Persistent local semantic memory for AI agents

Persistent Local Semantic Memory for AI Agents

For AI developers building agents that require robust, long-term memory, managing and retrieving information efficiently is paramount. Traditional methods often rely on cloud-based solutions or simpler key-value stores, which can introduce latency, privacy concerns, and scalability issues. This tool offers a decentralized, privacy-focused approach to semantic memory, designed to integrate seamlessly into your AI agent architecture.

What it Does

This project provides a persistent, 100% local semantic memory solution for AI agents. It enables agents to store and retrieve information based on its meaning, rather than just keywords. By leveraging MLX on Apple Silicon or CPU, it offers efficient processing for semantic search and knowledge graph operations directly on the user's machine. This ensures data privacy and reduces reliance on external services.

Key Features

• 100% Local: All data and processing remain on your local machine, ensuring maximum privacy and control.
• MLX Integration: Optimized for performance on Apple Silicon, with fallback CPU support.
• Hybrid Search: Combines semantic search with traditional keyword matching for comprehensive retrieval.
• Knowledge Graph: Builds and utilizes a knowledge graph to represent relationships between stored information, enabling more nuanced understanding and retrieval.
• Persistence: Memory is saved and loaded, allowing agents to retain context across sessions.
• Developer Focused: Designed for integration into custom AI agent frameworks.

Who it's For

This tool is ideal for AI developers and researchers who are building:

• Autonomous AI Agents: Agents that need to learn, adapt, and recall information over extended periods.
• Personal AI Assistants: Assistants that require secure and private memory of user interactions and preferences.
• Research Projects: Experiments requiring local, controllable data storage for AI models.
• Applications with Sensitive Data: Scenarios where cloud-based memory solutions are not feasible due to privacy or security constraints.