🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

🐫 CAMEL: Finding the Scaling Law of Agents. The first and the best multi-agent framework. https://www.camel-ai.org

🔍 An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT)

PraisonAI is a production-ready Multi AI Agents framework, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.

Harness LLMs with Multi-Agent Programming

A fast and lightweight framework for creating decentralized agents with ease.

Python implementation of a bunch of multi-robot path-planning algorithms.

A community-driven AI automation framework that builds upon the incredible work of the open source community. Our goal is to combine language models with specialized tools for tasks like web search, crawling, and Python code execution, while giving back to the community that made this possible.

KaibanJS is a JavaScript-native framework for building and managing multi-agent systems with a Kanban-inspired approach.

🏝️ OASIS: Open Agent Social Interaction Simulations with One Million Agents. https://oasis.camel-ai.org

Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail.

Resources of our paper "FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces". New versions in the making!

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for better understanding. ⭐ experience the forefront of progress in artificial intelligence with this repository!

灰狼优化算法(GWO)路径规划/轨迹规划/轨迹优化、多智能体/多无人机航迹规划

Build high-performance AI models with modular building blocks

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

Let's reproduce paper simulations of multi-robot systems, formation control, distributed optimization and cooperative manipulation.