Amazon OpenSearch Brings Selective Long-Term Memory to AI Agents

Amazon OpenSearch Service has introduced purpose-built agentic memory APIs through its ml-commons plugin, designed to give AI agents persistent and semantically searchable recall across conversations. The system addresses a core limitation of large language models: stuffing too many tokens into a context window degrades answer quality rather than improving it. Instead of replaying full conversation transcripts, OpenSearch organizes memory into sessions, working memory, and long-term storage, extracting key facts via an LLM and embedding them as vectors for precise retrieval. This means an agent can recall, for example, that a customer prefers conservative investments without re-reading months of chat history. The approach reframes AI memory as a search problem, retrieving only the most relevant facts needed at any given moment.