# JuliaSymbolics/Symbolics.jl

Symbolic programming for the next generation of numerical software

Repository: https://github.com/JuliaSymbolics/Symbolics.jl
Canonical: https://ross.abutalabs.com/products/symbolicsjl
Homepage: https://docs.sciml.ai/Symbolics/stable/
Language: Julia
License: NOASSERTION
License Family: other
Topics: symbolic-computing, high-performance, parallel-computing, computer-algebra-system, cas, mathematics, symbolic-math
Last push: 2026-08-26T17:24:05+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 2050, "days_push": 7, "days_rel": 27, "gap_med": 4, "n_releases_24m": 126}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1524, forks 188 (observed 2026-08-28T04:04:58.163655+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: math, compiler
- domain: mathematics, programming-languages, performance
- platform: cross-platform
- tags: computer-algebra-system, symbolic-computing, symbolic-differentiation, jacobian, automatic-differentiation, code-generation, sciml, algorithms, julia

## Member repositories
- JuliaSymbolics/Symbolics.jl (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:58.163655+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 2026-08-30T04:31:41.487982+00:00, confidence not recorded.
  - readme: https://github.com/JuliaSymbolics/Symbolics.jl (fetched 2026-08-28T04:04:58.163655+00:00, sha 5f6604bf7f7b)
  - homepage: https://docs.sciml.ai/Symbolics/stable/ (fetched 2026-08-29T11:34:25.375151+00:00, sha a0a02467135d)
  - site_page: https://docs.sciml.ai/DiffEqDocs (fetched 2026-08-29T11:34:25.384631+00:00, sha 82260832e07c)
  - site_page: https://docs.sciml.ai/DiffEqDevDocs (fetched 2026-08-29T11:34:25.386474+00:00, sha db0de492634b)
  - site_page: https://docs.sciml.ai/Symbolics/stable/manual/faq (fetched 2026-08-29T11:34:25.388041+00:00, sha 1bba38f4d931)
- Data as of 2026-08-30T08:39:29.467469+00:00.