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Why this server?
This server provides a persistent memory implementation using a local knowledge graph, which lets Claude remember information about users across conversations.
Why this server?
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.
Why this server?
This project is based on the Knowledge Graph Memory Server and retains its core functionality, so it also focuses on memory.
Why this server?
This server reduces token consumption by efficiently caching data between language model interactions, automatically storing and retrieving information to minimize redundant token usage.
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 a semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.
Why this server?
A local MCP server that enables AI applications like Claude Desktop to securely access and work with Obsidian vaults, providing capabilities for reading notes, executing templates, and performing semantic searches.
Why this server?
Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources.
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?
Provides memory/knowledge graph storage capabilities using Supabase, enabling multiple Claude instances to safely share and maintain a knowledge graph with features like entity storage, concurrent access safety, and full text search.