This repository is the official Python implementation of the algorithms from the textbook *Artificial Intelligence: A Modern Approach* (AIMA) by Russell and Norvig. It is primarily an educational library, providing clean, well-documented reference implementations of classic AI algorithms. The code is organized as a monolithic Python package (`aima/`) with modules corresponding to major AI subfields (search, logic, probability, learning, NLP, planning, agents, etc.), complemented by an extensive set of Jupyter notebooks that serve as interactive tutorials and a JupyterLite deployment for browser-based execution. The architecture emphasizes clarity and adherence to textbook pseudocode over production performance. The library demonstrates high craftsmanship in its core abstractions. The `Problem`/`Node` search framework, the `Expr` symbolic logic engine, the `BayesNet` probabilistic inference system, and the `CSP` constraint satisfaction framework are all well-isolated, reusable components that could be extracted for other projects. The code is extensively tested with pytest and actively maintained, with a very low open issue count relative to its popularity. While the core library is robust and written in pure Python with minimal dependencies, some modules (particularly `deep_learning.py` and `perception.py`) rely on heavy external libraries like TensorFlow/Keras and OpenCV, which have experienced significant API drift over time. The educational focus means the code prioritizes readability and algorithmic correctness over scalability or performance optimization. Overall, it is an invaluable resource for learning AI and a solid foundation for building educational tools and demonstrations.
Syntheseus is a Python package for retrosynthetic planning that provides a unified framework for integrating various single-step reaction models with search algorithms to find synthetic routes from target molecules to available starting materials. The package features a plugin architecture that allows users to plug in different reaction models (such as Chemformer, GLN, LocalRetro, etc.) and search algorithms (like MCTS, Retro*, Breadth-First, etc.) through well-defined abstract base classes. It includes command-line interfaces for running end-to-end searches and evaluating single-step model performance, along with tools for route analysis and visualization. The architecture emphasizes separation of concerns: the interface module defines core abstractions (molecule, reaction, bag, and models), the reaction_prediction module handles model loading and inference, and the search module implements the search algorithms and graph representations. This design enables easy extension and experimentation with new models and algorithms. The package is actively used in academic research and has been applied in recent publications for benchmarking retrosynthesis approaches.