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A year after GraphRAG left the lab, one statistic holds: it works where corporate information has dense relational structure, fails where there are only loose documents. Patterns, ingestion costs, and architectural decisions that have survived a year of real deployment.
For a decade, knowledge graphs were an academic idea with few real use cases, held back by the cost of building and maintaining the schema. LLMs have changed that equation: they now extract entities automatically and help anchor answers, audit reasoning, and support agents without hallucinating.
Graph RAG layers an explicit graph of entities and relationships on top of retrieval, so a question can be answered by traversing connections rather than by vector similarity alone. Microsoft Research published the GraphRAG paper in April 2024 and open-sourced the code that July. Lighter variants such as LightRAG and HippoRAG followed, running on Neo4j or Memgraph.
