#Multi-Agent

ChatArena offers a flexible, user-friendly library for developing and researching autonomous LLM agents through multi-agent language games. It supports Markov Decision Processes and features intuitive Web UI and CLI for easy interaction. Comprehensive documentation facilitates custom environment creation, making it a valuable tool for researchers and developers to test and train models.

Swarms orchestrates agents to automate real-world tasks, offering Python 3.10+ support and API integration. Its CLI onboarding ensures efficient deployment in fields like finance and logistics. Discover its documentation for effective implementation.

SMARTS is a simulation platform designed for multi-agent reinforcement learning and autonomous driving research. Created by Huawei Noah's Ark Lab, it emphasizes realistic and varied interactions, forming part of the broader XingTian RL platform suite. Suitable for researchers and developers focusing on autonomous driving advancements, SMARTS facilitates extensive experiments and learning in complex settings. Being open-source, it encourages community involvement and innovation. Detailed documentation is available for further insights into its features and application.

The project facilitates advanced research in multi-agent reinforcement learning using scalable JAX-based algorithms, enabling efficient parallel execution on various devices. It supports prominent MARL frameworks like CTDE and DTDE and includes adaptable environment wrappers for diverse tasks such as robotics and foraging. Mava ensures precise evaluation with comprehensive JSON logging, aiding in detailed analysis and performance optimization across different hardware environments. The open-source model supports community contributions and seamless integration into complex research workflows.