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vectordb

epsilla-cloud/vectordb

High performance Vector Database Management System

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

46views this month

875 stars45 forksLast push Nov 29, 2025 C++ GPL-3.0

Decision brief

vectordb is a high-performance vector database management system written in C++ under the GPL-3.0 license, designed for applications involving embeddings and neural search.

Good fit when

  • When you need a high-performance vector database management system that supports embeddings and neural search.
  • If your application requires efficient storage and retrieval of vector data, vectordb can be a suitable choice.

Avoid when

  • If your project requires a proprietary license, vectordb's GPL-3.0 license may not be compatible.
  • If you are looking for a solution that does not require setting up a Docker container or building Python bindings, vectordb might not be the best fit.

Observed Sep 18, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Slowing (292d since push)
As of Sep 18, 2026
Provenance
Not a fork · Organization account
As of Sep 18, 2026
Security (OSV)
No lockfile
As of Sep 18, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/epsilla-cloud/vectordb

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Epsilla is a vector database management system that supports high-performance vector storage and retrieval, suitable for applications involving embeddings and neural search.

Capability facts

Languages
c++

Source: github.language · Sep 18, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 18, 2026)

**2. Interact with Python Client**
Source link

Tags

README

Quick Start using Docker 1. Run Backend in Docker 2. Interact with Python Client shell cd engine/scripts (If on Ubuntu, run this first: bash setup dev.sh) bash install oatpp modules.sh cd .. bash build.sh ls lh build/ .so shell cd engine export PYTHONPATH=./build/ export DB PATH=...

For agents

This page has a .md twin and JSON over the API.

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