index.rst

Mesa: Agent-based modeling in Python

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

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.

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

Getting started quickly:

pip install 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:

• Mesa Introductory Tutorial
• Mesa Visualization Tutorial
• GitHub Issue Tracker
• Email list
• PyPI

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!

• Contributors guide
• Github

Mesa Packages

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

• See the Packages
• :ref:`Mesa-Packages <packages>`

.. toctree::
   :hidden:
   :maxdepth: 7

   Mesa Overview <overview>
   tutorials/intro_tutorial
   tutorials/visualization_tutorial
   Best Practices <best-practices>
   How-to Guide <howto>
   API Documentation <apis/api_main>
   Mesa Packages <packages>
   tutorials/adv_tutorial_legacy.ipynb

Indices and tables

• :ref:`genindex`
• :ref:`modindex`
• :ref:`search`