Build a production-oriented Multi-Agent AI System using LangGraph, Model Context Protocol (MCP), Supervisor Agents, Guardrails, Observability, and Human-in-the-Loop workflows.
This project demonstrates how to move beyond a simple AI chatbot and build a coordinated team of specialized AI agents that can collaborate, use external tools, follow safety policies, and request human approval when required.
Tech Stack : LangGraph — Build stateful, cyclic, and controllable agent workflows. Multi-Agent Architecture — Create specialized agents for different tasks. Supervisor Agent — Dynamically route tasks to the appropriate agent. MCP Integration — Connect agents with reusable MCP tools and external resources. Guardrails — Validate inputs, outputs, and tool usage for safer AI execution. Human-in-the-Loop — Pause workflows and request human approval for sensitive operations. Observability — Track agent decisions, tool calls, execution flow, errors, and performance. Production Patterns — Organize the system for scalability, reliability, and maintainability.
The goal is to provide a hands-on reference architecture for building real-world agentic AI applications where multiple agents collaborate under controlled orchestration, tools are exposed through MCP, risky actions require human approval, and the complete workflow can be monitored and debugged.
This repository is designed for developers who want to understand how modern agentic AI systems are actually structured and implemented.