Your data has relationships. Make them queryable.
Transform raw data into structured, connected knowledge, queryable, traversable, and ready to power both AI systems and real-time analytics.
No disconnected data. A web of relationships.
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.
One knowledge graph. Two workloads.
Foundation for AI
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.
Real-time analytics
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.
Why Memgraph.
Sub-millisecond traversals. Native vector search. Real-time graph updates. Built for knowledge graphs at production scale.
Sub-millisecond traversals
Deep relationship queries without latency, whether for AI pipelines or analyst-driven pattern detection.
Real-time updates
Concurrent writes so your graph reflects reality as it changes.
Native vector search
Semantic similarity and structural traversal in one engine. One query, one database.
Scale to production
100 GB to 4 TB. 1,000+ tx/sec. ACID with persistence. Enterprise security and multi-tenancy.
From data to knowledge graph, fast.
Part of the open-source Memgraph AI Toolkit. From raw data to queryable knowledge graph in minutes, not months.
Unstructured data
Unstructured2Graph
Convert PDFs, DOCX, and TXT into a connected knowledge graph. Parses documents, chunks content, and uses LightRAG to extract entities and relationships automatically.
Relational databases
SQL2Graph
Migrate MySQL or PostgreSQL into a graph. Analyzes your schema, generates an optimized graph model using HyGM, and migrates data while maintaining referential integrity.
Another graph database
Neo4j Migration
Migrate from Neo4j using Cypher compatibility, Bolt protocol, and Memgraph's built-in migration module - stream data directly with a single Cypher query.
GraphRAG JumpStart programme.
A structured engagement with the Memgraph engineering team to build a production-ready GraphRAG pipeline using your data, schema, and retrieval needs.
Trusted in production
What teams are building with Memgraph
“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.”
David Meza, NASA
“Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
Jason H. Moore, Cedars-Sinai
“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.”
Derick Schmidt, Capitec Bank