Knowledge graphs & GraphRAG

Ground your AI in facts and the relationships between them — graph, vector and full-text search in one store.

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A knowledge graph in GreyCat: entities linked by their relationships

The problem

RAG built on vector similarity alone loses the relationships between entities — and stitching a graph DB, vector DB and keyword index together is brittle and slow.

How GreyCat solves it

One store

Graph, vector and full-text search live together — no separate databases to sync.

Hybrid retrieval

Combine entities, relationships and embeddings in a single query.

Agents query directly

A built-in MCP server lets AI agents reach the graph with no glue code.

Point-in-time-correct retrieval that grounds LLM answers in the facts and the connections between them.

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