Significant, wide-spread changes have been made recently to the design of the library. Once these are more stable they will be released as version 0.9. This should happen in the first week or two of 2017.
NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. This project is a Python implementation of NEAT. It was forked from the excellent project by @MattKallada, and is in the process of being updated to provide more features and a (hopefully) simpler and documented API.
For further information regarding general concepts and theory, please see Selected Publications on Stanley's website.
neat-python is licensed under the 3-clause BSD license.
If you want to try neat-python, please check out the repository, start playing with the examples (examples/xor is a good place to start) and then try creating your own experiment.
The documentation, which is still a work in progress, is available on Read The Docs.