This repository aggregates a comprehensive collection of multi-agent system components and distributed AI coordination techniques. It provides implementations of consensus algorithms, contract net negotiation protocols, auction-based task allocation, coalition formation strategies, and inter-agent communication frameworks. Users can leverage built-in simulation environments to model and test agent behaviors under varied network topologies, latency scenarios, and failure modes. The modular design allows developers and researchers to integrate, extend, or customize individual coordination modules for applications in robotics swarms, IoT device collaboration, smart grids, and distributed decision-making systems.

This repository provides a hands-on demonstration of two-way communication between agents built on the JADE framework. It includes example Java classes showing agent setup, FIPA-ACL compliant message creation, and asynchronous behavior handling. Developers can study Agent A sending a REQUEST, Agent B processing the request, and returning an INFORM message. The code illustrates registering agents with the Directory Facilitator, using cyclic and one-shot behaviors, applying message templates to filter messages, and logging conversation sequences. It’s an ideal starting point for prototyping multi-agent exchanges, custom protocols, or integrating JADE agents into larger distributed AI systems.

A2A (Agent-to-Agent Architecture) is a Google open-source framework enabling the development and operation of distributed AI agents working together. It offers modular components to define agent roles, communication channels, and shared memory. Developers can integrate various LLM providers, customize agent behaviors, and orchestrate multi-step workflows. A2A includes built-in monitoring, error management, and replay capabilities to trace agent interactions. By providing a standardized protocol for agent discovery, message passing, and task allocation, A2A simplifies complex coordination patterns and enhances reliability when scaling agent-based applications across diverse environments.