Persistent Memory for a Python Agent: Start with a Tested Store

Persistent memory for Python agents requires storing user-specific context outside individual model requests and retrieving relevant records before responding, beginning with a transparent, testable system of explicit preferences rather than automatic fact extraction. The example uses SQLite to create a durable fact store scoped by tenant and authenticated user, supporting updates, retrieval, deletion, and persistence across database reopenings while emphasizing that database filtering does not replace authentication. Applications should inject relevant preferences, such as a scheduling timezone, into model context without inventing missing facts or treating assistant suggestions as confirmed user data, while transactional information should remain in the system that owns it. Storage tests should independently verify persistence, updates, deletion, and cross-user or cross-tenant isolation, while separate tests assess whether the model appropriately uses stored information and resists treating it as instructions. Semantic search and managed services such as Supermemory become useful for free-form conversations, document retrieval, profiles, and correction workflows, but require secure server-side API use, consistent authorization scopes, processing-state awareness, and explicit mappings for updates and deletion.

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