Gemini, Neo4j, and MCP Combined to Build Multi-Hop Reasoning AI Agents

Developers are moving beyond standard Retrieval-Augmented Generation (RAG), which relies on vector search, to address its limitations in handling complex, multi-step relational queries. The proposed architecture combines Google's Gemini as a reasoning engine, Neo4j as a structured knowledge graph, and the Model Context Protocol (MCP) as a standardized interface between tools. Unlike vector databases that match semantic similarity, graph databases store explicit typed relationships between entities, enabling multi-hop traversal across connected data points. This allows an AI agent to actively query a knowledge graph, evaluate results, and chain evidence together rather than simply retrieving loosely related text chunks. The approach targets real-world use cases such as identifying customer exposure to vendor outages or tracing dependencies in complex systems.