Show HN: Mnemosyne – Cognitive memory OS for AI agents (zero LLM calls) (github.com)

The launch of Mnemosyne marks a significant advancement in AI memory systems, addressing the critical limitation of stateless AI agents that struggle with persistence and learning. Unlike traditional models, Mnemosyne employs a 5-layer cognitive architecture that mimics human memory processing, allowing AI agents to remember, share, and improve over time without relying on costly large language model (LLM) calls. Its zero-LLM ingestion pipeline enables agents to process and store information in under 50ms, making it an efficient solution for production environments. This innovative memory system introduces ten advanced capabilities such as reinforcement learning, real-time memory updates, and a bi-temporal knowledge graph, enabling features like proactive recall and theory of mind for collaborative AI. With 13,000 memories already operational across a network of ten agents, Mnemosyne demonstrates practicality and readiness for real-world applications, setting a new standard for autonomous AI systems. By leveraging Mnemosyne, developers can create smarter, more adaptive agents that not only remember past interactions but also leverage that information to enhance future performance, representing a crucial step toward achieving autonomous intelligence.