# VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture

arXiv: https://arxiv.org/abs/2509.22218 · HTML: https://mlsanity.com/paper/2509.22218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 7 of 7)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study](https://mlsanity.com/paper/2404.17136/) (2024)
- [Text Understanding from Scratch](https://mlsanity.com/paper/1502.01710/) (2015)
- [Large Language Model Enhanced Text-to-SQL Generation: A Survey](https://mlsanity.com/paper/2410.06011/) (2024)
- [PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback](https://mlsanity.com/paper/2502.00988/) (2025)
- [Natural Language Interfaces to Data](https://mlsanity.com/paper/2212.13074/) (2022)
- [Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities](https://mlsanity.com/paper/2505.23838/) (2025)

## Abstract (arXiv)

Data visualization is essential for interpreting complex datasets, yet traditional tools often require technical expertise, limiting accessibility. VizGen is an AI-assisted graph generation system that empowers users to create meaningful visualizations using natural language. Leveraging advanced NLP and LLMs like Claude 3.7 Sonnet and Gemini 2.0 Flash, it translates user queries into SQL and recommends suitable graph types. Built on a multi-agent architecture, VizGen handles SQL generation, graph creation, customization, and insight extraction. Beyond visualization, it analyzes data for patterns, anomalies, and correlations, and enhances user understanding by providing explanations enriched with contextual information gathered from the internet. The system supports real-time interaction with SQL databases and allows conversational graph refinement, making data analysis intuitive and accessible. VizGen democratizes data visualization by bridging the gap between technical complexity and user-friendly design.