MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization

MultiVis-Agent is a multi-agent framework with explicit logic rules for reliable, comprehensive cross-modal data visualization, supporting inputs such as natural language, code, and images. This repository contains both the research artifact for the SIGMOD 2026 paper and the interactive demo artifact presented at SIGMOD 2026 Demo. It also provides a web-based demonstration and video presentation of MultiVis-Agent, MultiVis-Bench, a benchmark for text-to-vis and visualization modification tasks, and an automatic metric suite for visualization quality.

Publications

SIGMOD 2026 (Research Track): MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization
SIGMOD 2026 (Demonstration Track): Demonstration of MultiVis-Agent: Interactively Generating Reliable Visualizations via Logic-Rule Agents

Demonstration

https://github.com/user-attachments/assets/9f27ceab-fde1-417a-bf1a-fc88bf8c8881

Repository Overview

MultiVis-Agent/: Core multi-agent system (coordinator, tool manager, config, database/query and validation agents).
MultiVis-Agent_demo/: Front-end of multi-agent system.
MultiVis-Bench/: Benchmark datasets and reference implementations for text-to-vis and vis-modify tasks.
metric/: Visualization evaluation metrics used in the paper.
run_system.py: Example entry script to run the multi-agent visualization system on a sample.
run_metric.py: Script to compute metrics over saved results.

Installation

Set up environment

git clone https://github.com/Jinwei-Lu/MultiVis.git
cd MultiVis
pip install -r requirements.txt

Configure LLM APIs

Edit MultiVis-Agent/utils/Config.py, MultiVis-Agent_demo/vis_system/utils/Config.py and replace the placeholder "xxx" values in MODEL_CONFIGS with your own API keys and endpoints.

Quick Start

Run a sample visualization generation case

python run_system.py

The script creates log and temporary folders and runs a sample item through the CoordinatorAgent, logging intermediate steps and final visualization code.

Run Front-end of multi-agent system

cd MultiVis-Agent_demo
python app.py

The script starts the frontend on a local port and users can interact with multi-agent system on the web page.

Evaluate generated results

Organize your results under ./results/{method_type}/{model_type}/{data_type}/results.json following the structure in run_metric.py, then run:

python run_metric.py

This produces metric.json, wrong_results.json, and correct_results.json for each data type.

Benchmark Data

MultiVis-Bench provides benchmark files:

text2vis.json, vis_modify.json, text2vis_with_img.json, text2vis_with_code.json under MultiVis-Bench/.
database/ and img/ subdirectories with databases and images used in benchmark tasks.
For the databases in MultiVis-Bench/database/, download the Spider dataset from Google Drive and extract all database files into the MultiVis-Bench/database/ directory.

License

Source code in this repository is released under the MIT License. This includes the framework under MultiVis-Agent/, metric scripts, top-level runnable scripts, and Python reference implementations under MultiVis-Bench/code/ and MultiVis-Bench/vis_modify/. See LICENSE.
Benchmark data and annotations distributed with this repository are released under CC BY 4.0. This includes the JSON benchmark files under MultiVis-Bench/ and other benchmark assets distributed with the repository unless otherwise noted. See LICENSE-DATA.
Third-party resources that must be downloaded separately, including the Spider databases placed under MultiVis-Bench/database/, are not covered by the licenses above and remain subject to their original licenses and terms.

Citation

If you find this project useful in your research, please cite:

@article{lu2026multivis,
  author    = {Lu, Jinwei and Song, Yuanfeng and Zhang, Chen and Wong, Raymond Chi-Wing},
  title     = {MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization},
  journal   = {Proc. ACM Manag. Data},
  volume    = {4},
  number    = {1},
  articleno = {56},
  year      = {2026},
  month     = {feb},
  publisher = {ACM},
  doi       = {10.1145/3786670}
}

@inproceedings{lu2026demonstration,
  title={Demonstration of MultiVis-Agent: Interactively Generating Reliable Visualizations via Logic-Rule Agents},
  author={Lu, Jinwei and Lu, Jiawei and Zhang, Chen and Song, Yuanfeng and Wong, Raymond Chi-Wing},
  booktitle={Companion of the 2026 International Conference on Management of Data},
  year={2026}
}

MultiVis

Install / Use

README