Show HN: MSAM – Memory system for AI agents that knows when it doesn't know (github.com)

A new cognitive memory architecture for AI agents called Multi-Stream Adaptive Memory (MSAM) has been introduced, designed to improve the way AI systems manage memory. MSAM offers a structured memory system that allows agents to persistently store, retrieve, and even forget information autonomously. The architecture relies on a blend of semantic, episodic, procedural, and working memory streams, employing an innovative hybrid retrieval pipeline that incorporates embedding similarity, keyword matching, and knowledge graphs. This system not only produces outputs proportional to an agent's confidence but also ensures that it avoids generating incorrect information, admitting when it does not know something. The significance of MSAM in the AI/ML community lies in its advanced capabilities for efficient memory management and its production readiness, as evidenced by its deployment and impressive performance metrics. The architecture achieves 99.3% compression during startup and drastically reduces session token usage by 89% compared to traditional flat file systems. Key features such as intentional forgetting, temporal awareness through a structured knowledge graph, and adaptive retrieval make MSAM a groundbreaking advancement for developing robust and flexible AI agents. The deployment is facilitated by a REST API, making it accessible across different programming languages and integrations.