Mesa: Agent-based modeling in Python#

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Mesa is an Apache2 licensed agent-based modeling (or ABM) framework in Python.

The original conference paper is available here.

Mesa allows users to quickly create agent-based models using built-in core components (such as spatial grids and agent schedulers) or customized implementations; visualize them using a browser-based interface; and analyze their results using Python’s data analysis tools. Its goal is to be the Python-based counterpart to NetLogo, Repast, or MASON.

A screenshot of the Schelling Model in Mesa|100%

Above: A Mesa implementation of the Schelling segregation model, being visualized in a browser window and analyzed in a Jupyter notebook.

Features#

  • Modular components

  • Browser-based visualization

  • Built-in tools for analysis

Using Mesa#

To install our latest stable release (2.4.x), run:

pip install -U mesa

To install our latest pre-release (3.0 alpha), run:

pip install -U --pre mesa

To launch an example model, clone the repository folder and invoke mesa runserver for one of the examples/ subdirectories:

mesa runserver examples/wolf_sheep

For more help on using Mesa, check out the following resources:

Contributing back to Mesa#

If you run into an issue, please file a ticket for us to discuss. If possible, follow up with a pull request.

If you would like to add a feature, please reach out via ticket or the [email list] for discussion. A feature is most likely to be added if you build it!

Mesa Packages#

ABM features users have shared that you may want to use in your model

Indices and tables#