How to Deploy LangGraph + MCP Agents as Stable Production Services

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Developers running LangGraph and Model Context Protocol (MCP) agents locally often face crashes and erratic behavior when moving to production environments. The core challenge lies in structuring the agent as a long-running service capable of handling continuous requests, recovering from failures, and adapting to distributed system realities. Using Python's signal module, developers can implement graceful shutdown handling for SIGTERM and SIGINT signals to prevent abrupt termination. A key production concern is state persistence — the langgraph.Checkpointer class enables periodic saving and restoration of agent state, ensuring recovery after unexpected crashes. The article presents a foundational service pattern as a starting point for building more scalable and resilient agent deployments.

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