Knowledge Graphs with Memgraph
Your data has relationships. Make them queryable.
A knowledge graph structures entities and their connections into a format that machines — and people — can reason over. Not flat tables. Not disconnected documents. A web of relationships you can traverse, query, and build on.
One knowledge graph. Two workloads.
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.
Why Memgraph.
From data to knowledge graph — fast.
Convert PDFs, DOCX, and TXT into a connected knowledge graph. Parses documents, chunks content, and uses LightRAG to extract entities and relationships automatically.
Migrate MySQL or PostgreSQL into a graph. Analyzes your schema, generates an optimized graph model using HyGM, and migrates data while maintaining referential integrity.
Migrate from Neo4j using Cypher compatibility, Bolt protocol, and Memgraph's built-in migration module — stream data directly with a single Cypher query.
Part of the open-source Memgraph AI Toolkit. From raw data to queryable knowledge graph in minutes, not months. View AI Toolkit on GitHub →
GraphRAG JumpStart programme.
Trusted in production.
