PyPhi#

The toolbox for Integrated Information Theory

PyPhi 2.0 is out as a release candidate. Until the final release, give the version when installing; a bare pip install pyphi installs 1.2 instead.

pip install "pyphi>=2.0.0rc1"

import pyphi

# The IIT 4.0 paper's Fig. 1A network, analyzing units A and B
substrate = pyphi.examples.iit4_2023_fig1a_substrate()
analysis = pyphi.analyze(substrate, state=(0, 1, 1), subset=(0, 1))

analysis.phi      # φₛ ≈ 0.04, system integrated information
analysis.big_phi  # Φ ≈ 1.56, structure integrated information
Getting started

Install PyPhi and compute your first φ.

Getting started
Tutorials

Learn the library through worked, executable examples.

Tutorials
How-to guides

Build a substrate, read a result, size a run, configure, parallelize, export.

How-to guides
Theory

How IIT’s mathematics maps onto PyPhi’s types and functions.

Theory
Reference

The API reference, the glossary, configuration options, and conventions.

Reference
Migration

Moving to PyPhi 2.0 from earlier versions and related tools.

Migration
Learn IIT with an AI assistant

IIT Expert answers questions about the theory from its primary literature, with citations. A work in progress.

Use PyPhi with an AI assistant
Compute with an AI assistant

PyPhi’s MCP server lets an assistant build substrates, size runs, and analyze them. This site is also readable by AI agents.

The PyPhi MCP server

If you use this software in your research, please cite the software 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. https://doi.org/10.1371/journal.pcbi.1006343

The theory it implements, IIT 4.0, is described in:

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. https://doi.org/10.1371/journal.pcbi.1011465

Mayner WGP, Marshall W, Tononi G. (2026). Intrinsic cause–effect power: the tradeoff between differentiation and specification. Entropy 28(4): 410. https://doi.org/10.3390/e28040410

BibTeX entries are on the Citing PyPhi page. To report issues, use the issue tracker. For general discussion, join the pyphi-users group.

Everything in the docs#