# Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence

arXiv: https://arxiv.org/abs/2503.03800 · HTML: https://mlsanity.com/paper/2503.03800/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 14 of 14)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory](https://mlsanity.com/paper/2305.17144/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Collaborating with language models for embodied reasoning](https://mlsanity.com/paper/2302.00763/) (2023)
- [Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence](https://mlsanity.com/paper/2410.11163/) (2024)
- [Exploring the Intersection of Large Language Models and Agent-Based Modeling via Prompt Engineering](https://mlsanity.com/paper/2308.07411/) (2023)
- [Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design](https://mlsanity.com/paper/2312.01059/) (2023)
- [LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs](https://mlsanity.com/paper/2410.11387/) (2024)
- [SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents](https://mlsanity.com/paper/2404.09331/) (2024)
- [Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks](https://mlsanity.com/paper/2212.08484/) (2022)

## Built on by (most cited first, 14 of 14)

- [MAGPIE: A benchmark for Multi-AGent contextual PrIvacy Evaluation](https://mlsanity.com/paper/2510.15186/) (2025)
- [Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control](https://mlsanity.com/paper/2508.05702/) (2025)
- [From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation](https://mlsanity.com/paper/2507.19364/) (2025)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges](https://mlsanity.com/paper/2510.09404/) (2025)
- [HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution](https://mlsanity.com/paper/2509.00189/) (2025)
- [Agentic Inequality](https://mlsanity.com/paper/2510.16853/) (2025)
- [DAO-Agent: Zero Knowledge-Verified Incentives for Decentralized Multi-Agent Coordination](https://mlsanity.com/paper/2512.20973/) (2025)
- [From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence](https://mlsanity.com/paper/2508.15447/) (2025)
- [Three AI-agents walk into a bar . . . . `Lord of the Flies' tribalism emerges among smart AI-Agents](https://mlsanity.com/paper/2602.23093/) (2026)
- [On Theoretically-Driven LLM Agents for Multi-Dimensional Discourse Analysis](https://mlsanity.com/paper/2602.13713/) (2026)
- [LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain](https://mlsanity.com/paper/2509.00510/) (2025)
- [When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets](https://mlsanity.com/paper/2512.04988/) (2025)
- [Multi-Agentic Approach for History Matching of Oil Reservoirs](https://mlsanity.com/paper/2605.15028/) (2026)

## Abstract (arXiv)

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 https://github.com/crjimene/swarm_gpt.