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⇱ A simple memory MCP server with read/write tools and vector database search | Glama


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A simple memory MCP server with read/write tools and vector database search

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  • Why this server?

    This server explicitly uses Qdrant, a vector database, for storing and retrieving information with semantic search capabilities, aligning perfectly with the 'vectordb to search intelligently' and 'read'/'write' requirements for a simple memory MCP.

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    A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
    Last updated
    Apache 2.0
  • Why this server?

    Utilizes TxtAI for semantic search and memory management, focusing on storing, retrieving, and managing text-based memories, which directly translates to simple 'read' and 'write' operations with intelligent vector search.

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    Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also
    Last updated
    14
  • Why this server?

    Provides persistent memory capabilities using OpenAI's vector stores, enabling AI assistants to 'save' and 'search' through memories, fulfilling the need for a vector database and simple memory operations.

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    A Model Context Protocol (MCP) server that provides persistent memory capabilities using OpenAI's vector stores, allowing AI assistants to save and search through memories across conversations.
    Last updated
    2
  • Why this server?

    Integrates the Mem0 framework with local models and vector storage for persistent memory, allowing users to store, search, and manage contextual information, which maps to simple read/write and intelligent search.

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    A fully self-hosted MCP server that integrates the Mem0 framework to provide persistent memory capabilities for AI assistants using local models and vector storage. It enables users to store, search, and manage contextual information across conversations through a Docker-based deployment.
    Last updated
    3
    MIT
  • Why this server?

    A high-performance persistent memory system using LibSQL with vector search capabilities for efficient knowledge storage. This offers a simple memory solution with intelligent search, supporting core 'read'/'write' functionality.

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    A high-performance, persistent memory system for the Model Context Protocol (MCP) providing vector search capabilities and efficient knowledge storage using libSQL as the backing store.
    Last updated
    6
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    MIT
  • Why this server?

    Similar to id 1721, this server uses LibSQL for persistent memory and vector search, enabling efficient semantic knowledge storage, which provides a simple 'read'/'write' memory with intelligent search.

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    A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.
    Last updated
    6
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  • Why this server?

    This LibSQL-powered memory system features vector search and semantic knowledge storage, making it a strong candidate for a simple memory MCP with intelligent read/write capabilities.

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    🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.
    Last updated
    7
    MIT
  • Why this server?

    A local, offline MCP memory server using SQLite with semantic vector search capabilities. Its 'memory server' description and explicit vector search align well with the user's request for a simple memory with intelligent search.

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    A local, fully-offline MCP memory server that enables persistent storage and retrieval of information using SQLite with both keyword and semantic vector search capabilities.
    Last updated
    10
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    MIT
  • Why this server?

    Integrates with mem0.ai to store, retrieve, and search information, which implies simple memory operations ('read' and 'write') backed by an intelligent vector search mechanism.

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    An MCP server that integrates with mem0.ai to help users store, retrieve, and search coding preferences for more consistent programming practices.
    Last updated
    9
    655
    Apache 2.0