Pydantic
Published reports for this stack, with severity, affected version, and a direct path into each diagnostic.
The `TypeSafeClassifier` input schema generation fails with `PydanticUserError` because the private recursive `_StateValue` type alias is not resolvable in the dynamically created `TypeSafeClassifierInput` model namespace. This breaks `get_input_jsonschema()`, `as_tool()`, schemaful graph serialization, and Runnable composition schema introspection.
langchain-typesafe's TypeSafeClassifier does not validate that the 'questions' mapping is non-empty before making an HTTP request, causing invalid API calls and a regression from previous Pydantic-based validation.
A tool using `InjectedToolCallId` receives the wrong tool_call_id when the custom args_schema omits the injected field and the ToolCall args contain a key `tool_call_id`. The runtime ToolCall.id should always win, but currently the user-provided value from args is used, causing mismatch between tool body and ToolMessage.
When `from __future__ import annotations` is used, stringized type annotations are not resolved in the correct namespace for `RunnableLambda` and `RunnableGenerator`, causing `PydanticUserError` or empty tool argument schemas during `as_tool()` conversion, even though direct invocation works.
An injected tool argument declared with a Pydantic alias (e.g., Field(alias="private_token")) is not stripped from the inputs and input_str passed to on_tool_start callbacks, leaking internal sentinel values to tracing backends.