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Getting Started

Memorg is distributed as the memorg package on PyPI. It bundles a Python library, an interactive CLI (memorg), and an MCP server (memorg-mcp).

Prerequisites

  • Python 3.11 or 3.12 — declared in pyproject.toml
  • OpenAI API key — used for embeddings (text-embedding-ada-002, 1536-dim) and chat completions

Choose Your Interface

Memorg can be used in three different ways. Pick the one that matches your workflow:

Interface When to use it Entry point
CLI (memorg) Quick experimentation, single-user chat-with-memory memorg.cli_entry:main
Python library Embed memory in your own application or agent loop from memorg import MemorgSystem
MCP server (memorg-mcp) Expose memory to Claude Desktop or other MCP clients memorg.mcp.cli:main

All three interfaces sit on top of the same MemorgSystem core, so a database written by the CLI can be read by the library or the MCP server, and vice versa.

Storage Footprint

A fresh Memorg install creates two artifacts on disk (default base name memorg):

  • memorg.db — SQLite database holding sessions, conversations, topics, exchanges, and FTS5 indexes
  • memorg.usearch — USearch vector index (1536-dimensional, cosine metric)

Both files share the same base path, so passing --db-path ./data/memorg.db writes ./data/memorg.usearch next to it.

Next Steps

  1. Installation — install from PyPI or source
  2. Quick Start — first session, conversation, topic, and search
  3. Configuration — environment variables and component options

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