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Why this server?
Provides knowledge graph functionality for managing entities, relations, and observations in memory.
Why this server?
Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.
Why this server?
Provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.
Why this server?
This project is based on the Knowledge Graph Memory Server from the MCP servers repository and retains its core functionality.
Why this server?
Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.
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?
A Model Context Protocol server that optimizes token usage by caching data during language model interactions, compatible with any language model and MCP client.
Why this server?
This advanced memory server facilitates neural memory-based sequence learning and prediction, enhancing code generation and understanding through state maintenance and manifold optimization as inspired by Google Research's framework.
Why this server?
A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
Why this server?
A TypeScript-based server that provides a memory system for Large Language Models (LLMs), allowing users to interact with multiple LLM providers while maintaining conversation history and offering tools for managing providers and model configurations.