memgraph: an in-memory graph database providing atomic GraphRAG and real-time connected context for AI agents

What it solves

Memgraph is a high-performance, in-memory graph database designed to provide real-time connected context for AI systems. It eliminates the need to scatter retrieval pipelines across multiple systems by combining graph traversals, vector similarity search, and text search into a single atomic database operation.

How it works

Built in C/C++, Memgraph uses an in-memory architecture to achieve sub-millisecond multi-hop traversals. It is fully compatible with the Cypher query language and provides built-in vector and text indexes. The system includes the MAGE library for graph algorithms (implemented in C++, Python, and CUDA) and a dedicated LLM utility module for formatting graph-aware context for large language models.

Who it’s for

It is intended for developers building GraphRAG pipelines, AI memory systems, and agentic workflows, as well as professionals performing real-time graph analytics for fraud detection, network analysis, and infrastructure monitoring.

Highlights

• Atomic GraphRAG: Allows pivot search, graph expansion, and prompt assembly to be executed as a single Cypher query.
• Hybrid Search: Combines vector indexes for similarity search with text and geospatial indexes in one query layer.
• MAGE Library: Over 40 graph algorithms including PageRank, community detection, and GNN-based link prediction.
• Extensibility: Supports custom query modules written in Python, Rust, and C++.
• Real-time Ingestion: Native support for streaming data from Kafka, Pulsar, and RedPanda.