Bridging the Prototype-Production Gap: A Multi-Agent System for Notebooks Transformation

TL;DR

This paper introduces Codelevate, a multi-agent system that automatically converts Jupyter notebooks into production-quality Python code, addressing the challenge of transitioning from exploratory to maintainable software.

Contribution

It presents a novel multi-agent system that autonomously transforms notebooks into structured, production-ready code, enhancing software engineering practices for data science workflows.

Findings

Improves code quality metrics significantly

Maintains computational semantics during transformation

Bridges the gap between prototype and production code

Abstract

The increasing adoption of Jupyter notebooks in data science and machine learning workflows has created a gap between exploratory code development and production-ready software systems. While notebooks excel at iterative development and visualization, they often lack proper software engineering principles, making their transition to production environments challenging. This paper presents Codelevate, a novel multi-agent system that automatically transforms Jupyter notebooks into well-structured, maintainable Python code repositories. Our system employs three specialized agents - Architect, Developer, and Structure - working in concert through a shared dependency tree to ensure architectural coherence and code quality. Our experimental results validate Codelevate's capability to bridge the prototype-to-production gap through autonomous code transformation, yielding quantifiable improvements in code quality metrics while preserving computational semantics.