Memgraph

Memgraph is a high-performance, in-memory graph database that powers real-time AI context and graph analytics at scale.

Vector search finds what's similar. Graph reasoning finds what's connected — following relationships, dependencies, and hierarchies that similarity alone can't capture. Modern AI systems need both, and Memgraph is the graph layer - surfacing precise structural context with full audit trails in sub-millisecond time.

It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows — a single high-performance layer for any system that needs structured, connected context. The same in-memory architecture drives real-time graph analytics for fraud detection, network analysis, infrastructure monitoring, and other operational workloads where milliseconds matter.

NASA uses Memgraph to connect people, skills, and projects across the agency into a queryable knowledge graph that powers real-time expert discovery and workforce planning. Cedars-Sinai uses it to link genes, drugs, and clinical pathways in an Alzheimer's knowledge graph spanning over 230,000 entities that drives drug repurposing research and multi-hop biomedical reasoning. Organizations across cybersecurity, finance, retail, and other knowledge-intensive domains rely on Memgraph for the same reason: sub-millisecond graph traversals for the structured context and real-time insight that modern systems demand.

Keywords: in-memory graph database, real-time analytics, graph computation, streaming data, fraud detection, cybersecurity, network analysis, knowledge graphs, openCypher, ACID transactions, data relationships, GraphRAG, vector search, MAGE, Dominik Tomicevic, Marko Budiselic, real-time graph applications, enterprise AI, supply chain optimization, data exploration, identity and access management, recommendation engines, developer tools