A graph database and a vector database in one.Native relationships, approximate-nearest-neighbour search and full-text ranking in one query language over one copy of the data.
Native graph model
Nodes, edges and properties as records, traversed with arrow syntax to any depth.
Recursive traversal
Walk chains and dependency trees with recursive graph queries, filtering edges as you go.
HNSW vector index
Approximate-nearest-neighbour search with Cosine, Euclidean, Manhattan and inner-product metrics, on HNSW or DiskANN indexes.
Hybrid search
Full-text BM25 and vector similarity in one query, fused with reciprocal rank fusion.
Scope first, rank second
A structured filter narrows the candidates before the vector search runs.
One copy of the data
Graph edges, vectors and documents share the same records.
CONNECTED DATA
Relationships and similarity, together
Edges are documents you traverse with arrow syntax; vectors are an index on the same table. One query walks the graph and ranks by meaning.
Edges are records you traverse
RELATE customer:alice->purchased->product:widget_pro
SET quantity = 2, date = time::now(), source = 'web';
SELECT ->purchased[WHERE date > time::now() - 30d]->product.name
FROM customer:alice;
Similarity on the same records
DEFINE INDEX product_embedding ON product
FIELDS embedding HNSW DIMENSION 1536 DIST COSINE;
SELECT id, name, vector::distance::knn() AS distance
FROM product
WHERE category = 'tools'
AND embedding <|10,40|> $query_embedding
ORDER BY distance;
Graph and vector as the same records
One query, both models
One copy, in step
One pass
THE PLATFORM
Everything an application and its agents know. Five surfaces, one engine.
Database
Document, graph, vector, time-series and relational in one engine.
Agent Memory
What an agent learns, with its source and its time, in the same engine.
Cloud
Managed clusters in the regions you choose, scaled on demand.
Studio
Query, explore and design the schema from the browser.
MCP
Every model that speaks MCP reaches the database and the memory directly.
IN PRODUCTION
Trusted at scale. Samsung, Nvidia, Verizon, Tencent, and Walmart run on SurrealDB.
14,000+
Developers building on SurrealDB Cloud
4M+
Developers building on SurrealDB worldwide
FROM THE TEAMS
SurrealDB gives us a foundation where we can unify semantic search, knowledge graphs, and AI-driven decision making without stitching together multiple systems. Collapsing responsibility into SurrealDB has become our default engineering posture.
VP of Engineering, Later
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