Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation for decision-making, with the aim of making the resulting decisions faithfully explainable to and contestable by humans. Here we propose a web-based system implementing ArgLLM-empowered agents for binary tasks. ArgLLM-App supports visualisation of the produced explanations and interaction with human users, allowing them to identify and contest any mistakes in the system's reasoning. It is highly modular and enables drawing information from trusted external sources. ArgLLM-App is publicly available at https://argllm.app, with a video demonstration at https://youtu.be/vzwlGOr0sPM.
@article{arxiv.2602.24172,
title = {ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models},
author = {Adam Dejl and Deniz Gorur and Francesca Toni},
journal= {arXiv preprint arXiv:2602.24172},
year = {2026}
}