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securityA
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qualityA basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
Last updated -
9
14,700
51,950
Python
MIT License
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Why this server?
This server provides a basic implementation of persistent memory using a local knowledge graph, allowing Claude to remember information about the user across chats.
Why this server?
This server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.
Why this server?
Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.
Why this server?
Provides knowledge graph functionality for managing entities, relations, and observations in memory with strict validation rules to maintain data consistency.
Why this server?
A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.
Why this server?
This project is intended as a both MCP server connecting to Qdrant, a vector search engine, enabling memory creation using vector embeddings.
Why this server?
A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage for creating memories.
Why this server?
Model Context Protocol 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.
Why this server?
A Model Context Protocol server that enables semantic search and retrieval of Apple Notes content, allowing AI assistants to access, search, and create notes using on-device embeddings, which is like building a memory.
Why this server?
Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis, helpful for creating context for memories.