MCMAS : a toolkit for developing agent-based simulations on many-core architectures Laville, G. Lang, C. Herrmann, B. Philippe, L. Mazouzi, K. /Marilleau, Nicolas Multi-agent systems parallel computing GPGPU many-core Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generic toolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. The toolkit provides few famous algorithms as diffusion, path-finding or population dynamics that are frequently used in an agent based models. For further needs, MCMAS is based on a flexible architecture that can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with three models and their performance analysis. 2015 text https://www.documentation.ird.fr/hor/fdi:010068858 fdi:010068858 Laville G., Lang C., Herrmann B., Philippe L., Mazouzi K., Marilleau Nicolas. MCMAS : a toolkit for developing agent-based simulations on many-core architectures. 2015, 11 (1), 15-31 EN