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Python SDK Overview

Status: Work in ProgressThe Python SDK is currently under active development. The API described here reflects the target design. For a production-ready SDK today, see the Rust SDK.
The Stoffel Python SDK provides a clean, high-level interface for integrating Stoffel’s secure Multi-Party Computation capabilities into Python applications. It offers a developer-friendly API that abstracts away cryptographic complexity while maintaining clear semantics for public and secret data.

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Repository

The Python SDK is being developed at: github.com/Stoffel-Labs/stoffel-python-sdk

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Design Philosophy

The SDK is built around two core principles:

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Separation of Concerns

  • StoffelProgram: Handles StoffelLang compilation, VM operations, and execution parameters
  • StoffelClient: Manages MPC network communication, data handling, and result reconstruction

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Explicit Data Visibility

The API makes a clear distinction between:
  • Secret Inputs: Private data that gets secret-shared across MPC nodes
  • Public Inputs: Configuration and parameters visible to all nodes

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Core Components

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StoffelProgram - VM Operations

Responsible for local program management and compilation: 
from stoffel import StoffelProgram

# Create and compile a program
program = StoffelProgram("secure_add.stfl")
program.compile(optimize=True)

# Set execution parameters
program.set_execution_params({
    "computation_id": "secure_addition",
    "function_name": "main",
    "expected_inputs": ["a", "b", "threshold"]
})

# Test locally before MPC execution
result = program.execute_locally({"a": 25, "b": 17})

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StoffelClient - Network Operations

Handles MPC network communication and data management: 
from stoffel import StoffelClient

# Configure MPC network connection
client = StoffelClient({
    "nodes": [
        "http://mpc-node1:9000",
        "http://mpc-node2:9000",
        "http://mpc-node3:9000"
    ],
    "client_id": "client_001",
    "program_id": "secure_addition"
})

# Execute with explicit public/secret inputs
result = await client.execute_with_inputs(
    secret_inputs={"a": 25, "b": 17},      # Private data
    public_inputs={"threshold": 50}        # Public configuration
)

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Quick Start

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Simple MPC Computation

import asyncio
from stoffel import StoffelProgram, StoffelClient

async def main():
    # 1. Program compilation and setup
    program = StoffelProgram("secure_add.stfl")
    program.compile()
    program.set_execution_params({
        "computation_id": "secure_addition",
        "function_name": "main",
        "expected_inputs": ["a", "b"]
    })

    # 2. MPC network client setup
    client = StoffelClient({
        "nodes": ["http://mpc-node1:9000", "http://mpc-node2:9000", "http://mpc-node3:9000"],
        "client_id": "my_client",
        "program_id": "secure_addition"
    })

    # 3. Execute secure computation
    result = await client.execute_with_inputs(
        secret_inputs={"a": 25, "b": 17}
    )

    print(f"Secure computation result: {result}")
    await client.disconnect()

asyncio.run(main())

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Development Status

Current implementation progress:
ComponentStatusNotes
Clean API Design✅ CompleteSeparation of concerns implemented
StoffelProgram🚧 In ProgressCompilation and VM operations
StoffelClient🚧 In ProgressNetwork communication interface
VM Bindings🚧 In ProgressFFI bindings to StoffelVM
MPC Network Integration📋 PlannedAwaiting MPC service infrastructure
Integration Tests📋 PlannedWith actual shared libraries

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Current Alternative: Rust SDK

While the Python SDK is being developed, you can use the fully-functional Rust SDK which provides:
  • Complete compilation and VM execution
  • MPC configuration with HoneyBadger protocol
  • Network-based client/server architecture
  • Local testing capabilities

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Contributing

The Python SDK is open source and contributions are welcome!

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Next Steps