# 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

Repository: https://github.com/casadi/casadi
Canonical: https://ross.abutalabs.com/products/casadi
Homepage: http://casadi.org
Language: C++
License: LGPL-3.0
License Family: copyleft
Topics: optimization, nonlinear, derivatives, code-generation, numerical-calculations, academic-project, scientific-computing, mathematics, symbolic-manipulation, parameter-estimation, optimal-control, matlab, octave, python, c-plus-plus, library, algorithmic-differentation, nonlinear-programming, julia-package, javascript
Last push: 2026-08-25T19:07:43+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 75, longevity 100
- inputs: {"age_days": 5054, "days_push": 8, "days_rel": 8, "gap_med": 147.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2281, forks 454 (observed 2026-08-28T04:06:33.821170+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: math, compiler, simulation, machine-learning
- domain: mathematics, simulation, robotics, data-science, developer-tools
- platform: python, cpp, cross-platform, cli, wasm, jvm-scripting
- tags: 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

## Member repositories
- casadi/casadi (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:33.821170+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 2026-08-30T02:41:20.811787+00:00, confidence not recorded.
  - readme: https://github.com/casadi/casadi (fetched 2026-08-28T04:06:33.821170+00:00, sha b912550a944e)
  - homepage: http://casadi.org (fetched 2026-08-29T10:21:39.183677+00:00, sha d4fd0bf7a5ff)
  - site_page: https://web.casadi.org/docs (fetched 2026-08-29T10:21:39.198259+00:00, sha ef28589cd9e7)
  - site_page: https://web.casadi.org/get (fetched 2026-08-29T10:21:39.193700+00:00, sha 6d000af043cf)
- Data as of 2026-08-30T08:39:29.467469+00:00.