Skip to main content

GAMMS v1.0.0

GAMMS (Graph based Adversarial Multiagent Modelling Simulator) is a Python library designed for simulating large scale multi-agent scenarios on environments represented as graphs. The library is a framework which focuses on providing a flexible and extensible architecture to facilitate implementing interactions between agents and the environment as well as controlling the information flow between them. The library is geared towards easing development of high level scenario dynamics and testing agent behaviours rather than low level details like the actual physics of real world interactions. GAMMS tries to solve the following problems:

  1. Run large simulations involving multiple agents on an everyday laptop.
  2. Provide a simple interface to the user to define the agents and the environment.
  3. Allow the user to work with the agents and the environment without worrying about the underlying packages.
  4. Create a simulater that is easy to use on any operating system (Windows, Mac, Linux) and is easy to install.

The uniqueness of GAMMS comes from what it is not trying to be. It is not a physics engine, game engine, deep learning library, or a reinforcement learning library. The goal of GAMMS is to provide a framework where the user can use any of these libraries to implement their own agents and environments. Other libraries like Unity, PyBullet, and OpenAI Gym are great for simulating environments but they are not designed to be used as a framework for multi-agent simulations on graphs. GAMMS is trying to address the niche use for researchers and developers who want to do a quick iterations on their ideas and test them in simulated environments that have limited interactions. GAMMS want to be a tool to create a standardized way for simulating multi-agent systems, making it easier to share and reproduce results.

Requirements

Python 3.9+ installed with pip.

Package dependecies:

  • pygame
  • shapely
  • networkx
  • cbor2
  • aenum
  • osmnx

Installation

Direct pip installation from PyPI:

pip install gamms

Installation support is directly from the source code. If you have git installed,

pip install "git+https://github.com/GAMMSim/gamms.git"

Another option is to download the source code locally and run the following command in the root directory of the project:

pip install .

Detailed installation and setup instructions are available in the Installation Guide.

Documentation

The documentation is available at GAMMS Documentation. The documentation is generated using mkdocs-materials with mike for versioning. You can do a local build by cloning the repository and serving the documentation using mkdocs. For new users, Tutorials is a good place to start.

Citation

If you use GAMMS in your work, please cite the paper:

@article{patil2026gamms,
 title={GAMMS: Graph based Adversarial Multiagent Modeling Simulator},
 author={Patil, Rohan and Malegaonkar, Jai and Jiang, Xiao and Dion, Andre and Sukhatme, Gaurav S and Christensen, Henrik I},
 journal={arXiv preprint arXiv:2602.05105},
 year={2026}
}

Metadata

Release files for gamms 1.0.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gamms 1.0.0
File Size Uploaded Provenance Actions
gamms-1.0.0.tar.gz 10.5 MB With provenance Details for gamms-1.0.0.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for gamms 1.0.0
File Interpreter ABI Platform Actions
gamms-1.0.0-py3-none-any.whl Python 3 none any Details for gamms-1.0.0-py3-none-any.whl

Total release size: 10.5 MB

Release files / gamms-1.0.0.tar.gz

Download URL gamms-1.0.0.tar.gz
Size 10.5 MB
Tags Source
SHA-256 checksum
How to use checksums
82b125b3d0d4c4d84d8c3fc81aa9f3d98aa11881d67f24db80f32c743fd91630
BLAKE2b-256 checksum
How to use checksums
b03fb0d9b6402e0659be9c43b0e670f050e590504d889ae20c45dd1d80a7a75a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 9, 2026.

Transparency log

Release files / gamms-1.0.0-py3-none-any.whl

Download URL gamms-1.0.0-py3-none-any.whl
Size 80.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
68dc7811f49480e23178acb06a8ab585aee7ae76ff1468c965b7b4563ffb6d38
BLAKE2b-256 checksum
How to use checksums
aef09ac6bfc74733036a72f8e1b957f0a6636d46c1b33e1c48320f221886b92d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 9, 2026.

Transparency log

Security for gamms 1.0.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for gamms-1.0.0.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 9, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page