Notice: While Javascript is not essential for this website, your interaction with the content will be limited. Please turn Javascript on for the full experience.

PEP 544 -- Protocols

PEP: 544
Title: Protocols
Author: Ivan Levkivskyi <levkivskyi at>, Jukka Lehtosalo <jukka.lehtosalo at>, Ɓukasz Langa <lukasz at>
Discussions-To: Python-Dev < python-dev at >
Status: Draft
Type: Standards Track
Created: 05-Mar-2017
Python-Version: 3.7


Type hints introduced in PEP 484 can be used to specify type metadata for static type checkers and other third party tools. However, PEP 484 only specifies the semantics of nominal subtyping. In this PEP we specify static and runtime semantics of protocol classes that will provide a support for structural subtyping (static duck typing).

Rationale and Goals

Currently, PEP 484 and the typing module [typing] define abstract base classes for several common Python protocols such as Iterable and Sized . The problem with them is that a class has to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484 :

from typing import Sized, Iterable, Iterator

class Bucket(Sized, Iterable[int]):
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

The same problem appears with user-defined ABCs: they must be explicitly subclassed or registered. This is particularly difficult to do with library types as the type objects may be hidden deep in the implementation of the library. Also, extensive use of ABCs might impose additional runtime costs.

The intention of this PEP is to solve all these problems by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket to be implicitly considered a subtype of both Sized and Iterable[int] by static type checkers using structural [wiki-structural] subtyping:

from typing import Iterator, Iterable

class Bucket:
    def __len__(self) -> int: ...
    def __iter__(self) -> Iterator[int]: ...

def collect(items: Iterable[int]) -> int: ...
result: int = collect(Bucket())  # Passes type check

Note that ABCs in typing module already provide structural behavior at runtime, isinstance(Bucket(), Iterable) returns True . The main goal of this proposal is to support such behavior statically. The same functionality will be provided for user-defined protocols, as specified below. The above code with a protocol class matches common Python conventions much better. It is also automatically extensible and works with additional, unrelated classes that happen to implement the required protocol.

Nominal vs structural subtyping

Structural subtyping is natural for Python programmers since it matches the runtime semantics of duck typing: an object that has certain properties is treated independently of its actual runtime class. However, as discussed in PEP 483 , both nominal and structural subtyping have their strengths and weaknesses. Therefore, in this PEP we do not propose to replace the nominal subtyping described by PEP 484 with structural subtyping completely. Instead, protocol classes as specified in this PEP complement normal classes, and users are free to choose where to apply a particular solution. See section on rejected ideas at the end of this PEP for additional motivation.


At runtime, protocol classes will be simple ABCs. There is no intent to provide sophisticated runtime instance and class checks against protocol classes. This would be difficult and error-prone and will contradict the logic of PEP 484 . As well, following PEP 484 and PEP 526 we state that protocols are completely optional :

  • No runtime semantics will be imposed for variables or parameters annotated with a protocol class.
  • Any checks will be performed only by third-party type checkers and other tools.
  • Programmers are free to not use them even if they use type annotations.
  • There is no intent to make protocols non-optional in the future.

Existing Approaches to Structural Subtyping

Before describing the actual specification, we review and comment on existing approaches related to structural subtyping in Python and other languages:

  • zope.interface [zope-interfaces] was one of the first widely used approaches to structural subtyping in Python. It is implemented by providing special classes to distinguish interface classes from normal classes, to mark interface attributes, and to explicitly declare implementation. For example:

    from zope.interface import Interface, Attribute, implementer
    class IEmployee(Interface):
        name = Attribute("Name of employee")
        def do(work):
            """Do some work"""
    class Employee:
        name = 'Anonymous'
        def do(self, work):
            return work.start()

    Zope interfaces support various contracts and constraints for interface classes. For example:

    from zope.interface import invariant
    def required_contact(obj):
        if not ( or
            raise Exception("At least one contact info is required")
    class IPerson(Interface):
        name = Attribute("Name")
        email = Attribute("Email Address")
        phone = Attribute("Phone Number")

    Even more detailed invariants are supported. However, Zope interfaces rely entirely on runtime validation. Such focus on runtime properties goes beyond the scope of the current proposal, and static support for invariants might be difficult to implement. However, the idea of marking an interface class with a special base class is reasonable and easy to implement both statically and at runtime.

  • Python abstract base classes [abstract-classes] are the standard library tool to provide some functionality similar to structural subtyping. The drawback of this approach is the necessity to either subclass the abstract class or register an implementation explicitly:

    from abc import ABC
    class MyTuple(ABC):
    assert issubclass(tuple, MyTuple)
    assert isinstance((), MyTuple)

    As mentioned in the rationale , we want to avoid such necessity, especially in static context. However, in a runtime context, ABCs are good candidates for protocol classes and they are already used extensively in the typing module.

  • Abstract classes defined in module [collections-abc] are slightly more advanced since they implement a custom __subclasshook__() method that allows runtime structural checks without explicit registration:

    from import Iterable
    class MyIterable:
        def __iter__(self):
            return []
    assert isinstance(MyIterable(), Iterable)

    Such behavior seems to be a perfect fit for both runtime and static behavior of protocols. As discussed in rationale , we propose to add static support for such behavior. In addition, to allow users to achieve such runtime behavior for user-defined protocols a special @runtime decorator will be provided, see detailed discussion below.

  • TypeScript [typescript] provides support for user-defined classes and interfaces. Explicit implementation declaration is not required and structural subtyping is verified statically. For example:

    interface LabeledItem {
        label: string;
        size?: int;
    function printLabel(obj: LabeledItem) {
    let myObj = {size: 10, label: "Size 10 Object"};

    Note that optional interface members are supported. Also, TypeScript prohibits redundant members in implementations. While the idea of optional members looks interesting, it would complicate this proposal and it is not clear how useful it will be. Therefore it is proposed to postpone this; see rejected ideas. In general, the idea of static protocol checking without runtime implications looks reasonable, and basically this proposal follows the same line.

  • Go [golang] uses a more radical approach and makes interfaces the primary way to provide type information. Also, assignments are used to explicitly ensure implementation:

    type SomeInterface interface {
        SomeMethod() ([]byte, error)
    if _, ok := someval.(SomeInterface); ok {
        fmt.Printf("value implements some interface")

    Both these ideas are questionable in the context of this proposal. See the section on rejected ideas.



We propose to use the term protocols for types supporting structural subtyping. The reason is that the term iterator protocol , for example, is widely understood in the community, and coming up with a new term for this concept in a statically typed context would just create confusion.

This has the drawback that the term protocol becomes overloaded with two subtly different meanings: the first is the traditional, well-known but slightly fuzzy concept of protocols such as iterator; the second is the more explicitly defined concept of protocols in statically typed code. The distinction is not important most of the time, and in other cases we propose to just add a qualifier such as protocol classes when referring to the static type concept.

If a class includes a protocol in its MRO, the class is called an explicit subclass of the protocol. If a class is a structural subtype of a protocol, it is said to implement the protocol and to be compatible with a protocol. If a class is compatible with a protocol but the protocol is not included in the MRO, the class is an implicit subtype of the protocol. (Note that one can explicitly subclass a protocol and still not implement it if a protocol attribute is set to None in the subclass, see Python [data-model] for details.)

The attributes (variables and methods) of a protocol that are mandatory for other class in order to be considered a structural subtype are called protocol members.

Defining a protocol

Protocols are defined by including a special new class typing.Protocol (an instance of abc.ABCMeta ) in the base classes list, typically at the end of the list. Here is a simple example:

from typing import Protocol

class SupportsClose(Protocol):
    def close(self) -> None:

Now if one defines a class Resource with a close() method that has a compatible signature, it would implicitly be a subtype of SupportsClose , since the structural subtyping is used for protocol types:

class Resource:
    def close(self) -> None:

Apart from few restrictions explicitly mentioned below, protocol types can be used in every context where a normal types can:

def close_all(things: Iterable[SupportsClose]) -> None:
    for t in things:

f = open('foo.txt')
r = Resource()
close_all([f, r])  # OK!
close_all([1])     # Error: 'int' has no 'close' method

Note that both the user-defined class Resource and the built-in IO type (the return type of open() ) are considered subtypes of SupportsClose , because they provide a close() method with a compatible type signature.

Protocol members

All methods defined in the protocol class body are protocol members, both normal and decorated with @abstractmethod . If any parameters of a protocol method are not annotated, then their types are assumed to be Any (see PEP 484 ). Bodies of protocol methods are type checked. An abstract method that should not be called via super() ought to raise NotImplementedError . Example:

from typing import Protocol
from abc import abstractmethod

class Example(Protocol):
    def first(self) -> int:     # This is a protocol member
        return 42

    def second(self) -> int:    # Method without a default implementation
        raise NotImplementedError

Static methods, class methods, and properties are equally allowed in protocols.

To define a protocol variable, one can use PEP 526 variable annotations in the class body. Additional attributes only defined in the body of a method by assignment via self are not allowed. The rationale for this is that the protocol class implementation is often not shared by subtypes, so the interface should not depend on the default implementation. Examples:

from typing import Protocol, List

class Template(Protocol):
    name: str        # This is a protocol member
    value: int = 0   # This one too (with default)

    def method(self) -> None:
        self.temp: List[int] = [] # Error in type checker

class Concrete:
    def __init__(self, name: str, value: int) -> None: = name
        self.value = value

var: Template = Concrete('value', 42)  # OK

To distinguish between protocol class variables and protocol instance variables, the special ClassVar annotation should be used as specified by PEP 526 . By default, protocol variables as defined above are considered readable and writable. To define a read-only protocol variable, one can use an (abstract) property.

Explicitly declaring implementation

To explicitly declare that a certain class implements a given protocol, it can be used as a regular base class. In this case a class could use default implementations of protocol members. typing.Sequence is a good example of a protocol with useful default methods. Static analysis tools are expected to automatically detect that a class implements a given protocol. So while it's possible to subclass a protocol explicitly, it's not necessary to do so for the sake of type-checking.

The default implementations cannot be used if the subtype relationship is implicit and only via structural subtyping -- the semantics of inheritance is not changed. Examples:

class PColor(Protocol):
    def draw(self) -> str:
    def complex_method(self) -> int:
        # some complex code here

class NiceColor(PColor):
    def draw(self) -> str:
        return "deep blue"

class BadColor(PColor):
    def draw(self) -> str:
        return super().draw()  # Error, no default implementation

class ImplicitColor:   # Note no 'PColor' base here
    def draw(self) -> str:
        return "probably gray"
    def comlex_method(self) -> int:
        # class needs to implement this

nice: NiceColor
another: ImplicitColor

def represent(c: PColor) -> None:
    print(c.draw(), c.complex_method())

represent(nice) # OK
represent(another) # Also OK

Note that there is little difference between explicit and implicit subtypes, the main benefit of explicit subclassing is to get some protocol methods "for free". In addition, type checkers can statically verify that the class actually implements the protocol correctly:

class RGB(Protocol):
    rgb: Tuple[int, int, int]

    def intensity(self) -> int:
        return 0

class Point(RGB):
    def __init__(self, red: int, green: int, blue: str) -> None:
        self.rgb = red, green, blue  # Error, 'blue' must be 'int'

    # Type checker might warn that 'intensity' is not defined

A class can explicitly inherit from multiple protocols and also form normal classes. In this case methods are resolved using normal MRO and a type checker verifies that all subtyping are correct. The semantics of @abstractmethod is not changed, all of them must be implemented by an explicit subclass before it can be instantiated.

Merging and extending protocols

The general philosophy is that protocols are mostly like regular ABCs, but a static type checker will handle them specially. Subclassing a protocol class would not turn the subclass into a protocol unless it also has typing.Protocol as an explicit base class. Without this base, the class is "downgraded" to a regular ABC that cannot be used with structural subtyping. The rationale for this rule is that we don't want to accidentally have some class act as a protocol just because one of its base classes happens to be one. We still slightly prefer nominal subtyping over structural subtyping in the static typing world.

A subprotocol can be defined by having both one or more protocols as immediate base classes and also having typing.Protocol as an immediate base class:

from typing import Sized, Protocol

class SizedAndClosable(Sized, Protocol):
    def close(self) -> None:

Now the protocol SizedAndClosable is a protocol with two methods, __len__ and close . If one omits Protocol in the base class list, this would be a regular (non-protocol) class that must implement Sized . Alternatively, one can implement SizedAndClosable protocol by merging the SupportsClose protocol from the example in the definition section with typing.Sized :

from typing import Sized

class SupportsClose(Protocol):
    def close(self) -> None:

class SizedAndClosable(Sized, SupportsClose, Protocol):

The two definitions of SizedAndClosable are equivalent. Subclass relationships between protocols are not meaningful when considering subtyping, since structural compatibility is the criterion, not the MRO.

If Protocol is included in the base class list, all the other base classes must be protocols. A protocol can't extend a regular class, see rejected ideas for rationale. Note that rules around explicit subclassing are different from regular ABCs, where abstractness is simply defined by having at least one abstract method being unimplemented. Protocol classes must be marked explicitly .

Generic protocols

Generic protocols are important. For example, SupportsAbs , Iterable and Iterator are generic protocols. They are defined similar to normal non-protocol generic types:

class Iterable(Protocol[T]):
    def __iter__(self) -> Iterator[T]:

Protocol[T, S, ...] is allowed as a shorthand for Protocol, Generic[T, S, ...] .

Declaring variance is not necessary for protocol classes, since it can be inferred from a protocol definition. Examples:

class Box(Protocol[T]):
    def content(self) -> T:

box: Box[float]
second_box: Box[int]
box = second_box  # This is OK due to the inferred covariance of 'Box'.

class Sender(Protocol[T]):
    def send(self, data: T) -> int:

sender: Sender[float]
new_sender: Sender[int]
new_sender = sender  # OK, type checker finds that 'Sender' is contravariant.

class Proto(Protocol[T]):
    attr: T  # this class is invariant, since it has a mutable attribute

var: Proto[float]
another_var: Proto[int]
var = another_var  # Error! 'Proto[float]' is incompatible with 'Proto[int]'.

Recursive protocols

Recursive protocols are also supported. Forward references to the protocol class names can be given as strings as specified by PEP 484 . Recursive protocols are useful for representing self-referential data structures like trees in an abstract fashion:

class Traversable(Protocol):
    def leaves(self) -> Iterable['Traversable']:

Note that for recursive protocols, a class is considered a subtype of the protocol in situations where the decision depends on itself. Continuing the previous example:

class SimpleTree:
    def leaves(self) -> List['SimpleTree']:

root: Traversable = SimpleTree()  # OK

class Tree(Generic[T]):
    def leaves(self) -> List['Tree[T]']:

def walk(graph: Traversable) -> None:
tree: Tree[float] = Tree(0, [])
walk(tree)  # OK, 'Tree[float]' is a subtype of 'Traversable'

Using Protocols

Subtyping relationships with other types

Protocols cannot be instantiated, so there are no values whose runtime type is a protocol. For variables and parameters with protocol types, subtyping relationships are subject to the following rules:

  • A protocol is never a subtype of a concrete type.
  • A concrete type X is a subtype of protocol P if and only if X implements all protocol members of P with compatible types. In other words, subtyping with respect to a protocol is always structural.
  • A protocol P1 is a subtype of another protocol P2 if P1 defines all protocol members of P2 with compatible types.

Generic protocol types follow the same rules of variance as non-protocol types. Protocol types can be used in all contexts where any other types can be used, such as in Union , ClassVar , type variables bounds, etc. Generic protocols follow the rules for generic abstract classes, except for using structural compatibility instead of compatibility defined by inheritance relationships.

Unions and intersections of protocols

Union of protocol classes behaves the same way as for non-protocol classes. For example:

from typing import Union, Optional, Protocol

class Exitable(Protocol):
    def exit(self) -> int:
class Quittable(Protocol):
    def quit(self) -> Optional[int]:

def finish(task: Union[Exitable, Quittable]) -> int:
class DefaultJob:
    def quit(self) -> int:
        return 0
finish(DefaultJob()) # OK

One can use multiple inheritance to define an intersection of protocols. Example:

from typing import Sequence, Hashable

class HashableFloats(Sequence[float], Hashable, Protocol):

def cached_func(args: HashableFloats) -> float:
cached_func((1, 2, 3)) # OK, tuple is both hashable and sequence

If this will prove to be a widely used scenario, then a special intersection type construct could be added in future as specified by PEP 483 , see rejected ideas for more details.

Type[] with protocols

Variables and parameters annotated with Type[Proto] accept only concrete (non-protocol) subtypes of Proto . The main reason for this is to allow instantiation of parameters with such type. For example:

class Proto(Protocol):
    def meth(self) -> int:
class Concrete:
    def meth(self) -> int:
        return 42

def fun(cls: Type[Proto]) -> int:
    return cls().meth() # OK
fun(Proto)              # Error
fun(Concrete)           # OK

The same rule applies to variables:

var: Type[Proto]
var = Proto    # Error
var = Concrete # OK
var().meth()   # OK

Assigning an ABC or a protocol class to a variable is allowed if it is not explicitly typed, and such assignment creates a type alias. For normal (non-abstract) classes, the behavior of Type[] is not changed.

NewType() and type aliases

Protocols are essentially anonymous. To emphasize this point, static type checkers might refuse protocol classes inside NewType() to avoid an illusion that a distinct type is provided:

from typing import NewType, Protocol, Iterator

class Id(Protocol):
    code: int
    secrets: Iterator[bytes]

UserId = NewType('UserId', Id)  # Error, can't provide distinct type

In contrast, type aliases are fully supported, including generic type aliases:

from typing import TypeVar, Reversible, Iterable, Sized

T = TypeVar('T')
class SizedIterable(Iterable[T], Sized, Protocol):
CompatReversible = Union[Reversible[T], SizedIterable[T]]

@runtime decorator and narrowing types by isinstance()

The default semantics is that isinstance() and issubclass() fail for protocol types. This is in the spirit of duck typing -- protocols basically would be used to model duck typing statically, not explicitly at runtime.

However, it should be possible for protocol types to implement custom instance and class checks when this makes sense, similar to how Iterable and other ABCs in and typing already do it, but this is limited to non-generic and unsubscripted generic protocols ( Iterable is statically equivalent to Iterable[Any]`). The ``typing module will define a special @runtime class decorator that provides the same semantics for class and instance checks as for classes, essentially making them "runtime protocols":

from typing import runtime, Protocol

class Closable(Protocol):
    def close(self):

assert isinstance(open('some/file'), Closable)

Static type checkers will understand isinstance(x, Proto) and issubclass(C, Proto) for protocols defined with this decorator (as they already do for Iterable etc.). Static type checkers will narrow types after such checks by the type erased Proto (i.e. with all variables having type Any and all methods having type Callable[..., Any] ). Note that isinstance(x, Proto[int]) etc. will always fail in agreement with PEP 484 . Examples:

from typing import Iterable, Iterator, Sequence

def process(items: Iterable[int]) -> None:
    if isinstance(items, Iterator):
        # 'items' has type 'Iterator[int]' here
    elif isinstance(items, Sequence[int]):
        # Error! Can't use 'isinstance()' with subscripted protocols

Note that instance checks are not 100% reliable statically, this is why this behavior is opt-in, see section on rejected ideas for examples.

Using Protocols in Python 2.7 - 3.5

Variable annotation syntax was added in Python 3.6, so that the syntax for defining protocol variables proposed in specification section can't be used if support for earlier versions is needed. To define these in a manner compatible with older versions of Python one can use properties. Properties can be settable and/or abstract if needed:

class Foo(Protocol):
    def c(self) -> int:
        return 42         # Default value can be provided for property...

    def d(self) -> int:   # ... or it can be abstract
        return 0

Also function type comments can be used as per PEP 484 (for example to provide compatibility with Python 2). The typing module changes proposed in this PEP will also be backported to earlier versions via the backport currently available on PyPI.

Runtime Implementation of Protocol Classes

Implementation details

The runtime implementation could be done in pure Python without any effects on the core interpreter and standard library except in the typing module:

  • Define class typing.Protocol similar to typing.Generic .
  • Implement metaclass functionality to detect whether a class is a protocol or not. Add a class attribute __protocol__ = True if that is the case. Verify that a protocol class only has protocol base classes in the MRO (except for object).
  • Implement @runtime that adds all attributes to __subclasshook__() .
  • All structural subtyping checks will be performed by static type checkers, such as mypy [mypy] . No additional support for protocol validation will be provided at runtime.

Changes in the typing module

The following classes in typing module will be protocols:

  • Callable
  • Awaitable
  • Iterable , Iterator
  • AsyncIterable , AsyncIterator
  • Hashable
  • Sized
  • Container
  • Collection
  • Reversible
  • Sequence , MutableSequence
  • Mapping , MutableMapping
  • ContextManager , AsyncContextManager
  • SupportsAbs (and other Supports* classes)

Most of these classes are small and conceptually simple. It is easy to see what are the methods these protocols implement, and immediately recognize the corresponding runtime protocol counterpart. Practically, few changes will be needed in typing since some of these classes already behave the necessary way at runtime. Most of these will need to be updated only in the corresponding typeshed stubs [typeshed] .

All other concrete generic classes such as List , Set , IO , Deque , etc are sufficiently complex that it makes sense to keep them non-protocols (i.e. require code to be explicit about them). Also, it is too easy to leave some methods unimplemented by accident, and explicitly marking the subclass relationship allows type checkers to pinpoint the missing implementations.


The existing class introspection machinery ( dir , __annotations__ etc) can be used with protocols. In addition, all introspection tools implemented in the typing module will support protocols. Since all attributes need to be defined in the class body based on this proposal, protocol classes will have even better perspective for introspection than regular classes where attributes can be defined implicitly -- protocol attributes can't be initialized in ways that are not visible to introspection (using setattr() , assignment via self , etc.). Still, some things like types of attributes will not be visible at runtime in Python 3.5 and earlier, but this looks like a reasonable limitation.

There will be only limited support of isinstance() and issubclass() as discussed above (these will always fail with TypeError for subscripted generic protocols, since a reliable answer could not be given at runtime in this case). But together with other introspection tools this give a reasonable perspective for runtime type checking tools.

Rejected/Postponed Ideas

The ideas in this section were previously discussed in [several] [discussions] [elsewhere] .

Make every class a protocol by default

Some languages such as Go make structural subtyping the only or the primary form of subtyping. We could achieve a similar result by making all classes protocols by default (or even always). However we believe that it is better to require classes to be explicitly marked as protocols, for the following reasons:

  • Protocols don't have some properties of regular classes. In particular, isinstance() , as defined for normal classes, is based on the nominal hierarchy. In order to make everything a protocol by default, and have isinstance() work would require changing its semantics, which won't happen.
  • Protocol classes should generally not have many method implementations, as they describe an interface, not an implementation. Most classes have many method implementations, making them bad protocol classes.
  • Experience suggests that many classes are not practical as protocols anyway, mainly because their interfaces are too large, complex or implementation-oriented (for example, they may include de facto private attributes and methods without a __ prefix).
  • Most actually useful protocols in existing Python code seem to be implicit. The ABCs in typing and are rather an exception, but even they are recent additions to Python and most programmers do not use them yet.
  • Many built-in functions only accept concrete instances of int (and subclass instances), and similarly for other built-in classes. Making int a structural type wouldn't be safe without major changes to the Python runtime, which won't happen.

Allow protocols subclassing normal classes

The main rationale to prohibit this is to preserve transitivity of subtyping, consider this example:

from typing import Protocol

class Base:
    attr: str

class Proto(Base, Protocol):
    def meth(self) -> int:

class C:
    attr: str
    def meth(self) -> int:
        return 0

Now, C is a subtype of Proto , and Proto is a subtype of Base . But C cannot be a subtype of Base (since the latter is not a protocol). This situation would be really weird. In addition, there is an ambiguity about whether attributes of Base should become protocol members of Proto .

Support optional protocol members

We can come up with examples where it would be handy to be able to say that a method or data attribute does not need to be present in a class implementing a protocol, but if it is present, it must conform to a specific signature or type. One could use a hasattr() check to determine whether they can use the attribute on a particular instance.

Languages such as TypeScript have similar features and apparently they are pretty commonly used. The current realistic potential use cases for protocols in Python don't require these. In the interest of simplicity, we propose to not support optional methods or attributes. We can always revisit this later if there is an actual need.

Allow only protocol methods and force use of getters and setters

One could argue that protocols typically only define methods, but not variables. However, using getters and setters in cases where only a simple variable is needed would be quite unpythonic. Moreover, the widespread use of properties (that often act as type validators) in large code bases is partially due to previous absence of static type checkers for Python, the problem that PEP 484 and this PEP are aiming to solve. For example:

# without static types

class MyClass:
    def my_attr(self):
        return self._my_attr
    def my_attr(self, value):
        if not isinstance(value, int):
            raise ValidationError("An integer expected for my_attr")
        self._my_attr = value

# with static types

class MyClass:
    my_attr: int

Support non-protocol members

There was an idea to make some methods "non-protocol" (i.e. not necessary to implement, and inherited in explicit subclassing), but it was rejected, since this complicates things. For example, consider this situation:

class Proto(Protocol):
    def first(self) -> int:
        raise NotImplementedError
    def second(self) -> int:
        return self.first() + 1

def fun(arg: Proto) -> None:

The question is should this be an error? We think most people would expect this to be valid. Therefore, to be on the safe side, we need to require both methods to be implemented in implicit subclasses. In addition, if one looks at definitions in , there are very few methods that could be considered "non-protocol". Therefore, it was decided to not introduce "non-protocol" methods.

There is only one downside to this: it will require some boilerplate for implicit subtypes of Mapping and few other "large" protocols. But, this applies to few "built-in" protocols (like Mapping and Sequence ) and people are already subclassing them. Also, such style is discouraged for user-defined protocols. It is recommended to create compact protocols and combine them.

Make protocols interoperable with other approaches

The protocols as described here are basically a minimal extension to the existing concept of ABCs. We argue that this is the way they should be understood, instead of as something that replaces Zope interfaces, for example. Attempting such interoperabilities will significantly complicate both the concept and the implementation.

On the other hand, Zope interfaces are conceptually a superset of protocols defined here, but using an incompatible syntax to define them, because before PEP 526 there was no straightforward way to annotate attributes. In the 3.6+ world, zope.interface might potentially adopt the Protocol syntax. In this case, type checkers could be taught to recognize interfaces as protocols and make simple structural checks with respect to them.

Use assignments to check explicitly that a class implements a protocol

In the Go language the explicit checks for implementation are performed via dummy assignments [golang] . Such a way is also possible with the current proposal. Example:

class A:
    def __len__(self) -> float:
        return ...

_: Sized = A()  # Error: A.__len__ doesn't conform to 'Sized'
                # (Incompatible return type 'float')

This approach moves the check away from the class definition and it almost requires a comment as otherwise the code probably would not make any sense to an average reader -- it looks like dead code. Besides, in the simplest form it requires one to construct an instance of A , which could be problematic if this requires accessing or allocating some resources such as files or sockets. We could work around the latter by using a cast, for example, but then the code would be ugly. Therefore we discourage the use of this pattern.

Support isinstance() checks by default

The problem with this is instance checks could be unreliable, except for situations where there is a common signature convention such as Iterable . For example:

class P(Protocol):
    def common_method_name(self, x: int) -> int: ...

class X:
    <a bunch of methods>
    def common_method_name(self) -> None: ... # Note different signature

def do_stuff(o: Union[P, X]) -> int:
    if isinstance(o, P):
        return o.common_method_name(1)  # oops, what if it's an X instance?

Another potentially problematic case is assignment of attributes after instantiation:

class P(Protocol):
    x: int

class C:
    def initialize(self) -> None:
        self.x = 0

c = C()
isinstance(c1, P)  # False
isinstance(c, P)  # True

def f(x: Union[P, int]) -> None:
    if isinstance(x, P):
        # static type of x is P here
        # type of x is "int" here?
        print(x + 1)

f(C())   # oops

We argue that requiring an explicit class decorator would be better, since one can then attach warnings about problems like this in the documentation. The user would be able to evaluate whether the benefits outweigh the potential for confusion for each protocol and explicitly opt in -- but the default behavior would be safer. Finally, it will be easy to make this behavior default if necessary, while it might be problematic to make it opt-in after being default.

Provide a special intersection type construct

There was an idea to allow Proto = All[Proto1, Proto2, ...] as a shorthand for:

class Proto(Proto1, Proto2, ..., Protocol):

However, it is not yet clear how popular/useful it will be and implementing this in type checkers for non-protocol classes could be difficult. Finally, it will be very easy to add this later if needed.

Prohibit explicit subclassing of protocols by non-protocols

This was rejected for the following reasons:

  • Backward compatibility: People are already using ABCs, including generic ABCs from typing module. If we prohibit explicit subclassing of these ABCs, then quite a lot of code will break.
  • Convenience: There are existing protocol-like ABCs (that will be turned into protocols) that have many useful "mix-in" (non-abstract) methods. For example in the case of Sequence one only needs to implement __getitem__ and __len__ in an explicit subclass, and one gets __iter__ , __contains__ , __reversed__ , index , and count for free.
  • Explicit subclassing makes it explicit that a class implements a particular protocol, making subtyping relationships easier to see.
  • Type checkers can warn about missing protocol members or members with incompatible types more easily, without having to use hacks like dummy assignments discussed above in this section.
  • Explicit subclassing makes it possible to force a class to be considered a subtype of a protocol (by using # type: ignore together with an explicit base class) when it is not strictly compatible, such as when it has an unsafe override.

Support adapters and adaptation

Adaptation was proposed by PEP 246 (rejected) and is supported by zope.interface , see . Adapters is quite an advanced concept, and PEP 484 supports unions and generic aliases that can be used instead of adapters. This can be illustrated with an example of Iterable protocol, there is another way of supporting iteration by providing __getitem__ and __len__ . If a function supports both this way and the now standard __iter__ method, then it could be annotated by a union type:

class OldIterable(Sized, Protocol[T]):
    def __getitem__(self, item: int) -> T: ...

CompatIterable = Union[Iterable[T], OldIterable[T]]

class A:
    def __iter__(self) -> Iterator[str]: ...
class B:
    def __len__(self) -> int: ...
    def __getitem__(self, item: int) -> str: ...

def iterate(it: CompatIterable[str]) -> None:

iterate(A())  # OK
iterate(B())  # OK

Since there is a reasonable alternative for such cases with existing tooling, it is therefore proposed not to include adaptation in this PEP.

Backwards Compatibility

This PEP is almost fully backwards compatible. Few collection classes such as Sequence and Mapping will be turned into runtime protocols, therefore results of isinstance() checks are going to change in some edge cases. For example, a class that implements the Sequence protocol but does not explicitly inherit from Sequence currently returns False in corresponding instance and class checks. With this PEP implemented, such checks will return True .


A working implementation of this PEP for mypy type checker is found on GitHub repo at , corresponding typeshed stubs for more flavor are found at . Installation steps:

git clone --recurse-submodules
cd mypy && git checkout protocols && cd typeshed
git remote add proto
git fetch proto && git checkout proto/protocols
cd .. && git add typeshed && sudo python3 -m pip install -U .

The runtime implementation of protocols in typing module is found at .