Release: 2.0.45 | Release Date: December 9, 2025

# SQLAlchemy 2.0 Documentation

# Mypy / Pep-484 Support for ORM Mappings

Support for PEP 484 typing annotations as well as the MyPy type checking tool when using SQLAlchemy declarative mappings that refer to the `Column` object directly, rather than the `mapped_column()` construct introduced in SQLAlchemy 2.0.

Deprecated since version 2.0: The SQLAlchemy Mypy Plugin is DEPRECATED, and will be removed in the SQLAlchemy 2.1 release. We would urge users to please migrate away from it ASAP. The mypy plugin also works only up until mypy version 1.10.1. version 1.11.0 and greater may not work properly.

This plugin cannot be maintained across constantly changing releases of mypy and its stability going forward CANNOT be guaranteed.

Modern SQLAlchemy now offers fully pep-484 compliant mapping syntaxes; see the linked section for migration details.

SQLAlchemy Mypy Plugin Status Update

Updated July 2024

The mypy plugin is supported only up until mypy 1.10.1, and it will have issues running with 1.11.0 or greater. Use with mypy 1.11.0 or greater may have error conditions which currently cannot be resolved.

For SQLAlchemy 2.0, the Mypy plugin continues to work at the level at which it reached in the SQLAlchemy 1.4 release. SQLAlchemy 2.0 however features an all new typing system for ORM Declarative models that removes the need for the Mypy plugin and delivers much more consistent behavior with generally superior capabilities. Note that this new capability is not part of SQLAlchemy 1.4, it is only in SQLAlchemy 2.0.

The SQLAlchemy Mypy plugin, while it has technically never left the “alpha” stage, should now be considered as deprecated in SQLAlchemy 2.0, even though it is still necessary for full Mypy support when using SQLAlchemy 1.4.

The Mypy plugin itself does not solve the issue of supplying correct typing with other typing tools such as Pylance/Pyright, Pytype, Pycharm, etc, which cannot make use of Mypy plugins. Additionally, Mypy plugins are extremely difficult to develop, maintain and test, as a Mypy plugin must be deeply integrated with Mypy’s internal datastructures and processes, which itself are not stable within the Mypy project itself. The SQLAlchemy Mypy plugin has lots of limitations when used with code that deviates from very basic patterns which are reported regularly.

For these reasons, new non-regression issues reported against the Mypy plugin are unlikely to be fixed. Existing code that passes Mypy checks using the plugin with SQLAlchemy 1.4 installed will continue to pass all checks in SQLAlchemy 2.0 without any changes required, provided the plugin is still used. SQLAlchemy 2.0’s API is fully backwards compatible with the SQLAlchemy 1.4 API and Mypy plugin behavior.

End-user code that passes all checks under SQLAlchemy 1.4 with the Mypy plugin may incrementally migrate to the new structures, once that code is running exclusively on SQLAlchemy 2.0. See the section ORM Declarative Models for background on how this migration may proceed.

Code that is running exclusively on SQLAlchemy version 2.0 and has fully migrated to the new declarative constructs will enjoy full compliance with pep-484 as well as working correctly within IDEs and other typing tools, without the need for plugins.

## Installation

For SQLAlchemy 2.0 only: No stubs should be installed and packages like sqlalchemy-stubs and sqlalchemy2-stubs should be fully uninstalled.

The Mypy package itself is a dependency.

Mypy may be installed using the “mypy” extras hook using pip:

    pip install sqlalchemy[mypy]

The plugin itself is configured as described in Configuring mypy to use Plugins, using the `sqlalchemy.ext.mypy.plugin` module name, such as within `setup.cfg`:

    [mypy]
    plugins = sqlalchemy.ext.mypy.plugin

## What the Plugin Does

The primary purpose of the Mypy plugin is to intercept and alter the static definition of SQLAlchemy declarative mappings so that they match up to how they are structured after they have been instrumented by their `Mapper` objects. This allows both the class structure itself as well as code that uses the class to make sense to the Mypy tool, which otherwise would not be the case based on how declarative mappings currently function. The plugin is not unlike similar plugins that are required for libraries like dataclasses which alter classes dynamically at runtime.

To cover the major areas where this occurs, consider the following ORM mapping, using the typical example of the `User` class:

    from sqlalchemy import Column, Integer, String, select
    from sqlalchemy.orm import declarative_base

    # "Base" is a class that is created dynamically from the
    # declarative_base() function
    Base = declarative_base()

    class User(Base):
        __tablename__ = "user"

        id = Column(Integer, primary_key=True)
        name = Column(String)

    # "some_user" is an instance of the User class, which
    # accepts "id" and "name" kwargs based on the mapping
    some_user = User(id=5, name="user")

    # it has an attribute called .name that's a string
    print(f"Username: {some_user.name}")

    # a select() construct makes use of SQL expressions derived from the
    # User class itself
    select_stmt = select(User).where(User.id.in_([3, 4, 5])).where(User.name.contains("s"))

Above, the steps that the Mypy extension can take include:

- Interpretation of the `Base` dynamic class generated by `declarative_base()`, so that classes which inherit from it are known to be mapped. It also can accommodate the class decorator approach described at Declarative Mapping using a Decorator (no declarative base).

- Type inference for ORM mapped attributes that are defined in declarative “inline” style, in the above example the `id` and `name` attributes of the `User` class. This includes that an instance of `User` will use `int` for `id` and `str` for `name`. It also includes that when the `User.id` and `User.name` class-level attributes are accessed, as they are above in the `select()` statement, they are compatible with SQL expression behavior, which is derived from the `InstrumentedAttribute` attribute descriptor class.

- Application of an `__init__()` method to mapped classes that do not already include an explicit constructor, which accepts keyword arguments of specific types for all mapped attributes detected.

When the Mypy plugin processes the above file, the resulting static class definition and Python code passed to the Mypy tool is equivalent to the following:

    from sqlalchemy import Column, Integer, String, select
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm.decl_api import DeclarativeMeta

    class Base(metaclass=DeclarativeMeta):
        __abstract__ = True

    class User(Base):
        __tablename__ = "user"

        id: Mapped[Optional[int]] = Mapped._special_method(
            Column(Integer, primary_key=True)
        )
        name: Mapped[Optional[str]] = Mapped._special_method(Column(String))

        def __init__(self, id: Optional[int] = ..., name: Optional[str] = ...) -> None: ...

    some_user = User(id=5, name="user")

    print(f"Username: {some_user.name}")

    select_stmt = select(User).where(User.id.in_([3, 4, 5])).where(User.name.contains("s"))

The key steps which have been taken above include:

- The `Base` class is now defined in terms of the `DeclarativeMeta` class explicitly, rather than being a dynamic class.

- The `id` and `name` attributes are defined in terms of the `Mapped` class, which represents a Python descriptor that exhibits different behaviors at the class vs. instance levels. The `Mapped` class is now the base class for the `InstrumentedAttribute` class that is used for all ORM mapped attributes.

  `Mapped` is defined as a generic class against arbitrary Python types, meaning specific occurrences of `Mapped` are associated with a specific Python type, such as `Mapped[Optional[int]]` and `Mapped[Optional[str]]` above.

- The right-hand side of the declarative mapped attribute assignments are removed, as this resembles the operation that the `Mapper` class would normally be doing, which is that it would be replacing these attributes with specific instances of `InstrumentedAttribute`. The original expression is moved into a function call that will allow it to still be type-checked without conflicting with the left-hand side of the expression. For Mypy purposes, the left-hand typing annotation is sufficient for the attribute’s behavior to be understood.

- A type stub for the `User.__init__()` method is added which includes the correct keywords and datatypes.

## Usage

The following subsections will address individual uses cases that have so far been considered for pep-484 compliance.

### Introspection of Columns based on TypeEngine

For mapped columns that include an explicit datatype, when they are mapped as inline attributes, the mapped type will be introspected automatically:

    class MyClass(Base):
        # ...

        id = Column(Integer, primary_key=True)
        name = Column("employee_name", String(50), nullable=False)
        other_name = Column(String(50))

Above, the ultimate class-level datatypes of `id`, `name` and `other_name` will be introspected as `Mapped[Optional[int]]`, `Mapped[Optional[str]]` and `Mapped[Optional[str]]`. The types are by default always considered to be `Optional`, even for the primary key and non-nullable column. The reason is because while the database columns “id” and “name” can’t be NULL, the Python attributes `id` and `name` most certainly can be `None` without an explicit constructor:

    >>> m1 = MyClass()
    >>> m1.id
    None

The types of the above columns can be stated explicitly, providing the two advantages of clearer self-documentation as well as being able to control which types are optional:

    class MyClass(Base):
        # ...

        id: int = Column(Integer, primary_key=True)
        name: str = Column("employee_name", String(50), nullable=False)
        other_name: Optional[str] = Column(String(50))

The Mypy plugin will accept the above `int`, `str` and `Optional[str]` and convert them to include the `Mapped[]` type surrounding them. The `Mapped[]` construct may also be used explicitly:

    from sqlalchemy.orm import Mapped

    class MyClass(Base):
        # ...

        id: Mapped[int] = Column(Integer, primary_key=True)
        name: Mapped[str] = Column("employee_name", String(50), nullable=False)
        other_name: Mapped[Optional[str]] = Column(String(50))

When the type is non-optional, it simply means that the attribute as accessed from an instance of `MyClass` will be considered to be non-None:

    mc = MyClass(...)

    # will pass mypy --strict
    name: str = mc.name

For optional attributes, Mypy considers that the type must include None or otherwise be `Optional`:

    mc = MyClass(...)

    # will pass mypy --strict
    other_name: Optional[str] = mc.name

Whether or not the mapped attribute is typed as `Optional`, the generation of the `__init__()` method will still consider all keywords to be optional. This is again matching what the SQLAlchemy ORM actually does when it creates the constructor, and should not be confused with the behavior of a validating system such as Python `dataclasses` which will generate a constructor that matches the annotations in terms of optional vs. required attributes.

### Columns that Don’t have an Explicit Type

Columns that include a `ForeignKey` modifier do not need to specify a datatype in a SQLAlchemy declarative mapping. For this type of attribute, the Mypy plugin will inform the user that it needs an explicit type to be sent:

    # .. other imports
    from sqlalchemy.sql.schema import ForeignKey

    Base = declarative_base()

    class User(Base):
        __tablename__ = "user"

        id = Column(Integer, primary_key=True)
        name = Column(String)

    class Address(Base):
        __tablename__ = "address"

        id = Column(Integer, primary_key=True)
        user_id = Column(ForeignKey("user.id"))

The plugin will deliver the message as follows:

    $ mypy test3.py --strict
    test3.py:20: error: [SQLAlchemy Mypy plugin] Can't infer type from
    ORM mapped expression assigned to attribute 'user_id'; please specify a
    Python type or Mapped[<python type>] on the left hand side.
    Found 1 error in 1 file (checked 1 source file)

To resolve, apply an explicit type annotation to the `Address.user_id` column:

    class Address(Base):
        __tablename__ = "address"

        id = Column(Integer, primary_key=True)
        user_id: int = Column(ForeignKey("user.id"))

### Mapping Columns with Imperative Table

In imperative table style, the `Column` definitions are given inside of a `Table` construct which is separate from the mapped attributes themselves. The Mypy plugin does not consider this `Table`, but instead supports that the attributes can be explicitly stated with a complete annotation that must use the `Mapped` class to identify them as mapped attributes:

    class MyClass(Base):
        __table__ = Table(
            "mytable",
            Base.metadata,
            Column(Integer, primary_key=True),
            Column("employee_name", String(50), nullable=False),
            Column(String(50)),
        )

        id: Mapped[int]
        name: Mapped[str]
        other_name: Mapped[Optional[str]]

The above `Mapped` annotations are considered as mapped columns and will be included in the default constructor, as well as provide the correct typing profile for `MyClass` both at the class level and the instance level.

### Mapping Relationships

The plugin has limited support for using type inference to detect the types for relationships. For all those cases where it can’t detect the type, it will emit an informative error message, and in all cases the appropriate type may be provided explicitly, either with the `Mapped` class or optionally omitting it for an inline declaration. The plugin also needs to determine whether or not the relationship refers to a collection or a scalar, and for that it relies upon the explicit value of the `relationship.uselist` and/or `relationship.collection_class` parameters. An explicit type is needed if neither of these parameters are present, as well as if the target type of the `relationship()` is a string or callable, and not a class:

    class User(Base):
        __tablename__ = "user"

        id = Column(Integer, primary_key=True)
        name = Column(String)

    class Address(Base):
        __tablename__ = "address"

        id = Column(Integer, primary_key=True)
        user_id: int = Column(ForeignKey("user.id"))

        user = relationship(User)

The above mapping will produce the following error:

    test3.py:22: error: [SQLAlchemy Mypy plugin] Can't infer scalar or
    collection for ORM mapped expression assigned to attribute 'user'
    if both 'uselist' and 'collection_class' arguments are absent from the
    relationship(); please specify a type annotation on the left hand side.
    Found 1 error in 1 file (checked 1 source file)

The error can be resolved either by using `relationship(User, uselist=False)` or by providing the type, in this case the scalar `User` object:

    class Address(Base):
        __tablename__ = "address"

        id = Column(Integer, primary_key=True)
        user_id: int = Column(ForeignKey("user.id"))

        user: User = relationship(User)

For collections, a similar pattern applies, where in the absence of `uselist=True` or a `relationship.collection_class`, a collection annotation such as `List` may be used. It is also fully appropriate to use the string name of the class in the annotation as supported by pep-484, ensuring the class is imported with in the TYPE_CHECKING block as appropriate:

    from typing import TYPE_CHECKING, List

    from .mymodel import Base

    if TYPE_CHECKING:
        # if the target of the relationship is in another module
        # that cannot normally be imported at runtime
        from .myaddressmodel import Address

    class User(Base):
        __tablename__ = "user"

        id = Column(Integer, primary_key=True)
        name = Column(String)
        addresses: List["Address"] = relationship("Address")

As is the case with columns, the `Mapped` class may also be applied explicitly:

    class User(Base):
        __tablename__ = "user"

        id = Column(Integer, primary_key=True)
        name = Column(String)

        addresses: Mapped[List["Address"]] = relationship("Address", back_populates="user")

    class Address(Base):
        __tablename__ = "address"

        id = Column(Integer, primary_key=True)
        user_id: int = Column(ForeignKey("user.id"))

        user: Mapped[User] = relationship(User, back_populates="addresses")

### Using @declared_attr and Declarative Mixins

The `declared_attr` class allows Declarative mapped attributes to be declared in class level functions, and is particularly useful when using declarative mixins. For these functions, the return type of the function should be annotated using either the `Mapped[]` construct or by indicating the exact kind of object returned by the function. Additionally, “mixin” classes that are not otherwise mapped (i.e. don’t extend from a `declarative_base()` class nor are they mapped with a method such as `registry.mapped()`) should be decorated with the `declarative_mixin()` decorator, which provides a hint to the Mypy plugin that a particular class intends to serve as a declarative mixin:

    from sqlalchemy.orm import declarative_mixin, declared_attr

    @declarative_mixin
    class HasUpdatedAt:
        @declared_attr
        def updated_at(cls) -> Column[DateTime]:  # uses Column
            return Column(DateTime)

    @declarative_mixin
    class HasCompany:
        @declared_attr
        def company_id(cls) -> Mapped[int]:  # uses Mapped
            return mapped_column(ForeignKey("company.id"))

        @declared_attr
        def company(cls) -> Mapped["Company"]:
            return relationship("Company")

    class Employee(HasUpdatedAt, HasCompany, Base):
        __tablename__ = "employee"

        id = Column(Integer, primary_key=True)
        name = Column(String)

Note the mismatch between the actual return type of a method like `HasCompany.company` vs. what is annotated. The Mypy plugin converts all `@declared_attr` functions into simple annotated attributes to avoid this complexity:

    # what Mypy sees
    class HasCompany:
        company_id: Mapped[int]
        company: Mapped["Company"]

### Combining with Dataclasses or Other Type-Sensitive Attribute Systems

The examples of Python dataclasses integration at Applying ORM Mappings to an existing dataclass (legacy dataclass use) presents a problem; Python dataclasses expect an explicit type that it will use to build the class, and the value given in each assignment statement is significant. That is, a class as follows has to be stated exactly as it is in order to be accepted by dataclasses:

    mapper_registry: registry = registry()

    @mapper_registry.mapped
    @dataclass
    class User:
        __table__ = Table(
            "user",
            mapper_registry.metadata,
            Column("id", Integer, primary_key=True),
            Column("name", String(50)),
            Column("fullname", String(50)),
            Column("nickname", String(12)),
        )
        id: int = field(init=False)
        name: Optional[str] = None
        fullname: Optional[str] = None
        nickname: Optional[str] = None
        addresses: List[Address] = field(default_factory=list)

        __mapper_args__ = {  # type: ignore
            "properties": {"addresses": relationship("Address")}
        }

We can’t apply our `Mapped[]` types to the attributes `id`, `name`, etc. because they will be rejected by the `@dataclass` decorator. Additionally, Mypy has another plugin for dataclasses explicitly which can also get in the way of what we’re doing.

The above class will actually pass Mypy’s type checking without issue; the only thing we are missing is the ability for attributes on `User` to be used in SQL expressions, such as:

    stmt = select(User.name).where(User.id.in_([1, 2, 3]))

To provide a workaround for this, the Mypy plugin has an additional feature whereby we can specify an extra attribute `_mypy_mapped_attrs`, that is a list that encloses the class-level objects or their string names. This attribute can be conditional within the `TYPE_CHECKING` variable:

    @mapper_registry.mapped
    @dataclass
    class User:
        __table__ = Table(
            "user",
            mapper_registry.metadata,
            Column("id", Integer, primary_key=True),
            Column("name", String(50)),
            Column("fullname", String(50)),
            Column("nickname", String(12)),
        )
        id: int = field(init=False)
        name: Optional[str] = None
        fullname: Optional[str]
        nickname: Optional[str]
        addresses: List[Address] = field(default_factory=list)

        if TYPE_CHECKING:
            _mypy_mapped_attrs = [id, name, "fullname", "nickname", addresses]

        __mapper_args__ = {  # type: ignore
            "properties": {"addresses": relationship("Address")}
        }

With the above recipe, the attributes listed in `_mypy_mapped_attrs` will be applied with the `Mapped` typing information so that the `User` class will behave as a SQLAlchemy mapped class when used in a class-bound context.