Python Type Safety

Python Type Safety

Leverage Python's type system to catch errors at static analysis time. Type annotations serve as enforced documentation that tooling validates automatically.

Tooling: Prefer ty for static analysis, managed with uv (uv add --dev ty, uv run ty check), matching the FastAPI other-tools reference and python-code-style. mypy and pyright remain valid if a project or organization already standardized on them.

When to Use This Skill

• Adding type hints to existing code
• Creating generic, reusable classes
• Defining structural interfaces with protocols
• Configuring ty (or mypy / pyright) for strict checking
• Understanding type narrowing and guards
• Building type-safe APIs and libraries

Core Concepts

1. Type Annotations

Declare expected types for function parameters, return values, and variables.

2. Generics

Write reusable code that preserves type information across different types.

3. Protocols

Define structural interfaces without inheritance (duck typing with type safety).

4. Type Narrowing

Use guards and conditionals to narrow types within code blocks.

Quick Start

Run the type checker from the project environment:

uv add --dev ty
uv run ty check

Ad hoc check without adding ty to the project: uvx ty check. See ty type checking for behavior and options.

def get_user(user_id: str) -> User | None:
    """Return type makes 'might not exist' explicit."""
    ...

# Type checker enforces handling None case
user = get_user("123")
if user is None:
    raise UserNotFoundError("123")
print(user.name)  # Type checker knows user is User here

Fundamental Patterns

Pattern 1: Annotate All Public Signatures

Every public function, method, and class should have type annotations.

def get_user(user_id: str) -> User:
    """Retrieve user by ID."""
    ...

def process_batch(
    items: list[Item],
    max_workers: int = 4,
) -> BatchResult[ProcessedItem]:
    """Process items concurrently."""
    ...

class UserRepository:
    def __init__(self, db: Database) -> None:
        self._db = db

    async def find_by_id(self, user_id: str) -> User | None:
        """Return User if found, None otherwise."""
        ...

    async def find_by_email(self, email: str) -> User | None:
        ...

    async def save(self, user: User) -> User:
        """Save and return user with generated ID."""
        ...

Run uv run ty check in CI to catch type errors early. Treat ty’s rule levels and overrides like gradual strictness for large codebases. With mypy or pyright, use their strict modes and incremental overrides instead.

Pattern 2: Use Modern Union Syntax

Python 3.10+ provides cleaner union syntax.

# Preferred (3.10+)
def find_user(user_id: str) -> User | None:
    ...

def parse_value(v: str) -> int | float | str:
    ...

# Older style (still valid, needed for 3.9)
from typing import Optional, Union

def find_user(user_id: str) -> Optional[User]:
    ...

Pattern 3: Type Narrowing with Guards

Use conditionals to narrow types for the type checker.

def process_user(user_id: str) -> UserData:
    user = find_user(user_id)

    if user is None:
        raise UserNotFoundError(f"User {user_id} not found")

    # Type checker knows user is User here, not User | None
    return UserData(
        name=user.name,
        email=user.email,
    )

def process_items(items: list[Item | None]) -> list[ProcessedItem]:
    # Filter and narrow types
    valid_items = [item for item in items if item is not None]
    # valid_items is now list[Item]
    return [process(item) for item in valid_items]

Pattern 4: Generic Classes

Create type-safe reusable containers.

from typing import TypeVar, Generic

T = TypeVar("T")
E = TypeVar("E", bound=Exception)

class Result(Generic[T, E]):
    """Represents either a success value or an error."""

    def __init__(
        self,
        value: T | None = None,
        error: E | None = None,
    ) -> None:
        if (value is None) == (error is None):
            raise ValueError("Exactly one of value or error must be set")
        self._value = value
        self._error = error

    @property
    def is_success(self) -> bool:
        return self._error is None

    @property
    def is_failure(self) -> bool:
        return self._error is not None

    def unwrap(self) -> T:
        """Get value or raise the error."""
        if self._error is not None:
            raise self._error
        return self._value  # type: ignore[return-value]

    def unwrap_or(self, default: T) -> T:
        """Get value or return default."""
        if self._error is not None:
            return default
        return self._value  # type: ignore[return-value]

# Usage preserves types
def parse_config(path: str) -> Result[Config, ConfigError]:
    try:
        return Result(value=Config.from_file(path))
    except ConfigError as e:
        return Result(error=e)

result = parse_config("config.yaml")
if result.is_success:
    config = result.unwrap()  # Type: Config

Advanced Patterns

Pattern 5: Generic Repository

Create type-safe data access patterns.

from typing import TypeVar, Generic
from abc import ABC, abstractmethod

T = TypeVar("T")
ID = TypeVar("ID")

class Repository(ABC, Generic[T, ID]):
    """Generic repository interface."""

    @abstractmethod
    async def get(self, id: ID) -> T | None:
        """Get entity by ID."""
        ...

    @abstractmethod
    async def save(self, entity: T) -> T:
        """Save and return entity."""
        ...

    @abstractmethod
    async def delete(self, id: ID) -> bool:
        """Delete entity, return True if existed."""
        ...

class UserRepository(Repository[User, str]):
    """Concrete repository for Users with string IDs."""

    async def get(self, id: str) -> User | None:
        row = await self._db.fetchrow(
            "SELECT * FROM users WHERE id = $1", id
        )
        return User(**row) if row else None

    async def save(self, entity: User) -> User:
        ...

    async def delete(self, id: str) -> bool:
        ...

Pattern 6: TypeVar with Bounds

Restrict generic parameters to specific types.

from typing import TypeVar
from pydantic import BaseModel

ModelT = TypeVar("ModelT", bound=BaseModel)

def validate_and_create(model_cls: type[ModelT], data: dict) -> ModelT:
    """Create a validated Pydantic model from dict."""
    return model_cls.model_validate(data)

# Works with any BaseModel subclass
class User(BaseModel):
    name: str
    email: str

user = validate_and_create(User, {"name": "Alice", "email": "a@b.com"})
# user is typed as User

# Type error: str is not a BaseModel subclass
result = validate_and_create(str, {"name": "Alice"})  # Error!

Pattern 7: Protocols for Structural Typing

Define interfaces without requiring inheritance.

Ellipsis in protocols: Using ... as the body of a Protocol method (below) is standard typing practice. That is not the same as using ... for required FastAPI parameters or Pydantic field defaults—the Official FastAPI skill forbids the latter only; see Do not use Ellipsis for path operations or Pydantic models there.

from typing import Protocol, runtime_checkable

@runtime_checkable
class Serializable(Protocol):
    """Any class that can be serialized to/from dict."""

    def to_dict(self) -> dict:
        ...

    @classmethod
    def from_dict(cls, data: dict) -> "Serializable":
        ...

# User satisfies Serializable without inheriting from it
class User:
    def __init__(self, id: str, name: str) -> None:
        self.id = id
        self.name = name

    def to_dict(self) -> dict:
        return {"id": self.id, "name": self.name}

    @classmethod
    def from_dict(cls, data: dict) -> "User":
        return cls(id=data["id"], name=data["name"])

def serialize(obj: Serializable) -> str:
    """Works with any Serializable object."""
    return json.dumps(obj.to_dict())

# Works - User matches the protocol
serialize(User("1", "Alice"))

# Runtime checking with @runtime_checkable
isinstance(User("1", "Alice"), Serializable)  # True

Pattern 8: Common Protocol Patterns

Define reusable structural interfaces.

from typing import Protocol

class Closeable(Protocol):
    """Resource that can be closed."""
    def close(self) -> None: ...

class AsyncCloseable(Protocol):
    """Async resource that can be closed."""
    async def close(self) -> None: ...

class Readable(Protocol):
    """Object that can be read from."""
    def read(self, n: int = -1) -> bytes: ...

class HasId(Protocol):
    """Object with an ID property."""
    @property
    def id(self) -> str: ...

class Comparable(Protocol):
    """Object that supports comparison."""
    def __lt__(self, other: "Comparable") -> bool: ...
    def __le__(self, other: "Comparable") -> bool: ...

Pattern 9: Type Aliases

Create meaningful type names.

Note: The type Alias = ... statement syntax (PEP 695) was introduced in Python 3.12, not 3.10. For projects targeting earlier versions (including 3.10/3.11), use the TypeAlias annotation (PEP 613, available since Python 3.10).

# Python 3.12+ type statement (PEP 695)
type UserId = str
type UserDict = dict[str, Any]

# Python 3.12+ type statement with generics (PEP 695)
type Handler[T] = Callable[[Request], T]
type AsyncHandler[T] = Callable[[Request], Awaitable[T]]

# Python 3.10-3.11 style (needed for broader compatibility)
from typing import TypeAlias
from collections.abc import Callable, Awaitable

UserId: TypeAlias = str
Handler: TypeAlias = Callable[[Request], Response]

# Usage
def register_handler(path: str, handler: Handler[Response]) -> None:
    ...

Pattern 10: Callable Types

Type function parameters and callbacks.

from collections.abc import Callable, Awaitable

# Sync callback
ProgressCallback = Callable[[int, int], None]  # (current, total)

# Async callback
AsyncHandler = Callable[[Request], Awaitable[Response]]

# With named parameters (using Protocol)
class OnProgress(Protocol):
    def __call__(
        self,
        current: int,
        total: int,
        *,
        message: str = "",
    ) -> None: ...

def process_items(
    items: list[Item],
    on_progress: ProgressCallback | None = None,
) -> list[Result]:
    for i, item in enumerate(items):
        if on_progress:
            on_progress(i, len(items))
        ...

Configuration

ty (preferred for FastAPI-aligned projects)

Configure ty under [tool.ty] in pyproject.toml. Full reference: ty configuration.

# pyproject.toml
[tool.ty.environment]
python-version = "3.12"  # Optional; ty can infer from requires-python / the venv

[tool.ty.rules]
all = "error"  # Enabled rules report as errors; tune individual rules or overrides as needed

Example: relax specific rules under tests/ while keeping the rest strict:

[[tool.ty.overrides]]
include = ["tests/**", "**/test_*.py"]

[tool.ty.overrides.rules]
possibly-unresolved-reference = "warn"

Run locally and in CI:

uv run ty check

See overrides and suppression comments to tighten coverage over time without blocking the whole repo.

Alternative checkers (mypy, pyright)

If the team already uses mypy or pyright, keep one checker in CI and apply the same strictness goals below. Example mypy strict baseline:

# pyproject.toml
[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = true
warn_unused_ignores = true
disallow_untyped_defs = true
disallow_incomplete_defs = true
no_implicit_optional = true

Example pyright strict baseline:

[tool.pyright]
pythonVersion = "3.12"
typeCheckingMode = "strict"

Strictness goals (any checker)

These targets apply whether you use ty, mypy, or pyright:

• All function parameters annotated
• All return types annotated
• Class attributes annotated
• Minimize Any usage (acceptable for truly dynamic data)
• Generic collections use type parameters (list[str] not list)

For mypy, enable stricter checking per module with # mypy: strict or [[tool.mypy.overrides]]. For ty, use [tool.ty.rules] and per-file overrides linked above.

Best Practices Summary

1. Annotate all public APIs - Functions, methods, class attributes
2. Use T | None - Modern union syntax over Optional[T]
3. Run strict type checking in CI - Prefer uv run ty check; mypy/pyright equivalents if those are the project standard
4. Use generics - Preserve type info in reusable code
5. Define protocols - Structural typing for interfaces
6. Narrow types - Use guards to help the type checker
7. Bound type vars - Restrict generics to meaningful types
8. Create type aliases - Meaningful names for complex types
9. Minimize Any - Use specific types or generics. Any is acceptable for truly dynamic data or when interfacing with untyped third-party code
10. Document with types - Types are enforceable documentation