Extensible memoizing collections and decorators
Clone or download



This module provides various memoizing collections and decorators, including variants of the Python 3 Standard Library @lru_cache function decorator.

>>> from cachetools import LRUCache
>>> cache = LRUCache(maxsize=2)
>>> cache.update([('first', 1), ('second', 2)])
>>> cache
LRUCache([('second', 2), ('first', 1)], maxsize=2, currsize=2)
>>> cache['third'] = 3
>>> cache
LRUCache([('second', 2), ('third', 3)], maxsize=2, currsize=2)
>>> cache['second']
>>> cache['fourth'] = 4
>>> cache
LRUCache([('second', 2), ('fourth', 4)], maxsize=2, currsize=2)

For the purpose of this module, a cache is a mutable mapping of a fixed maximum size. When the cache is full, i.e. by adding another item the cache would exceed its maximum size, the cache must choose which item(s) to discard based on a suitable cache algorithm. In general, a cache's size is the total size of its items, and an item's size is a property or function of its value, e.g. the result of sys.getsizeof(value). For the trivial but common case that each item counts as 1, a cache's size is equal to the number of its items, or len(cache).

Multiple cache classes based on different caching algorithms are implemented, and decorators for easily memoizing function and method calls are provided, too.

For more information, please refer to the online documentation.


Install cachetools using pip:

pip install cachetools

Project Resources

Latest PyPI version Travis CI build status Test coverage Documentation Status


Copyright (c) 2014-2018 Thomas Kemmer.

Licensed under the MIT License.