mcp-duckdb-memory-server
by IzumiSy·★ 57·Score 46
A production-ready MCP memory server using DuckDB as backend for knowledge graph storage and retrieval.
Overview
This MCP server implements a memory system with a DuckDB backend, replacing the original JSON file-based storage. It provides a structured approach to storing and retrieving knowledge graph data including entities, observations, and relationships. The implementation combines SQL queries with Fuse.js for flexible entity searching, offering both structured queries and fuzzy matching capabilities. It maintains transaction integrity and supports indexing for better performance with large datasets.
Try asking AI
After installing, here are 5 things you can ask your AI assistant:
When to choose this
Choose this when you need a persistent, scalable memory system for AI applications with structured knowledge graphs and efficient search capabilities.
When NOT to choose this
Don't choose this if you need a simple in-memory solution for very small datasets or if you prefer JSON-based storage without external dependencies.
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Installation
Installation
Via Smithery
npx -y @smithery/cli install @IzumiSy/mcp-duckdb-memory-server --client claudeManual Install
Add to claude_desktop_config.json:
{
"mcpServers": {
"graph-memory": {
"command": "npx",
"args": [
"-y",
"@izumisy/mcp-duckdb-memory-server"
],
"env": {
"MEMORY_FILE_PATH": "/path/to/your/memory.data"
}
}
}
}Docker
docker build -t mcp-duckdb-graph-memory .
docker run -dit mcp-duckdb-graph-memoryFAQ
- What is the difference from the original memory server?
- This implementation replaces the JSON file backend with DuckDB, providing better performance, scalability, and query flexibility with SQL support.
- How does the fuzzy search work?
- The system combines DuckDB SQL queries with Fuse.js. SQL retrieves base data while Fuse.js provides fuzzy matching capabilities on top of the retrieved data.
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Last updated · Auto-generated from public README + GitHub signals.