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Calkit helps you manage and automate research projects like a software
engineer.
Define computational environments,
steps that process your data, create figures,
presentations, and publications, connect to external tools,
then iterate quickly and painlessly until your research questions are
answered, tracking changes to all files along the way.
At the end, deliver your entire project as a self-contained, self-documenting,
version-controlled, and
single button reproducible
"calculation kit" so you and others can easily verify
and build upon the results.
Guiding principles
- Quality comes from iteration. Automation reduces the time and effort
needed to iterate, thereby increasing iteration and quality.
- Automating a step can and should take roughly
the same amount of time as doing it once manually,
therefore it's almost always worth it.
- Working in a "quick and dirty" way can easily become not quick when
the dirtiness results in mistakes and/or discourages working in small steps.
Features
- A simplified version control
interface that unifies Git and DVC (Data Version Control),
so all materials can be kept in the same project repository.
This way, code doesn't need to be siloed away from other
important artifacts like datasets, models, figures, or article PDFs,
allowing you to work on all parts of a project without hopping around to
different tools.
- Computational environment management with support for many
languages and environment managers: Conda, Docker, uv, Julia, Renv, and more.
No need to create and update environments on your own. Calkit will handle
them as needed.
- An environment-aware build system or pipeline with
a simple declarative syntax and
output caching so you don't need to think about which steps or stages
need to be rerun after changing any part of the project.
Simply call
calkit run.
Compose your pipeline from many different kinds of stages,
including simple scripts, commands, Jupyter Notebooks, LaTeX, and more.
- A complementary self-hostable and GitHub-integrated
cloud platform
to facilitate backup, collaboration,
and sharing throughout the entire research lifecycle.
- Overleaf integration, so
analysis, visualization, and writing can all stay in sync
(no more manual uploads!)
- Support for running on high performance computing (HPC) systems that use
SLURM schedulers.
- Support for running with GitHub Actions.
- Extensions for doing all of the above graphically in
JupyterLab and
VS Code.
Installation
On Linux, macOS, or Windows Git Bash,
install Calkit and uv
(if not already installed) with:
curl -LsSf install.calkit.org | sh
Or with Windows Command Prompt or PowerShell:
powershell -ExecutionPolicy ByPass -c "irm install-ps1.calkit.org | iex"
If you already have uv installed, install Calkit with:
uv tool install calkit-python
You can also install with your system Python:
pip install calkit-python
To effectively use Calkit, you'll want to ensure Git
is installed and properly configured.
You may also want to install Docker,
since that is the default method by which LaTeX environments are created.
If you want to use the Calkit Cloud
for collaboration and backup as a DVC remote,
you can set up cloud integration with:
calkit cloud login
If you use AI agents like Claude, Copilot, or Codex,
see AI tools
to learn how to install agent skills for working with Calkit.
Use without installing
If you want to use Calkit without installing it,
you can use uv's uvx command to run it directly:
uvx calk9 --help
Calkit Assistant
