Now with ODE & PDE support

MathHook

SymbolicPower.EducationalClarity.NativeSpeed.

A Rust computer algebra system with native Python and Node.js bindings. No GC, no compromises.

<300ns

Integration

<2μs

Derivatives

32B

Expression size

Get Started

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use mathhook_core::prelude::*;

// 🎓 Step-by-step derivative of eˣ·sin(x)

let expr = parse!("exp(x) * sin(x)");

let steps = expr.derivative_with_steps("x");

// ✨ 7 educational steps:

// 1. Identify Product → 2. State Product Rule

// 3. d/dx[eˣ] = eˣ → 4. d/dx[sin(x)] = cos(x)

// 5. Result: cos(x)·eˣ + eˣ·sin(x)

for step in &steps { println!("{}", step); }

🦀 Crates.io 🐍 PyPI 📦 npm ⭐ GitHub 📚 Docs

FEATURES

Key Features

A high-performance educational computer algebra system

🎓

Step-by-Step Learning

Educational explanations showing every rule applied

∫

Symbolic Calculus

Derivatives, integrals, limits with product/chain rules

∂

PDEs & ODEs

Heat, wave, Laplace equations with Fourier series

📝

Multiple Input Formats

Parse LaTeX, Wolfram Language, and standard notation

⚖️

Equation Solving

Linear, quadratic, polynomial, and systems of equations

⚡

High Performance

Rust-based core with SIMD optimizations

🔗

Language Bindings

Native support for Python and Node.js

🚀

Production Ready

Zero-copy parsing, 32B expressions, thread-safe

PERFORMANCE

Blazing Fast

Nanosecond-to-microsecond performance for symbolic operations

Operation Benchmarks

Elementary integration (cos, exp)< 300 ns

Simple derivatives< 2 μs

Polynomial simplification (deg 50)< 10 μs

Complex calculus (chain + product)< 500 μs

Benchmarked with Criterion.rs

Why So Fast?

32B

32-byte expressions

Two fit per CPU cache line

Zero-copy parsing

Direct AST construction without allocations

⚡

SIMD operations

Vectorized arithmetic for bulk operations

🦀

No interpreter overhead

Native Rust, no garbage collector

🔒

Thread-safe

Immutable expressions, lock-free operations

DOCUMENTATION

Comprehensive Math Coverage

From basic algebra to advanced differential equations

Explore all topics

USE CASES

Built For

From research to production

Machine Learning

Symbolic gradients for neural networks and optimization

Education

Step-by-step solutions for teaching and learning

Research

High-performance symbolic computation for science

Development

Embed math in your applications with native speed

INSTALLATION

Get Started in Seconds

Choose your language and start computing

🦀Rust

cargo add mathhook

🐍Python

pip install mathhook

📜Node.js

npm install mathhook-node

Ready to Solve?

Join developers using MathHook for high-performance symbolic math

Read the Docs

View on GitHub