I've been working on NeuG (pronounced "new-gee"), an embeddable graph database that follows the same philosophy as sqlite and DuckDB — in-process, zero configuration, just pip install and query.

What's different from existing embedded graph DBs:

- Dual-mode: start embedded, flip one line to expose as a network service — same data, same queries, no migration - Built on GraphScope Flex, the engine behind the current LDBC SNB Interactive world record (80k+ QPS)

Local benchmark highlights on LDBC SNB SF1 (~3M nodes, 17M edges):

Embedded mode vs LadybugDB (Kuzu-based): NeuG wins 8/9 LSQB queries single-threaded vs LadybugDB's best multi-threaded result. 287x on triangle patterns (Q3), 91x on two-hop filtering (Q2).

Service mode vs Neo4j: 617 QPS vs Neo4j's 12 QPS on LDBC SNB Interactive — 50.6x throughput. P95 latency 20ms vs Neo4j's 1,728ms.

Currently Python only. Node.js bindings and GraphRAG/vector extensions are on the roadmap.

Would love feedback — especially from anyone who's tried Kùzu, LadybugDB, or runs Neo4j in production.

GitHub: https://github.com/alibaba/neug Blog post with full details: https://graphscope.io/blog/tech/2026/04/12/neug-one-engine-t...