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pyahocorasick is a fast and memory efficient library for exact or approximate multi-pattern string search meaning that you can find multiple key strings occurrences at once in some input text. The library provides an ahocorasick Python module that you can use as a plain dict-like Trie or convert a Trie to an automaton for efficient Aho-Corasick search.

It is implemented in C and tested on Python 2.7 and 3.4+. It works on Linux, Mac and Windows.

The license is BSD-3-clause. Some utilities, such as tests and the pure Python automaton are dedicated to the Public Domain.


The full documentation including the API reference is published on readthedocs.

Quick start

This module is written in C. You need a C compiler installed to compile native CPython extensions. To install:

pip install pyahocorasick

Then create an Automaton:

>>> import ahocorasick
>>> A = ahocorasick.Automaton()

You can use the Automaton class as a trie. Add some string keys and their associated value to this trie. Here we associate a tuple of (insertion index, original string) as a value to each key string we add to the trie:

>>> for idx, key in enumerate('he her hers she'.split()):
...   A.add_word(key, (idx, key))

Then check if some string exists in the trie:

>>> 'he' in A
>>> 'HER' in A

And play with the get() dict-like method:

>>> A.get('he')
(0, 'he')
>>> A.get('she')
(3, 'she')
>>> A.get('cat', 'not exists')
'not exists'
>>> A.get('dog')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>

Now convert the trie to an Aho-Corasick automaton to enable Aho-Corasick search:

>>> A.make_automaton()

Then search all occurrences of the keys (the needles) in an input string (our haystack).

Here we print the results and just check that they are correct. The Automaton.iter() method return the results as two-tuples of the end index where a trie key was found in the input string and the associated value for this key. Here we had stored as values a tuple with the original string and its trie insertion order:

>>> for end_index, (insert_order, original_value) in A.iter(haystack):
...     start_index = end_index - len(original_value) + 1
...     print((start_index, end_index, (insert_order, original_value)))
...     assert haystack[start_index:start_index + len(original_value)] == original_value
(1, 2, (0, 'he'))
(1, 3, (1, 'her'))
(1, 4, (2, 'hers'))
(4, 6, (3, 'she'))
(5, 6, (0, 'he'))

You can also create an eventually large automaton ahead of time and pickle it to re-load later. Here we just pickle to a string. You would typically pickle to a file instead:

>>> import cPickle
>>> pickled = cPickle.dumps(A)
>>> B = cPickle.dumps(pickled)
>>> B.get('he')
(0, 'he')
See also:


With an Aho-Corasick automaton you can efficiently search all occurrences of multiple strings (the needles) in an input string (the haystack) making a single pass over the input string. With pyahocorasick you can eventually build large automatons and pickle them to reuse them over and over as an indexed structure for fast multi pattern string matching.

One of the advantages of an Aho-Corasick automaton is that the typical worst-case and best-case runtimes are about the same and depends primarily on the size of the input string and secondarily on the number of matches returned. While this may not be the fastest string search algorithm in all cases, it can search for multiple strings at once and its runtime guarantees make it rather unique. Because pyahocorasick is based on a Trie, it stores redundant keys prefixes only once using memory efficiently.

A drawback is that it needs to be constructed and “finalized” ahead of time before you can search strings. In several applications where you search for several pre-defined “needles” in a variable “haystacks” this is actually an advantage.

Aho-Corasick automatons are commonly used for fast multi-pattern matching in intrusion detection systems (such as snort), anti-viruses and many other applications that need fast matching against a pre-defined set of string keys.

Internally an Aho-Corasick automaton is typically based on a Trie with extra data for failure links and an implementation of the Aho-Corasick search procedure.

Behind the scenes the pyahocorasick Python library implements these two data structures: a Trie and an Aho-Corasick string matching automaton. Both are exposed through the Automaton class.

In addition to Trie-like and Aho-Corasick methods and data structures, pyahocorasick also implements dict-like methods: The pyahocorasick Automaton is a Trie a dict-like structure indexed by string keys each associated with a value object. You can use this to retrieve an associated value in a time proportional to a string key length.

pyahocorasick is available in two flavors:

  • a CPython C-based extension, compatible with Python 2 and 3.
  • a simpler pure Python module, compatible with Python 2 and 3. This is only available in the source repository (not on Pypi) under the py/ directory and has a slightly different API.

Some background about pyahocorasick internals

I wrote this article about different trie representations. These are experiments I made while creating this module.

Other Aho-Corasick implementations for Python you can consider

While pyahocorasick tries to be the finest and fastest Aho Corasick library for Python you may consider these other libraries:

  • Written in pure Python.
  • Poor performance.
  • Written in C. Does not return overlapping matches.
  • Does not compile on Windows (July 2016).
  • No support for the pickle protocol.
  • Written in Cython.
  • Large automaton may take a long time to build (July 2016)
  • No support for a dict-like protocol to associate a value to a string key.
  • Written in C.
  • seems unmaintained (last update in 2005).
  • GPL-licensed.

API overview

This is a quick tour of the API for the C ahocorasick module. See the full API doc for more details. The pure Python module has a slightly different interface.

The module ahocorasick contains a few constants and the main Automaton class.

Module constants

  • ahocorasick.unicode — see Unicode and bytes
  • ahocorasick.STORE_ANY, ahocorasick.STORE_INTS, ahocorasick.STORE_LENGTH — see Automaton class
  • ahocorasick.EMPTY, ahocorasick.TRIE, ahocorasick.AHOCORASICK — see Automaton Attributes
  • ahocorasick.MATCH_EXACT_LENGTH, ahocorasick.MATCH_AT_MOST_PREFIX, ahocorasick.MATCH_AT_LEAST_PREFIX — see description of the keys method

Automaton class

Note: Automaton instances are pickle-able meaning that you can create ahead of time an eventually large automaton then save it to disk and re-load it later to reuse it over and over as a persistent multi-string search index. Internally, Automaton implements the __reduce__() magic method.

Create a new empty Automaton optionally passing a value_type to indicate what is the type of associated values (default to any Python object type)

Automaton Trie methods

The Automaton class has the following main trie-like methods:

add_word(key, [value]) => bool
Add a key string to the dict-like trie and associate this key with a value.
exists(key) => bool or key in ...
Return True if the key is present in the trie.
match(key) => bool
Return True if there is a prefix (or key) equal to key present in the trie.

Automaton Dictionary-like methods

A pyahocorasick Automaton trie behaves more or less like a Python dictionary and implements a subset of dict-like methods. Some of them are:

get(key[, default])
Return the value associated with the key string. Similar to dict.get().
keys([prefix, [wildcard, [how]]]) => yield strings
Return an iterator on keys.
values([prefix, [wildcard, [how]]]) => yield object
Return an iterator on values associated with each keys.
items([prefix, [wildcard, [how]]]) => yield tuple (string, object)
Return an iterator on tuples of (key, value).

Aho-Corasick methods

The Automaton class has the following main Aho-Corasick methods:

Finalize and create the Aho-Corasick automaton.
iter(string, [start, [end]])
Perform the Aho-Corasick search procedure using the provided input string. Return an iterator of tuples (end_index, value) for keys found in string.

AutomatonSearchIter class

Instances of this class are returned by the iter method of an Automaton. This iterator can be manipulated through its set() method.

set(string, [reset]) => None

Set a new string to search eventually keeping the current Automaton state to continue searching for the next chunk of a string.

For example:

>>> it = A.iter(b"")
>>> while True:
...     buffer = receive(server_address, 4096)
...     if not buffer:
...         break
...     it.set(buffer)
...     for index, value in it:
...         print(index, '=>', value)

When reset is True then processing is restarted. For example this code:

>>> for string in string_set:
...     for index, value in A.iter(string)
...         print(index, '=>', value)

does the same job as:

>>> it = A.iter(b"")
>>> for string in string_set:
...     it.set(it, True)
...     for index, value in it:
...         print(index, '=>', value)

Automaton Attributes

The Automaton class has the following attributes:

kind [readonly]
Return the state of the Automaton instance.
store [readonly]
Return the type of values stored in the Automaton as specified at creation.

Other Automaton methods

The Automaton class has a few other interesting methods:

dump() => (list of nodes, list of edges, list of fail links)
Return a three-tuple of lists describing the Automaton as a graph of (nodes, edges, failure links). The source repository and source package also contains the script that converts dump() results to a graphviz dot format for convenient visualization of the trie and Automaton data structure.
get_stats() => dict
Return a dictionary containing Automaton statistics. Note that the real size occupied by the data structure could be larger because of internal memory fragmentation that can occur in a memory manager.
__sizeof__() => int
Return the approximate size in bytes occupied by the Automaton instance. Also available by calling sys.getsizeof(automaton instance).


>>> import ahocorasick
>>> A = ahocorasick.Automaton()

>>> # add some key words to trie
>>> for index, word in enumerate('he her hers she'.split()):
...   A.add_word(word, (index, word))

>>> # test that these key words exists in the trie all right
>>> 'he' in A
>>> 'HER' in A
>>> A.get('he')
(0, 'he')
>>> A.get('she')
(3, 'she')
>>> A.get('cat', '<not exists>')
'<not exists>'
>>> A.get('dog')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>

>>> # convert the trie in an Aho-Corasick automaton
>>> A.make_automaton()

>>> # then find all occurrences of the stored keys in a string
>>> for item in A.iter('_hershe_'):
...  print(item)
(2, (0, 'he'))
(3, (1, 'her'))
(4, (2, 'hers'))
(6, (3, 'she'))
(6, (0, 'he'))

Example of the keys method behavior

>>> import ahocorasick
>>> A = ahocorasick.Automaton()

>>> # add some key words to trie
>>> for index, word in enumerate('cat catastropha rat rate bat'.split()):
...   A.add_word(word, (index, word))

>>> # Search some prefix
>>> list(A.keys('cat'))
['cat', 'catastropha']

>>> # Search with a wildcard: here '?' is used as a wildcard. You can use any character you like.
>>> list(A.keys('?at', '?', ahocorasick.MATCH_EXACT_LENGTH))
['bat', 'cat', 'rat']

>>> list(A.keys('?at?', '?', ahocorasick.MATCH_AT_MOST_PREFIX))
['bat', 'cat', 'rat', 'rate']

>>> list(A.keys('?at?', '?', ahocorasick.MATCH_AT_LEAST_PREFIX))

Build and install

To install for common operating systems, use pip. Pre-built wheels should be available on Pypi at some point in the future:

pip install pyahocorasick

To build from sources you need to have a C compiler installed and configured which should be standard on Linux and easy to get on MacOSX.

On Windows and Python 2.7 you need the Microsoft Visual C++ Compiler for Python 2.7 (aka. Visual Studio 2008). There have been reports that pyahocorasick does not build yet with MinGW. It may build with cygwin but this has not been tested. If you get this working with these platforms, please report in a ticket!

To build from sources, clone the git repository or download and extract the source archive.

Install pip (and its setuptools companion) and then run (in a virtualenv of course!):

pip install .

If compilation succeeds, the module is ready to use.

Unicode and bytes

The type of strings accepted and returned by Automaton methods are either unicode or bytes, depending on a compile time settings (preprocessor definition of AHOCORASICK_UNICODE as set in

The Automaton.unicode attributes can tell you how the library was built. On Python 3, unicode is the default. On Python 2, bytes is the default and only value.


When the library is built with unicode support on Python 3, an Automaton will store 2 or 4 bytes per letter, depending on your Python installation. When built for bytes, only one byte per letter is needed.

Unicode is NOT supported on Python 2 for now.


The source repository contains several tests. To run them use:

make test


Support is available through the GitHub issue tracker to report bugs or ask questions.


You can submit contributions through GitHub pull requests.


The main author: Wojciech Muła, This library would not be possible without help of many people, who contributed in various ways. They created pull requests, reported bugs as GitHub issues or via direct messages, proposed fixes, or spent their valuable time on testing.

Thank you.