@article{60897,
  abstract     = {{<jats:p>This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a toolchain that integrates LLMs with the NetLogo simulation platform, leveraging its Python extension to enable communication with GPT-4o via the OpenAI API. This toolchain facilitates prompt-driven behavior generation, allowing agents to respond adaptively to environmental data. For both example applications mentioned above, we employ both structured, rule-based prompts and autonomous, knowledge-driven prompts. Our work demonstrates how this toolchain enables LLMs to study self-organizing processes and induce emergent behaviors within multi-agent environments, paving the way for new approaches to exploring intelligent systems and modeling swarm intelligence inspired by natural phenomena. We provide the code, including simulation files and data at <jats:ext-link>https://github.com/crjimene/swarm_gpt</jats:ext-link>.</jats:p>}},
  author       = {{Jimenez-Romero, Cristian and Yegenoglu, Alper and Blum, Christian}},
  issn         = {{2624-8212}},
  journal      = {{Frontiers in Artificial Intelligence}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Multi-agent systems powered by large language models: applications in swarm intelligence}}},
  doi          = {{10.3389/frai.2025.1593017}},
  volume       = {{8}},
  year         = {{2025}},
}