广告合作
MCP.so
登录

MCP Memory Server with Qdrant Persistence

@delorenj

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

delorenj

关于 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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Memory Server with Qdrant Persistence 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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.

评论

记忆与知识 分类下的更多 MCP 服务器

Agimem

Persistent memory for AI agents. Hosted MCP server with capsule-scoped key/value storage, tags, TTL expiration, bulk ops, and RBAC.

Groundwork

Rarefied-Earth

Groundwork: company memory for your AI tools. Docs public. Source closed. Public proof needs no account.

1

AOCI-CODE

aoci-spec

Local MCP server and CLI that keeps a Git-versioned, model-written index of a codebase and its database schema, so coding agents start each task with the whole system in view.

1.7K

HandbookHub MCP

veryayskiy

Search saved employee handbooks from your AI assistant. Four read-only tools list authorized handbooks, browse policies, search content, and read source pages with citation links. Uses scoped bearer tokens and Streamable

PLUR

plur-ai

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference

226
广告合作