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Vector DBFreemiumOpen Source
MEMGRAPH
In-memory graph database for real-time AI, GraphRAG, and agentic memory
NOASSERTION
ABOUT
AI agents and real-time applications need sub-millisecond graph queries combined with vector search, but traditional disk-based graph databases cannot deliver the low latency required for interactive AI experiences. Memgraph solves this with an in-memory graph engine featuring vector search support, Cypher query compatibility, and streaming data ingestion for real-time GraphRAG and agentic memory applications.
INSTALL
docker run -p 7687:7687 memgraph/memgraph-platform:latestINTEGRATION GUIDE
1. Build an agentic memory layer that stores agent experiences as a graph with vector-searchable semantic context
2. Create a real-time GraphRAG pipeline that ingests streaming data and performs sub-millisecond semantic graph queries
3. Implement a fraud detection system analyzing transaction patterns in real time with graph traversal and similarity search
4. Power a conversational AI with an in-memory knowledge graph that tracks conversation state and entity relationships
5. Develop a recommendation engine that delivers instant results by combining in-memory graph access with vector similarity
TAGS
graph-databasein-memoryvector-searchgraph-ragcypherreal-timestreamingdocker