Transform raw data into structured, connected knowledge, queryable, traversable, and ready to power both AI systems and real-time analytics.
A knowledge graph structures entities and their connections into a format that machines, and people, can reason over. Multi-hop relationships, entity hierarchies, temporal chains, and traceable reasoning paths that vector databases cannot represent.
Knowledge graphs are what GraphRAG, AI memory, and agentic systems traverse. They provide the structured context that vector search alone can't deliver - multi-hop relationships, entity hierarchies, temporal chains, and traceable reasoning paths.
Query your knowledge graph directly for pattern detection, entity resolution, community discovery, and impact analysis. In-memory architecture handles complex traversals across large graphs without batch processing.
Sub-millisecond traversals. Native vector search. Real-time graph updates. Built for knowledge graphs at production scale.
How knowledge graphs enhance LLM responses, which tools and techniques to use, and how to build GraphRAG systems with Memgraph, all in one document.
Part of the open-source Memgraph AI Toolkit. From raw data to queryable knowledge graph in minutes, not months.
A structured engagement with the Memgraph engineering team to build a production-ready GraphRAG pipeline using your data, schema, and retrieval needs.
“Memgraph gave us a more cost-effective way to build on the graph capabilities we already knew, with a minimal learning curve for our Python and R team.”
“Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
“Being in memory, Memgraph is fast and really performant. We score 3.5 million-plus clients daily, and the entire infrastructure runs start to end in two hours on average.”
