---
id: aimmspy
version: "26.1.8.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# aimmspy — Python bindings for the AIMMS optimization platform, built with pybind11 for seamless C++ integration. Enables efficient data exchange and interaction with AIMMS projects using pandas, polars, and pyarrow. Ideal for advanced optimization workflows requiring high-performance native code.

License: permissive · Maintenance: active · Downloads: 77.2K/mo

## What it is and what it does

aimmspy is a Python library that bridges Python scripts to AIMMS optimization models via C++ bindings. It allows you to programmatically assign data to AIMMS identifiers, execute AIMMS procedures (such as solves), and retrieve results—all from Python. The library supports flexible data exchange using Python dictionaries, pandas DataFrames, Polars DataFrames, and PyArrow tables, making it suitable for batch optimization runs, automated pipelines, and embedding AIMMS solvers into external applications.

The package requires AIMMS Developer to be installed on your machine along with a valid AIMMS license (free Academic Licenses are available). It is built on pybind11 for efficient C++ integration and is distributed as pre-compiled wheels for Python 3.10 through 3.14 on Linux and Windows. The library is designed for "Python-in-the-lead" workflows where Python drives the optimization, complementing the reverse "AIMMS-in-the-lead" pattern supported by the pyaimms library.

Use it for:

- Run batch optimization jobs from Python scripts, assigning input data and retrieving solver results without opening the AIMMS IDE.
- Automate supply chain, logistics, or financial optimization workflows by triggering AIMMS solves from a Python orchestration layer.
- Embed AIMMS optimization capabilities into external applications or web services that need to call optimization models programmatically.
- Integrate AIMMS models into data pipelines where pandas or Polars DataFrames are already in use for preprocessing and result analysis.
- Prototype and test optimization models by iteratively assigning scenarios and running procedures from a Python notebook or script.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

aimmspy provides Python bindings to run AIMMS optimization models headless from Python scripts, exchanging data via pandas, polars, or PyArrow and executing AIMMS procedures programmatically.

Yes, if you have AIMMS Developer installed and a valid license. aimmspy is actively maintained, has no known vulnerabilities, and offers a clean Python interface to AIMMS models with flexible data exchange. The medium install friction is acceptable given the compiled-wheel distribution and the external AIMMS dependency. Not suitable if you do not have AIMMS Developer or a license.

## Install

pip install aimmspy
uv add aimmspy
poetry add aimmspy

## Installing aimmspy

Before you install: Medium install friction due to compiled wheels for specific Python versions (3.10–3.14) and platforms (Linux, Windows). Active maintenance with a release 17 days ago. Requires AIMMS Developer to be installed separately and a valid license.

License in practice: MIT license is permissive and imposes no restrictions on use, modification, or distribution of aimmspy itself.

Quickstart:

pip install aimmspy

from aimmspy.project.project import Project, Model
from aimmspy.utils import find_aimms_path

project = Project(
    aimms_path=find_aimms_path("25.5.4.3"),
    aimms_project_file="path/to/project.aimms",
    license_url="wss://licensing.aimms.cloud/your-license-url"
)
model = project.get_model(__file__)
model.Supply.assign({"Factory1": 35})
model.TransportSolve()
results = model.Shipments.data()

Requires AIMMS Developer installed, a valid AIMMS license (or free Academic License), and an existing AIMMS project file. Python 3.10 or later.

Verify before relying:

- Whether AIMMS Developer and aimmspy versions must be aligned (e.g., does aimmspy 26.1.8.1 require AIMMS 26.x).
- Performance characteristics and typical throughput for bulk data operations via multi_assign() and multi_data().
- Compatibility with AIMMS model types beyond LP/MIP (e.g., stochastic or robust optimization).

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 77.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

AIMMS Python integration, optimization model automation, headless AIMMS execution, mathematical modeling from Python, operations research Python bridge, data exchange with AIMMS, optimization workflow automation, optimization-modeling, operations-research, c++-bindings

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