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Python Enhancement Proposals

PEP 724 – Stricter Type Guards

Rich Chiodo <rchiodo at>, Eric Traut <erictr at>, Erik De Bonte <erikd at>
Jelle Zijlstra <jelle.zijlstra at>
Discourse thread
Standards Track
30-Dec-2021, 19-Sep-2023

Table of Contents


This PEP is withdrawn. The Typing Council was unable to reach consensus on this proposal, and the authors decided to withdraw it.


PEP 647 introduced the concept of a user-defined type guard function which returns True if the type of the expression passed to its first parameter matches its return TypeGuard type. For example, a function that has a return type of TypeGuard[str] is assumed to return True if and only if the type of the expression passed to its first input parameter is a str. This allows type checkers to narrow types when a user-defined type guard function returns True.

This PEP refines the TypeGuard mechanism introduced in PEP 647. It allows type checkers to narrow types when a user-defined type guard function returns False. It also allows type checkers to apply additional (more precise) type narrowing under certain circumstances when the type guard function returns True.


User-defined type guard functions enable a type checker to narrow the type of an expression when it is passed as an argument to the type guard function. The TypeGuard mechanism introduced in PEP 647 is flexible, but this flexibility imposes some limitations that developers have found inconvenient for some uses.

Limitation 1: Type checkers are not allowed to narrow a type in the case where the type guard function returns False. This means the type is not narrowed in the negative (“else”) clause.

Limitation 2: Type checkers must use the TypeGuard return type if the type guard function returns True regardless of whether additional narrowing can be applied based on knowledge of the pre-narrowed type.

The following code sample demonstrates both of these limitations.

def is_iterable(val: object) -> TypeGuard[Iterable[Any]]:
    return isinstance(val, Iterable)

def func(val: int | list[int]):
    if is_iterable(val):
        # The type is narrowed to 'Iterable[Any]' as dictated by
        # the TypeGuard return type
        reveal_type(val)  # Iterable[Any]
        # The type is not narrowed in the "False" case
        reveal_type(val)  # int | list[int]

    # If "isinstance" is used in place of the user-defined type guard
    # function, the results differ because type checkers apply additional
    # logic for "isinstance"

    if isinstance(val, Iterable):
        # Type is narrowed to "list[int]" because this is
        # a narrower (more precise) type than "Iterable[Any]"
        reveal_type(val)  # list[int]
        # Type is narrowed to "int" because the logic eliminates
        # "list[int]" from the original union
        reveal_type(val)  # int

PEP 647 imposed these limitations so it could support use cases where the return TypeGuard type was not a subtype of the input type. Refer to PEP 647 for examples.


There are a number of issues where a stricter TypeGuard would have been a solution:


The use of a user-defined type guard function involves five types:

  • I = TypeGuard input type
  • R = TypeGuard return type
  • A = Type of argument passed to type guard function (pre-narrowed)
  • NP = Narrowed type (positive)
  • NN = Narrowed type (negative)
def guard(x: I) -> TypeGuard[R]: ...

def func1(val: A):
    if guard(val):
        reveal_type(val)  # NP
        reveal_type(val)  # NN

This PEP proposes some modifications to PEP 647 to address the limitations discussed above. These limitations are safe to eliminate only when a specific condition is met. In particular, when the output type R of a user-defined type guard function is consistent [1] with the type of its first input parameter (I), type checkers should apply stricter type guard semantics.

# Stricter type guard semantics are used in this case because
# "Kangaroo | Koala" is consistent with "Animal"
def is_marsupial(val: Animal) -> TypeGuard[Kangaroo | Koala]:
    return isinstance(val, Kangaroo | Koala)

# Stricter type guard semantics are not used in this case because
# "list[T]"" is not consistent with "list[T | None]"
def has_no_nones(val: list[T | None]) -> TypeGuard[list[T]]:
    return None not in val

When stricter type guard semantics are applied, the application of a user-defined type guard function changes in two ways.

  • Type narrowing is applied in the negative (“else”) case.
def is_str(val: str | int) -> TypeGuard[str]:
    return isinstance(val, str)

def func(val: str | int):
    if not is_str(val):
        reveal_type(val)  # int
  • Additional type narrowing is applied in the positive “if” case if applicable.
def is_cardinal_direction(val: str) -> TypeGuard[Literal["N", "S", "E", "W"]]:
    return val in ("N", "S", "E", "W")

def func(direction: Literal["NW", "E"]):
    if is_cardinal_direction(direction):
        reveal_type(direction)  # "Literal[E]"
        reveal_type(direction)  # "Literal[NW]"

The type-theoretic rules for type narrowing are specificed in the following table.

Non-strict type guard Strict type guard
Applies when R not consistent with I R consistent with I
NP is .. R AR
NN is .. A A∧¬R

In practice, the theoretic types for strict type guards cannot be expressed precisely in the Python type system. Type checkers should fall back on practical approximations of these types. As a rule of thumb, a type checker should use the same type narrowing logic – and get results that are consistent with – its handling of “isinstance”. This guidance allows for changes and improvements if the type system is extended in the future.

Additional Examples

Any is consistent [1] with any other type, which means stricter semantics can be applied.

 # Stricter type guard semantics are used in this case because
 # "str" is consistent with "Any"
def is_str(x: Any) -> TypeGuard[str]:
    return isinstance(x, str)

def test(x: float | str):
    if is_str(x):
        reveal_type(x)  # str
        reveal_type(x)  # float

Backwards Compatibility

This PEP proposes to change the existing behavior of TypeGuard. This has no effect at runtime, but it does change the types evaluated by a type checker.

def is_int(val: int | str) -> TypeGuard[int]:
    return isinstance(val, int)

def func(val: int | str):
    if is_int(val):
        reveal_type(val)  # "int"
        reveal_type(val)  # Previously "int | str", now "str"

This behavioral change results in different types evaluated by a type checker. It could therefore produce new (or mask existing) type errors.

Type checkers often improve narrowing logic or fix existing bugs in such logic, so users of static typing will be used to this type of behavioral change.

We also hypothesize that it is unlikely that existing typed Python code relies on the current behavior of TypeGuard. To validate our hypothesis, we implemented the proposed change in pyright and ran this modified version on roughly 25 typed code bases using mypy primer to see if there were any differences in the output. As predicted, the behavioral change had minimal impact. The only noteworthy change was that some # type: ignore comments were no longer necessary, indicating that these code bases were already working around the existing limitations of TypeGuard.

Breaking change

It is possible for a user-defined type guard function to rely on the old behavior. Such type guard functions could break with the new behavior.

def is_positive_int(val: int | str) -> TypeGuard[int]:
    return isinstance(val, int) and val > 0

def func(val: int | str):
    if is_positive_int(val):
        reveal_type(val)  # "int"
        # With the older behavior, the type of "val" is evaluated as
        # "int | str"; with the new behavior, the type is narrowed to
        # "str", which is perhaps not what was intended.

We think it is unlikley that such user-defined type guards exist in real-world code. The mypy primer results didn’t uncover any such cases.

How to Teach This

Users unfamiliar with TypeGuard are likely to expect the behavior outlined in this PEP, therefore making TypeGuard easier to teach and explain.

Reference Implementation

A reference implementation of this idea exists in pyright.

To enable the modified behavior, the configuration flag enableExperimentalFeatures must be set to true. This can be done on a per-file basis by adding a comment:

# pyright: enableExperimentalFeatures=true

Rejected Ideas


A new StrictTypeGuard construct was proposed. This alternative form would be similar to a TypeGuard except it would apply stricter type guard semantics. It would also enforce that the return type was consistent [1] with the input type. See this thread for details: StrictTypeGuard proposal

This idea was rejected because it is unnecessary in most cases and added unnecessary complexity. It would require the introduction of a new special form, and developers would need to be educated about the subtle difference between the two forms.

TypeGuard with a second output type

Another idea was proposed where TypeGuard could support a second optional type argument that indicates the type that should be used for narrowing in the negative (“else”) case.

def is_int(val: int | str) -> TypeGuard[int, str]:
    return isinstance(val, int)

This idea was proposed here.

It was rejected because it was considered too complicated and addressed only one of the two main limitations of TypeGuard. Refer to this thread for the full discussion.



Last modified: 2024-02-09 02:51:39 GMT