I am doing a multi-model graph database in pure Rust with Cypher, SQL, Gremlin, and native GNN looking for extreme speed and performance

Reddit r/artificial / 4/2/2026

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A PhD student shared an open-source Rust-based, embeddable multi-model graph database engine intended to combine graph querying with vector/embedding and native GNN capabilities.
The project, “BikoDB,” is motivated by perceived limitations of existing systems, including Neo4j’s JVM-heavy single-model approach, ArcadeDB’s slower graph algorithms, and vector databases’ lack of native graph awareness.
The engine aims for extreme speed and performance while supporting multiple query interfaces/languages, including Cypher, SQL, and Gremlin.
The author published the code recently and is explicitly seeking feedback from practitioners who work with graph databases daily to identify improvements and tradeoffs.

Hi guys,

I'm a PhD student in Applied AI and I've been building an embeddable graph database engine from scratch in Rust. I'd love feedback from people who actually work with graph databases daily.

I got frustrated with the tradeoffs: Neo4j is mature but JVM-heavy and single-model. ArcadeDB is multi-model but slow on graph algorithms. Vector databases like Milvus handle embeddings but have zero graph awareness. I wanted one engine that does all three natively.

So I would like if someone could give me feedback or points to improve it, I am very open mind for whatever opinion

I was working several months with my university professors and I decided to publish the code yesterday night because I guessed its more or less reddit to try it.

The repo is: https://github.com/DioCrafts/BikoDB

Guys, as I told you, whatever feedback is more than welcome.

PD: Obviously is open source project.

Cheers!

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