Memini: Persistent memory for MCP agents with hybrid retrieval

Memini: Persistent Memory for MCP Agents

Memini is a Python library designed to equip MCP agents with robust and persistent memory capabilities. It addresses the critical need for agents to retain and effectively recall information across sessions, enhancing their ability to learn, adapt, and perform complex tasks. By leveraging a hybrid retrieval system, Memini offers a sophisticated approach to memory management for AI developers working with MCP frameworks.

What it Does

Memini enables MCP agents to store and retrieve information persistently. This means that data learned or generated by an agent is not lost when the agent stops running. The library implements a tiered storage system, allowing for efficient management of different types of memory. Crucially, it combines both vector similarity search and keyword-based retrieval, providing a flexible and powerful mechanism for agents to access relevant past experiences or knowledge.

Key Features

• Persistent Storage: Ensures agent memory is saved and reloaded across sessions.
• Tiered Memory System: Organizes memory into distinct levels for optimized access and management.
• Hybrid Retrieval: Combines vector similarity search (for semantic understanding) with keyword search (for exact matching).
• MCP Agent Integration: Specifically designed for seamless integration with MCP agent architectures.
• Source Code Available: Open-source on GitHub, allowing for inspection and customization.

Who it's For

Memini is an essential tool for AI developers building sophisticated MCP agents. This includes developers working on agents that require long-term learning, contextual understanding, and the ability to recall specific details from previous interactions. If your MCP agent needs to remember user preferences, past conversations, learned strategies, or any other form of persistent data to improve its performance over time, Memini provides the foundational memory infrastructure.