#GraphRAG
4 related articles
DeepAgents + Neo4j: A Three-Layer Graph Unifies Agent Memory and Knowledge
Agentic GraphRAG uses Neo4j to build three-layer knowledge, work, and decision graphs with DeepAgents and LangGraph, unifying agent memory and knowledge bases. We analyze its extraction, hybrid retrieval, and citation validation design, and discuss the cost trade-offs of schema-driven approaches.
Teaching the Enterprise Knowledge Graph to Say "I Don't Know": A Citation-First GraphRAG Practice
ZEHN Core builds a citation-first enterprise knowledge graph on FalkorDB, forcing the model to say "I don't know" without citations and grading its own graph paths—reusable engineering practices for trustworthy GraphRAG.
VIDRAFT Open-Sources Six Projects: From GraphRAG to Quantum Error Correction
VIDRAFT open-sources six projects on GitHub: ONGRID for verifiable GraphRAG, FINAL Bench for model metacognition, POCKET for laptop-local inference, ZTC for zero-token answer trust scoring, plus quantum benchmark scoring tools and an aggregated leaderboard.
The GraphRAG Paradox: How Knowledge Graphs Can Avoid Token Collapse
GraphRAG faces Token collapse and latency spikes in production. This article explains why brute-force graph traversal burns money and offers a hybrid architecture: deterministic Cypher queries replace LLM graph traversal, cutting latency by 95% and Token spending by 84% while maintaining multi-hop reasoning accuracy.