base on Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors. <p align="center"><em>For GSoC, checkout our <a href="https://github.com/projectmesa/mesa/wiki/Google-Summer-of-Code-2026">Google Summer of Code 2026</a> guide</em></p> # Mesa: Agent-based modeling in Python | | | |---------| --- | | CI/CD | [](https://github.com/mesa/mesa/actions) [](https://codecov.io/gh/mesa/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. An online demo is [available here](https://py.cafe/app/EwoutH/mesa-solara-basic-examples).* ## 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/mesa/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: - [Getting Started](https://mesa.readthedocs.io/stable/getting_started.html) (A collection of tutorials that will walk you through a basic model.) - [GSoC at Mesa — Candidates Guide](https://github.com/mesa/mesa/blob/main/docs/GSoC.md) (For candidates interested in participating in the Google Summer of Code at Mesa) - [Mesa Examples](https://mesa.readthedocs.io/stable/examples.html) (A repository of seminal ABMs that are part of the Mesa[rec] install and use the most current Mesa release) - [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/mesa/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/mesa/mesa/discussions)). > - Just check out the code on [GitHub](https://github.com/mesa/mesa/). If you run into an issue, please file a [ticket](https://github.com/mesa/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/mesa/mesa/issues) or join a dev session (see [Mesa discussions](https://github.com/mesa/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/mesa/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/mesa/mesa/blob/main/CITATION.cff) can be used to generate APA, BibTeX and other citation formats. ", Assign "at most 3 tags" to the expected json: {"id":"12170","tags":[]} "only from the tags list I provide: []" returns me the "expected json"