# Memgraph

> Memgraph is the graph engine for AI context, built for real-time graph
> reasoning. It complements vector search with structured, connected context and
> traceable multi-hop reasoning across enterprise data in milliseconds. One
> in-memory engine serves two workloads: AI context — GraphRAG, AI memory, and
> agentic reasoning — and real-time graph analytics, on the same architecture.

This file indexes the Memgraph company site. The product documentation lives at
https://memgraph.com/docs and is indexed separately, page by page, at
https://memgraph.com/docs/llms.txt — start there for installation, Cypher
syntax, configuration, clustering, and the algorithm reference.

## Documentation

- [Documentation](https://memgraph.com/docs): installation, querying, deployment, and the full reference manual
- [Documentation index for LLMs](https://memgraph.com/docs/llms.txt): every documentation page, with a Markdown version of each
- [Download](https://memgraph.com/download): get started for free with the fully functional Community edition

## Product

- [Memgraph database](https://memgraph.com/memgraphdb): the in-memory graph engine behind both AI context and real-time analytics
- [Memgraph Lab](https://memgraph.com/lab): visualize and query graph data, with natural-language querying through LLMs
- [Memgraph Zero](https://memgraph.com/memgraph-zero): query the databases you already run as one unified graph through a single GQL interface, with zero ETL
- [Vector search](https://memgraph.com/vector-search): similarity and structure in one engine, running vector search alongside graph traversal
- [Enterprise](https://memgraph.com/enterprise): production-grade graph infrastructure with high availability, security, and support
- [AI Platform](https://memgraph.com/ai-platform): the Enterprise engine priced for AI workloads, with unlimited vector indexes
- [Pricing](https://memgraph.com/pricing): plans and licensing
- [Tools and integrations](https://memgraph.com/tools): supplementary tools that extend Memgraph

## AI workloads

- [GraphRAG](https://memgraph.com/graphrag): standard RAG retrieves text chunks by similarity; GraphRAG traverses a knowledge graph to follow multi-hop relationships across entities, connecting information similarity matching cannot reach
- [AI memory](https://memgraph.com/ai-memory): LLMs are stateless, and vector-based memory retrieves what sounds similar rather than what is structurally relevant; Memgraph connects semantic, episodic, and procedural long-term memory as one graph any AI system can query in real time
- [Agentic AI](https://memgraph.com/agentic-ai): a reasoning graph makes "what should I do next" explicit — an action space where graph algorithms find the highest-scoring path from current state to goal
- [Knowledge graphs](https://memgraph.com/knowledge-graph): unify siloed data into a queryable graph of entities and relationships that any team can traverse
- [RAG vs. GraphRAG demo](https://memgraph.com/rag-vs-graphrag-demo): the same questions run against vector RAG and GraphRAG on one dataset, showing where similarity search falls short

## Graph analytics

- [All use cases](https://memgraph.com/use-cases-list): how teams use Memgraph to uncover connections in real time across industries
- [Fraud and risk detection](https://memgraph.com/fraud-and-risk-detection): eliminate chargeback fees and unrecoverable fraud in real time by mining relationships between entities
- [360 data and network exploration](https://memgraph.com/360-data-and-network-exploration): a 360° view of activity — risk is multi-faceted, so stop looking at entities in isolation
- [Identity and access management](https://memgraph.com/identity-access-management): build IAM systems that track complex permissions and check access rules in milliseconds at scale
- [Data lineage](https://memgraph.com/data-lineage): ensure the reliability of your data and prevent its misuse
- [Supply chain and network optimization](https://memgraph.com/network-resource-optimization): model multi-tier supplier networks, simulate disruption, and reroute in real time
- [Logistics and network optimization](https://memgraph.com/logistics-and-network-optimization): optimize flows and prevent failures across supply chains, power grids, telecom networks, and pipelines
- [Cybersecurity](https://memgraph.com/cybersecurity): graph analysis for security data
- [Recommendation engine](https://memgraph.com/recommendation-engine): recommendations over the connections between customers and products
- [Energy management](https://memgraph.com/energy-management-system): manage energy network systems for stable, reliable service

## Comparisons and migration

- [Memgraph vs Neo4j](https://memgraph.com/memgraph-for-neo4j-developers): both are property graph databases that support Cypher, but the architecture underneath differs in ways that matter for real-time AI workloads
- [Memgraph vs Amazon Neptune](https://memgraph.com/memgraph-vs-amazon-neptune)
- [Memgraph vs ArangoDB](https://memgraph.com/memgraph-vs-arangodb)
- [Memgraph vs FalkorDB](https://memgraph.com/memgraph-vs-falkordb)
- [Memgraph for NetworkX developers](https://memgraph.com/memgraph-for-networkx): keep writing NetworkX code against a persistent in-memory graph database, without reloading data every run
- [Memgraph for Python developers](https://memgraph.com/memgraph-for-python-developers): connect with GQLAlchemy, map graph entities to Python objects, and query without writing Cypher
- [Memgraph for developers](https://memgraph.com/for-developers): the graph database designed by developers for developers
- [Benchmark](https://memgraph.com/benchmark): Benchgraph, an open-source benchmarking tool for graph databases

## Learn

- [Blog](https://memgraph.com/blog): articles on graph database technology, GraphRAG, and real-time analytics
- [Customer stories](https://memgraph.com/customer-stories): how teams at NASA, Cedars-Sinai, Capitec and others run Memgraph in production
- [Learn Cypher query language](https://memgraph.com/learn-cypher-query-language): a free 10-day course on Cypher
- [Learn graph modeling](https://memgraph.com/learn-graph-modeling): a free 10-day course on graph modeling
- [Learn to build a knowledge graph](https://memgraph.com/learn-to-build-knowledge-graph): a free 9-day knowledge graph course
- [How to GraphRAG course](https://memgraph.com/how-to-graphrag-course): build GraphRAG pipelines in 10 practical lessons
- [Webinars](https://memgraph.com/webinars): sessions on advances in graph database technology
- [On demand](https://memgraph.com/on-demand): recorded talks and sessions

## Company

- [About us](https://memgraph.com/about-us): the team and the company
- [Careers](https://memgraph.com/careers): open roles at Memgraph
- [Contact us](https://memgraph.com/contact-us): get in touch
- [Partners](https://memgraph.com/partners): the Memgraph partnership program
- [Media](https://memgraph.com/media): stories, interviews, and press coverage
- [Events](https://memgraph.com/events): upcoming events

## Optional

- [Book a demo](https://memgraph.com/general-demo): see Memgraph in action
- [Enterprise trial](https://memgraph.com/enterprise-trial): a 30-day free trial of Memgraph Enterprise
- [Office hours](https://memgraph.com/office-hours): expert advice on data modeling, query optimization, and migration
- [CTO hours](https://memgraph.com/cto-hours): a 1:1 on scaling, infrastructure, and production support
- [Support](https://memgraph.com/support): L3 support by default
- [Legal](https://memgraph.com/legal): terms, privacy, and policies