MCP (Multi-Context Protocol) lets you turn AI agents into modular world-builders—splitting behaviors like planning, reasoning and execution into swappable contexts—and wire them up in real time multiplayer games using Python, CrewAI and FastAPI. You can hot-swap LLMs (GPT-4, Claude, Mixtral) on the fly and immediately see how changing models reshapes agent play, without rewriting your core code.
On top of that, you get built-in A/B testing: switch models mid-game and capture structured metrics (planning time, replanning frequency, context-switch impact) to benchmark performance beyond raw token counts or latency. The open-source repo walkthrough shows how this protocol-first design cleans up “spaghetti” orchestration and levels up any LLM-powered multi-agent system.
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