diff --git a/Doc/library/functools.rst b/Doc/library/functools.rst
index b2d69de..a9819f2 100644
--- a/Doc/library/functools.rst
+++ b/Doc/library/functools.rst
@@ -37,57 +37,6 @@
 
    .. versionadded:: 3.2
 
-.. decorator:: lfu_cache(maxsize)
-
-   Decorator to wrap a function with a memoizing callable that saves up to the
-   *maxsize* most frequent calls.  It can save time when an expensive or I/O
-   bound function is periodically called with the same arguments.
-
-   The *maxsize* parameter defaults to 100.  Since a dictionary is used to cache
-   results, the positional and keyword arguments to the function must be
-   hashable.
-
-   The wrapped function is instrumented with two attributes, :attr:`hits`
-   and :attr:`misses` which count the number of successful or unsuccessful
-   cache lookups.  These statistics are helpful for tuning the *maxsize*
-   parameter and for measuring the cache's effectiveness.
-
-   The wrapped function also has a :attr:`clear` attribute which can be
-   called (with no arguments) to clear the cache.
-
-   A `LFU (least frequently used) cache
-   <http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used>`_
-   is indicated when the pattern of calls does not change over time, when
-   more the most common calls already seen are the best predictors of the
-   most common upcoming calls.
-
-   .. versionadded:: 3.2
-
-.. decorator:: lru_cache(maxsize)
-
-   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.
-
-   The *maxsize* parameter defaults to 100.  Since a dictionary is used to cache
-   results, the positional and keyword arguments to the function must be
-   hashable.
-
-   The wrapped function is instrumented with two attributes, :attr:`hits`
-   and :attr:`misses` which count the number of successful or unsuccessful
-   cache lookups.  These statistics are helpful for tuning the *maxsize*
-   parameter and for measuring the cache's effectiveness.
-
-   The wrapped function also has a :attr:`clear` attribute which can be
-   called (with no arguments) to clear the cache.
-
-   A `LRU (least recently used) cache
-   <http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used>`_
-   is indicated when the pattern of calls changes over time, such as
-   when more recent calls are the best predictors of upcoming calls.
-
-   .. versionadded:: 3.2
-
 .. decorator:: total_ordering
 
    Given a class defining one or more rich comparison ordering methods, this
diff --git a/Lib/functools.py b/Lib/functools.py
index 863706d..1a1f22e 100644
--- a/Lib/functools.py
+++ b/Lib/functools.py
@@ -4,17 +4,10 @@
 # to allow utilities written in Python to be added
 # to the functools module.
 # Written by Nick Coghlan <ncoghlan at gmail.com>
-# and Raymond Hettinger <python at rcn.com>
-#   Copyright (C) 2006-2010 Python Software Foundation.
+#   Copyright (C) 2006 Python Software Foundation.
 # See C source code for _functools credits/copyright
 
-__all__ = ['update_wrapper', 'wraps', 'WRAPPER_ASSIGNMENTS', 'WRAPPER_UPDATES',
-           'total_ordering', 'cmp_to_key', 'lfu_cache', 'lru_cache']
-
 from _functools import partial, reduce
-from collections import OrderedDict, Counter
-from heapq import nsmallest
-from operator import itemgetter
 
 # update_wrapper() and wraps() are tools to help write
 # wrapper functions that can handle naive introspection
@@ -104,88 +97,3 @@
         def __hash__(self):
             raise TypeError('hash not implemented')
     return K
-
-def lfu_cache(maxsize=100):
-    '''Least-frequently-used cache decorator.
-
-    Arguments to the cached function must be hashable.
-    Cache performance statistics stored in f.hits and f.misses.
-    Clear the cache using f.clear().
-    http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used
-
-    '''
-    def decorating_function(user_function):
-        cache = {}                      # mapping of args to results
-        use_count = Counter()           # times each key has been accessed
-        kwd_mark = object()             # separate positional and keyword args
-
-        @wraps(user_function)
-        def wrapper(*args, **kwds):
-            key = args
-            if kwds:
-                key += (kwd_mark,) + tuple(sorted(kwds.items()))
-            use_count[key] += 1         # count a use of this key
-            try:
-                result = cache[key]
-                wrapper.hits += 1
-            except KeyError:
-                result = user_function(*args, **kwds)
-                cache[key] = result
-                wrapper.misses += 1
-                if len(cache) > maxsize:
-                    # purge the 10% least frequently used entries
-                    for key, _ in nsmallest(maxsize // 10,
-                                            use_count.items(),
-                                            key=itemgetter(1)):
-                        del cache[key], use_count[key]
-            return result
-
-        def clear():
-            'Clear the cache and cache statistics'
-            cache.clear()
-            use_count.clear()
-            wrapper.hits = wrapper.misses = 0
-
-        wrapper.hits = wrapper.misses = 0
-        wrapper.clear = clear
-        return wrapper
-    return decorating_function
-
-def lru_cache(maxsize=100):
-    '''Least-recently-used cache decorator.
-
-    Arguments to the cached function must be hashable.
-    Cache performance statistics stored in f.hits and f.misses.
-    Clear the cache using f.clear().
-    http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
-
-    '''
-    def decorating_function(user_function):
-        cache = OrderedDict()           # ordered least recent to most recent
-        kwd_mark = object()             # separate positional and keyword args
-
-        @wraps(user_function)
-        def wrapper(*args, **kwds):
-            key = args
-            if kwds:
-                key += (kwd_mark,) + tuple(sorted(kwds.items()))
-            try:
-                result = cache.pop(key)
-                wrapper.hits += 1
-            except KeyError:
-                result = user_function(*args, **kwds)
-                wrapper.misses += 1
-                if len(cache) >= maxsize:
-                    cache.popitem(0)    # purge least recently used cache entry
-            cache[key] = result         # record recent use of this key
-            return result
-
-        def clear():
-            'Clear the cache and cache statistics'
-            cache.clear()
-            wrapper.hits = wrapper.misses = 0
-
-        wrapper.hits = wrapper.misses = 0
-        wrapper.clear = clear
-        return wrapper
-    return decorating_function
diff --git a/Lib/test/test_functools.py b/Lib/test/test_functools.py
index a02d37c..f6ccc87 100644
--- a/Lib/test/test_functools.py
+++ b/Lib/test/test_functools.py
@@ -4,7 +4,6 @@
 from test import support
 from weakref import proxy
 import pickle
-from random import choice
 
 @staticmethod
 def PythonPartial(func, *args, **keywords):
@@ -455,50 +454,6 @@
             class A:
                 pass
 
-class TestLRU(unittest.TestCase):
-
-    def test_lru(self):
-        def orig(x, y):
-            return 3*x+y
-        f = functools.lru_cache(maxsize=20)(orig)
-
-        domain = range(5)
-        for i in range(1000):
-            x, y = choice(domain), choice(domain)
-            actual = f(x, y)
-            expected = orig(x, y)
-            self.assertEquals(actual, expected)
-        self.assert_(f.hits > f.misses)
-        self.assertEquals(f.hits + f.misses, 1000)
-
-        f.clear()   # test clearing
-        self.assertEqual(f.hits, 0)
-        self.assertEqual(f.misses, 0)
-        f(x, y)
-        self.assertEqual(f.hits, 0)
-        self.assertEqual(f.misses, 1)
-
-    def test_lfu(self):
-        def orig(x, y):
-            return 3*x+y
-        f = functools.lfu_cache(maxsize=20)(orig)
-
-        domain = range(5)
-        for i in range(1000):
-            x, y = choice(domain), choice(domain)
-            actual = f(x, y)
-            expected = orig(x, y)
-            self.assertEquals(actual, expected)
-        self.assert_(f.hits > f.misses)
-        self.assertEquals(f.hits + f.misses, 1000)
-
-        f.clear()   # test clearing
-        self.assertEqual(f.hits, 0)
-        self.assertEqual(f.misses, 0)
-        f(x, y)
-        self.assertEqual(f.hits, 0)
-        self.assertEqual(f.misses, 1)
-
 def test_main(verbose=None):
     test_classes = (
         TestPartial,
@@ -506,8 +461,7 @@
         TestPythonPartial,
         TestUpdateWrapper,
         TestWraps,
-        TestReduce,
-        TestLRU,
+        TestReduce
     )
     support.run_unittest(*test_classes)
 
diff --git a/Misc/NEWS b/Misc/NEWS
index d8f64a4..d04beaa 100644
--- a/Misc/NEWS
+++ b/Misc/NEWS
@@ -504,8 +504,6 @@
 - Issue #4179: In pdb, allow "list ." as a command to return to the currently
   debugged line.
 
-- Add lfu_cache() and lru_cache() decorators to the functools module.
-
 - Issue #4108: In urllib.robotparser, if there are multiple 'User-agent: *'
   entries, consider the first one.