Memgraph is an open-source, in-memory graph database built in C++ for high-throughput, low-latency transactional and analytical workloads. Fully compatible with the openCypher query language, Memgraph is designed for real-time streaming graph applications where microsecond-level query response times are critical. ¹⁾

Founded in 2016 in Zagreb, Croatia by software engineers Dominik Tomicevic and Marko Budiselic, the company has raised approximately $14.2 million in funding from investors including Microsoft's M12, Heavybit Industries, In-Q-Tel, Counterview Capital, Mundi Ventures, and Techstars. ^{2) 3)}

Memgraph uses a hybrid in-memory property graph architecture optimized for speed through its C++ implementation, avoiding JVM garbage collection pauses that affect Java-based graph databases. ⁴⁾

Key architectural features include:

• In-memory storage – nodes and edges reside in RAM for microsecond to low-millisecond query response times
• Durability – write-ahead logs (WAL) and periodic snapshots provide crash recovery
• Concurrency – fine-grained locking, lock-free skiplists, and Multi-Version Concurrency Control (MVCC) prevent writer-reader blocking
• Storage modes – three modes for transactional, analytical, or large-dataset workloads exceeding available RAM
• ACID compliance – full transactional guarantees with in-memory persistence
• Encryption in transit – secure data communication between clients and the database

Memgraph supports automatic failover through a coordinator service using the Raft consensus protocol (implemented via NuRaft in C++). ⁵⁾

• The Community Edition offers WAL-based replication
• The Enterprise Edition adds cluster coordinators, multi-tenancy with role-based access control, and multi-data-center deployment support
• A leader coordinator manages replicas for reads and writes, supporting configurations such as MAIN + REPLICA across multiple data centers

Scalability is primarily vertical (more RAM and CPU cores), with replication providing read scaling. Write operations are directed to a single MAIN instance.

Memgraph natively supports openCypher, providing high compatibility with Neo4j's Cypher query language. This allows developers familiar with Neo4j to transition with minimal query modifications. ⁶⁾

The platform extends query capabilities through the MAGE (Memgraph Advanced Graph Extensions) library, which provides:

• Pre-optimized graph algorithms including BFS, DFS, Weighted Shortest Path, and All Shortest Paths
• User-defined graph analytics modules and procedures
• Community-contributed algorithm implementations
• Python, C, and C++ extensibility for custom procedures

Memgraph is specifically designed for real-time graph computing, evolving static graphs into active, event-driven systems: ⁷⁾

• Highly concurrent data ingestion for always-fresh graph data
• On-the-fly triggers, algorithms, and rules on incoming data streams
• Dynamic algorithms that recalculate only changed analytical results rather than reprocessing entire graphs
• Native integration with Apache Kafka and other streaming platforms
• Support for over 1,000 transactions per second in high-velocity environments

Ideal use cases include fraud detection, cybersecurity threat analysis, real-time recommendation engines, network monitoring, and IoT data processing.

Memgraph excels in streaming and real-time workloads where data fits in RAM, while Neo4j is better suited for massive, durable graph datasets requiring horizontal scaling. ⁸⁾

Memgraph Lab is the visual interface for interacting with the database. Version 3.0 introduced: ⁹⁾

• Parallel GraphChat – an AI-powered Cypher assistant supporting multi-threaded model comparison and debugging
• Auto-updating schema – displays node and relationship counts and property distributions for faster query development
• Cluster management visibility – shows leader/follower roles to prevent accidental writes to replicas

• Neo4j
• Graph Databases
• Apache Kafka