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Wiring the Reasoning Loop: Gemini + Neo4j + MCP for Multi-Hop AI Agents

This story is from 2026-08-25. It is preserved in the archive; the latest stories are on the live feed.

Standard Retrieval-Augmented Generation (RAG) solved the initial LLM hallucination problem by grounding outputs in vector embeddings. However, pure vector search falls short when queries require multi-hop reasoning, structural context, or relational aggregation across disparate data points. Moving…

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  1. 2026-08-25 03:18 · DEV Community — AI
    Wiring the Reasoning Loop: Gemini + Neo4j + MCP for Multi-Hop AI Agents

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