How does M3 Memory retrieve information?

It utilizes a powerful hybrid retrieval system that combines FTS5 keyword matching, semantic vector similarity, and Maximal Marginal Relevance (MMR) diversity re-ranking for comprehensive and accurate recall.

Can I use M3 Memory across multiple devices?

Absolutely. M3 Memory offers optional, easy-to-add bi-directional delta synchronization via PostgreSQL or ChromaDB, allowing your AI agent's memories and context to follow you across different machines seamlessly.

Which AI agents are compatible with M3 Memory?

M3 Memory is compatible with Claude Code, Gemini CLI, Aider, OpenCode, and any MCP (Model Context Protocol) compatible agent. It provides over 60 MCP tools for extensive agent interaction.

Does M3 Memory support data privacy and GDPR compliance?

Yes, M3 Memory is designed with GDPR compliance in mind. It includes built-in tools like `gdpr_export` for data export and `gdpr_forget` for the right to be forgotten, ensuring robust data management.

M3 Memory provides a persistent, local-first memory layer for AI agents, enabling them to retain and recall long-term knowledge across sessions and devices without re-explaining context.

M3 Memory

Name: M3 Memory
Author: skynetcmd

byskynetcmd

•

Herramientas para Desarrolladores

Ciencia de Datos y ML

Productividad y Flujo de Trabajo

Provides a persistent, local-first memory layer for AI agents, enabling long-term knowledge retention and recall across sessions and devices.

M3 Memory

byskynetcmd

•

Herramientas para Desarrolladores

Ciencia de Datos y ML

Productividad y Flujo de Trabajo

Provides a persistent, local-first memory layer for AI agents, enabling long-term knowledge retention and recall across sessions and devices.

M3 Memory equips AI agents with a robust, local-first long-term memory, eliminating the need to repeatedly provide context across sessions. It offers a structured, persistent memory layer that handles facts, decisions, and preferences, allowing agents to retain knowledge across different machines and platforms without relying on cloud services. This framework ensures your AI agents remember past interactions, project details, and evolving information, fostering more efficient and consistent collaboration while upholding data privacy and local control.

Características Principales

01Over 60 MCP tools for comprehensive agent interaction, including chat log capture and multi-agent orchestration.

0210 GitHub stars

03Automatic contradiction handling with bitemporal versioning to preserve full history.

04GDPR compliance with built-in tools for data export (`gdpr_export`) and the right to be forgotten (`gdpr_forget`).

05Hybrid retrieval combining FTS5 keyword matching, semantic vector similarity, and MMR diversity re-ranking.

06Persistent, local-first memory stored in SQLite with cross-device synchronization options.

Casos de Uso

01Providing long-term, persistent memory for AI agents like Claude Code, Gemini CLI, Aider, or any MCP-compatible agent.

02Coordinating multiple AI agents on a shared local knowledge base that automatically syncs across machines.

03Enabling AI agents to retain context, architecture decisions, and troubleshooting steps across sessions and different operating systems.