MCP Server for reduce use of Token (github.com)

The recent launch of nxm-memory introduces a local memory and search engine specifically designed for AI assistants, significantly enhancing their functionality by indexing and retrieving information from any collection of files, including code, documentation, and contracts—all while keeping data private on users' machines. Key features include a powerful token reduction mechanism that compresses source code and document content, allowing for more efficient queries. This efficiency is particularly crucial for AI/ML applications, where managing token usage can dramatically reduce costs and improve performance. Configured as a Model Context Protocol (MCP) server, nxm-memory seamlessly integrates with various AI agents like Claude Code and Opencode. Its advanced search capabilities leverage a hybrid approach that combines exact matches with meaning-based queries, ensuring users can obtain relevant answers quickly. The system runs locally, updating its index in real-time as files change, thus functioning as an AI assistant's long-term memory dedicated to specific projects. This combination of local operation, privacy, and reduced token expenditure positions nxm-memory as a vital tool for developers and AI practitioners looking to enhance their workflows and maintain efficient AI interactions.