KAG is a reasoning and retrieval framework that combines OpenSPG knowledge graph engine with LLMs to build Q&A systems for professional domain knowledge bases. It addresses limitations of traditional RAG by using logical form-guided reasoning instead of pure vector similarity. The framework supports multi-hop fact questioning and logical reasoning across structured knowledge, aiming to improve accuracy over standard retrieval approaches.