AutoML has advanced in handling complex tasks using the integration of LLMs, yet its efficiency remains limited by dependence on specific underlying tools. In this paper, we introduce LightAutoDS-Tab, a multi-AutoML agentic system for tasks with tabular data, which combines an LLM-based code generation with several AutoML tools. Our approach improves the flexibility and robustness of pipeline design, outperforming state-of-the-art open-source solutions on several data science tasks from Kaggle. The code of LightAutoDS-Tab is available in the open repository https://github.com/sb-ai-lab/LADS
@article{arxiv.2507.13413,
title = {LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data},
author = {Aleksey Lapin and Igor Hromov and Stanislav Chumakov and Mile Mitrovic and Dmitry Simakov and Nikolay O. Nikitin and Andrey V. Savchenko},
journal= {arXiv preprint arXiv:2507.13413},
year = {2025}
}