optlang
Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).
Decision gist · record as of 2026-08-14
Yes. Optlang is actively maintained, has low install friction, carries no known vulnerabilities, and offers a clean Pythonic API for optimization problems. It's a good fit if you need to formulate and solve LP/MILP/QP problems with solver flexibility. Install it if you're doing operations research, scientific computing, or constraint-based modeling in Python.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.9; swiglpk (GLPK interface) installs by default but optional solvers (cplex, gurobipy, scipy, osqp) must be installed separately if needed.
- Low friction: pure Python wheel with two runtime dependencies (sympy and swiglpk).
- Actively maintained as of June 2026 with recent releases.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive; you may use, modify, and distribute optlang freely in commercial or proprietary projects provided you include the license notice.
last release 2026-06-02 (73 days) · last repo commit 2026-06-02 · 271 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,241 downloads/mo, #14,089 on PyPI
Alternatives
Verify before relying
pip install optlang
from optlang import Model, Variable, Constraint, Objective
x1 = Variable('x1', lb=0)
x2 = Variable('x2', lb=0)
c1 = Constraint(x1 + x2, ub=100)
obj = Objective(10*x1 + 6*x2, direction='max')
model = Model()
model.objective = obj
model.add([c1])
status = model.optimize()
print(model.objective.value)- Whether the package handles numerical stability or precision guarantees for large-scale problems.
- Performance characteristics when solving problems with thousands of variables or constraints.
- Completeness of support for all GLPK solver features through the swiglpk interface.
What it is and what it does
Optlang is a Python library that lets you define linear, mixed-integer, and quadratic optimization problems using symbolic math expressions (via sympy), then solve them through a unified interface to multiple solver backends. Instead of writing problems in a solver-specific format, you declare variables with bounds, build constraints and objectives from symbolic expressions, combine them into a Model, and call optimize()—the same code can then switch between GLPK, CPLEX, Gurobi, or other solvers without rewriting the problem formulation.
The library is built on sympy for symbolic math and swiglpk for GLPK access by default, with optional support for commercial solvers (CPLEX, Gurobi) and open-source alternatives (scipy, osqp). It's actively maintained, supports Python 3.9–3.13, and carries no known security vulnerabilities. It's commonly used in scientific and operations research workflows where problem portability and clean problem definition matter.
Use it for
- Define and solve linear programming problems (e.g., resource allocation, production planning) without learning solver-specific syntax.
- Prototype optimization models in Python that can later be solved with different backends (GLPK, CPLEX, Gurobi) by changing one import.
- Formulate mixed-integer programs for scheduling, routing, or combinatorial optimization using symbolic constraints.
- Build quadratic programming solvers for portfolio optimization or least-squares problems via optional solvers.
- Integrate optimization into scientific workflows where sympy expressions are already used for symbolic math.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Optlang is actively maintained, has low install friction, carries no known vulnerabilities, and offers a clean Pythonic API for optimization problems. It's a good fit if you need to formulate and solve LP/MILP/QP problems with solver flexibility. Install it if you're doing operations research, scientific computing, or constraint-based modeling in Python.
Install
optlang on PyPI
Before you install
Low friction: pure Python wheel with two runtime dependencies (sympy and swiglpk). Actively maintained as of June 2026 with recent releases.
Requires Python >= 3.9; swiglpk (GLPK interface) installs by default but optional solvers (cplex, gurobipy, scipy, osqp) must be installed separately if needed.
License in practice
Apache-2.0 is permissive; you may use, modify, and distribute optlang freely in commercial or proprietary projects provided you include the license notice.
Quickstart
pip install optlang
from optlang import Model, Variable, Constraint, Objective
x1 = Variable('x1', lb=0)
x2 = Variable('x2', lb=0)
c1 = Constraint(x1 + x2, ub=100)
obj = Objective(10*x1 + 6*x2, direction='max')
model = Model()
model.objective = obj
model.add([c1])
status = model.optimize()
print(model.objective.value)Verify before relying
- Whether the package handles numerical stability or precision guarantees for large-scale problems.
- Performance characteristics when solving problems with thousands of variables or constraints.
- Completeness of support for all GLPK solver features through the swiglpk interface.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesswiglpksympy |
| Maintenance | Actively maintained 73 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,241 / month, #14,089 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/StableIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Mathematics |
Evidence: optlang-1.9.1-py2.py3-none-any.whl
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