AgoraSim: A Hybrid Agent-Based Modeling Framework
Quick Answer
AgoraSim is a hybrid agent-based modeling framework that facilitates scenario-oriented social reaction analysis by integrating various agent types, including LLMs and classical agents.
Quick Take
AgoraSim is a hybrid agent-based modeling framework that facilitates scenario-oriented social reaction analysis by integrating various agent types, including LLMs and classical agents. It allows users to compare simulation outputs with classical dynamics, providing a structured decision object for consistent interaction and metrics. The framework is accessible via a local UI, Python SDK/CLI, and REST API for enhanced user inspection and validation.
Key Points
- AgoraSim resolves textual artifacts into editable agent-based model configurations.
- It runs ratio-controlled populations mixing LLM, vision-language, and classical agents.
- Users can compare simulation scenarios against matched classical reference dynamics.
- The framework emits a shared structured decision object for consistent metrics.
- Accessible via local UI, Python SDK/CLI, and REST API for user interaction.
Paper Resources
📖 Reader Mode
~2 min readAbstract:LLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics. We present AgoraSim, a hybrid agent-based modeling framework for scenario-oriented social reaction analysis. AgoraSim resolves textual or multimodal artifacts into editable ABM configurations, runs ratio-controlled populations that mix LLM, vision-language, custom-endpoint, random, and classical agents, and compares the same scenario against matched classical reference dynamics. All agents emit a shared structured decision object, enabling common action spaces, interaction protocols, metrics, and audit records. Exposed through a local UI, Python SDK/CLI, and REST API, AgoraSim helps users inspect scenario trajectories, compare modeling assumptions, and identify cases that warrant empirical validation.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.05999 [cs.AI] |
| (or arXiv:2607.05999v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05999 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chung-Chi Chen [view email]
[v1]
Tue, 7 Jul 2026 08:37:17 UTC (2,920 KB)
— Originally published at arxiv.org
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