AI agents often lose context between interactions, limiting personalization, continuity, and long-term learning. This session introduces persistent agentic memory with OpenSearch, enabling context-aware agents that remember, learn, and improve over time.

We explain how OpenSearch agentic memory provides a durable, searchable memory layer where agents can store and retrieve session history, working context, long-term knowledge, and audit logs. You’ll learn how configurable memory processing extracts facts, preferences, and summaries from interactions, turning raw conversations into lasting intelligence. We also cover namespace design for isolating memory by user, agent, or session to support secure, scalable systems.

In this talk, we’ll demonstrates how to integrate agentic memory with existing AI frameworks using standard REST APIs, allowing seamless connection to LangChain, LangGraph, or custom agent pipelines without vendor lock-in. At the end, we’ll show how memory retrieval directly influences agent decisions and improves personalization over time.