Neo4j vs SurrealDB
Neo4j and SurrealDB both handle connected, graph-shaped data, and developers evaluating where to store relationships often weigh them against each other — Neo4j as the dedicated, mature graph database, and SurrealDB as the newer multi-model database that unifies document, graph, relational, and vector data behind one query language. Both ship official MCP servers, and the comparison is really about a focused graph engine versus a flexible multi-model one. Neo4j's official server gives an agent structured access over Cypher: it first introspects the schema with get-schema so it doesn't guess at labels and relationship types, then runs read-only Cypher to answer questions and, when permitted, write Cypher to mutate the graph; it also lists Graph Data Science procedures, and works against every Neo4j deployment shape (Aura, self-managed, Docker, Desktop, Sandbox). SurrealDB's official server (SurrealMCP) gives an agent first-class access via SurrealQL: run arbitrary queries, perform structured select/insert/create/upsert/update/delete and relate operations, traverse the connection model by switching namespace and database, and even manage SurrealDB Cloud organizations and instances. Here is how the two compare for an agent.
How they compare
| Dimension | Neo4j | SurrealDB |
|---|---|---|
| Data model | Dedicated graph database — nodes, relationships, and properties as the native model. | Multi-model — document, graph, relational, and vector data unified behind one engine and query language. |
| Query language | Cypher — get-schema to introspect, then read-cypher and write-cypher for queries and mutations. | SurrealQL — a query tool for arbitrary SurrealQL plus structured select/insert/create/upsert/update/delete and relate. |
| Schema awareness | Introspects schema first so the agent uses real labels and relationship types rather than guessing. | Traverses the connection model by switching namespace/database; structured operations map cleanly to records. |
| Analytics and cloud | Lists Graph Data Science (GDS) procedures for graph algorithms; runs on Aura, self-managed, Docker, Desktop, and Sandbox. | Manages SurrealDB Cloud organizations and instances in addition to data operations, plus namespace/database listing. |
| Best-fit task | Teams whose data is genuinely graph-shaped and want a mature engine, Cypher, and graph algorithms. | Teams that want one flexible database for documents, graphs, relations, and vectors operated through SurrealQL. |
Verdict
Choose Neo4j's server when your data is genuinely graph-shaped and you want the mature, dedicated graph engine: Cypher with schema introspection so the agent uses real labels and relationship types, plus access to Graph Data Science procedures for algorithms, across every Neo4j deployment shape. Choose SurrealDB's server when you want one flexible, multi-model database — documents, graphs, relations, and vectors behind a single SurrealQL surface — and value structured operations plus the ability to manage SurrealDB Cloud from the agent. The split is focused-graph-and-mature (Neo4j) versus multi-model-and-flexible (SurrealDB). If relationships and graph algorithms are the heart of your workload, lean Neo4j; if you want one database to cover several data shapes, lean SurrealDB.
FAQ
- Is SurrealDB a graph database like Neo4j?
- SurrealDB is multi-model — it can do graph-style relationships (via the relate operation and SurrealQL) but also document, relational, and vector data. Neo4j is a dedicated graph database focused on nodes, relationships, and graph algorithms via Cypher and GDS.
- Does either help the agent avoid guessing the schema?
- Neo4j's server exposes get-schema so the agent introspects labels and relationship types before querying. SurrealDB lets the agent traverse and switch namespaces/databases and run structured operations, which similarly keeps queries grounded in the actual data model.