Mesa: Agent-based modeling in Python 3+

It 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 3-based alternative 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
• Example model library

Using Mesa

Getting started quickly:

$ pip install mesa

You can also use pip to install the github version:

$ pip install -e git+https://github.com/projectmesa/mesa#egg=mesa

Or any other (development) branch on this repo or your own fork:

$ pip install -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa

Take a look at the examples folder for sample models demonstrating Mesa features.

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

• Intro to Mesa Tutorial
• Docs
• Email list for users
• PyPI

Running Mesa in Docker

You can run Mesa in a Docker container in a few ways.

If you are a Mesa developer, first install Docker Compose and then, in the folder containing the Mesa Git repository, you run:

$ docker compose up
# If you want to make it run in the background, you instead run
$ docker compose up -d

This runs the wolf-sheep predation model, as an example.

With the docker-compose.yml file in this Git repository, the docker compose up command does two important things:

• It mounts the mesa root directory (relative to the docker-compose.yml file) into /opt/mesa and runs pip install -e on that directory so your changes to mesa should be reflected in the running container.
• It binds the docker container's port 8521 to your host system's port 8521 so you can interact with the running model as usual by visiting localhost:8521 on your browser

If you are a model developer that wants to run Mesa on a model, you need to:

• make sure that your model folder is inside the folder containing the docker-compose.yml file
• change the MODEL_DIR variable in docker-compose.yml to point to the path of your model
• make sure that the model folder contains a run.py file

Then, you just need to run docker compose up -d to make it accessible from localhost:8521.

Contributing to Mesa

Want to join the Mesa team or just curious about what is happening with Mesa? You can...

• Join our Matrix chat room in which questions, issues, and ideas can be (informally) discussed.
• Come to a monthly dev session (you can find dev session times, agendas and notes on Mesa discussions).
• Just check out the code on GitHub.

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 join a dev session (see Mesa discussions). A feature is most likely to be added if you build it!

Don't forget to checkout the Contributors guide.

Citing Mesa

To cite Mesa in your publication, you can use the CITATION.bib.