Where is memory data stored?
All memory is stored as local markdown files in `~/.ide-memory/projects/`. No database or cloud service is required; data never leaves your machine.

@prasanna00019
基本情報
IDE Memory MCP gives AI coding agents a persistent memory layer that works across any IDE supporting the Model Context Protocol. Write project context once — the AI remembers it everywhere.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"IDE-Memory-MCP": {
"command": "uv",
"args": [
"pip",
"install",
"ide-memory-mcp"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
Cross‑IDE persistent memory for AI coding agents. It remembers project context—architecture, decisions, progress—across any IDE that supports the Model Context Protocol (Cursor, VS Code, Windsurf, Claude Desktop). Built for developers who use AI assistants and want to stop repeating themselves.
Install the package with pip install ide-memory-mcp (or uv pip install ide-memory-mcp). Run ide-memory-mcp setup to auto‑configure your IDEs, then restart your IDE. Manual configuration is also available via IDE‑specific JSON files.
All memory is stored as local markdown files in `~/.ide-memory/projects/`. No database or cloud service is required; data never leaves your machine.
Install with `pip install ide-memory-mcp` or `uv pip install ide-memory-mcp`. The server requires Python 3.11+.
It automatically detects sections not updated for >7 days (stale), sections exceeding 10k characters (large), and sections older than 30 days (pr
Project context your AI can query through MCP — with source evidence.
Local MCP server and CLI that keeps a Git-versioned, model-written index of a codebase and its database schema, so coding agents start each task with the whole system in view.

One permissioned folder your team and your AI agents both log into.
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference
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