Mastra and Supermemory: Native Memory, Processors, and Scope

Mastra supports multiple forms of memory, including conversation history, working memory, semantic recall, and observational memory, so Supermemory should be added only when its retrieval, user profiles, or cross-application context addresses needs not covered by native capabilities. Each memory store should have a distinct responsibility, with business systems retaining transactional state and shared retrieval services handling deliberately shared documents or context. The Supermemory integration uses `withSupermemory` and should be configured with server-authorized user scopes and unique conversation IDs, initially disabling automatic writes to validate retrieval behavior and avoid storing unverified model output. Compatibility should be verified through pinned package versions and typechecking, while native Mastra resource identities and Supermemory scopes should consistently map to the intended user or workspace. Before enabling persistent writes, teams should define what information may be retained, test corrections, deletions, and excluded messages, and inspect both memory systems for duplicate or conflicting data. Evaluation should compare assembled model inputs across native-only, external-only, and combined-memory configurations, measure token duplication and source attribution, protect application policy from retrieved instructions, and validate behavior across multiple users and conversations before making performance claims.

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