# Memgraph Memgraph is an in-memory graph database for GraphRAG, AI memory, agentic AI, and real-time graph analytics. It uses Cypher and provides tools for querying, visualization, algorithms, and streaming data workflows. - Findings24: - Website: - Pricing: Freemium - Categories: AI Knowledge Graph ## What is Memgraph? Memgraph is an in-memory graph database for applications that need connected context and real-time graph queries. It is positioned for GraphRAG, AI memory, agentic AI, and graph analytics workloads such as fraud detection, network analysis, supply chain modeling, and data lineage. The database represents entities and their relationships as a graph that applications can traverse with Cypher. Memgraph combines in-memory processing with ACID transactions and on-disk persistence, and its documentation covers graph modeling, text and vector search, streaming, replication, high availability, and deployment. For AI systems, Memgraph can complement vector search with structured, multi-hop context. Its graph model can also represent semantic, episodic, and procedural memory and provide an inspectable path behind an agent's reasoning or action. ## What can Memgraph do? ### In-memory graph database Processes connected data in memory for real-time graph workloads, with ACID transactions and on-disk persistence for durability. ### Cypher querying and graph exploration Uses the Cypher query language. Memgraph Lab lets users run queries, visualize results, manage databases, and organize shared query collections. ### GraphRAG and AI context Adds structured graph context to retrieval workflows by traversing relationships and supporting multi-hop reasoning beyond similarity-based text retrieval. ### Graph algorithms and extensions MAGE includes 60+ algorithms such as PageRank, community detection, centrality, node2vec, and graph-native machine-learning procedures. Custom query modules can be written in Python, C++, or Rust, with cuGraph support for GPU acceleration. ### Streaming and ecosystem integrations Connects with Kafka, Redpanda, and Pulsar for streaming sources, and works with NetworkX, igraph, cuGraph, and Elasticsearch. ### Visual and Python developer tools GraphChat converts plain-English questions into Cypher, while GQLAlchemy provides a Python object graph mapper for modeling, querying, and updating graph data. ## Use Cases ### GraphRAG applications Build retrieval systems that follow relationships across documents, data sources, and entities to provide connected context and multi-hop answers. ### AI memory and agentic AI Represent long-term semantic, episodic, and procedural memory in one graph, or model an agent's possible actions so graph algorithms can help evaluate paths from a current state to a goal. ### Real-time fraud detection Model relationships among customers, transactions, accounts, and other entities to identify connected risk patterns and support real-time fraud scoring. ### Network and supply-chain analysis Analyze interconnected activity, supplier networks, and dependencies; graph models can support disruption simulation, rerouting, and broader risk assessment. ### Knowledge graphs and data lineage Unify siloed information into traversable entities and relationships, or track data origins and dependencies to support reliability and responsible use. ## Frequently Asked Questions ### How do developers query Memgraph? Memgraph uses the Cypher query language. Users can write and run queries in Memgraph Lab, use GraphChat to turn plain-English questions into Cypher, or interact from Python through GQLAlchemy. ### Can Memgraph be used for GraphRAG and AI agents? Yes. The product is designed for GraphRAG, AI memory, and agentic AI. Its graph model provides structured, connected context and supports multi-hop traversal, memory modeling, and reasoning paths. ### What integrations are documented? The product site identifies streaming integrations for Kafka, Redpanda, and Pulsar; algorithm integrations for NetworkX, igraph, and cuGraph; and a search-provider integration for Elasticsearch. ### What deployment options are available? The documentation covers Docker, Linux distributions, Kubernetes, WSL, direct downloads, cloud environments, and Memgraph Cloud. The Cloud offering is managed and AWS-hosted, while Community Edition can be downloaded and run by developers. ### Is there a free way to start? Yes. Community Edition is free forever and open source. Memgraph Cloud offers a free trial, and the Enterprise page states that a free 30-day Enterprise trial can be requested. ## Quick Facts - **Product category**: In-memory graph database - **Primary workloads**: GraphRAG, AI memory, agentic AI, and real-time graph analytics - **Query language**: Cypher - **Developer tools**: Memgraph Lab, GraphChat, GQLAlchemy, and MAGE - **Deployment options**: Community Edition, managed Cloud, Docker, Linux, Kubernetes, and cloud environments - **Source domain**: memgraph.com