# Mesa: Agent-based modeling in Python
| | |
| --- | --- |
| CI/CD | [](https://github.com/projectmesa/mesa/actions) [](https://codecov.io/gh/projectmesa/mesa) |
| Package | [](https://pypi.org/project/Mesa/) [](https://pypi.org/project/Mesa/) [](https://pypi.org/project/Mesa/) |
| Meta | [](https://github.com/astral-sh/ruff) [](https://github.com/psf/black) [](https://github.com/pypa/hatch) |
| Chat | [](https://matrix.to/#/#project-mesa:matrix.org) |
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 alternative to NetLogo,
Repast, or MASON.
*Above: A Mesa implementation of the Schelling segregation model, this
can be displayed in browser windows or Jupyter.*
## Features
- Modular components
- Browser-based visualization
- Built-in tools for analysis
- Example model library
## Using Mesa
Getting started quickly:
``` bash
pip install mesa
```
You can also use `pip` to install the github version:
``` bash
pip install -U -e git+https://github.com/projectmesa/mesa@main#egg=mesa
```
Or any other (development) branch on this repo or your own fork:
``` bash
pip install -U -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa
```
For resources or help on using Mesa, check out the following:
- [Intro to Mesa Tutorial](http://mesa.readthedocs.org/en/stable/tutorials/intro_tutorial.html) (An introductory model, the Boltzmann
Wealth Model, for beginners or those new to Mesa.)
- [Complexity Explorer Tutorial](https://www.complexityexplorer.org/courses/172-agent-based-models-with-python-an-introduction-to-mesa) (An advanced-beginner model,
SugarScape with Traders, with instructional videos)
- [Mesa Examples](https://github.com/projectmesa/mesa-examples/tree/main/examples) (A repository of seminal ABMs using Mesa and
examples of employing specific Mesa Features)
- [Docs](http://mesa.readthedocs.org/) (Mesa's documentation, API and useful snippets)
- [Discussions](https://github.com/projectmesa/mesa/discussions) (GitHub threaded discussions about Mesa)
- [Matrix Chat](https://matrix.to/#/#project-mesa:matrix.org) (Chat Forum via Matrix to talk about Mesa)
## 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](https://docs.docker.com/compose/install/) and then, in the
folder containing the Mesa Git repository, you run:
``` bash
$ docker compose up
# If you want to make it run in the background, you instead run
$ docker compose up -d
```
This runs the Schelling 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 8765 to your host system's
port 8765 so you can interact with the running model as usual by
visiting localhost:8765 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 an app.py file
Then, you just need to run `docker compose up -d` to have it
accessible from `localhost:8765`.
## 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](https://matrix.to/#/#project-mesa:matrix.org) 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](https://github.com/projectmesa/mesa/discussions)).
> - Just check out the code on [GitHub](https://github.com/projectmesa/mesa/).
If you run into an issue, please file a [ticket](https://github.com/projectmesa/mesa/issues) 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](https://github.com/projectmesa/mesa/issues) or
join a dev session (see [Mesa discussions](https://github.com/projectmesa/mesa/discussions)). A feature is most likely
to be added if you build it!
Don't forget to checkout the [Contributors guide](https://github.com/projectmesa/mesa/blob/main/CONTRIBUTING.md).
## Citing Mesa
To cite Mesa in your publication, you can use the [CITATION.bib](https://github.com/projectmesa/mesa/blob/main/CITATION.bib).
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"description": "# Mesa: Agent-based modeling in Python\n\n| | |\n| --- | --- |\n| CI/CD | [](https://github.com/projectmesa/mesa/actions) [](https://codecov.io/gh/projectmesa/mesa) |\n| Package | [](https://pypi.org/project/Mesa/) [](https://pypi.org/project/Mesa/) [](https://pypi.org/project/Mesa/) |\n| Meta | [](https://github.com/astral-sh/ruff) [](https://github.com/psf/black) [](https://github.com/pypa/hatch) |\n| Chat | [](https://matrix.to/#/#project-mesa:matrix.org) |\n\nMesa allows users to quickly create agent-based models using built-in\ncore components (such as spatial grids and agent schedulers) or\ncustomized implementations; visualize them using a browser-based\ninterface; and analyze their results using Python's data analysis\ntools. 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