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

from cachetools import cached, LRUCache, TTLCache
# speed up calculating Fibonacci numbers with dynamic programming
@cached(cache={})
def fib(n):
    return n if n < 2 else fib(n - 1) + fib(n - 2)
# cache least recently used Python Enhancement Proposals
@cached(cache=LRUCache(maxsize=32))
def get_pep(num):
    url = 'https://www.python.org/dev/peps/pep-%04d/' % num
    with urllib.request.urlopen(url) as s:
        return s.read()
# cache weather data for no longer than ten minutes
@cached(cache=TTLCache(maxsize=1024, ttl=600))
def get_weather(place):
    return owm.weather_at_place(place).get_weather()

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.

This module provides multiple cache classes based on different cache algorithms, as well as decorators for easily memoizing function and method calls.

cachetools is available from PyPI and can be installed by running:

pip install cachetools

Typing stubs for this package are provided by typeshed and can be installed by running:

pip install types-cachetools

• Documentation
• Issue tracker
• Source code
• Change log

• asyncache: Helpers to use cachetools with asyncio.
• cachetools-async: Helpers to use cachetools with asyncio.
• cacheing: Pure Python Cacheing Library.
• CacheToolsUtils: Stackable cache classes for sharing, encryption, statistics and more on top of cachetools, redis and memcached.
• shelved-cache: Persistent cache implementation for Python cachetools.

Copyright (c) 2014-2025 Thomas Kemmer.

Licensed under the MIT License.