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🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code support via MCP protocol. Ranked #1 in agent-based frameworks.
A research toolkit for particle swarm optimization in Python
A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)
🐝 The First Graph Agentic Framework with RL and Prompt Optimization
OpenHuFu is an open-sourced data federation system to support collaborative queries over multi databases with security guarantee.
EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.
Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm
Neural Architecture Search Powered by Swarm Intelligence 🐜
Python microframework for building nature-inspired algorithms. Official docs: https://niapy.org
A (still growing) paper list of Evolutionary Computation (EC) published in some (rather all) top-tier (and also EC-focused) journals and conferences. For EC-focused publications, only Parallel/Distributed EC are covered in the current version.
Swarming algorithms like PSO, Ant Colony, Sakana, and more in PyTorch 😊
The goal of this repository is to introduce a new, customizable, scalable, and fully opensource mobile robot platform, called SMARTmBOT. This repository provides a guide, and all design files and source codes so that you can build your own SMARTmBOT. SMARTmBOT can be useful for studying the basics of robotics, especially mobile robotics. It can also be used to study advanced topics such as swarm robotics.
This is a Boids Simulation, written in Python with Pygame.
Python implementation of QBSO-FS : a Reinforcement Learning based Bee Swarm Optimization metaheuristic for Feature Selection problem.
Implementation of Firefly Algorithm in Python
Advanced Agentic Development Environment Supporting Devpods, Github Codespaces, Google Cloud Shell, and more! Features 600+ AI agents, Claude Flow, SPARC methodology, and automatic context loading! Deploy intelligent multi-agent swarms, coordinate autonomous workflows.
Digimon Engine — Multi-Agent, Multi-Player Framework for AI-Native Games and Agentic Metaverse
Solving Travelling Salesman Problem using Ant Colony Optimization
In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html