The Agent Communication Protocol (ACP) is a formal framework designed to enable seamless interaction among autonomous AI agents. ACP specifies a set of message types, headers, and payload conventions, along with agent discovery and registry mechanisms. It supports conversation tracking, version negotiation, and standardized error reporting. By providing language-agnostic JSON schemas and transport-agnostic bindings, ACP reduces integration complexity and allows developers to compose scalable, interoperable multi-agent systems for use in customer service bots, robotic swarms, IoT orchestration, and collaborative AI workflows.

AI Agent Marketplace is built with Next.js and React to provide a centralized hub where users can browse, evaluate, and deploy a wide range of AI agents. The platform pulls agent metadata from community contributions, offering detailed descriptions, capability tags, and live in-browser demos. Users can filter agents by domain, function, or technology provider. For developers, the open-source repository includes a modular architecture with support for adding new agent entries, configuring API endpoints, and customizing UI components. Deployment options include hosting on Vercel or local Docker containers. By consolidating disparate AI agent projects into one searchable interface, the marketplace accelerates experimentation, collaboration, and integration into production workflows.

AI Agent Marketplace is a community-driven directory for AI agents, allowing developers, researchers, and enthusiasts to discover, evaluate, and contribute agents. Users can filter agents by category, view detailed functionality and integration instructions, and submit their own agents via pull requests. The platform aggregates metadata, links, and examples for each agent, making it easier to compare capabilities and find the right tool for specific use cases.

Multi-Agent Coordination provides a lightweight API to define AI agents, register them with a central coordinator, and dispatch tasks for collaborative problem solving. It handles message routing, concurrency control, and result aggregation. Developers can plug in custom agent behaviors, extend communication channels, and monitor interactions through built-in logging and hooks. This framework simplifies the development of distributed AI workflows, where each agent specializes in a subtask and the coordinator ensures smooth collaboration.