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hydradb vs pgGraph

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. — versus — PostgreSQL extension adding graph search, traversal and shortest-path over your existing tables — a derived graph index queried from plain SQL, no separate graph DB or query language. Rust.

The curated verdict

pggraph adds graph traversal to tables you already have in Postgres; hydradb is a dedicated graph engine — choose pggraph if the data lives in Postgres, hydradb if the graph is the primary store.

hydradbpgGraph
Stars3.1k1.0k
Forks66085
LanguageRustRust
LicenseAGPL-3.0NOASSERTION
Last activity23 days ago20 days ago
Topicsknowledge-graphs, storageknowledge-graphs, rag
Curated connections44

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.

pgGraph — the curator's take

The zero-migration bet: your tables stay the source of truth and the graph is a derived index, which makes it the cheapest possible on-ramp to GraphRAG over data already in Postgres. When NOT: the license is NOASSERTION with a managed-service funnel (polygres.com) — read the terms before production; graph-native workloads at scale still favor a dedicated store; ★898 and v1.0-young.