MPLang: A Programming Language for Multi-Party Computation

MPLang is a Python-native library for building and executing multi-party and multi-device programs. It simplifies secure computation by allowing developers to write a single program that orchestrates multiple parties in a synchronous, SPMD (Single Program, Multiple Data) fashion.

Features

Single-Controller SPMD: Write one program that runs across multiple parties in lockstep.
Explicit Device Placement: Clearly annotate and control where data lives and computation happens (e.g., on party P0, P1, or a secure SPU).
Function-Level Compilation: Use the @mplang.function decorator to compile Python functions into an auditable, optimizable graph representation.
Pluggable Architecture: Easily extend MPLang with new frontends (like JAX) and backends (like StableHLO, SPU).

Getting Started

Installation

You'll need a modern Python environment (3.11+). We recommend using uv for fast installation.

# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install MPLang from PyPI
uv pip install mplang

Quick Example

Here's a taste of what MPLang looks like. This example shows a "millionaire's problem" where two parties compare their wealth without revealing it.

import mplang as mp
from numpy.random import randint

# Use a decorator to compile this function for multi-party execution
@mp.function
def millionaire():
    # Alice's value, placed on device P0
    x = mp.device("P0")(randint)(0, 1000000)
    # Bob's value, placed on device P1
    y = mp.device("P1")(randint)(0, 1000000)
    # The comparison happens on a secure device (SPU)
    z = mp.device("SP0")(lambda a, b: a < b)(x, y)
    return z

# Set up a local simulator with 2 parties
sim = mp.Simulator.simple(2)

# Evaluate the compiled function
result = mp.evaluate(sim, millionaire)

# Securely fetch the result (reveals SPU value)
print("Is Alice poorer than Bob?", mp.fetch(sim, result))

Learn More

Tutorials: Check out the tutorials/ directory for in-depth, runnable examples covering conditions, loops, and more.
Contributing: We welcome contributions! See our Contributing Guide to get started with the development setup.

License

MPLang is licensed under the Apache 2.0 License.

Metadata

Release files for mplang-nightly 0.1.dev236

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mplang-nightly 0.1.dev236
File	Size	Uploaded
mplang_nightly-0.1.dev236.tar.gz	617.8 kB	Dec 10, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for mplang-nightly 0.1.dev236
File	Interpreter	ABI	Platform	Reset
mplang_nightly-0.1.dev236-py3-none-any.whl 430.3 kB Dec 10, 2025	Python 3	none	any	Details

Total release size: 1.0 MB

Release files / mplang_nightly-0.1.dev236.tar.gz

Download URL	mplang_nightly-0.1.dev236.tar.gz
Size	617.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`c954fb9a694813259070aa0137d9380f47c351a3487abda377a9c81d7b496d3f`
BLAKE2b-256 checksum How to use checksums	`77fa7600dfdfabf47bb3989f255eb99cf59f79823a71bc02d608e8485f5fbc77`
Upload date	Dec 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Hatch/1.16.2 cpython/3.11.14 HTTPX/0.28.1`

Release files / mplang_nightly-0.1.dev236-py3-none-any.whl

Download URL	mplang_nightly-0.1.dev236-py3-none-any.whl
Size	430.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`ef7409ab5076d2856dc14c8e733d1c3d29d245aad2e76372fbfe3ed753c97e76`
BLAKE2b-256 checksum How to use checksums	`7658c1067d7d35794c09b7b33cdafb59e517ff1fab288db9d2edd730cce0168e`
Upload date	Dec 10, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Hatch/1.16.2 cpython/3.11.14 HTTPX/0.28.1`