MCP Crew AI is a developer-focused framework that simplifies the creation and coordination of GPT-based AI agents in collaborative teams. By defining manager, worker, and monitor agent roles, it automates task delegation, execution, and oversight. The package offers built-in support for OpenAI’s API, a modular architecture for custom agent plugins, and a CLI for running and monitoring your Crew. MCP Crew AI accelerates multi-agent system development, making it easier to build scalable, transparent, and maintainable AI-driven workflows.

DEf-MARL (Decentralized Execution Framework for Multi-Agent Reinforcement Learning) provides a robust infrastructure to execute and train cooperative agents without centralized controllers. It leverages peer-to-peer communication protocols to share policies and observations among agents, enabling coordination through local interactions. The framework integrates seamlessly with common RL toolkits like PyTorch and TensorFlow, offering customizable environment wrappers, distributed rollout collection, and gradient synchronization modules. Users can define agent-specific observation spaces, reward functions, and communication topologies. DEf-MARL supports dynamic agent addition and removal at runtime, fault-tolerant execution by replicating critical state across nodes, and adaptive communication scheduling to balance exploration and exploitation. It accelerates training by parallelizing environment simulations and reducing central bottlenecks, making it suitable for large-scale MARL research and industrial simulations.

LobeChat provides a unified chat UI for popular large language models, allowing users to switch between ChatGPT, Claude, Gemini and more without leaving the platform. It features cloud-based message synchronization, custom assistant creation, and a plugin framework to extend functionality—covering text, image, video and voice AI tasks. With built-in workflow automation and multi-modal support, users can automate repetitive tasks, enhance creativity and manage diverse AI agents in one place.

multi_agent_celar is designed as a modular AI platform enabling emergent-language communication among multiple intelligent agents in simulated environments. Users can define agent behaviors via policy files, configure environment parameters, and launch coordinated training sessions where agents evolve their own communication protocols to solve cooperative tasks. The framework includes evaluation scripts, visualization tools, and support for scalable experiments, making it ideal for research on multi-agent collaboration, emergent language, and decision-making processes.