Kernel-FFI: Transparent Foreign Function Interfaces for Interactive Notebooks

TL;DR

Kernel-FFI introduces a transparent, language-agnostic framework that simplifies cross-language function calls and object management in interactive notebooks, addressing limitations of existing FFIs with source-level transformation and novel communication mechanisms.

Contribution

It provides automatic source-level rewriting for cross-language calls, supports object-oriented features, and introduces a side-channel for recursive and asynchronous foreign calls in notebooks.

Findings

Enables seamless multi-language interaction in notebooks.

Reduces manual configuration and boilerplate code.

Supports recursive and asynchronous foreign function calls.

Abstract

Foreign Function Interfaces (FFIs) are essential for enabling interoperability between programming languages, yet existing FFI solutions are ill-suited for the dynamic, interactive workflows prevalent in modern notebook environments such as Jupyter. Current approaches require extensive manual configuration, introduce significant boilerplate, and often lack support for recursive calls and object-oriented programming (OOP) constructs-features critical for productive, multi-language development. We present Kernel-FFI, a transparent, language-agnostic framework that enables seamless cross-language function calls and object manipulation within interactive notebooks. Kernel-FFI employs source-level transformation to automatically rewrite cross-language invocations, eliminating the need for manual bindings or boilerplate. Kernel-FFI provides robust support for OOP by enabling foreign object referencing and automatic resource management across language boundaries. Furthermore, to address the blocking nature of Jupyter kernels and support recursive and asynchronous foreign calls, we introduce a novel side-channel communication mechanism. Our tool will be open-sourced and available at https://codepod.io/docs/kernel-ffi