Memgraph

Memgraph is an open-source, in-memory graph database designed for real-time graph analytics and AI context management, providing a server-based engine that supports sub-millisecond multi-hop traversals. The software can be deployed on Linux, macOS, Windows, or via Kubernetes using Helm charts, and it is also available as a managed cloud service on AWS.

Users typically employ Memgraph to power GraphRAG pipelines, AI memory systems, and agentic workflows. It allows for the combination of similarity searches and graph traversals within a single atomic operation, removing the need to scatter retrieval pipelines across multiple systems. The system is ACID-compliant and uses the Cypher query language.

Key features

• Built-in vector, text, and geospatial indexes for hybrid retrieval and similarity search.
• MAGE library containing over 40 graph algorithms implemented in C++, Python, and CUDA.
• Native ingestion from Kafka, Pulsar, and RedPanda streams for real-time data processing.
• Direct data loading from Parquet and JSONL files via local disk, S3, or HTTP endpoints.
• Support for custom query modules written in Python, Rust, and C++.
• Raft-based coordination for high availability and automatic failover.
• Role-based and label-based access control at the node and edge level.
• Real-time schema introspection for Text2Cypher and AI agent integration.

Memgraph is built in C/C++ for high performance and supports parallel query execution to handle high-throughput workloads. It integrates with various agentic frameworks and provides a dedicated LLM utility module for formatting graph-aware context for large language models. For data visualization and query testing, the software includes Memgraph Lab, a user interface for exploring and manipulating stored data, alongside the mgconsole and various drivers for Python and C++.

This database is positioned for developers and enterprises requiring a high-performance, in-memory solution for connected data, fraud detection, network analysis, and real-time AI analytics.