I think this is a pretty reasonable gap to discuss. I’ve also found myself using lru_cache for simple cases, but once you need things like TTL, manual invalidation, or async functions, it starts feeling like you’re either writing your own decorator or reaching for another library.

I’d personally be in favor of having a few more common patterns supported or at least documented in functools, especially TTL caching. I don’t think functools needs to become a full caching framework though there are already good libraries for more advanced use cases.

Even just having some recommended, standard approaches for things like async memoization, TTL, and invalidation would make things easier. It would save people from having 20 slightly different implementations of the same “simple” cache scattered across projects.

So yeah, I’d definitely be interested in seeing what a minimal API for this could look like without making functools overly complicated.

A few concrete observations that might help scope this:

What functools already covers

• functools.cache / lru_cache handle the sync memoization case, and cache_clear(), cache_info() and cache_parameters() cover basic introspection.
• There's no per-key invalidation and no expiry.

The async gap is a correctness issue, not just a missing feature

lru_cache on an async def caches the coroutine object, which can only be awaited once. The second cache hit therefore raises RuntimeError: cannot reuse already awaited coroutine.

A common workaround is to cache the Task/Future instead:

import asyncio
from functools import wraps

def async_cache(fn):
    cache = {}

    @wraps(fn)
    def wrapper(*args):
        task = cache.get(args)
        if task is None:
            task = cache[args] = asyncio.ensure_future(fn(*args))
        return task

    return wrapper

This is deliberately minimal. It leaves exception and cancellation eviction unspecified (a failed or cancelled task stays cached), and those are exactly the semantics a stdlib API would need to define, along with whether the cache is tied to an event loop and how concurrent calls for the same key are deduplicated.

TTL is possible today, but only crudely

import time
from functools import lru_cache

def _bucket(seconds):
    return int(time.monotonic() // seconds)

@lru_cache(maxsize=128)
def _load(key, _bucket_id):
    ...

def load(key):
    return _load(key, _bucket(60))

The trade-offs are that everything expires at the same bucket boundary and stale entries stay in the cache until LRU eviction. A real TTL needs per-entry timestamps, which changes the underlying data structure.

Suggestion

Since the size/TTL/async combinations multiply quickly, the most realistic outcome may be documentation: a "caching patterns" section in the functools docs covering the async pitfall and common TTL/invalidation patterns, while pointing to libraries such as cachetools / async-lru for more advanced needs.

A TTL parameter on lru_cache is a much bigger API commitment, so I'd expect maintainers to want a clear use case that existing third-party libraries can't serve.