Ever wanted to build a real-time multiplayer game where AI agents not only make decisions but also “chat” through distinct planning, reasoning and execution phases? That’s exactly what the Multi-Context Protocol (MCP) does: wire up modular agent logic in Python (using CrewAI and FastAPI) and hot-swap LLMs—GPT-4, Claude, Mixtral—on the fly, no code rewrites required.
On top of that, MCP lets you run live A/B tests by swapping models mid-game and collecting structured metrics (planning time, replanning frequency, context-switch impact, etc.), so you can benchmark LLMs beyond mere latency or token counts. We’ll dive into the open-source repo, break down the architecture, and show how this protocol-based design can tame spaghetti orchestration in any multi-agent system.
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