Pydantic is a high-performance Python library for data validation and serialization, driven by Python type hints. Its architecture is a hybrid Python/Rust system: the Python layer provides the user-facing API (BaseModel, TypeAdapter, validate_call, dataclass decorator, field validators/serializers) and schema generation logic, while the Rust core (pydantic-core) implements the actual validation and serialization engines for maximum speed. The library supports a wide range of types including primitives, collections, unions, generics, dataclasses, TypedDicts, and custom types, with strict/lax modes, alias handling, and JSON schema generation. Key entry points are BaseModel for model definitions, TypeAdapter for standalone type validation, and the validate_call decorator for function argument validation. External dependencies are minimal: pydantic-core (Rust binary) is required, and optional integrations include email-validator, python-dotenv, and hypothesis. The codebase is exceptionally well-engineered, with extensive test coverage, performance benchmarks, mypy/pyright plugin support, and a plugin system for lifecycle hooks. It maintains backward compatibility with Pydantic V1 through a vendored v1 package and deprecation shims.