diff --git a/docs/patterns/index.rst b/docs/patterns/index.rst
index 78a66a1d..2737d695 100644
--- a/docs/patterns/index.rst
+++ b/docs/patterns/index.rst
@@ -34,7 +34,7 @@ Snippet Archives <http://flask.pocoo.org/snippets/>`_.
    jquery
    errorpages
    lazyloading
-   mongokit
+   mongoengine
    favicon
    streaming
    deferredcallbacks
diff --git a/docs/patterns/mongoengine.rst b/docs/patterns/mongoengine.rst
new file mode 100644
index 00000000..61697171
--- /dev/null
+++ b/docs/patterns/mongoengine.rst
@@ -0,0 +1,140 @@
+.. mongoengine-pattern:
+
+MongoEngine in Flask
+====================
+
+Using a document database rather than a full DBMS gets more common these days.
+This pattern shows how to use MongoEngine, a document mapper library, to
+integrate with MongoDB.
+
+This pattern requires a running MongoDB server, MongoEngine_ and Flask-MongoEngine_
+libraries installed::
+
+    pip install flask-mongoengine
+
+.. _MongoEngine: http://mongoengine.org
+.. _Flask-MongoEngine: http://docs.mongoengine.org/projects/flask-mongoengine/en/latest/>`_
+
+Configuration
+-------------
+
+Basic setup can be done by defining ``MONGODB_SETTINGS`` on App config and then
+creating a ``MongoEngine`` instance::
+
+    from flask import Flask
+    from flask_mongoengine import MongoEngine
+
+    app = Flask(__name__)
+    app.config['MONGODB_SETTINGS'] = {
+        'host': "mongodb://localhost:27017/mydb"
+    }
+    db = MongoEngine(app)
+
+
+Mapping Documents
+-----------------
+
+To declare models that will represent your Mongo documents, just create a class that
+inherits from ``Document`` and declare each of the fields::
+
+    from mongoengine import *
+
+
+    class Movie(Document):
+
+        title = StringField(required=True)
+        year = IntField()
+        rated = StringField()
+        director = StringField()
+        actors = ListField()
+
+If the model has embedded documents, use ``EmbeddedDocument`` to defined the fields of
+the embedded document and ``EmbeddedDocumentField`` to declare it on the parent document::
+
+    class Imdb(EmbeddedDocument):
+
+        imdb_id = StringField()
+        rating = DecimalField()
+        votes = IntField()
+
+
+    class Movie(Document):
+
+        ...
+        imdb = EmbeddedDocumentField(Imdb)
+
+
+Creating Data
+-------------
+
+Just create the objects and call ``save()``::
+
+    bttf = Movie(title="Back To The Future", year=1985)
+    bttf.actors = [
+        "Michael J. Fox",
+        "Christopher Lloyd"
+    ]
+    bttf.imdb = Imdb(imdb_id="tt0088763", rating=8.5)
+    bttf.save()
+
+
+Queries
+-------
+
+Use the class ``objects`` attribute to make queries::
+
+    bttf = Movies.objects(title="Back To The Future").get()  # Throw error if not unique
+
+``objects`` is an iterable. Query operators may be user by concatenating it with the document
+key using a double-underscore::
+
+    some_theron_movie = Movie.objects(actors__in=["Charlize Theron"]).first()
+
+    for recents in Movie.objects(year__gte=2017):
+        print(recents.title)
+
+Available operators are as follows:
+
+* ``ne`` -- not equal to
+* ``lt`` -- less than
+* ``lte`` -- less than or equal to
+* ``gt`` -- greater than
+* ``gte`` -- greater than or equal to
+* ``not`` -- negate a standard check, may be used before other operators (e.g.
+  ``Q(age__not__mod=5)``)
+* ``in`` -- value is in list (a list of values should be provided)
+* ``nin`` -- value is not in list (a list of values should be provided)
+* ``mod`` -- ``value % x == y``, where ``x`` and ``y`` are two provided values
+* ``all`` -- every item in list of values provided is in array
+* ``size`` -- the size of the array is
+* ``exists`` -- value for field exists
+
+String queries
+--------------
+
+The following operators are available as shortcuts to querying with regular
+expressions:
+
+* ``exact`` -- string field exactly matches value
+* ``iexact`` -- string field exactly matches value (case insensitive)
+* ``contains`` -- string field contains value
+* ``icontains`` -- string field contains value (case insensitive)
+* ``startswith`` -- string field starts with value
+* ``istartswith`` -- string field starts with value (case insensitive)
+* ``endswith`` -- string field ends with value
+* ``iendswith`` -- string field ends with value (case insensitive)
+* ``match``  -- performs an $elemMatch so you can match an entire document within an array
+
+Accessing PyMongo MongoClient
+-----------------------------
+
+If, for some reason, you want to access PyMongo instance, use ``get_connection`` function::
+
+    from mongoengine.connection import get_connection
+
+    conn = get_connection()
+    collection = conn.mydb.movie
+    collection({'title': u'Days of Thunder'})
+
+For more information about MongoEngine, head over to the
+`website <http://docs.mongoengine.org/>`_.
diff --git a/docs/patterns/mongokit.rst b/docs/patterns/mongokit.rst
deleted file mode 100644
index 9d1b3e2a..00000000
--- a/docs/patterns/mongokit.rst
+++ /dev/null
@@ -1,144 +0,0 @@
-.. mongokit-pattern:
-
-MongoKit in Flask
-=================
-
-Using a document database rather than a full DBMS gets more common these days.
-This pattern shows how to use MongoKit, a document mapper library, to
-integrate with MongoDB.
-
-This pattern requires a running MongoDB server and the MongoKit library
-installed.
-
-There are two very common ways to use MongoKit.  I will outline each of them
-here:
-
-
-Declarative
------------
-
-The default behavior of MongoKit is the declarative one that is based on
-common ideas from Django or the SQLAlchemy declarative extension.
-
-Here an example :file:`app.py` module for your application::
-
-    from flask import Flask
-    from mongokit import Connection, Document
-
-    # configuration
-    MONGODB_HOST = 'localhost'
-    MONGODB_PORT = 27017
-
-    # create the little application object
-    app = Flask(__name__)
-    app.config.from_object(__name__)
-
-    # connect to the database
-    connection = Connection(app.config['MONGODB_HOST'],
-                            app.config['MONGODB_PORT'])
-
-
-To define your models, just subclass the `Document` class that is imported
-from MongoKit.  If you've seen the SQLAlchemy pattern you may wonder why we do
-not have a session and even do not define a `init_db` function here.  On the
-one hand, MongoKit does not have something like a session.  This sometimes
-makes it more to type but also makes it blazingly fast.  On the other hand,
-MongoDB is schemaless.  This means you can modify the data structure from one
-insert query to the next without any problem.  MongoKit is just schemaless
-too, but implements some validation to ensure data integrity.
-
-Here is an example document (put this also into :file:`app.py`, e.g.)::
-
-    from mongokit import ValidationError
-
-    def max_length(length):
-        def validate(value):
-            if len(value) <= length:
-                return True
-            # must have %s in error format string to have mongokit place key in there
-            raise ValidationError('%s must be at most {} characters long'.format(length))
-        return validate
-
-    class User(Document):
-        structure = {
-            'name': unicode,
-            'email': unicode,
-        }
-        validators = {
-            'name': max_length(50),
-            'email': max_length(120)
-        }
-        use_dot_notation = True
-        def __repr__(self):
-            return '<User %r>' % (self.name)
-
-    # register the User document with our current connection
-    connection.register([User])
-
-
-This example shows you how to define your schema (named structure), a
-validator for the maximum character length and uses a special MongoKit feature
-called `use_dot_notation`.  Per default MongoKit behaves like a python
-dictionary but with `use_dot_notation` set to ``True`` you can use your
-documents like you use models in nearly any other ORM by using dots to
-separate between attributes.
-
-You can insert entries into the database like this:
-
->>> from yourapplication.database import connection
->>> from yourapplication.models import User
->>> collection = connection['test'].users
->>> user = collection.User()
->>> user['name'] = u'admin'
->>> user['email'] = u'admin@localhost'
->>> user.save()
-
-Note that MongoKit is kinda strict with used column types, you must not use a
-common `str` type for either `name` or `email` but unicode.
-
-Querying is simple as well:
-
->>> list(collection.User.find())
-[<User u'admin'>]
->>> collection.User.find_one({'name': u'admin'})
-<User u'admin'>
-
-.. _MongoKit: http://bytebucket.org/namlook/mongokit/
-
-
-PyMongo Compatibility Layer
----------------------------
-
-If you just want to use PyMongo, you can do that with MongoKit as well.  You
-may use this process if you need the best performance to get.  Note that this
-example does not show how to couple it with Flask, see the above MongoKit code
-for examples::
-
-    from MongoKit import Connection
-
-    connection = Connection()
-
-To insert data you can use the `insert` method.  We have to get a
-collection first, this is somewhat the same as a table in the SQL world.
-
->>> collection = connection['test'].users
->>> user = {'name': u'admin', 'email': u'admin@localhost'}
->>> collection.insert(user)
-
-MongoKit will automatically commit for us.
-
-To query your database, you use the collection directly:
-
->>> list(collection.find())
-[{u'_id': ObjectId('4c271729e13823182f000000'), u'name': u'admin', u'email': u'admin@localhost'}]
->>> collection.find_one({'name': u'admin'})
-{u'_id': ObjectId('4c271729e13823182f000000'), u'name': u'admin', u'email': u'admin@localhost'}
-
-These results are also dict-like objects:
-
->>> r = collection.find_one({'name': u'admin'})
->>> r['email']
-u'admin@localhost'
-
-For more information about MongoKit, head over to the
-`website <https://github.com/namlook/mongokit>`_.