Eigenvue Documentation

Choose Your Path

I want to learn algorithms

Browse the algorithm catalog and watch algorithms execute step-by-step with synchronized code highlighting and explanations.

I want to use the Python package

Install with pip install eigenvue and visualize algorithms in Jupyter notebooks and scripts. Get started →

I want to contribute

Add new algorithms, improve visualizations, or fix bugs. Our contributor guide walks you through every step. Contribute →

I'm reviewing this project

JOSS reviewers and researchers: read the project overview, tool comparison, and citation info.

What Is Eigenvue?

Eigenvue is an open-source platform for visualizing algorithms across four domains: classical algorithms, deep learning, generative AI, and quantum computing. It is distributed as two products:

• Web Application at eigenvue.web.app — interactive visualizations in the browser, shareable via deep links.
• Python Package via pip install eigenvue — visualizations in Jupyter notebooks, scripts, and research workflows.

Both products share the same algorithm definitions and produce identical visualizations. The platform currently includes 22 algorithms with step-by-step execution, synchronized code highlighting, plain-language explanations, and educational content.

Algorithm Domains

Domain	Algorithms	Accent
Classical	Binary Search, Bubble Sort, QuickSort, Merge Sort, BFS, DFS, Dijkstra	Blue
Deep Learning	Perceptron, Feedforward Network, Backpropagation, Convolution, Gradient Descent	Purple
Generative AI	Tokenization (BPE), Token Embeddings, Self-Attention, Multi-Head Attention, Transformer Block	Pink
Quantum Computing	Qubit Bloch Sphere, Quantum Gates, Superposition & Measurement, Grover’s Search, Quantum Teleportation	Cyan

Quick Links

• Step Format Specification — the universal data contract
• Generator API (TypeScript) — how algorithms produce steps
• Python Public API — the Python package public API
• Architecture Overview — how the system fits together