  The next generation of telescopes such as the SKA and the Rubin Observatory
will produce enormous data sets, requiring automated anomaly detection to
enable scientific discovery. Here, we present an overview and friendly user
guide to the Astronomaly framework for active anomaly detection in astronomical
data. Astronomaly uses active learning to combine the raw processing power of
machine learning with the intuition and experience of a human user, enabling
personalised recommendations of interesting anomalies. It makes use of a Python
backend to perform data processing, feature extraction and machine learning to
detect anomalous objects; and a JavaScript frontend to allow interaction with
the data, labelling of interesting anomalous and active learning. Astronomaly
is designed to be modular, extendable and run on almost any type of
astronomical data. In this paper, we detail the structure of the Astronomaly
code and provide guidelines for basic usage.
