Overview

What is memtomem?

memtomem gives your AI agent memory that persists across sessions and across agents. It runs as a local MCP server — your agent uses the same tool-calling it already does, and past information becomes searchable.

Use It When

• You keep re-explaining yesterday’s decisions in today’s session — memtomem solves the “every new session is a blank slate” problem. Walk through the flow in Memory Persistence Across Sessions.
• You want notes or docs to be searchable by your agent — point mm index ~/notes at a folder of Markdown / structured files and every MCP-connected agent can query it.
• Multiple agents need to share the same knowledge — Claude Code, Cursor, Codex CLI, and any other MCP client share one memory store.

Start in 3 Steps

uv tool install 'memtomem[all]'                       # 1. install
mm init                                               # 2. interactive setup
claude mcp add memtomem -s user -- memtomem-server    # 3. connect your agent

Full walkthrough (including other MCP clients) in Quick Start.

Core Concepts

• Hybrid Search — BM25 keyword + dense vector search merged via RRF, so exact identifiers and meaning-based queries both land. See Hybrid Search.
• Namespaces — Per-agent private spaces (agent-runtime:{id}) plus a shared space for cross-agent knowledge. See Multi-Agent Collaboration.
• Lifecycle Policies — auto_archive / auto_expire / auto_promote / auto_tag run on a background scheduler, so memories are aged and promoted automatically.

Architecture

AI Agent (Claude Code, Cursor, Gemini CLI, …)
    ↕  MCP protocol
memtomem server
    ↕
SQLite (FTS5 + sqlite-vec)

memtomem runs as a local MCP server. All data stays on your machine — SQLite for storage, ONNX for embeddings. No GPU, no external services, no account required.

Relationship to STM

	LTM (memtomem)	STM (memtomem-stm)
Role	Persistent storage & search	Real-time proxy & compression
Required?	Yes (core)	Optional
How it works	Agent calls mem_search when needed	Relevant memories auto-injected into every tool response

The default setup is LTM alone. If you want token-optimized responses with proactive memory injection, add memtomem-stm as a proxy in front.

Package Info

PyPI	memtomem
Latest release	0.2.2
CLI	mm
License	Apache 2.0
GitHub	memtomem/memtomem

Next Steps

• Quick Start — Install and store your first memory in 5 minutes
• Memory Persistence Across Sessions — Save in session A, recall in session B
• Hybrid Search — How the search engine works
• Multi-Agent Collaboration — Namespace design and sharing workflows
• Context Gateway — Define agents / skills / commands once, sync across runtimes
• MCP Tools — Full tool reference
• CLI Reference — mm command reference