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.
@article{arxiv.2511.07257,
title = {Bridging the Prototype-Production Gap: A Multi-Agent System for Notebooks Transformation},
author = {Hanya Elhashemy and Youssef Lotfy and Yongjian Tang},
journal= {arXiv preprint arXiv:2511.07257},
year = {2025}
}