The neurosymbolic database

The database that thinks in two minds.

Oxid fuses logical reasoning and vector search into one engine. One query language. One mind.

FIND?xWHERE?xIS-ABeerNEAR?xTO"crisp pale lager"

THE STATUS QUO

You shouldn't need two databases and a layer of glue to ask one question.

VECTOR DB

Vector DB

Embeddings & similarity search

glue code
KNOWLEDGE GRAPH

Knowledge Graph

Entities & explicit relations

Two stores means two truths. Embeddings drift from the graph, identity gets duplicated across systems, the sync glue turns brittle with every schema change, and every query ends in fragile post-filtering: accuracy quietly falling through the cracks.

Oxidone database · one query

HYBRID OXQL  ·  ONE PASS

One query. One pass. One system.

query.oxql
OxQL

// narrow by logic, then rank by vectors

FIND ?x

WHERE ?xIS-A Beer

NEAR ?x TO"crisp pale lager"

LIMIT 5

→  5 results · single pass · 12ms

Pilsner Urquell0.94
Stella Artois0.90
Heineken0.86
Bitburger0.83
Amstel0.79
knowledge_graph5 of 4.2M nodes  ·  single pass
WineCoffeeCheeseBreadCarCityDogTeaBeerAleLagerPilsnerPilsner Urquell0.94Stella Artois0.90Heineken0.86"crisp pale lager"① logic narrows to Beer② vectors rank survivorsquery vector
subClassOfinstanceOfsimilarity rankfiltered out
Logic narrows.Vectors rank.In one pass.

The ontology filters candidates during the search, not after. No noise, no post-filtering, no second system.

The reasoner and the vector index share one identity space.   No glue.   No drift.   No second system.
BUILT LIKE INFRASTRUCTURE

The guarantees you expect from a real database.

The neurosymbolic features ride on a storage engine that behaves like one: durable, transactional, recoverable.

01

ACID & durable

Write-ahead log, crash recovery, point-in-time recovery.

02

Time travel

AS OF snapshot queries over your knowledge's history.

03

MVCC snapshot isolation

Lock-free reads under concurrent writes.

04

One identity space

Every entity is one ID to reasoner and vector index: zero-copy joins.

05

Auto-embedding

Insert text, get vectors: local ONNX, no external service.

06

Knowledge-graph embeddings

Train TransE / RotatE structural vectors in-engine.

07

Single binary

Embedded graph UI, zero runtime dependencies.

08

Typed SDKs

Node.js & Python.

FOR THE AGENT ERA

Built for the agents that will actually use it.

Agents ask in language. Oxid turns the question into an OxQL query, reasons over the graph, and hands back an answer it can defend.

Drops into Claude Code & Cursor
MCP server

One server, every client. Your agent gets typed tools, not a REST wrapper.

Bring your own model
AnthropicOpenAIGeminiOllama
agent · oxid sessionconnected

Which suppliers shipped late to our Berlin plant last quarter, and what did they have in common?

OxQLcompiled from natural language

FIND?supplier

WHERE?sSHIPPED-TOBerlin_Plant

AND?sDELAYED-BY> 0

NEAR?causeTO"shared delay"

tool: parse·natural language → OxQL
tool: search·graph pattern match
tool: vector.match·similar delay signatures
tool: reason·TransE path inference
ANSWER

All late shipments trace to one shared upstream: the Hamburg hub, after the March port closure. The link came from the graph, not a keyword match.

THE VISION

We gave the database a mind so your data can finally think.

Perception, reasoning, and memory in one system.
This is the future of databases.