PyPhi is a Python library for computing integrated information.

The latest formalism of Integrated Information Theory (IIT 4.0) is outlined in this paper:

Albantakis L, Barbosa L, Findlay G, Grasso M, … Tononi G. (2023)
Integrated information theory (IIT) 4.0: formulating the properties of phenomenal existence in physical terms.
PLoS Computational Biology 19(10): e1011465.

If you use this software in your research, please cite the paper:

Mayner WGP, Marshall W, Albantakis L, Findlay G, Marchman R, Tononi G. (2018)
PyPhi: A toolbox for integrated information theory.
PLOS Computational Biology 14(7): e1006343.

A jupyter notebook illustrating how to use PyPhi is available as a supplement to the IIT 4.0 paper.

To report issues, use the issue tracker on the GitHub repository. Bug reports and pull requests are welcome.

For general discussion, you are welcome to join the pyphi-users group.


To install the latest stable release, run

pip install pyphi

To install the latest development version, which is a work in progress and may have bugs, run

pip install "git+"


For detailed instructions on how to install PyPhi on macOS, see the Detailed installation guide for macOS.


Windows users: PyPhi is only supported on Linux and macOS operating systems. However, you can run it on Windows by using the Anaconda Python distribution and installing PyPhi with conda: conda install -c wmayner pyphi