Is there a pythonic way to support keyword arguments for a memoize decorator in Python?

I'd suggest something like the following:

import inspect

class key_memoized(object):
    def __init__(self, func):
       self.func = func
       self.cache = {}

    def __call__(self, *args, **kwargs):
        key = self.key(args, kwargs)
        if key not in self.cache:
            self.cache[key] = self.func(*args, **kwargs)
        return self.cache[key]

    def normalize_args(self, args, kwargs):
        spec = inspect.getargs(self.func.__code__).args
        return dict(kwargs.items() + zip(spec, args))

    def key(self, args, kwargs):
        a = self.normalize_args(args, kwargs)
        return tuple(sorted(a.items()))

Example:

@key_memoized
def foo(bar, baz, spam):
    print 'calling foo: bar=%r baz=%r spam=%r' % (bar, baz, spam)
    return bar + baz + spam

print foo(1, 2, 3)
print foo(1, 2, spam=3)         #memoized
print foo(spam=3, baz=2, bar=1) #memoized

Note that you can also extend key_memoized and override its key() method to provide more specific memoization strategies, e.g. to ignore some of the arguments:

class memoize_by_bar(key_memoized):
    def key(self, args, kwargs):
        return self.normalize_args(args, kwargs)['bar']

@memoize_by_bar
def foo(bar, baz, spam):
    print 'calling foo: bar=%r baz=%r spam=%r' % (bar, baz, spam)
    return bar

print foo('x', 'ignore1', 'ignore2')
print foo('x', 'ignore3', 'ignore4')

Try lru_cache:

@functools.lru_cache(maxsize=128, typed=False)

Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can save time when an expensive or I/O bound function is periodically called with the same arguments.

lru_cache added in python 3.2, but can be backported into 2.x