Memgraph - In-Memory Graph Database

Open-source in-memory real-time graph database M Voice & Memory

Basic Information

  • Product Name: Memgraph
  • Developer Company: Memgraph Ltd.
  • Country/Region: UK/Croatia
  • Official Website: https://memgraph.com/
  • GitHub: https://github.com/memgraph/memgraph
  • Type: Open-source in-memory real-time graph database
  • Query Language: Cypher (compatible with Neo4j)
  • Latest Version: Memgraph 3.5

Product Description

Memgraph is an open-source in-memory real-time graph database designed for applications requiring low-latency processing of connected data. It supports ACID transactions in memory for fast access while persisting all writes to permanent storage. Memgraph is compatible with the Cypher query language, supports both analytical and transactional workload modes, and actively embraces the AI/GraphRAG ecosystem.

Core Features/Characteristics

  • In-Memory Native: Data resides in memory, enabling millisecond-level query responses
  • ACID Transactions: Full transaction support in memory
  • Cypher Query Language: Compatible with Neo4j
  • Dual-Mode Storage: Seamless switching between analytical and transactional modes
  • Custom Query Modules: Native execution of Python, Rust, C/C++ code
  • AI Graph Toolkit: Automatically imports SQL and unstructured data into knowledge graphs
  • GraphRAG Support: Official support for GraphRAG development starting February 2025
  • MCP Server: Modular architecture, experimental features, vector search tools
  • Memgraph 3.5 New Features: Enhanced data security, smarter Cypher queries, Schema tools, high availability control
  • Vector Search: 85% vector memory savings
  • Parallel Runtime: Concurrent edge writes on super nodes

Business Model

  • Community Edition: Open-source and free
  • Enterprise Edition: Commercial license
  • High-availability clusters
  • Enterprise-grade security
  • Advanced monitoring
  • Memgraph Cloud: Managed services

Target Users

  • Applications requiring real-time graph queries (recommendations, fraud detection)
  • AI/GraphRAG application developers
  • Teams migrating from Neo4j (Cypher compatibility)
  • IoT and streaming data graph analysis
  • Proxy systems requiring low-latency graph operations

Competitive Advantages

  • In-memory native, extremely low query latency
  • Cypher compatibility, easy migration for Neo4j users
  • Open-source and actively embracing the AI ecosystem
  • Flexible custom query modules
  • MCP Server support, integration with AI agent ecosystem
  • 85% vector memory savings

Relationship with the OpenClaw Ecosystem

Memgraph's in-memory graph database characteristics make it suitable for OpenClaw's latency-sensitive real-time scenarios. OpenClaw agents require rapid responses when querying knowledge graphs, and Memgraph's millisecond-level query latency meets this need. Its MCP Server support also allows OpenClaw agents to interact directly with the graph database via the MCP protocol.

External References

Learn more from these authoritative sources: