Query Data Where It Lives
Memgraph Zero connects to the databases you already run and lets you query across them as a unified graph. No pipelines. One GQL interface.
Data is scattered. Copying it doesn't scale.
Data lives in silos
Postgres, ClickHouse, Neo4j, Iceberg - each serves a purpose. None holds the full picture.
ETL is expensive and stale
Expensive to build, expensive to maintain, and outdated by the time it runs. It creates a new silo while trying to solve the silo problem.
Agents make it harder
They need data from multiple systems per task and spend tokens rediscovering what other agents already found.
Federated graph queries across any backend
Memgraph Zero is a product line built around a single principle: leave data where it is, query it as a graph.
MemGQL
A federated GQL query engine implementing the ISO/IEC 39075 standard. Write a single GQL query - MemGQL translates it into the native language of each target backend and pushes execution down to the source. Zero ETL needed.
How it works
GQL query arrives
A GQL query is sent via Bolt protocol on port 7688 (the default).
MemGQL resolves target backends
Reads configured graph mappings to determine which backend holds each part of the query.
Query transpiled to native language
GQL is translated into Cypher or SQL and execution is pushed down to each source.
Results return
Results from each backend are merged and returned through a single interface.
Eight connectors. One query layer.
Graph databases
Relational databases
Real-time OLAP
Embedded & lakehouse
On the roadmap: MongoDB, Redis, Elasticsearch, Oracle, CSV/Parquet, REST and GraphQL APIs, unstructured data sources.
What you can do today
Federated GQL
Execute GQL queries across heterogeneous backends in a single session. Query Memgraph and Postgres in one statement. Union results from Neo4j and ClickHouse. The engine handles translation and routing.
Public-private data
GDPR-regulated records stay in Postgres. Public product catalogs stay in Memgraph. One GQL query joins both without moving regulated data across system boundaries.
Agentic data access
Agents connect via MCP, discover available data sources, and execute GQL queries without knowing where each dataset lives. When one agent finds something, others don't repeat the work.
Cross-system context
Org charts in one database. Product hierarchies in another. Ontologies in a third. MemGQL gives every team a unified graph view without forcing migration.
Complementary engines. Different trade-offs
Use them independently or together.
Memgraph
The in-memory graph engine
Sub-millisecond traversals, real-time writes, deep path algorithms, graph analytics. Data lives in memory for maximum performance.
Best for: GraphRAG, AI Memory, fraud detection, network analysis.
Memgraph Zero
The federated query layer
Data stays in source systems. Queries are translated and pushed down. Best when data is distributed and copying it is impractical or prohibited.
Best for: distributed data, regulated data, multi-system agents.
Together
Best of both
Memgraph serves as the high-performance caching and analytics layer inside Memgraph Zero. Pull data from Postgres or Iceberg into Memgraph for deep traversals, then let MemGQL route queries to whichever backend fits.
Community and Enterprise
Community
Free · Available on Docker Hub
For developers evaluating and building with Memgraph Zero.
2 connectors- 2 simultaneous connections
- MCP server
- Agentic mapping
- Full GQL query support
Enterprise
Custom pricing
For teams running Memgraph Zero in production.
- Unlimited connectors
- Unlimited simultaneous connections
- Priority support
- Compliance and security features as they ship
Early and moving fast
Launched May 2025. GQL-to-Cypher translation has the strongest coverage today. SQL push-down support is expanding across all backends.
