Memgraph Documentation

Welcome to Memgraph Documentation.
Memgraph is a high-performance, in-memory graph database that powers real-time AI context. It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows - delivering sub-millisecond multi-hop traversals with full provenance for any system that needs structured, connected context alongside semantic search.

The same architecture that makes Memgraph the context layer for AI also drives real-time graph analytics across fraud detection, network analysis, infrastructure monitoring, and other operational use cases where speed and connectivity matter.

Learn how to utilize Memgraph to migrate and analyze your data.

Memgraph provides a public MCP server for its documentation. Connect it to any MCP-capable AI assistant to ask questions about Memgraph and get answers grounded in the current docs, with sources.

Memgraph ecosystem

To get started with Memgraph, explore everything the Memgraph ecosystem offers, including Memgraph Lab, Memgraph Cloud, MAGE graph library and more.

Client libraries

To start using Memgraph in your application, use one of the following client libraries and follow their getting started guide.

Migrate to Memgraph

You can migrate your data from an existing graph or SQL database using CSV or JSON files, and import data using queries within a CYPHERL file.

Migration guides

Supported source systems

Query and analyze data

You can query Memgraph using Cypher query language, use algorithms available in Memgraph’s MAGE library on your graph, and explore visualizations and query your data using Memgraph Lab.

Configure Memgraph to your needs

Whether you are running Memgraph on-prem or in production, here are the fundamental concepts of how the database operates and the tools you need to configure it to your needs.

Deploy Memgraph

Explore deployment guides to run Memgraph across various environments, including Docker, native Linux, Kubernetes and cloud platforms like AWS, Azure and GCP. Learn best practices, optimize for specific workloads and benchmark performance to ensure your graph applications scale efficiently in production.

Important changes

New releases might affect your existing code, queries or configuration. Ensure alignment with the latest updates and changes.