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agent-based-modeling
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Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.
The Simple Agent Development Kit.
turtles, patches, and links for kids, teachers, and scientists
Make social simulations on real maps! Agent-based modeling for the web.
Agent-based modeling framework in Julia
🤖 A comprehensive list of 1,500+ resources and tools related to AI agents.
Learning to trade under the reinforcement learning framework
large population models
Julia and Python complex system applications in ecology, epidemiology, sociology, economics & finance; network science models including Bianconi-Barabási, Barabási-Albert, Watts-Strogatz, Waxman Model & Erdős-Rényi; graph theory algorithms involving Gillespie, Bron Kerbosch, Ramsey, Bellman Ford, A*, Kruskal, Borůvka, Prim, Dijkstra, DSatur, Randomized Distributed, Vizing, Topological Sort, DFS, BFS
AgentPy is an open-source framework for the development and analysis of agent-based models in Python.
Main repository for developing the 1.x versions of GAMA
Mathematical Modeling of Infectious Disease Dynamics
krABMaga: A modern developing art for reliable and efficient Agent-based Model (ABM) simulation with the Rust language
GIS Extension for Mesa Agent-Based Modeling
depthmapX is a multi-platform Spatial Network Analysis Software
User and showcase agent-based models developed using Mesa
TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.
PhysiCell: Scientist end users should use latest release! Developers please fork the development branch and submit PRs to the dev branch. Thanks!
Fast, general-purpose interactive applications for complex systems
The Multi-Agent Reasoning framework creates an interactive chatbot where AI agents collaborate via structured reasoning and Swarm Integration for optimal answers. Simulating a team that discusses, debates, and refines responses, it enables complex problem-solving and precise results. Now with Prompt Caching to reduce latency and costs.