Kernel-FFI is a transparent, language-agnostic framework that enables seamless cross-language function calls and object manipulation within interactive notebooks. It allows you to call functions, access variables, and work with objects across different programming languages as if they were native to your current language.


Kernel-FFI solves the challenge of multi-language development in interactive environments like Jupyter Notebooks. Traditional approaches require extensive manual configuration, boilerplate code, and often lack support for modern programming constructs like object-oriented programming and recursive calls.

- **Transparent Cross-Language Calls**: Call functions in other languages as if they were native

- **Object-Oriented Programming Support**: Create and manipulate objects across language boundaries

- **No Boilerplate Code**: Automatic source-level transformation eliminates manual bindings

- **Interactive Development**: Works seamlessly with Jupyter's dynamic, exploratory programming model

- **Language Agnostic**: Supports multiple programming languages with different paradigms

Kernel-FFI supports a wide range of programming languages:

- **Python**: Data science, machine learning, web development

- **JavaScript/TypeScript**: Web frontend/backend, Node.js applications

- **Julia**: Scientific computing, multiple dispatch

- **Ruby**: Web development (Rails), metaprogramming

- **Racket**: Functional programming, DSLs

- **Rust**: Memory safety, systems programming

- **Go**: Cloud services, microservices

- **C++**: Systems programming, performance-critical applications

- **C#**: .NET ecosystem, Windows development

Kernel-FFI can be directly used in CodePod. Just create pods of different languages, and call each other as if they are native!

<img src={require("./assets/codepod-unified-framework.png").default} alt="unified-framework" width="600" />

<img src={require("./assets/simple-loop.png").default} alt="simple-loop" width="800" />

Kernel-FFI automatically discovers and maintains a registry of programming constructs (functions, classes, variables) defined in each kernel. As you add, modify, or remove code, the registry updates to reflect the current state.

When you use a function or object from another language, Kernel-FFI automatically transforms your code into an intermediate representation that can be executed across language boundaries.

The transformed code is executed in the target language kernel, and results are automatically converted back to your native language format.

<img src={require("./assets/objRefSequence.png").default} alt="objRefSequence" width="800" />

Combine Python's data science libraries with Julia's performance or Rust's memory safety:

import numpy as np

from julia import fast_algorithm

data = np.array([1, 2, 3, 4, 5])

result = fast_algorithm(data) # Calls Julia function

Use TypeScript for frontend logic while leveraging Python's backend capabilities:

import { processData } from './python-module';

const userData = { name: "John", age: 30 };

const processed = await processData(userData); // Calls Python function

Combine high-level languages with low-level performance:

use python_ml::{train_model, predict};

let model = train_model(training_data); // Calls Python function

let prediction = predict(model, input); // Calls Python function

- Jupyter Notebook environment

- Supported language kernels installed

- Kernel-FFI framework

1. Install the required language kernels

2. Configure Kernel-FFI in your Jupyter environment

3. Start creating multi-language notebooks

**Host Language (Python):**

def calculate_sum(a, b):

return a + b

class DataProcessor:

def __init__(self, name):

self.name = name

def process(self, data):

return f"Processed {data} by {self.name}"

**Client Language (TypeScript):**

const result = await calculate_sum(5, 3); // Returns 8

const processor = new DataProcessor("TS Client");

const processed = await processor.process("test data");

Kernel-FFI supports full OOP across languages:

- **Object Instantiation**: Create objects in one language and use them in another

- **Method Calls**: Call methods on foreign objects transparently

- **State Management**: Maintain object state across language boundaries

- **Automatic Cleanup**: Resources are automatically managed

Support for complex multi-language workflows:

- **Non-blocking Communication**: Side-channel communication prevents blocking

- **Recursive Function Calls**: Functions can call back to other languages

- **Asynchronous Support**: Handle async operations across languages

Maintains type safety across different language paradigms:

- **Dynamic Languages**: Runtime type checking and conversion

- **Static Languages**: Compile-time type information preservation

- **Automatic Serialization**: Handles complex data structures

Kernel-FFI uses a three-layer architecture:

1. **Source Analysis**: Parses and identifies cross-language usage patterns

2. **Intermediate Representation**: Converts operations to language-agnostic format

3. **Target Execution**: Generates and executes code in target languages

- **Jupyter Protocol**: Leverages existing Jupyter messaging

- **Side-Channel**: HTTP-based communication for non-blocking operations

- **Object Store**: Global registry for managing cross-language objects

- **Reduced Complexity**: No need for manual FFI bindings

- **Increased Productivity**: Focus on logic, not language integration

- **Interactive Development**: Immediate feedback in notebook environment

- **Language Flexibility**: Choose the best language for each task

- **Skill Utilization**: Leverage team members' language expertise

- **Code Reuse**: Share functionality across language boundaries

- **Maintenance**: Single source of truth for cross-language functionality

- **Documentation**: [https://codepod.io/docs/kernel-ffi](https://codepod.io/docs/kernel-ffi)

- **Source Code**: Open-source implementation available

- **Community**: Join discussions and contribute

Kernel-FFI represents a significant advancement in multi-language development for interactive environments. By providing transparent, language-agnostic cross-language function calls, it enables developers to leverage the strengths of different programming languages without the traditional overhead of FFI setup and maintenance.

Whether you're working on data science projects that benefit from multiple language ecosystems, building web applications that span frontend and backend languages, or developing systems that require both high-level expressiveness and low-level performance, Kernel-FFI provides the tools you need to work seamlessly across language boundaries.