lean-py

Effortless interop between Lean 4 and Python, in both directions.

• Lean -> Python. Annotate any Lean definition with @[python "name"] and call it from Python with automatic type marshalling. derive_python exposes inductives and structures as Python constructors.
• Python -> Lean. LeanPy.Python gives Lean code a Py type with import_, eval, exec, getAttr, call, etc. CPython is loaded lazily via dlopen.
• Kernel facade. LeanPy.Kernel wraps the Pantograph library so a Python process can drive Lean's type-checker, elaborator, and tactic engine without spawning a subprocess.

Install

Python side

uv pip install "lean_py @ git+https://github.com/kiranandcode/lean.py"

or in pyproject.toml:

[project]
dependencies = ["lean_py @ git+https://github.com/kiranandcode/lean.py"]

The Python package discovers lean.h and libleanshared from the active Lean toolchain at import time. You need a working elan install (lean on PATH).

Lean side

Add to your lakefile.toml:

[[require]]
name = "LeanPy"
git  = "https://github.com/kiranandcode/lean.py"

[[lean_lib]]
name = "MyLib"
# These three lines are required:
moreLinkObjs = [
  "LeanPy/LeanPy:static",
  "LeanPy/leanPyNative:static",
  "Pantograph/Pantograph:static",
]
precompileModules = true
defaultFacets = ["shared"]
# macOS only — allows install_name_tool to rewrite @rpath references:
moreLinkArgs = ["-Wl,-headerpad_max_install_names"]

Why three static libs? LeanPy:static is the Lean module, leanPyNative:static is the C bridge (python_bridge.c), and Pantograph:static is the proof-assistant kernel that LeanPy.Kernel depends on. All three must be linked into the shared library that Python loads.

Then build:

lake build           # fetches LeanPy + Pantograph, compiles everything

Using additional Lake dependencies

If your project depends on other Lean libraries (Batteries, Mathlib, your own packages, etc.), add them as normal [[require]] entries in your lakefile.toml. Any library whose symbols are called at runtime through the Python-loaded .so/.dylib must also appear in moreLinkObjs:

[[require]]
name = "LeanPy"
git  = "https://github.com/kiranandcode/lean.py"

[[require]]
name = "batteries"
git  = "https://github.com/leanprover-community/batteries"
rev  = "main"

[[lean_lib]]
name = "MyLib"
moreLinkObjs = [
  "LeanPy/LeanPy:static",
  "LeanPy/leanPyNative:static",
  "Pantograph/Pantograph:static",
  # Add any additional deps whose symbols you call at runtime:
  "batteries/Batteries:static",
]
precompileModules = true
defaultFacets = ["shared"]
moreLinkArgs = ["-Wl,-headerpad_max_install_names"]

Rule of thumb: if lake build succeeds but Python fails with symbol not found, add the missing package to moreLinkObjs as "<package>/<LibName>:static". The pattern is always "<lake-package-name>/<lean_lib-name>:static".

If you only import a library at compile time (e.g. for notation or macros) but don't call its functions at runtime, you don't need it in moreLinkObjs.

Quick start

1. Write Lean code

-- MyLib.lean
import LeanPy
open LeanPy

@[python "add"]
def add (a b : Int) : Int := a + b

structure Point where
  x : Int
  y : Int

derive_python Point

@[python "origin"]
def origin (_ : Unit) : Point := { x := 0, y := 0 }

#export_python_registry "MyLib"   -- makes the registry visible to Python

2. Call from Python

from lean_py import LeanLibrary

lib = LeanLibrary.from_lake("path/to/lake/project", "MyLib", build=True)

lib.add(3, 4)          # 7
lib.origin(None)       # Point.mk(0, 0)
lib.Point(10, 20)      # Point.mk(10, 20) — constructed in Python

from_lake finds the .lake/build/lib/lib<Name>.{dylib,so} produced by lake build. Pass build=True to run lake build automatically.

Calling Python from Lean

open LeanPy.Python in
@[python "numpy_dot"]
def numpyDot (xs ys : Array Int) : IO Int := do
  init ()                         -- dlopens libpython once
  let np ← import_ "numpy"
  let dot ← np.getAttr "dot"
  let a ← Py.ofList (xs.toList.map Py.ofInt)
  let b ← Py.ofList (ys.toList.map Py.ofInt)
  (← dot.call #[← a, ← b]).toInt

lib.numpy_dot([1, 2, 3], [4, 5, 6])   # 32

Kernel facade (Pantograph)

Drive Lean's type-checker and tactic engine from Python:

from lean_py import LeanLibrary
from lean_py.kernel import Kernel

lib = LeanLibrary.from_lake("path/to/project", "MyLib", build=True)
k = Kernel(lib)
k.load(["Init"])

# Create a goal and run tactics
state = k.goal_create("∀ n : Nat, n + 0 = n")
print(state.pretty())             # ⊢ ∀ (n : Nat), n + 0 = n

result = state.try_tactic("intro n")
print(result.state.pretty())      # n : Nat\n⊢ n + 0 = n

result2 = result.state.try_tactic("simp")
print(result2.state.is_solved())  # True

The kernel API also exposes environment introspection (catalog, decl_type, module_of, ...), expression elaboration (infer_type, pretty_print, whnf), frontend processing, and goal-state pickling. See lean_py/kernel.py for the full surface.

Bidirectional introspection

Lean's kernel ADTs (Lean.Expr, Lean.Name, Lean.Level, Lean.Syntax, ...) are exposed as Python values via derive_python (registered in LeanPy/Reflect.lean):

Name = lib.Name
Expr = lib.Expr

# Build a Lean.Expr tree in Python
nat  = Name.str(Name.anonymous, "Nat")
succ = Expr.const(Name.str(nat, "succ"), [])
zero = Expr.const(Name.str(nat, "zero"), [])
e    = Expr.app(succ, zero)       # Nat.succ Nat.zero

# Pass it to any @[python] function expecting Lean.Expr
lib.describe_expr(e)

Going the other way, Py values returned from Lean land as live Python objects:

lib.makeList123(None)   # [1, 2, 3]  (not an opaque handle)

Exceptions

Errors carry type information across the boundary:

from lean_py import LeanError, LeanPyCallbackError

try:
    lib.some_io_function()
except LeanPyCallbackError as e:    # Python error inside a Lean callback
    print(e.python_type, e.python_message)
except LeanError as e:              # Lean IO error
    print(e.kind, e.message)

Examples

examples/
  01_basic/             tiny end-to-end demo
  02_pantograph_kernel/ Pantograph-style kernel facade
  03_numpy_typed/       numpy with Lean-checked dependent shapes
  04_sympy_tactic/      `by sympy` — Lean tactic backed by SymPy via Expr trees
  05_knuckledragger/    `by knuckle` — Lean tactic backed by Z3 via Expr trees

Each is a self-contained Lake + uv project.

Tests

uv sync --dev
lake build
cd tests/lean && lake build TestLib:shared && cd ../..
uv run pytest tests -v

125 tests covering: FFI primitives, all marshalled types, typed exceptions, bidirectional introspection, kernel facade (goal state, tactics, environment, elaboration, frontend, serialisation), Python-in-Lean demos, and refcount stress tests.

How it works

1. @[python "name"] sets @[export] and registers metadata (parameter types, return type) in a persistent env extension.
2. derive_python TypeName walks an inductive's constructors and adds them to the same registry.
3. #export_python_registry "Prefix" serialises the registry to JSON and emits two @[export]'d C functions returning that JSON.
4. On the Python side, LeanLibrary dlopens the .dylib/.so, calls Prefix_funcs_json() / Prefix_types_json(), builds TypeWrappers, and exposes one Python callable per registered function.
5. The C bridge (LeanPy/native/python_bridge.c) implements the Python-in-Lean direction: a lean_external_class over PyObject* plus lean_py_* externs.

Repository layout

LeanPy.lean              root import
LeanPy/
  Attr.lean              @[python] attribute, derive_python
  Export.lean            #export_python_registry
  Python.lean            Py type + @[extern] bridge
  Reflect.lean           derive_python for Lean.Expr/Name/Level/...
  Kernel.lean            Pantograph kernel API
  Kernel/                Frontend, Compat, ...
  native/python_bridge.c C bridge (dlopen, no Python.h)

lean_py/
  __init__.py            public API
  library.py             LeanLibrary loader
  marshal.py             Lean <-> Python marshalling
  kernel.py              Kernel / GoalState / TacticResult
  registry.py            TypeRepr / FuncInfo mirrors
  _runtime.py            dynamic ctypes FFI from lean.h
  _parse.py              lean.h parser (pycparser)
  utils.py               toolchain helpers

examples/                self-contained demos
tests/                   125-test suite

License

Apache 2.0. See LICENSE.