MnemoX Lite
Provides a persistent, semantic memory system for large language models to overcome context limitations across conversations.
About
MnemoX Lite is an experimental Model Context Protocol (MCP) server designed to address a fundamental limitation of Large Language Models: their inability to retain context across separate conversations. It implements a sophisticated semantic memory system that intelligently chunks, contextualizes, and stores information using vector embeddings. This allows LLMs to recall relevant insights, project decisions, and accumulated knowledge through natural language queries, operating transparently in the background to ensure persistent, curated memory.
Key Features
- Automatic memory curation for conflict resolution and redundancy elimination.
- 2 GitHub stars
- Transparent integration as an MCP server with tools like Claude Desktop.
- Core semantic memory operations: remember, recall, create/list projects.
- Intelligent semantic chunking and automatic context emergence from content.
- Project-based memory segregation ensuring complete isolation between knowledge spaces.
Use Cases
- Enabling persistent memory for LLM interactions in conversational AI tools like Claude Desktop and Cursor.
- Managing and retrieving project-specific knowledge that spans multiple LLM conversations.
- Experimenting with advanced semantic memory concepts for AI systems.