What is Memory and its primary function?

Memory acts as an MCP server, exposing your local persistent knowledge to AI agents like Claude, enabling them to instantly access and utilize structured information.

What format does Memory use for storing knowledge?

Knowledge is stored in standard Markdown files, enhanced with YAML Front Matter for rich metadata and structured information within each note.

How does Memory help organize vast amounts of information?

It combines the robust PARA method (Projects, Areas, Resources, Archives) for high-level organization with Zettelkasten principles (UIDs, backlinks, link integrity) for atomic, interconnected notes.

What makes Memory's search and association capabilities unique?

Memory features advanced search via SQLite FTS5 and a link graph, alongside the Olima engine for contextual reranking and intelligent knowledge recommendations.

Memory

Name: Memory
Author: inchan

byinchan

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生産性とワークフロー

学習とドキュメンテーション

コンテンツ管理

Exposes local persistent memory as a server for AI agents to access and utilize knowledge.

Memory is a project that transforms local persistent memory into an MCP server, making it instantly accessible to Claude and other MCP-compatible AI agents. It leverages a robust system for knowledge management, including Markdown and YAML Front Matter for storage, PARA (Projects/Areas/Resources/Archives) for organization, and Zettelkasten principles (UID, backlinks, link integrity) for powerful connections. Enhanced search capabilities with SQLite FTS5 and a link graph are complemented by an Olima context-based reranking and recommendation engine, allowing for intelligent knowledge retrieval and association.

主な機能

01Stores knowledge in Markdown with YAML Front Matter

02Organizes information using the PARA (Projects/Areas/Resources/Archives) method

03Connects notes via Zettelkasten principles, including UIDs, backlinks, and link integrity

04Provides advanced search with SQLite FTS5 and a link graph (extensible to embedding similarity)

05Enables contextual association and recommendation using the Olima engine

061 GitHub stars

ユースケース

01Enhancing AI agent capabilities with context-based memory access and recall

02Providing a personal, persistent knowledge base for AI agents

03Integrating structured and connected notes with AI-driven workflows