Features
Cypher-compatible graph query language
In-memory ACID transactions with persistence
Vector search for semantic AI
Built-in GraphRAG pipelines
AI memory (semantic, episodic, procedural)
Agentic AI execution graph support
Sub-millisecond multi-hop graph traversal
Real-time fraud detection and network analysis
MAGE algorithm library (centrality, community, GNN)
Stream connectors (Kafka, Pulsar, Redpanda)
Memgraph Lab visual interface
Multi-tenancy and RBAC (Enterprise)
Federated query (MemGQL) across data sources
Memgraph MCP Server for AI agent integration
Graph neural network (GNN) support
High-fidelity synthetic data generation via TabularARGN
Agentic data science automation for training/sampling
Natural language AI Assistant executing Python code
Mock data generation for safe experimentation and testing
Simulated data for edge-case and what-if modeling
Multi-table synthesis with referential integrity
Time-series support and data rebalancing
Differential privacy with temperature control
Deployment on Kubernetes or Red Hat OpenShift
Open-source Synthetic Data SDK under Apache v2 license
Data connectors for Databricks, AWS, Snowflake, BigQuery
Real-time data access from production systems (e.g., Databricks)
Star schema and nested sequences support
REST API and Python Client for programmatic access
Conditional simulation and seeded generation
