# aimacode/aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

Repository: https://github.com/aimacode/aima-python
Canonical: https://ross.abutalabs.com/products/aima-python
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-06-30T13:24:50+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 90, release rhythm 35, longevity 100
- inputs: {"age_days": 3866, "days_push": 64, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8799, forks 4037 (observed 2026-08-28T04:10:25.811877+00:00)

## What it is
Python implementations of the pseudocode algorithms from Russell and Norvig's 'Artificial Intelligence: A Modern Approach' (4th edition), organized as an importable package plus explanatory Jupyter notebooks. It serves as a companion for AI courses and self-study.

## Use cases
- learn AI algorithms from AIMA with runnable Python code
- study search algorithms for an AI course
- understand logic and planning algorithms with examples
- explore machine learning and deep learning basics in notebooks
- implement AIMA pseudocode assignments in Python
- self-study artificial intelligence fundamentals

## When to choose
- you are following the AIMA textbook or an AI course based on it
- you want readable reference implementations of classic AI algorithms
- you prefer learning through Jupyter notebooks with examples

## When to avoid
- you need production-grade, optimized AI libraries
- you want modern ML frameworks rather than educational implementations
- you need the 3rd edition code specifically

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, search-engine, nlp, deep-learning, simulation
- domain: artificial-intelligence, education, tutorials
- platform: python, cross-platform
- tags: textbook-companion, jupyter-notebooks, algorithms, aima, education

## Member repositories
- aimacode/aima-python (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:25.811877+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:25:11.483220+00:00, confidence not recorded.
  - readme: https://github.com/aimacode/aima-python (fetched 2026-08-28T04:10:25.811877+00:00, sha b0818063c4e9)
- Data as of 2026-08-30T08:39:29.467469+00:00.