ArticleFrontiers in artificial intelligence2025
Multi-agent systems powered by large language models: applications in swarm intelligence.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Ivy Optimization Algorithm Combining Sine-Cosine Operator and Adaptive T-Distribution and Its Engineering Application.Biomimetics (Basel, Switzerland) · 2026Article
- A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis.Methods and protocols · 2026Review
- Swarm Intelligence in Drug Discovery Applications: Unlocking Deeper Insights on the Identification and Optimization of Potential Drug Candidates.Drug design, development and therapy · 2026Review
- Editorial: Advanced integration of large language models for autonomous systems and critical decision support.Frontiers in artificial intelligence · 2026Article
- Ethical issues in multi-agent AI systems for healthcare: a narrative review.Frontiers in public health · 2026Review
- Blueprint2Code: a multi-agent pipeline for reliable code generation via blueprint planning and repair.Frontiers in artificial intelligence · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.