Embeddable graph database built in pure Rust. Dual data models (LPG + RDF), six query languages, HNSW vector search, MVCC transactions. Fastest graph database in our graph-bench suite (which includes workloads inspired by the LDBC Social Network Benchmark), in both embedded and server configurations, with a low memory footprint. Now in beta.

^{Try it live at grafeo.ai}

• 6 query languages: GQL (ISO), Cypher, Gremlin, GraphQL, SPARQL, SQL/PGQ. Use what you know or want to learn.
• LPG + RDF in one engine: Property graphs and triples with optimized storage for each.
• Vector search: HNSW indexes with scalar, binary, and product quantization. SIMD-accelerated.
• Embeddable: Link as a library (Rust, Python, Node.js, Go, C#, Dart, WASM) or run as a server. No external processes.
• Fast and lean: Fastest graph database in our graph-bench suite (includes LDBC-inspired workloads) in both embedded and server modes, with a lower memory footprint than other in-memory databases. Push-based vectorized execution, morsel-driven parallelism, columnar storage with zone maps. Also benchmarked with ann-benchmarks.

import grafeo

db = grafeo.GrafeoDB()

# Build a graph
db.execute("INSERT (:Person {name: 'Alice', age: 30})-[:KNOWS {since: 2020}]->(:Person {name: 'Bob', age: 25})")
db.execute("INSERT (:Person {name: 'Bob'})-[:KNOWS]->(:Person {name: 'Carol', age: 28})")

# Traverse it
for row in db.execute("""
    MATCH (p:Person)-[:KNOWS]->(friend)-[:KNOWS]->(fof)
    WHERE fof <> p
    RETURN p.name, fof.name AS friend_of_friend
"""):
    print(row)

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