FalkorDB vs hydradb
Low-latency, multi-tenant property graph database that runs queries as sparse-matrix linear algebra (GraphBLAS); OpenCypher over the Redis protocol, built for GraphRAG and agent memory. — versus — Object-store-native distributed graph DB in Rust: graph on SlateDB over S3, snapshot-consistent OpenCypher, GraphBLAS traversal, Neo4j Bolt 5.x compatibility, disaggregated data nodes and indexers.
Both execute graph traversal as GraphBLAS sparse-matrix algebra with OpenCypher; HydraDB is object-store-native and distributed over S3, FalkorDB an in-memory, Redis-protocol server.
| FalkorDB | hydradb | |
|---|---|---|
| Stars | 9.0k | 13k |
| Forks | 544 | 5.5k |
| Language | Rust | Rust |
| License | NOASSERTION | AGPL-3.0 |
| Last activity | today | 3 days ago |
| Topics | knowledge-graphs, rag | knowledge-graphs, storage |
| Curated connections | 3 | 6 |
FalkorDB — the curator's take
Choose it when a knowledge graph or agent memory sits in the hot path and Neo4j feels heavy — Redis-speaking, millisecond OpenCypher, many isolated graphs per instance, one `docker run` with a browser UI. The license is SSPLv1, not OSI open source: fine to use internally, a problem if you plan to offer it as a hosted service. Need vectors and full-text fused in one engine? helix-db.
hydradb — the curator's take
Consider it when you want a Neo4j-shaped graph (Cypher, Bolt drivers) but with cloud-native durability — S3 is the only copy that matters, and compute nodes are disposable and scale independently. That makes it a fit for large agent-memory or knowledge graphs on a budget where you'd rather not run a stateful Neo4j cluster. Avoid it for embedded or single-process use (latticedb, pggraph or plain SQLite are simpler), for early-stage projects that need battle-tested drivers and tooling, and note the AGPL-3.0 licence if you embed it in a product. Still 0.x; verify with a round-tripped write, as its own README insists.