Installation

Clone the repository including submodules:

git clone --recurse-submodules https://github.com/Kripner/leantree.git

Ensure Lean 4.19 is installed via elan.

lake --version

At the moment, LeanTree only supports Lean 4.19.

Make sure pip is installed. Then, run:

make install

Alternatively, use Poetry explicitly:

pip install poetry
poetry install

For running tests or experiments, refer to the Development section.

Basic Usage

Note: You must create a Lean project to use Lean. See Lean project guide for more information.

Start by creating or loading a Lean project:

from leantree import LeanProject

project = LeanProject.create("path/to/project")

# or load an existing one:
project = LeanProject("path/to/project")

# or decide automatically:
project = LeanProject("path/to/project", create=True)

The created project includes Mathlib by default. If no path is provided in LeanProject.create, the project will be created in or loaded from the leantree_project subdirectory of the current directory.

Starting a Proof

Using the environment, you can initialize a proof search by supplying a theorem with one or more sorry keywords.

with project.environment() as env:
    env.send_command("import Mathlib\nopen BigOperators Real Nat Topology Rat")
    branch = env.proof_from_sorry("theorem succ_less_double_succ (n : Nat) : n > 0 → n < 2 * n := by sorry")
    zero, succ = branch.apply_tactic("cases n")
    print("Factorized proof states after `cases n`:")
    print(zero.state)
    print(succ.state)
    assert not zero.is_solved
    ...

Async API

async with project.environment() as env:
    await env.send_command_async("import Mathlib\nopen BigOperators Real Nat Topology Rat")
    branch = await env.proof_from_sorry_async("theorem succ_less_double_succ (n : Nat) : n > 0 → n < 2 * n := by sorry")
    zero, succ = await branch.apply_tactic_async("cases n")
    ...

Data Extraction

Using the project, you can parse a Lean file and build all proof trees. Then, you can use the environment to start a proof for each tactic block in the file.

file = project.load_file("Example.lean")

# Pretty-print all proof trees.
for thm in file.theorems:
    print(thm.load_source() + "\n")
    for by_block in thm.by_blocks:
        print(by_block.tree.pretty_print())
    print("-" * 100)

# Start proofs for each tactic block.
for thm, branch in env.file_proofs(file):
    if isinstance(branch, Exception):
        print(f"Could not start theorem '{thm}' due to exception: {branch}")
    ...

Dataset Generation

You can easily generate a dataset containing all Lean files in a directory. For production-ready dataset generation, see the Datasets section

import glob
import json

with open("dataset.json", "w") as f:
    for path in glob.glob('**/*.lean', recursive=True):
        file = project.load_file(path)
        f.write(json.dumps(file.serialize()) + "\n")

Save/Restore Environment State

The environment state can be saved to disk and restored later.

with project.environment() as env:
    env.send_command("import Mathlib\nopen BigOperators Real Nat Topology Rat")
    env.pickle("env.pkl")

with project.environment() as env:
    env.unpickle("env.pkl")
    branch = env.proof_from_sorry("theorem succ_less_double_succ (n : Nat) : n > 0 → n < 2 * n := by sorry")
    zero, succ = branch.apply_tactic("cases n")
    ...

Datasets

Assuming you have already create a Lean project in leantree_project, you can recreate the whole Mathlib dataset by running:

python dataset/tree_dataset.py generate --project_path leantree_project --source_files mathlib/Mathlib

Development

Install all development and experiments dependencies:

make install-dev

Running Tests

To run tests, first create a Lean 4.19 project called leantree_project in the LeanTree directory. You can use LeanTree for that - in the leantree directory, run the following Python code:

from leantree import LeanProject

project = LeanProject.create()

After the project is created, run:

make test

Debugging Tips

When working with a Lean environment, you can use env.take_control() to debug the underlying Lean REPL. This method connects your stdin/stdout to the REPL's stdin/stdout.

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Related Tools

• LeanDojo
• Pantograph

For a detailed comparison, refer to the LeanTree paper.

Reference

@inproceedings{
    kripner2025leantree,
    title={LeanTree: Accelerating White-Box Proof Search with Factorized States in Lean 4},
    author={Mat{\v{e}}j Kripner and Michal Sustr and Milan Straka},
    booktitle={2nd AI for Math Workshop @ ICML 2025},
    year={2025},
    url={https://openreview.net/forum?id=pROVJwTb6w}
}