Memgraph
High-performance in-memory graph database with vector search and streaming integrations, designed for real-time GraphRAG, AI memory systems, and low-latency knowledge graph applications
Overview
Memgraph is a high-performance, in-memory graph database built in C++ and designed for real-time applications where latency matters. Compatible with Neo4j via the openCypher query language, it enables sub-millisecond graph traversals while supporting graphs from 100 GB to 4 TB. The platform excels at GraphRAG pipelines for multi-hop reasoning, AI memory systems combining semantic and episodic memory, and agentic AI workflows. With native Kafka, Pulsar, and Redpanda integrations, Memgraph processes streaming data in real-time. The database includes built-in vector search capabilities and ships with MAGE, a comprehensive graph algorithm library. Trusted by NASA, Cedars-Sinai, Netflix, IBM, and Siemens for mission-critical workloads.
The Verdict
Who Should Use Memgraph?
Best For
- Real-time graph analytics requiring sub-millisecond latency
- GraphRAG and AI memory system implementations
- Teams migrating from Neo4j seeking better performance
- Streaming data pipelines with Kafka/Pulsar
- Fraud detection and network analysis workloads
- Knowledge graphs needing vector search integration
Not Ideal For
- Datasets exceeding available RAM (disk-first preferred)
- Teams needing trillion-edge deep analytics (consider TigerGraph)
- Multi-model requirements beyond graph (consider ArangoDB)
- Budget-constrained teams needing enterprise features
What's Great
- Sub-millisecond query latency with in-memory architecture
- 132x higher mixed workload throughput vs Neo4j (vendor benchmark)
- Neo4j compatible - uses openCypher, minimal retraining needed
- Native streaming integration with Kafka, Pulsar, Redpanda
- Built-in vector search for RAG applications
- MAGE algorithm library with 100+ graph algorithms
- Free Community Edition with core features
- 12 official client libraries including Python, Go, Rust
Watch Out For
- Requires dataset to fit in RAM for optimal performance
- Smaller community than Neo4j (less third-party tooling)
- Enterprise pricing starts at $25K/year minimum
- Business Source License (not OSI-approved open source)
- Fewer visualization options compared to Neo4j ecosystem
Pricing
View all features & details
Core Features
- In-memory graph storage with disk persistence
- ACID transactions with write-ahead logging
- openCypher query language (Neo4j compatible)
- Built-in vector search
- MAGE graph algorithm library
- High-availability replication
- Automatic failover (Enterprise)
- Multi-tenancy support
AI & RAG Integrations
- LangChain integration
- LlamaIndex integration
- GraphRAG pipelines
- AI memory systems
- Semantic search
- Multi-hop reasoning
- Agentic AI workflows
Streaming & Data Sources
- Apache Kafka
- Apache Pulsar
- Redpanda
- CSV, JSON, Parquet import
- Neo4j migration tools
- PostgreSQL, MySQL connectors
- Apache Spark integration
- Amazon S3 support
Deployment Options
- Docker / Kubernetes
- Linux (Debian, Ubuntu, CentOS, RHEL)
- AWS, GCP, Azure
- Memgraph Cloud (managed)
- AWS Marketplace
- Windows WSL
Client Libraries
- Python
- JavaScript / Node.js
- Java
- Go
- Rust
- C# / .NET
- PHP
- GraphQL
Enterprise Security
- Role-based access control (RBAC)
- Label-based access control
- SSO (Entra ID, Okta, OIDC, SAML)
- LDAP / PAM authentication
- Query audit logging
- Prometheus monitoring
How It Compares
| Feature | Memgraph | Neo4j | TigerGraph | ArangoDB |
|---|---|---|---|---|
| Architecture | In-memory | Disk-first | Disk-first | Disk-first |
| Query Language | openCypher | Cypher | GSQL | AQL |
| Real-time Streaming | Native | Via plugins | Limited | Limited |
| Vector Search | Built-in | Plugin | No | No |
| Free Tier | Full DB | Limited | Dev only | 100GB cap |
| Neo4j Compatible | Yes | - | No | No |
| Multi-model | Graph only | Graph only | Graph only | Graph+Doc |
| Enterprise Start | $25K/yr | $65K+/yr | Custom | $5K+/yr |
| Best For | Real-time RAG | General graph | Deep analytics | Multi-model |