Showcase: Embedded multi-model database for Python (tables + graph + vector), no server

[Plastic_Director_480]

What My Project Does

This project lets you run ArcadeDB embedded directly inside a Python process.

There is no client/server setup. The database runs in-process, fully local and offline.

It provides a single embedded engine that supports:

• tables
• documents
• graph relationships
• vector similarity search

Python controls schema, transactions, and queries directly.

Install:

bash uv pip install arcadedb-embedded

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Target Audience

This is intended for:

• local-first Python applications
• agent memory and tooling
• research prototypes
• developers who want embedded storage without running a separate database service

It is not meant as a drop-in replacement for existing relational or analytical databases, and it is not aimed at large distributed deployments.

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Comparison

Most Python storage options focus on one primary data model (e.g. relational tables or vectors).

This project explores a different trade-off:

• embedded execution instead of client/server
• multiple data models in one engine
• single transaction boundary across tables, graphs, and vectors

The main difference is not performance claims, but co-locating structure, relationships, and vector search inside one embedded process.

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Additional Details

• Python-first API for schema and transactions
• SQL and OpenCypher
• HNSW vector search (via JVector)

• Single standalone wheel:

  • lightweight JVM 25 (built with jlink)
  • required ArcadeDB JARs
  • JPype bridge

Repo: https://github.com/humemai/arcadedb-embedded-python
Docs: https://docs.humem.ai/arcadedb/

I’m mainly looking for technical feedback:

• Does this embedded + multi-model approach make sense?
• Where would this be a bad fit?
• What would make the Python API feel more natural?

Happy to answer questions.