Lean projects #

In general, if you just open a single .lean file in your text editor and try to compile it, you'll get a bunch of confusing errors. Every non-trivial piece of Lean code needs to live inside a Lean project (sometimes also called a Lean package). A "Lean project" is more than just a folder that you've named "My Lean stuff". Rather, it's a folder containing some very specific things: in particular, a git repository and a file leanpkg.toml that gathers information about dependencies of the project, including for instance the version of Lean that should be used.

If you're interested in contributing to mathlib you only need to set up a Lean project once, which you can use for all your contributions — you don't need to set up a new Lean project for each new contribution.

Setting up and managing all this is done by a little python program called leanproject. This page describes the basic use of this tool, and should be sufficient for everyday use. If this is not enough for your purposes, you can read the full leanproject documentation. If you are really curious, you can also read how pieces fit together.

There is a video walkthrough of these instructions on YouTube.

Working on an existing project #

Suppose you want to work on an existing project. As example, we will take the tutorial project. Open a terminal.

• If you have not logged in since you installed Lean and mathlib, then you may need to first type source ~/.profile or source ~/.bash_profile depending on your OS.

• Go the the directory where you would like this package to live.

• Run leanproject get tutorials.

• Launch VS Code, either through your application menu or by typing code tutorials. (MacOS users need to take a one-off extra step to be able to launch VS Code from the command line.)

• If you launched VS Code from a menu, on the main screen, or in the File menu, click "Open folder" (just "Open" on a Mac), and choose the folder tutorials (not one of its subfolders).

• Using the file explorer on the left-hand side, explore everything you want in tutorials/src. See the tutorials instructions for advice about how to do the exercises in this project.

Creating a Lean project #

We will now create a new project depending on mathlib. The following commands should be typed in a terminal.

• Go to a folder where you want to create a project in a subfolder my_project, and type leanproject new my_project. If you get an error message saying leanproject is an unknown command and you have not logged in since you installed Lean and mathlib, then you may need to first type source ~/.profile or source ~/.bash_profile.

• Launch VS Code, either through your application menu or by typing code my_project.

• If you launched VS Code through a menu: on the main screen, or in the File menu, click "Open folder" (on a Mac, just "Open"), and choose the folder my_project (not one of its subfolders).

• Your Lean code should now be put inside files with extension .lean living in my_project/src/ or a subfolder thereof. In the file explorer on the left-hand side of VS Code, you can right-click on src, choose New file, and type a filename to create a file there.

If you want to make sure everything is working, you can start by creating, say my_project/src/test.lean containing:

import topology.basic

#check topological_space

When the cursor is on the last line, the right hand part of VS Code should display a "Lean Goal" area saying: topological_space : Type u_1 → Type u_1

If, for some reason, you happen to lose the "Lean Goal" area, you can get it back with Ctrl-Shift-Enter (Cmd-Shift-Enter on MacOS). Also, you can get the Lean documentation inside VS Code using Ctrl-Shift-p (Cmd-Shift-p on MacOS) and then, inside the text field that appears, type "lean doc" and hit Enter. Then click "Theorem Proving in Lean" and enjoy.

Hosting your project on GitHub #

Your project is already a git repository, and as it grows, you may want to host it on GitHub. If you take this step, the community offers some GitHub Actions scripts that could help manage your repository. But don't worry if you don't know what this means. It's not necessary for using Lean.

Contributing to mathlib #

See How to contribute to mathlib.