Nornicdb

v1.0.0Databasesstable

Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes. Then, adding intelligent features like schema

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What is Nornicdb?

Nornicdb is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to nornicdb is a distributed low-latency, graph+vector, temporal mvcc with all sub-ms hnsw search, graph traversal, and writes. using neo4j bolt/cypher and qdrant's grpc means you can switch with no chan...

Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes. Then, adding intelligent features like schema

This server falls under the Databases category on MCPgee, the world's largest MCP server directory with 33,000+ servers.

Features

  • Nornicdb is a distributed low-latency, Graph+Vector, Tempora

Use Cases

Graph and vector search with sub-ms latency
Neo4j/Cypher compatibility
Temporal MVCC database operations
orneryd

Maintainer

LicenseMIT
Languagego
Versionv1.0.0
UpdatedMay 21, 2026
Statushealthy
Maintenanceactive

Works with

ClaudeOpenAIwindowsmacoslinux

Installation

Manual Installation

npx nornicdb

Configuration

Configuration Details

Config File

claude_desktop_config.json

Performance

Response Metrics

Response Time< 200ms
ThroughputMedium

Resource Usage

Memory UsageLow
CPU UsageLow

How to Set Up and Use Nornicdb

NornicDB is a distributed Graph+Vector database that delivers sub-millisecond HNSW vector search, graph traversal via Neo4j-compatible Cypher/Bolt protocol, and Qdrant-compatible gRPC — all in a single engine with temporal MVCC support. It features on-device embedding with BGE-M3 models, a knowledge decay system modeled on the Ebbinghaus forgetting curve, and over 950 APOC functions for in-database ML and text processing. Teams use it as a drop-in replacement for Neo4j or Qdrant that adds native AI memory management and graph-RAG capabilities without running multiple separate services.

Prerequisites

  • Docker installed (recommended for quickest setup) or Go 1.21+ for building from source
  • Ports 7474 (HTTP/Admin UI) and 7687 (Bolt) available
  • At least 4 GB RAM; 8 GB recommended for embedding workloads
  • NVIDIA GPU with CUDA drivers (optional, for GPU-accelerated vector search)
  • A Neo4j-compatible client library (e.g., neo4j Python driver) or Cypher-capable MCP client
1

Start NornicDB with Docker

Pull and run the appropriate Docker image for your hardware. Use the arm64-metal image for Apple Silicon, amd64-cpu for standard x86, or amd64-cuda for NVIDIA GPU acceleration.

# 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
docker run -d --name nornicdb -p 7474:7474 -p 7687:7687 \
  -v nornicdb-data:/data timothyswt/nornicdb-amd64-cpu-bge:latest
2

Verify the admin UI is running

Open the browser-based admin interface to confirm NornicDB started successfully and to run your first Cypher queries interactively.

open http://localhost:7474
3

Connect with a Neo4j-compatible client

Use any Neo4j Bolt driver to connect to NornicDB. The Python neo4j driver works without any code changes.

pip install neo4j

python3 -c "
from neo4j import GraphDatabase
driver = GraphDatabase.driver('bolt://localhost:7687')
with driver.session() as s:
    s.run(\"CREATE (n:Memory {content: 'Hello NornicDB'})\"
    result = s.run('MATCH (n:Memory) RETURN n.content')
    print([r['n.content'] for r in result])
"
4

Run a vector similarity search

Use Cypher's vector index query to perform semantic search over embedded content. NornicDB runs embeddings locally using BGE-M3.

CALL db.index.vector.queryNodes('embeddings', 10, 'machine learning')
YIELD node, score
RETURN node.content, score
ORDER BY score DESC
5

Configure decay profiles for memory management

Create a decay profile to model knowledge forgetting over time using the Ebbinghaus curve. This is useful for AI agent memory systems where older, unused facts should fade.

CREATE DECAY PROFILE working_memory OPTIONS {
  halfLifeSeconds: 604800,
  function: 'exponential',
  visibilityThreshold: 0.10
}
6

Connect Claude Desktop via MCP

Add NornicDB as an MCP server in Claude Desktop so Claude can query your graph and vector data using natural language.

Nornicdb Examples

Client configuration

Configure Claude Desktop to connect to a running NornicDB MCP server.

{
  "mcpServers": {
    "nornicdb": {
      "command": "npx",
      "args": ["nornicdb"],
      "env": {
        "NORNICDB_BOLT_URL": "bolt://localhost:7687",
        "NORNICDB_HTTP_URL": "http://localhost:7474"
      }
    }
  }
}

Prompts to try

Example prompts for querying graph and vector data in NornicDB through Claude.

- "Find the 5 most semantically similar nodes to 'neural network training'"
- "Show me all relationships between Person nodes in the knowledge graph"
- "Create a node representing our Q3 product roadmap and link it to relevant team members"
- "Query the temporal history of the 'config' node to see all changes in the last 30 days"
- "Which memories have decayed below the visibility threshold this week?"

Troubleshooting Nornicdb

Bolt port 7687 is already occupied by a running Neo4j instance

Stop your existing Neo4j service ('neo4j stop' or via Neo4j Desktop) before starting NornicDB, or change NornicDB's bolt_port in nornicdb.yaml to an unused port like 7688 and update your client connection strings accordingly.

Vector search returns no results even though data was inserted

Confirm embedding is enabled in nornicdb.yaml ('embedding.enabled: true') and that the BGE-M3 model file was downloaded. Check the Docker logs with 'docker logs nornicdb' for any model loading errors.

Docker container exits immediately on Apple Silicon

Ensure you are using the arm64-metal image, not the amd64 image. Run 'docker pull timothyswt/nornicdb-arm64-metal-bge:latest' explicitly and check that Docker Desktop is configured to use Apple Virtualization Framework rather than QEMU.

Frequently Asked Questions about Nornicdb

What is Nornicdb?

Nornicdb is a Model Context Protocol (MCP) server that nornicdb is a distributed low-latency, graph+vector, temporal mvcc with all sub-ms hnsw search, graph traversal, and writes. using neo4j bolt/cypher and qdrant's grpc means you can switch with no changes. then, adding intelligent features like schema It connects AI assistants to external tools and data sources through a standardized interface.

How do I install Nornicdb?

Follow the installation instructions on the Nornicdb GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.

Which AI clients work with Nornicdb?

Nornicdb works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.

Is Nornicdb free to use?

Yes, Nornicdb is open source and available under the MIT license. You can use it freely in both personal and commercial projects.

Browse More Databases MCP Servers

Explore all databases servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.

Quick Config Preview

{ "mcpServers": { "nornicdb": { "command": "npx", "args": ["-y", "nornicdb"] } } }

Add this to your claude_desktop_config.json or .cursor/mcp.json

Read the full setup guide →

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