Multi-agent frameworks are gaining popularityamong the research community as they provide efficient andscalable tools for modelling distributed and/or social systems,enabling to simulate and investigate the behaviour of complexsystems in a wide range of applications (such as road traffic,crowd evacuation, disease spreading, etc.). Existing MAS frame-works (such as GAMA, MADkit, JADE, etc.) offer stable andwidely used solutions for users that are already experiencedwith distributed systems simulation or the MAS paradigm. Theyare also often dedicated (or primarily designed) for a specificapplication (e.g., MATSim for road traffic). New users cansee the learning curve associated with each framework as anobstacle, especially when they lack the theoretical knowledgeabout computer science or agents and they seek to build and run avery first proof-of-concept simulation. The work presented in thispaper results from an effort towards providing more accessibleMAS simulation tools, possibly further popularizing their useacross different research fields. This paper introduces PAMELA:a novel generic collaborative open-source MAS framework thataims at being light, beginner-friendly, and that allows for fastprototyping through assisted scenario generation and powerfulconfiguration. The tool can work with or without (for fastersimulations) the integrated graphical user interface (designed forboth testing and visualization). To make is more attractive to newprogrammers and to enable an easier interfacing with trendingmachine learning frameworks, PAMELA is entirely written inPython and only relies on standard libraries. This choice makesit possible to make it a multi-platform tool that is easy todeploy and maintain in industry and in the laboratory. After adiscussion of existing MAS platform capabilities and limitations,we will present the general design of PAMELA and then wewill describe its core components. Through two applicationsareas, we will show the potential of PAMELA to quickly andeasily provide running prototypes that could be used as proof-of-concept simulations before building more complex use casesin the same or a more specific MAS framework.