Unlocking Agentic AI: Choosing the Right Long-Term Memory Backend
Why Long-Term Memory Matters in Agentic AI Agentic AI applications, such as Retrieval-Augmented Generation (RAG) with reasoning agents, require more than just a context window. They need long-term memory to preserve knowledge across sessions, personalize responses with historical context, scale to millions of documents or interactions, and support governance features such as access control, auditing, and compliance. Long-term memory ensures knowledge persists across interactions, enables context-aware personalization, and provides governance capabilities that enterprises need for responsible deployment. The Main Choices Several open-source and self-hostable technologies can serve as long-term memory backends in private cloud environments. Weaviate: Schema-based, supports hybrid search (vector + keyword), strong governance with access control and schema support. A strong candidate for enterprise backbones. Milvus: Built for extreme scale...