Sistemas Multiagente e Large Language Model: estudo de caso utilizando as ferramentas LM Studio e LangGraph
Resumo
Este artigo apresenta um estudo sobre Sistemas Multiagente (SMA) e os Large Language Models (LLM) em um estudo de caso utilizando a definição de modelagem entre os agentes desse sistema. Para a simulação multiagente foi considerado o grupo dos Produtores do jogo de RPG Gorim, com a interação entre o agente empresário e o agricultor para a negociação de compra e venda de insumos agrícolas. O sistema foi desenvolvido utilizando a linguagem de programação Python utilizando a biblioteca LangGraph para orquestrar a interação entre agentes autônomos impulsionados por um LLM executado localmente via LM Studio.
Referências
Anthis, J. R., Liu, R., Richardson, S. M., Kozlowski, A. C., Koch, B., Evans, J., Brynjolfsson, E., and Bernstein, M. (2025). Llm social simulations are a promising research method. arXiv preprint arXiv:2504.02234.
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? . In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 610–623, New York, NY, USA. Association for Computing Machinery.
Born, M., Aguiar, M., and Adamatti, D. (2023). Modeling organizational level of agents with moise+ to a natural resources management rpg. In The international FLAIRS conference proceedings, volume 36.
Born, M. B. (2022). Modelagem a nível organizacional de agentes em um jogo do tipo RPG: estudo da complexidade de seus personagens e de suas funcionalidades. PhD thesis, Universidade Federal de Pelotas.
Duarte, G. T. and Valente, M. T. (2024). Aplicaçao local e open-source de grandes modelos de linguagem na geraçao de testes de unidade.
Easin, A. M., Sourav, S., and Tamás, O. (2024). An intelligent llm-powered personalized assistant for digital banking using langgraph and chain of thoughts. In 2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY), pages 625–630. IEEE.
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., and Zhang, X. (2024). Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680.
Han, S., Zhang, Q., Yao, Y., Jin, W., and Xu, Z. (2024). Llm multi-agent systems: Challenges and open problems. arXiv preprint arXiv:2402.03578.
Hua, Y., Qu, L., and Haffari, G. (2024). Assistive large language model agents for socially-aware negotiation dialogues. arXiv preprint arXiv:2402.01737.
Jiang, Z., Shi, Y., Li, M., Xiao, H., Qin, Y., Wei, Q., Wang, Y., and Zhang, Y. (2024). Casevo: A cognitive agents and social evolution simulator. arXiv preprint arXiv:2412.19498.
Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1–22.
Piao, J., Yan, Y., Zhang, J., Li, N., Yan, J., Lan, X., Lu, Z., Zheng, Z., Wang, J. Y., Zhou, D., et al. (2025). Agentsociety: Large-scale simulation of llm-driven generative agents advances understanding of human behaviors and society. arXiv preprint arXiv:2502.08691.
Shanahan, M. (2024). Talking about large language models. Commun. ACM, 67(2):68–79.
Shethiya, A. S. (2023). Rise of llm-driven systems: Architecting adaptive software with generative ai. Spectrum of Research, 3(2).
Wooldridge, M. (2009). An introduction to multiagent systems. John wiley & sons.
Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al. (2024). A survey on large language models for recommendation. World Wide Web, 27(5):60.
Ye, Q., Axmed, M., Pryzant, R., and Khani, F. (2023). Prompt engineering a prompt engineer. arXiv preprint arXiv:2311.05661.
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? . In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 610–623, New York, NY, USA. Association for Computing Machinery.
Born, M., Aguiar, M., and Adamatti, D. (2023). Modeling organizational level of agents with moise+ to a natural resources management rpg. In The international FLAIRS conference proceedings, volume 36.
Born, M. B. (2022). Modelagem a nível organizacional de agentes em um jogo do tipo RPG: estudo da complexidade de seus personagens e de suas funcionalidades. PhD thesis, Universidade Federal de Pelotas.
Duarte, G. T. and Valente, M. T. (2024). Aplicaçao local e open-source de grandes modelos de linguagem na geraçao de testes de unidade.
Easin, A. M., Sourav, S., and Tamás, O. (2024). An intelligent llm-powered personalized assistant for digital banking using langgraph and chain of thoughts. In 2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY), pages 625–630. IEEE.
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., and Zhang, X. (2024). Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680.
Han, S., Zhang, Q., Yao, Y., Jin, W., and Xu, Z. (2024). Llm multi-agent systems: Challenges and open problems. arXiv preprint arXiv:2402.03578.
Hua, Y., Qu, L., and Haffari, G. (2024). Assistive large language model agents for socially-aware negotiation dialogues. arXiv preprint arXiv:2402.01737.
Jiang, Z., Shi, Y., Li, M., Xiao, H., Qin, Y., Wei, Q., Wang, Y., and Zhang, Y. (2024). Casevo: A cognitive agents and social evolution simulator. arXiv preprint arXiv:2412.19498.
Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1–22.
Piao, J., Yan, Y., Zhang, J., Li, N., Yan, J., Lan, X., Lu, Z., Zheng, Z., Wang, J. Y., Zhou, D., et al. (2025). Agentsociety: Large-scale simulation of llm-driven generative agents advances understanding of human behaviors and society. arXiv preprint arXiv:2502.08691.
Shanahan, M. (2024). Talking about large language models. Commun. ACM, 67(2):68–79.
Shethiya, A. S. (2023). Rise of llm-driven systems: Architecting adaptive software with generative ai. Spectrum of Research, 3(2).
Wooldridge, M. (2009). An introduction to multiagent systems. John wiley & sons.
Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al. (2024). A survey on large language models for recommendation. World Wide Web, 27(5):60.
Ye, Q., Axmed, M., Pryzant, R., and Khani, F. (2023). Prompt engineering a prompt engineer. arXiv preprint arXiv:2311.05661.
Publicado
29/09/2025
Como Citar
F. JUNIOR, Ulisses G.; BORN, Miriam B.; SANTOS, Agatha C. S.; GROSSMANN, Rodolfo B.; FACKLAMM, João V. S.; CASTILHOS, Vitor A. de; ALVES, Bruno C.; AGUIAR, Marilton S. de.
Sistemas Multiagente e Large Language Model: estudo de caso utilizando as ferramentas LM Studio e LangGraph. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 250-261.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2025.37540.
