NornicDB

NornicDB

Graph, vector, and historical truth in one database Neo4j-compatible • Hybrid graph + vector retrieval • Historical reads via MVCC Achieving Psygnosis for AI

Multi-arch support: CPU | CUDA | Metal | Vulkan

Quick Start • What It Is • Why NornicDB • Benchmarks • Features • Docs • Comparison • Contributors

Quick Start

# Homebrew
brew tap orneryd/nornicdb && brew install nornicdb && brew services start nornicdb

# arm64 / Apple Silicon
docker run -d --name nornicdb -p 7474:7474 -p 7687:7687 -v nornicdb-data:/data timothyswt/nornicdb-arm64-metal-bge:latest

# amd64 / CPU only
docker run -d --name nornicdb -p 7474:7474 -p 7687:7687 -v nornicdb-data:/data timothyswt/nornicdb-amd64-cpu-bge:latest

Open http://localhost:7474 for the admin UI. For NVIDIA CUDA hosts, use timothyswt/nornicdb-amd64-cuda-bge:latest. For Vulkan hosts, use timothyswt/nornicdb-amd64-vulkan-bge:latest.

Note: Docker on macOS does not expose Metal acceleration. The Apple Silicon image still runs, but GPU acceleration on macOS requires a native install from the releases page or a local build.

Writing queries? Start with the Hot-Path Cypher Cookbook — proven query shapes that route through the executor's specialized fast paths.

🤖 Building with Claude / agents? The docs/skills/ directory contains agent-ready skill files for every Cypher surface: query shapes, decay/promotion policies, managed embeddings, vector & hybrid search, and RAG procedures. Drop them into .claude/skills/ to make agents fluent in NornicDB.

What NornicDB Is

NornicDB is a graph database for workloads that need graph traversal, vector retrieval, and historical truth in the same system. It speaks Neo4j's language through Bolt and Cypher, exposes REST, GraphQL, and gRPC interfaces, and can preserve Qdrant-style client workflows where that helps migration.

The architecture draws from research in Temporal GraphRAG, agent memory systems, event-sourced decision memory, and persistence semantics for AI agents. These ideas appear in NornicDB as graph-native support for temporal facts, canonical knowledge versioning, replayable history, policy-driven memory retention, and audit-friendly retrieval.

It is built for knowledge systems, agent memory, Graph-RAG, and canonical truth stores where semantic search is only part of the query. The design goal is not to bolt a vector store onto a graph database. The design goal is one execution path for graph, vector, temporal, and audit-oriented workloads.

Why NornicDB Is Different