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Enable topic-based inter-agent messaging, question routing, and collaborative responses via RabbitMQ.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-mesh-mcp" yet — see the docs or source repo.
Using agent-mesh-mcp, design a multi-agent Q&A flow where a research assistant sends the question “find the latest vector database options” to research.topic, a research agent answers it, and the result is returned to the main agent. Provide topic design, agent responsibilities, and sample message formats.
A multi-agent communication design with topic routing, message schemas, agent roles, and return flow.
Explain how to use agent-mesh-mcp to send code review questions to the code.review topic and deployment check questions to the ops.check topic, with different agents consuming and answering them. Include recommended topic naming conventions and retry strategies.
A topic-based task routing plan including naming conventions, consumer logic, and retry handling.
Design an agent collaboration protocol for agent-mesh-mcp that supports asking, answering, timeouts, failure notifications, and correlation ID tracking. Provide field definitions and explain how a main agent aggregates answers from multiple sub-agents.
A practical collaboration protocol draft with message fields, state transitions, and aggregation strategy.
Register, discover, and delegate tasks across agents via MCP.
Manage RabbitMQ brokers, authentication, and mutative admin operations through APIs.
Enable AI agents to communicate, route messages, and collaborate through MCP.
Manage RabbitMQ clusters, queues, exchanges, users, and messages with natural language.
Enable AI coding agents to communicate, share state, and coordinate work in real time.
Run deterministic agent orchestration with task decomposition, subagents, and review feedback.