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

arXiv: https://arxiv.org/abs/2511.07257 · HTML: https://mlsanity.com/paper/2511.07257/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2025.

## Built on by (most cited first, 6 of 6)

- [LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2601.09822/) (2026)
- [LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review](https://mlsanity.com/paper/2604.16321/) (2026)
- [SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows](https://mlsanity.com/paper/2606.08049/) (2026)
- [Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases](https://mlsanity.com/paper/2607.01425/) (2026)
- [Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility](https://mlsanity.com/paper/2602.07195/) (2026)
- [Code Broker: A Multi-Agent System for Automated Code Quality Assessment](https://mlsanity.com/paper/2604.23088/) (2026)

## Abstract (arXiv)

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.