# 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) [](https://scientific-python.org/specs/spec-0000/) |
| Chat | [](https://matrix.to/#/#project-mesa:matrix.org) |
| Cite | [](https://doi.org/10.21105/joss.07668) |
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 WolfSheep 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
To install our latest stable release, run:
``` bash
pip install -U mesa
```
Starting with Mesa 3.0, we don't install all our dependencies anymore by default.
```bash
# You can customize the additional dependencies you need, if you want. Available are:
pip install -U "mesa[network,viz]"
# This is equivalent to our recommended dependencies:
pip install -U "mesa[rec]"
# To install all, including developer, dependencies:
pip install -U "mesa[all]"
```
You can also use `pip` to install the latest 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
```
## Resources
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.)
- [Visualization Tutorial](https://mesa.readthedocs.io/stable/tutorials/visualization_tutorial.html) (An introduction into our Solara visualization)
- [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) (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)
- [Development version docs](https://mesa.readthedocs.io/latest/) (the latest version docs if you're using a pre-release Mesa version)
- [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 refer to our peer-reviewed article in the Journal of Open Source Software (JOSS):
- ter Hoeven, E., Kwakkel, J., Hess, V., Pike, T., Wang, B., rht, & Kazil, J. (2025). Mesa 3: Agent-based modeling with Python in 2025. Journal of Open Source Software, 10(107), 7668. https://doi.org/10.21105/joss.07668
Our [CITATION.cff](https://github.com/projectmesa/mesa/blob/main/CITATION.cff) can be used to generate APA, BibTeX and other citation formats.
Raw data
{
"_id": null,
"home_page": null,
"name": "Mesa",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "ABM, agent, based, model, modeling, multi-agent, simulation",
"author": null,
"author_email": "Project Mesa Team <maintainers@projectmesa.dev>",
"download_url": "https://files.pythonhosted.org/packages/d6/95/d56a50fee2b6c7c3c371a96164feb49982a5ac0c64d7990d640480946f92/mesa-3.3.0.tar.gz",
"platform": null,
"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) [](https://scientific-python.org/specs/spec-0000/) |\n| Chat | [](https://matrix.to/#/#project-mesa:matrix.org) |\n| Cite | [](https://doi.org/10.21105/joss.07668) |\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. Its goal is to be the Python-based alternative to NetLogo,\nRepast, or MASON.\n\n\n\n*Above: A Mesa implementation of the WolfSheep model, this\ncan be displayed in browser windows or Jupyter.*\n\n## Features\n\n- Modular components\n- Browser-based visualization\n- Built-in tools for analysis\n- Example model library\n\n## Using Mesa\n\nTo install our latest stable release, run:\n\n``` bash\npip install -U mesa\n```\n\nStarting with Mesa 3.0, we don't install all our dependencies anymore by default.\n```bash\n# You can customize the additional dependencies you need, if you want. Available are:\npip install -U \"mesa[network,viz]\"\n\n# This is equivalent to our recommended dependencies:\npip install -U \"mesa[rec]\"\n\n# To install all, including developer, dependencies:\npip install -U \"mesa[all]\"\n```\n\nYou can also use `pip` to install the latest GitHub version:\n\n``` bash\npip install -U -e git+https://github.com/projectmesa/mesa@main#egg=mesa\n```\n\nOr any other (development) branch on this repo or your own fork:\n\n``` bash\npip install -U -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa\n```\n\n## Resources\nFor resources or help on using Mesa, check out the following:\n\n- [Intro to Mesa Tutorial](http://mesa.readthedocs.org/en/stable/tutorials/intro_tutorial.html) (An introductory model, the Boltzmann\n Wealth Model, for beginners or those new to Mesa.)\n- [Visualization Tutorial](https://mesa.readthedocs.io/stable/tutorials/visualization_tutorial.html) (An introduction into our Solara visualization)\n- [Complexity Explorer Tutorial](https://www.complexityexplorer.org/courses/172-agent-based-models-with-python-an-introduction-to-mesa) (An advanced-beginner model,\n SugarScape with Traders, with instructional videos)\n- [Mesa Examples](https://github.com/projectmesa/mesa-examples) (A repository of seminal ABMs using Mesa and\n examples of employing specific Mesa Features)\n- [Docs](http://mesa.readthedocs.org/) (Mesa's documentation, API and useful snippets)\n - [Development version docs](https://mesa.readthedocs.io/latest/) (the latest version docs if you're using a pre-release Mesa version)\n- [Discussions](https://github.com/projectmesa/mesa/discussions) (GitHub threaded discussions about Mesa)\n- [Matrix Chat](https://matrix.to/#/#project-mesa:matrix.org) (Chat Forum via Matrix to talk about Mesa)\n\n## Running Mesa in Docker\n\nYou can run Mesa in a Docker container in a few ways.\n\nIf you are a Mesa developer, first [install Docker\nCompose](https://docs.docker.com/compose/install/) and then, in the\nfolder containing the Mesa Git repository, you run:\n\n``` bash\n$ docker compose up\n# If you want to make it run in the background, you instead run\n$ docker compose up -d\n```\n\nThis runs the Schelling model, as an example.\n\nWith the docker-compose.yml file in this Git repository, the `docker compose up` command does two important things:\n\n- It mounts the mesa root directory (relative to the\n docker-compose.yml file) into /opt/mesa and runs pip install -e on\n that directory so your changes to mesa should be reflected in the\n running container.\n- It binds the docker container's port 8765 to your host system's\n port 8765 so you can interact with the running model as usual by\n visiting localhost:8765 on your browser\n\nIf you are a model developer that wants to run Mesa on a model, you need\nto:\n\n- make sure that your model folder is inside the folder containing the\n docker-compose.yml file\n- change the `MODEL_DIR` variable in docker-compose.yml to point to\n the path of your model\n- make sure that the model folder contains an app.py file\n\nThen, you just need to run `docker compose up -d` to have it\naccessible from `localhost:8765`.\n\n## Contributing to Mesa\n\nWant to join the Mesa team or just curious about what is happening with\nMesa? You can\\...\n\n> - Join our [Matrix chat room](https://matrix.to/#/#project-mesa:matrix.org) in which questions, issues, and\n> ideas can be (informally) discussed.\n> - Come to a monthly dev session (you can find dev session times,\n> agendas and notes on [Mesa discussions](https://github.com/projectmesa/mesa/discussions)).\n> - Just check out the code on [GitHub](https://github.com/projectmesa/mesa/).\n\nIf you run into an issue, please file a [ticket](https://github.com/projectmesa/mesa/issues) for us to discuss. If\npossible, follow up with a pull request.\n\nIf you would like to add a feature, please reach out via [ticket](https://github.com/projectmesa/mesa/issues) or\njoin a dev session (see [Mesa discussions](https://github.com/projectmesa/mesa/discussions)). A feature is most likely\nto be added if you build it!\n\nDon't forget to checkout the [Contributors guide](https://github.com/projectmesa/mesa/blob/main/CONTRIBUTING.md).\n\n## Citing Mesa\n\nTo cite Mesa in your publication, you can refer to our peer-reviewed article in the Journal of Open Source Software (JOSS):\n- ter Hoeven, E., Kwakkel, J., Hess, V., Pike, T., Wang, B., rht, & Kazil, J. (2025). Mesa 3: Agent-based modeling with Python in 2025. Journal of Open Source Software, 10(107), 7668. https://doi.org/10.21105/joss.07668\n\nOur [CITATION.cff](https://github.com/projectmesa/mesa/blob/main/CITATION.cff) can be used to generate APA, BibTeX and other citation formats.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Agent-based modeling (ABM) in Python",
"version": "3.3.0",
"project_urls": {
"homepage": "https://github.com/projectmesa/mesa",
"repository": "https://github.com/projectmesa/mesa"
},
"split_keywords": [
"abm",
" agent",
" based",
" model",
" modeling",
" multi-agent",
" simulation"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "6e57147b19686c6f12ee3b5395af7887e70079163e7141b8ed462812003762be",
"md5": "aaa155becee6b4aabaea687b9a14fed2",
"sha256": "f50039e6e1d3eca4455753205a2440d9d56db01495154ba071903b5b4975e548"
},
"downloads": -1,
"filename": "mesa-3.3.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "aaa155becee6b4aabaea687b9a14fed2",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.11",
"size": 239639,
"upload_time": "2025-09-06T20:14:44",
"upload_time_iso_8601": "2025-09-06T20:14:44.748808Z",
"url": "https://files.pythonhosted.org/packages/6e/57/147b19686c6f12ee3b5395af7887e70079163e7141b8ed462812003762be/mesa-3.3.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "d695d56a50fee2b6c7c3c371a96164feb49982a5ac0c64d7990d640480946f92",
"md5": "aec220883e7252738291b7daae6d9c90",
"sha256": "49b4945a03b88a063d85e6c2674498dff5ee8ed6ef08fcf465370dce75f71722"
},
"downloads": -1,
"filename": "mesa-3.3.0.tar.gz",
"has_sig": false,
"md5_digest": "aec220883e7252738291b7daae6d9c90",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.11",
"size": 675585,
"upload_time": "2025-09-06T20:14:46",
"upload_time_iso_8601": "2025-09-06T20:14:46.549674Z",
"url": "https://files.pythonhosted.org/packages/d6/95/d56a50fee2b6c7c3c371a96164feb49982a5ac0c64d7990d640480946f92/mesa-3.3.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-09-06 20:14:46",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "projectmesa",
"github_project": "mesa",
"travis_ci": false,
"coveralls": true,
"github_actions": true,
"lcname": "mesa"
}
JSON
