mnem-o-matic

Shared memory layer for LLMs. Store documents, knowledge and notes in a single portable database and access them from any MCP-compatible client — Claude Code, VS Code Copilot, ChatGPT, Mistral Vibe, custom agents, or anything that speaks MCP.

Runs privately in a Docker container or natively. Your data never leaves your machine.

The Problem

Every LLM session starts from scratch. Claude doesn't know what ChatGPT learned yesterday. Your Copilot session can't access the architectural decisions you discussed with Claude last week. Each tool operates in complete isolation.

Mnem-O-matic fixes this by providing a shared, persistent memory that any LLM can read from and write to.

What It Stores

Documents — reference material, code snippets, specs, configs, notes. Anything you want LLMs to have access to.

Knowledge — discrete facts, decisions, and observations. "The auth system uses JWT with RS256." "We chose Postgres over SQLite for the main database." "The deploy pipeline runs on GitHub Actions."

Notes — quick thoughts, ideas, observations, and voice transcripts. Informal content that LLMs should be aware of but that isn't structured enough to be a document or atomic enough to be a knowledge entry.

All types support namespaces (per-project or global), tags, and metadata. Everything is searchable via full-text and semantic search. Large documents are automatically split into chunks at store time, so search returns the most relevant passage rather than the entire file — giving agents focused context without burning their context window.

Embedding Model

Semantic search runs on a local embedding model bundled into the Docker image — nothing leaves your machine. Three models are selectable at build time via the EMBED_MODEL build argument: MiniLM (the default) is the smallest and fastest but also the most limited — English only, and the weakest at paraphrased queries; gte-multilingual-base adds strong multilingual retrieval at near-MiniLM query speed; EmbeddingGemma has the best retrieval quality of the three — it resolves paraphrased queries that share no words with the stored content — at a higher CPU and memory cost. You can also bypass the built-in model and point MNEMOMATIC_EMBED_URL at any OpenAI-compatible embedding endpoint. See choosing the built-in embedding model for the full comparison.

Agent Skill

A sample agent skill file is included at skills/mnemomatic/SKILL.md. It teaches an agent how to use Mnem-O-matic effectively — when to search, which search mode to pick, what content type to store, and how to retrieve full content after a search.

The skill is written for Claude Code but can be adapted to any agent framework that supports custom instructions or skill files. Tailor the wording, triggers, and examples to match your agent's terminology and workflow.

To install for Claude Code:

# Personal (available in all your projects)
mkdir -p ~/.claude/skills && cp -r skills/mnemomatic ~/.claude/skills/mnemomatic

# Project-only (available in the current project)
mkdir -p .claude/skills && cp -r skills/mnemomatic .claude/skills/mnemomatic

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