gekko
Machine learning and optimization for dynamic systems
Decision gist · record as of 2026-08-14
Yes. GEKKO is actively maintained, has no known vulnerabilities, installs with minimal friction (numpy only), and is licensed permissively. It fills a specialized niche in dynamic optimization and DAE solving that few Python packages address directly. Install if you need to solve differential algebraic equations, implement model predictive control, or tackle mixed-integer nonlinear optimization problems.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction; pure Python wheel with only numpy as a runtime dependency.
- Active maintenance with recent commits and a stable production release status.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and proprietary projects.
last release 2025-12-31 (226 days) · last repo commit 2026-06-30 · 709 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 141,112 downloads/mo, #11,251 on PyPI
Alternatives
Verify before relying
pip install gekko
import gekko
m = gekko.GEKKO()
x = m.Var()
m.Equation(x**2 == 4)
m.solve()
print(x.value)- Whether the bundled APOPT and IPOPT solvers are included in the wheel or require separate installation
- Performance characteristics and scalability limits for large-scale DAE systems
- Whether cloud/edge deployment modes require additional configuration or credentials
What it is and what it does
GEKKO is an optimization and machine learning framework that translates mathematical models of dynamic systems into byte-code and solves them using large-scale solvers. It specializes in problems involving differential algebraic equations (DAEs)—systems where variables evolve over time according to differential equations and algebraic constraints. The package supports a wide range of problem formulations: from simple linear and quadratic programs to complex mixed-integer nonlinear problems, as well as control and estimation tasks like model predictive control and moving horizon estimation.
The package works by building a symbolic representation of your problem—defining variables, equations, and objectives—then compiling that representation and passing it to industrial-grade solvers (APOPT and IPOPT) with automatically computed sparse derivatives. It includes data-cleaning utilities and is designed to run on Windows, Linux, macOS, and ARM processors. GEKKO is intended for engineers and researchers solving real-world optimization and control problems, from parameter estimation and regression to real-time optimization and advanced process control.
Use it for
- Solve differential algebraic equation systems with constraints and optimization objectives over a time horizon
- Build and tune model predictive control (MPC) systems for process automation or robotics
- Perform parameter estimation and data regression on dynamic system models
- Formulate and solve mixed-integer nonlinear programs (MINLP) for scheduling or resource allocation
- Implement moving horizon estimation (MHE) for state estimation in real-time systems
- Conduct sensitivity analysis and optimization of complex engineering systems
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
GEKKO is actively maintained, has no known vulnerabilities, installs with minimal friction (numpy only), and is licensed permissively. It fills a specialized niche in dynamic optimization and DAE solving that few Python packages address directly. Install if you need to solve differential algebraic equations, implement model predictive control, or tackle mixed-integer nonlinear optimization problems.
Install
gekko on PyPI
Before you install
Low install friction; pure Python wheel with only numpy as a runtime dependency. Active maintenance with recent commits and a stable production release status.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and proprietary projects.
Quickstart
pip install gekko
import gekko
m = gekko.GEKKO()
x = m.Var()
m.Equation(x**2 == 4)
m.solve()
print(x.value)Verify before relying
- Whether the bundled APOPT and IPOPT solvers are included in the wheel or require separate installation
- Performance characteristics and scalability limits for large-scale DAE systems
- Whether cloud/edge deployment modes require additional configuration or credentials
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=2.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 226 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 141,112 / month, #11,251 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: gekko-1.3.2-py3-none-any.whl
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