It is certainly a positive development to see the largest data platforms shifting towards model-flexible and framework-agnostic memory solutions. For instance, @databricks launched agent memory into beta on Oct 12, just after @MongoDB introduced the capability to Atlas last month.
Nevertheless, a significant issue persists. While this memory might function across different models and agents, it frequently stays anchored to a single data platform. As a result, the valuable customer insights acquired by your agents transform into yet another trapped asset that you cannot relocate.
Walrus Memory ensures that your data remains completely yours. It acts as an independent memory layer that accompanies your agent everywhere. Because it is not restricted to any specific platform, it can be accessed from any runtime or model. Storing a fact is as simple as using remember(), and bringing it back just takes recall().
https://docs.databricks.com/aws/en/release-notes/product/2026/october
Nevertheless, a significant issue persists. While this memory might function across different models and agents, it frequently stays anchored to a single data platform. As a result, the valuable customer insights acquired by your agents transform into yet another trapped asset that you cannot relocate.
Walrus Memory ensures that your data remains completely yours. It acts as an independent memory layer that accompanies your agent everywhere. Because it is not restricted to any specific platform, it can be accessed from any runtime or model. Storing a fact is as simple as using remember(), and bringing it back just takes recall().
https://docs.databricks.com/aws/en/release-notes/product/2026/october