Why this server?

A basic implementation of persistent memory using a local knowledge graph, allowing Claude to remember information about the user across chats.

Why this server?

An implementation of persistent memory for chat applications by utilizing a local knowledge graph to remember user information across interactions.

Why this server?

Provides a set of tools and resources for AI assistants to interact with Memory Banks, which are structured repositories of information that help maintain context and track progress across multiple sessions.

Why this server?

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

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?

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?

A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.

Why this server?

Maintains consistent LLM interaction styles across conversations by storing emoji-based context keys (emojikeys) that can be used across different devices and applications.

Why this server?

A Model Context Protocol server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

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.