Advertise
MCP.so
Sign In

MCP Memory Server with Qdrant Persistence

@delorenj

Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

delorenj

About MCP Memory Server with Qdrant Persistence

MCP server providing a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database

MCP Memory Server with Qdrant Persistence config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-qdrant-memory": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "mcp-qdrant-memory",
        "."
      ]
    }
  }
}

MCP Memory Server with Qdrant Persistence tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Memory Server with Qdrant Persistence?

This MCP server provides a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database. It stores entities and relations in a graph structure, persists data both in a local JSON file and as embeddings in Qdrant, and uses OpenAI for generating semantic embeddings. It is designed for developers building AI assistants or applications that need persistent, semantically searchable memory.

How to use MCP Memory Server with Qdrant Persistence?

Install dependencies with npm install, then build with npm run build. Set the required environment variables (OPENAI_API_KEY, QDRANT_URL, QDRANT_API_KEY, QDRANT_COLLECTION_NAME) and add the server configuration to your MCP settings file under mcpServers. The server exposes tools for creating and querying the knowledge graph and searching semantically.

Key features of MCP Memory Server with Qdrant Persistence

  • Graph-based knowledge representation with entities and relations
  • File-based persistence via a memory.json file
  • Semantic search using Qdrant vector database
  • OpenAI embeddings for semantic similarity
  • HTTPS support with reverse proxy compatibility
  • Docker support for easy deployment

Use cases of MCP Memory Server with Qdrant Persistence

  • Give an AI assistant a persistent, searchable memory of entities and their relationships
  • Enable semantic search across stored knowledge concepts and observations
  • Maintain a dynamic knowledge graph that synchronizes between local file storage and a vector database

Frequently asked questions

1

What runtime dependencies are required?

Node.js, an OpenAI API key, and a running Qdrant server with a configured collection are required.

2

Where does the data live?

Data is stored both in a local file (`memory.json`) holding the full graph structure and in Qdrant as semantic embeddings for similarity search.

3

What tools does the server expose?

The server provides tools for entity and relation management (`create_entities`, `create_relations`, `add_observations`, `delete_entities`, `delete_observations`, `delete_relations`, `read_graph`) and semantic search (`search_similar`).

4

How does synchronization between file storage and Qdrant work?

When entities or relations are modified, changes are written to `memory.json`, embeddings are generated via the OpenAI API, and the vectors are stored in Qdrant, keeping both systems consistent.

5

How is authentication and transport configured?

Authentication is handled via environment variables (`OPENAI_API_KEY`, `QDRANT_API_KEY`). The server supports HTTP and HTTPS connections to Qdrant and includes configuration for reverse proxies and custom SSL/TLS setups.

Comments

More Memory & Knowledge MCP servers

LoreKeeper RPG (lorekeeper.com)

LoreKeeper Ltd

Mess in, magic out: pour in your D&D and TTRPG notes and LoreKeeper keeps them as a private store for your world. Ask Claude or ChatGPT about it, or build a sourcebook.

Codebase Memory

DeusData

High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

46.7K

Snipara

alopez3006

MCP connector for Snipara, the Project Brain for AI coding agents

1

ctxfile

ctxfile

Local-first MCP server that snapshots your project's working state into one context object. Open-core, privacy-first.

2

priostack

ideaswave

Open-source Python SDK & MCP interface for Priostack Agent Context Network (ACN). Zero-setup, autonomous memory orchestration for AI agents.

Advertise