casadi/casadi

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript observed · 2026-08-28

github.com/casadi/casadi · homepage · C++ · LGPL-3.0 (copyleft) observed · 2026-08-28

Health v2 · maintenance only

91/100

Activity 99
Release rhythm 75
Longevity 100

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

gap_med: 147.5
age_days: 5054
days_rel: 8
days_push: 8
n_releases_24m: 5

Full methodology

Adoption not part of the score

2281 stars · 454 forks observed · 2026-08-28

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

CasADi is an open-source symbolic framework for gradient-based numerical optimization, implementing forward and reverse mode automatic differentiation on sparse matrix-valued expression graphs. It supports self-contained C-code generation, ODE/DAE integration and sensitivity analysis, and interfaces to solvers like IPOPT and SUNDIALS, usable from C++, Python, MATLAB/Octave, Julia, and JavaScript (WebAssembly).

Use cases

compute gradients, Jacobians and Hessians of mathematical expressions via automatic differentiation
formulate and solve nonlinear programming problems
implement optimal control and nonlinear model predictive control
integrate ODE/DAE systems with forward and adjoint sensitivity analysis
generate standalone C code from symbolic expressions for embedded deployment
estimate parameters in dynamic system models
run optimization in the browser or Node.js via WebAssembly

When to choose

you need efficient gradient-based optimization with sparse derivative structures
you are building optimal control or NMPC applications
you need automatic differentiation with C-code generation for deployment
you want a single symbolic API across Python, C++, MATLAB/Octave, Julia, or JavaScript

When to avoid

you need general-purpose computer algebra or exact symbolic simplification
you need derivative-free or global optimization methods
you want a fully interactive CAS-style symbolic math environment

Facets

library · maturity stable

math compiler simulation machine-learning mathematics simulation robotics data-science developer-tools python cpp cross-platform cli wasm jvm-scripting automatic-differentiation optimal-control nonlinear-optimization symbolic-framework code-generation model-predictive-control sparse-jacobians ipopt sundials scientific-computing nlp-solver ode-dae-integration algorithms

4 sources

readme: https://github.com/casadi/casadi · fetched 2026-08-28 · b912550a944e
homepage: http://casadi.org · fetched 2026-08-29 · d4fd0bf7a5ff
site_page: https://web.casadi.org/docs · fetched 2026-08-29 · ef28589cd9e7
site_page: https://web.casadi.org/get · fetched 2026-08-29 · 6d000af043cf

Member repositories

Repository	Role	Health v2
casadi/casadi	main	91

For agents

markdown · JSON · MCP: product_card(name="casadi/casadi")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem