Oracle is introducing AI Agent Memory 26.6, high-accuracy, low-latency, enterprise-grade memory for agents that operate where the stakes are real.
Agent Memory 26.6 introduces hybrid search, combining:
- Vector search for semantic understanding
- Keyword search for names, identifiers, and exact language
- scoped retrieval across users, agents, threads, record types, and metadata
It also supports durable memories, facts, guidelines, and preferences as distinct record types. The result is memory designed to be more accurate where enterprises need it, not merely finding related text, but retrieving the right information in the right context.
Oracle AI Agent Memory runs on Oracle AI Database, the database trusted with many of the world’s most mission-critical workloads, according to the company. That means memory, metadata, scopes, transactional state, vector search, keyword search, hybrid indexes, retention controls, and deletion workflows can live together.
With OracleDBEmbedder, embeddings can be generated in the database, reducing external network hops. With HNSW vector indexing and in-database hybrid search, teams can build for responsive retrieval without creating an archipelago of systems that must be synchronized, secured, and explained.
Oracle AI Agent Memory 26.6 adds full CRUD capabilities for threads, messages, memories, user profiles, and agent profiles. Teams can update messages and memories, manage thread metadata, and delete records with cascading cleanup.
The release also adds time-to-live controls and schema-level retention configuration. Applications can decide whether information should expire relative to when it was stored or when the underlying event occurred.
For more information about this news, visit www.oracle.com.