AnyIO: Mypy Incompatibility With Lru_cache Decorator

Dec 1, 2025 by Alex Johnson 53 views

Introduction

This article delves into a specific issue encountered when using the lru_cache decorator from the anyio.functools module in conjunction with the mypy static type checker. Specifically, we'll explore the incompatibility that arises when decorating a method within a subclass, and contrast it with the behavior of async_lru.alru_cache, which doesn't exhibit the same problem. Understanding this issue is crucial for developers aiming to leverage caching mechanisms in their asynchronous Python code while maintaining type safety with mypy. This article provides a detailed explanation, reproducible code examples, and a comparative analysis to help you navigate this potential pitfall.

The core problem lies in how mypy interprets the signature of a method decorated with lru_cache from anyio.functools when the method is part of a class inheriting from an abstract base class (abc). While the async_lru library's alru_cache decorator works seamlessly in similar scenarios, anyio's lru_cache triggers a type checking error. This discrepancy can lead to confusion and hinder the adoption of anyio's caching functionality in projects that heavily rely on static type checking.

The Problem: mypy Error with `lru_cache`

The crux of the issue is that when a method in a subclass is decorated with @lru_cache from anyio.functools, mypy raises an "incompatible signature" error. This happens because mypy misinterprets the return type of the decorated method, particularly when the method is overriding an abstract method from a parent class. Let's break down the scenario with a concrete example.

Code Example

Consider the following Python code snippet that demonstrates the problem:

import abc

from anyio.functools import lru_cache
from async_lru import alru_cache


class Iterface(abc.ABC):
    @abc.abstractmethod
    async def method(self, value: str) -> str: ...


class AnyIO(Iterface):  # error
    @lru_cache()
    async def method(self, value: str) -> str:
        return value


class AsyncLru(Iterface):  # fine
    @alru_cache()
    async def method(self, value: str) -> str:
        return value

In this code:

We define an abstract base class Iterface with an abstract method method.
AnyIO is a subclass that implements method and decorates it with @lru_cache from anyio.functools. This is where mypy flags an error.
AsyncLru is another subclass that implements method, but this time it's decorated with @alru_cache from the async_lru library. This does not produce an error with mypy.

The Error Message

When running mypy on this code, you'll encounter the following error:

test.py:14: error: Signature of "method" incompatible with supertype "Iterface"  [override]
test.py:14: note:      Superclass:
test.py:14: note:          def method(self, value: str) -> Coroutine[Any, Any, str]
test.py:14: note:      Subclass:
test.py:14: note:          AsyncLRUCacheWrapper[[AnyIO, str], Any]
Found 1 error in 1 file (checked 1 source file)

This error message indicates that mypy sees a discrepancy between the signature of method in the Iterface and AnyIO classes. Specifically, it perceives the return type of the decorated method in AnyIO as AsyncLRUCacheWrapper[[AnyIO, str], Any], which is incompatible with the expected coroutine type Coroutine[Any, Any, str] defined in the abstract base class.

Why This Happens

The root cause of this issue lies in how lru_cache wraps the decorated method and how mypy interprets this wrapping. The lru_cache decorator alters the function signature, and mypy struggles to reconcile this change with the original abstract method's signature. In contrast, alru_cache from the async_lru library handles this wrapping in a way that mypy can correctly interpret, thus avoiding the error.

Deep Dive into the Cause

To fully grasp the issue, it's essential to understand the mechanics of both the lru_cache decorator and mypy's type checking process.

How `lru_cache` Works

The lru_cache decorator, in essence, wraps the decorated function with a caching mechanism. When the decorated function is called, the decorator first checks if the result for the given arguments is already stored in the cache. If it is, the cached result is returned immediately. If not, the original function is called, its result is stored in the cache, and then returned. This caching behavior optimizes performance by avoiding redundant computations.

However, this wrapping process inherently changes the function's signature. The decorated function is no longer the original function; it's now a wrapper object that manages the caching logic. This is where mypy's interpretation comes into play.

`mypy`'s Type Checking

mypy is a static type checker for Python. It analyzes code without executing it and identifies potential type errors. When mypy encounters a method decorated with lru_cache, it attempts to infer the type signature of the resulting wrapper object. In the case of anyio.functools.lru_cache, mypy incorrectly deduces the return type, leading to the signature mismatch error when overriding abstract methods.

The Contrast with `async_lru.alru_cache`

The async_lru library provides an alternative alru_cache decorator specifically designed for asynchronous functions. This decorator implements the caching logic in a way that mypy can correctly interpret. The key difference likely lies in how alru_cache handles the function wrapping and how it exposes the type information to mypy. By avoiding the signature mismatch, alru_cache offers a smoother experience when working with asynchronous caching and static type checking.

Reproducing the Bug

To reproduce the bug, you can use the code example provided earlier. Ensure you have anyio, async_lru, and mypy installed in your Python environment.

pip install anyio async_lru mypy

Save the code example as a Python file (e.g., test.py) and run mypy on it:

mypy test.py

You should observe the error message described earlier, confirming the incompatibility between anyio.functools.lru_cache and mypy in this specific scenario.

Potential Solutions and Workarounds

While the issue stems from an interaction between anyio's lru_cache and mypy, there are several potential solutions and workarounds to consider.

1. Use `async_lru.alru_cache`

The simplest and often most effective solution is to use async_lru.alru_cache instead of anyio.functools.lru_cache. As demonstrated in the code example, alru_cache does not trigger the mypy error and provides similar caching functionality for asynchronous functions. This is often the preferred approach if you're primarily concerned with caching asynchronous methods and want to maintain type safety.

2. Type Ignore (as a Last Resort)

If you are committed to using anyio.functools.lru_cache and cannot switch to async_lru.alru_cache, you can use mypy's # type: ignore comment to suppress the error. However, this should be considered a last resort, as it effectively disables type checking for the affected line of code. This can mask potential type errors and reduce the benefits of using mypy.

To use type ignore, add the comment to the line where the error occurs:

class AnyIO(Iterface):  # error
    @lru_cache()
    async def method(self, value: str) -> str:  # type: ignore
        return value

3. Define a Custom Decorator (Advanced)

For more advanced users, it's possible to define a custom decorator that wraps the lru_cache functionality while preserving the correct type information for mypy. This approach requires a deeper understanding of both decorators and type hints in Python. However, it can provide a more robust solution that avoids the limitations of type ignores.

4. Contribute to AnyIO or mypy

If you are passionate about open-source and have the expertise, consider contributing to either the AnyIO or mypy projects. You could help investigate the root cause of the issue and propose a fix that resolves the incompatibility. This would benefit the broader Python community and improve the overall experience of using these tools.

Conclusion

The incompatibility between anyio.functools.lru_cache and mypy when decorating methods in subclasses is a notable issue for developers using asynchronous Python and static type checking. While the error message can be perplexing at first, understanding the underlying cause—the interaction between function wrapping and type inference—is crucial for finding effective solutions. By using async_lru.alru_cache, employing type ignores judiciously, or exploring more advanced techniques like custom decorators, you can mitigate the problem and ensure that your code remains both performant and type-safe.

Remember, the key takeaway is to be mindful of how decorators can affect function signatures and how static type checkers interpret these changes. By carefully considering your options and choosing the right approach, you can leverage caching effectively without compromising the benefits of static type checking.

For more information on AnyIO and its functionalities, you can visit the official AnyIO documentation here. This external resource provides comprehensive details about AnyIO and can further assist in understanding and utilizing its features effectively.