Memgraph

Memgraph is an in-memory graph database built in C and C++, aimed at workloads where a multi-hop traversal has to return in under a millisecond. It serves as the graph engine for GraphRAG pipelines, AI memory systems and agentic workflows, and equally for fraud detection, network analysis and infrastructure monitoring.

What makes the retrieval side work is that no second system is required. Vector and text indexes sit in the same query layer as graph traversal, so similarity search, graph expansion and ranking run as one atomic database operation. The query language is Cypher, compatible with Neo4j, and the database is ACID-compliant.

Several capabilities extend the core engine.

• MAGE library: more than forty graph algorithms in C++, Python and CUDA, from PageRank and community detection to link prediction and embeddings.
• Deep-path traversals: accumulators and path filtering handled in the query rather than in application code.
• Custom query modules: the engine is extended natively with Python, Rust or C and C++.
• Streaming ingestion: Kafka, Pulsar and Redpanda feed dynamic algorithms that react to changes as they arrive.
• Schema introspection: one command returns the full graph ontology for Text2Cypher and agent integration.

Data loads natively from Parquet and JSONL on local disk, S3 or HTTP endpoints. Installation covers Docker on Windows, macOS and Linux, native Linux packages, and official Helm charts for standalone or high-availability Kubernetes clusters. Raft-based failover, multi-tenancy, label-level access control and SSO belong to the enterprise build, and a hosted playground lets you try the query language without installing anything.