memo := self._tool_call_schema_memo) is not None:
            return memo

        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)
        subset_model = _create_subset_model(
            self.name, full_schema, fields, fn_description=self.description
        )
        _patch_json_schema_cache(subset_model)
        self._tool_call_schema_memo = subset_model
        return subset_model

    @functools.cached_property
    def _injected_args_keys(self) -> frozenset[str]:
        # Inspect the tool's `_run` (falling back to `_arun` for async-only
        # subclasses) for directly injected args like `ToolRuntime` or args
        # annotated with `InjectedToolArg`. These are supplied at call time
        # rather than by the model, so they must be excluded from the schema and
        # re-injected during execution. `StructuredTool` overrides this to
        # inspect its wrapped `func`/`coroutine` instead.
        #
        # Resolve annotations via `get_type_hints` (rather than reading raw
        # `signature` annotations) so postponed annotations -- e.g. from
        # `from __future__ import annotations` or quoted forward references --
        # are recognized. Fall back to the raw annotation per-parameter when a
        # hint can't be resolved.
        for method in (self._run, self._arun):
            params = signature(method).parameters
            hints = _get_type_hints(method, include_extras=True) or {}
            keys = frozenset(
                name
                for name, param in params.items()
                if _is_injected_arg_type(hints.get(name, param.annotation))
            )
            if keys:
                return keys
        return _EMPTY_SET

    # --- Runnable ---

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> TypeBaseModel:
        """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[str, Any] | 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[str, Any] | 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[str, Any], 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}"  # type: ignore[unreachable]
                    raise TypeError(msg)
            return tool_input

        if input_args is not None:
            if isinstance(input_args, dict):
                return tool_input
            result: BaseModel | BaseModelV1
            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_v2 = input_args.model_validate(tool_input)
                result_dict = result_v2.model_dump()
                provided_fields = result_v2.model_fields_set
                result = result_v2
            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_v1 = input_args.parse_obj(tool_input)
                result_dict = result_v1.dict()
                provided_fields = result_v1.__fields_set__
                result = result_v1
            else:
                msg = (  # type: ignore[unreachable]
                    f"args_schema must be a Pydantic BaseModel, got {self.args_schema}"
                )
                raise NotImplementedError(msg)

            # Include fields from tool_input, fields provided through Pydantic aliases,
            # plus fields with explicit defaults. This applies Pydantic defaults (like
            # Field(default=1)) while excluding synthetic "args"/"kwargs" fields that
            # Pydantic creates for *args/**kwargs.
            field_info = get_fields(input_args)
            validated_input = {}
            for k in result_dict:
                if k in tool_input:
                    # Field was provided in input - include it (validated)
                    validated_input[k] = getattr(result, k)
                elif k in provided_fields:
                    # Field was provided through a Pydantic alias - include it.
                    validated_input[k] = getattr(result, k)
                elif k in field_info and k not in {"args", "kwargs"}:
                    # Check if field has an explicit default defined in the schema.
                    # Exclude "args"/"kwargs" as these are synthetic fields for variadic
                    # parameters that should not be passed as keyword arguments.
                    fi = field_info[k]
                    # Pydantic v2 uses is_required() method, v1 uses required attribute
                    has_default = (
                        not fi.is_required()
                        if hasattr(fi, "is_required")
                        else not getattr(fi, "required", True)
                    )
                    if has_default:
                        validated_input[k] = getattr(result, k)

            for k in self._injected_args_keys:
                if k in tool_input:
                    validated_input[k] = tool_input[k]
                elif 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

            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[str, Any]) -> dict[str, Any]:
        """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)

        # Add injected args from function signature (e.g., ToolRuntime parameters)
        filtered_keys.update(self._injected_args_keys)

        # If we have an args_schema, use it to identify injected args
        # Skip if args_schema is a dict (JSON Schema) as it's not a Pydantic model
        if self.args_schema is not None and not isinstance(self.args_schema, dict):
            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:
                # If we can't get annotations, just use FILTERED_ARGS
                _logger.debug(
                    "Failed to get args_schema annotations for filtering.",
                    exc_info=True,
                )

        # 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[str, Any], tool_call_id: str | None
    ) -> tuple[tuple[str, ...], dict[str, Any]]:
        """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)}"  # type: ignore[unreachable]
        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,
            tool_call_id=tool_call_id,
            **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, tool_call_id=tool_call_id)
            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[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 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,
            tool_call_id=tool_call_id,
            **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, 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:
            await run_manager.on_tool_error(error_to_raise, tool_call_id=tool_call_id)
            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 = (  # type: ignore[unreachable]
            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], ToolExceptionHandlerOutput]
    | None,
) -> ToolExceptionHandlerOutput:
    """Convert a `ToolException` into handled tool output content.

    Args:
        e: The tool exception that occurred.
        flag: How to handle the error. `True` uses the exception message, a string
            replaces the message, and a callable computes replacement content from
            the exception.

    Returns:
        The handled error content. This may be plain text or structured message
            content blocks; callers pass it through normal tool
            output formatting.

    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[str, Any] | ToolCall,
    config: RunnableConfig | None,
    **kwargs: Any,
) -> tuple[str | dict[str, Any], dict[str, Any]]:
    """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)
    tool_input: str | dict[str, Any]
    if _is_tool_call(value):
        tool_call_id: str | None = cast("ToolCall", value)["id"]
        tool_input = cast("ToolCall", value)["args"].copy()
    else:
        tool_call_id = None
        tool_input = cast("str | dict[str, Any]", 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`, the original content,
        or an unchanged list of `ToolOutputMixin` instances.
    """
    if (
        isinstance(content, list)
        and content
        and all(isinstance(item, ToolOutputMixin) for item in content)
    ):
        return content
    if isinstance(content, ToolOutputMixin) or tool_call_id is None:
        return content
    normalized_content = _normalize_message_content(content)
    content = _stringify(content) if normalized_content is None else normalized_content
    return ToolMessage(
        content,
        artifact=artifact,
        tool_call_id=tool_call_id,
        name=name,
        status=status,
    )


def _normalize_message_content(obj: Any) -> str | list[MessageContentBlock] | None:
    """Coerce valid message content to the shape expected by `ToolMessage`.

    A string passes through unchanged; any `Sequence` of valid content blocks
    (e.g. a list or tuple) is materialized into a `list`. Returning `None`
    signals the caller (`_format_output`) that `obj` is not message content and
    should be stringified instead.

    Args:
        obj: The object to normalize.

    Returns:
        The normalized content, or `None` if `obj` is not valid message content.
    """
    if isinstance(obj, str):
        return obj
    # Validate lazily before materializing: `all` short-circuits on the first
    # invalid element, so a large non-content sequence (e.g. `range(10**12)`)
    # falls back to stringification without allocating it.
    if isinstance(obj, Sequence) and all(_is_message_content_block(e) for e in obj):
        return list(obj)
    return None


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[..., Any], *, include_extras: bool = False
) -> dict[str, type] | None:
    """Get type hints from a function, handling partial functions.

    Args:
        func: The function to get type hints from.
        include_extras: Whether to preserve `Annotated` metadata in the resolved
            hints (e.g. to detect `Annotated[..., InjectedToolArg]`).

    Returns:
        `dict` of type hints, or `None` if extraction fails.
    """
    if isinstance(func, functools.partial):
        func = func.func
    try:
        return get_type_hints(func, include_extras=include_extras)
    except Exception:
        return None


def _get_runnable_config_param(func: Callable[..., Any]) -> 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`.

    For example, `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.
    """
    orig_bases: tuple[type, ...]
    # 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 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 = 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[TypeVar, ...] = 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 cast("type", _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.
        """
