PyReason is a powerful Python-based temporal first-order logic explainable AI system supporting multi-step inference, uncertainty, open-world reasoning, and graph-based syntax.

PyReason is a logic based framework designed for neuro symbolic AI.

NEW PyReason Gym is now available:

Check out our talk at AAAI-MAKE:

• Supports generalized annotated logic with temporal, graphical and uncertainty extensions, capturing a wide variety of fuzzy, real-valued, interval, and temporal logics

• Modern Python-based system supporting reasoning on graph-based data structures (e.g., exported from Neo4j, GraphML, etc.)

• Rule-based reasoning in a manner that support uncertainty, open-world reasoning, non-ground rules, quantification, etc., agnostic to selection of t-norm, etc.

• Fast, highly optimized, correct fixpoint-based deduction allows for explainable AI reasoning, scales to graphs with over 30 million edges

Read the PyReason paper (w. supplement):

Introductory blog post:

Open source Python library is available at:

PyReason codebase can be found at:

Install with pip:
pip install pyreason

Technical talk:

Introduction to PyReason v 1.1

Bibtex citation:
title = {{PyReason}: Software for Open World Temporal Logic},
booktitle = {{AAAI} Spring Symposium},
author = {Aditya, Dyuman and Mukherji, Kaustuv and Balasubramanian, Srikar and Chaudhary, Abhiraj and Shakarian, Paulo},
year = {2023} }