JuliaSymbolics/Symbolics.jl

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1524 stars · 188 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Symbolics.jl is a fast, modern Computer Algebra System (CAS) written in Julia for high-performance symbolic mathematics. It supports symbolic arithmetic, differentiation, simplification, equation solving, symbolic linear algebra, and generation of fast parallel numerical functions from symbolic expressions.

Use cases

• compute symbolic derivatives and jacobians of equations
• simplify and substitute symbolic algebraic expressions
• solve systems of symbolic equations
• generate fast parallel Julia functions from symbolic expressions
• automatically detect sparsity and build sparse jacobians and hessians
• convert Julia code into symbolic expressions
• perform symbolic linear algebra like factorizations and eigencomputations

When to choose

• you need a high-performance CAS integrated with the Julia language and its multiple dispatch
• you are building scientific machine learning or modeling tools (e.g. with ModelingToolkit.jl)
• you need symbolic-to-numeric code generation with parallelism
• you want to build a custom CAS on top of a rule-rewriting foundation

When to avoid

• your algorithms are not quasi-static and cannot be traced symbolically (e.g. value-dependent loops or conditionals)
• you need a CAS in Python, Mathematica, or another non-Julia ecosystem
• you only need lightweight numeric computation without symbolic manipulation

Facets

library · maturity active

math compiler mathematics programming-languages performance cross-platform computer-algebra-system symbolic-computing symbolic-differentiation jacobian automatic-differentiation code-generation sciml algorithms julia

Member repositories

For agents

markdown · JSON · MCP: product_card(name="JuliaSymbolics/Symbolics.jl")