memtomem 0.1.0.post1

Markdown-first memory infrastructure for AI agents with hybrid search

memtomem

Markdown-first long-term memory infrastructure for AI agents. Hybrid keyword + semantic search across your notes, docs, and code via the Model Context Protocol.

Core philosophy: .md files are the source of truth and the vector database is a derived cache. Manage memories as plain text files — memtomem makes them instantly searchable.

Built for:

• AI agents (Claude Code, Cursor, Windsurf, Claude Desktop) that need to remember between sessions
• Developers who want a searchable knowledge base built from their existing markdown notes — no proprietary database, no vendor lock-in
• Multilingual content (English, Korean, Japanese, Chinese) via bge-m3 embeddings

Installation

# As an MCP server (most common — no install needed, uvx handles it)
ollama pull nomic-embed-text         # one-time embedding model

# Add to Claude Code
claude mcp add memtomem -s user -- uvx --from memtomem memtomem-server

# Or add to .mcp.json for Cursor / Windsurf / Claude Desktop

{
  "mcpServers": {
    "memtomem": {
      "command": "uvx",
      "args": ["--from", "memtomem", "memtomem-server"],
      "env": {
        "MEMTOMEM_INDEXING__MEMORY_DIRS": "/path/to/your/notes"
      }
    }
  }
}

For terminal use, install the CLI separately:

uv tool install memtomem    # or: pipx install memtomem
mm init                     # 7-step interactive wizard

Quick Start

In your AI editor, ask:

"Index my notes folder"   →  mem_index(path="~/notes")
"Search for deployment"   →  mem_search(query="deployment checklist")
"Remember this insight"   →  mem_add(content="...", tags="ops")

That's it. Your agent now has a long-term memory built from plain markdown files.

Key Features

• 🔍 Hybrid search — BM25 (FTS5) + dense vectors (sqlite-vec) merged via Reciprocal Rank Fusion. Exact terms via keyword, meaning via semantic, both at once.
• 📦 Semantic chunking — heading-aware Markdown, AST-based Python, tree-sitter JS/TS, structure-aware JSON/YAML/TOML
• ♻️ Incremental indexing — chunk-level SHA-256 diff means only changed chunks get re-embedded
• 🏷️ Namespaces — scope memories into groups (work / personal / project) with optional auto-derivation from folder names
• 🧹 Maintenance — near-duplicate detection with merge, time-based score decay, TTL expiration, auto-tagging
• 🔄 Export / import — JSON bundle backup and restore with re-embedding
• 🌐 Web UI — full-featured SPA dashboard for search, sources, indexing, tags, sessions, health monitoring
• 🛠️ 72 MCP tools — full feature surface as MCP tools, with mem_do meta-tool routing 64 actions in core mode (default) for minimal context usage

Documentation

Full documentation lives in the memtomem GitHub repo:

Guide	Topic
Getting Started	Start here — install, setup wizard, first use
Hands-On Tutorial	Follow-along with example files
User Guide	Complete feature walkthrough — all tools and patterns
Configuration	All MEMTOMEM_* environment variables
Embeddings	Ollama and OpenAI providers, model dimensions, switching models
MCP Client Setup	Editor-specific configuration
Agent Memory Guide	Sessions, working memory, procedures, multi-agent
Web UI Guide	Visual dashboard reference
Hooks	Claude Code hooks for automatic indexing and search
memtomem-stm	Optional STM proxy for proactive memory surfacing (separate package)

License

Apache License 2.0 — see LICENSE for details.