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Isolating AI Memory across Clients: Preferences, Conversations and Shared Sources

Memory makes it easier to do repetitive work and may carry information from one client to another. This article refers to persistence and sensitive information risk materials, discusses the different boundaries of thread status, long-term preferences, and shared knowledge, and gives account switching test ideas. Don't think of "model remembered" as database permissions. Reads and updates still require identity and scope confirmation by the application.

Chinese edition published 23 Oct 2026 · 阅读中文原文 ↗

Source timingPublic information and official documents; verified on October 23, 2026.

LangGraph distinguishes between thread checkpoints and cross-thread storage, and OWASP reminds that model applications may leak sensitive information. This article discusses customer isolation design and does not provide security certification for specific platforms.

First define what information is allowed to cross tasks

For analog design services, user-preferred file formats can be reused within the same client, but quotations and unpublished materials from another client should not be shared. Each long-term record needs to clearly identify its owner, purpose and maintainer.

Check the access scope before and after retrieval

You cannot just write "Do not disclose other customer information" in the prompt word. When applying query storage, the identity and space should be limited, and the source of the returned results needs to be verified. Use a test account to create similar questions and check whether crosstalk occurs.

Deletion and error correction must be able to overwrite the relevant storage

Users who request to modify their preferences or clean up their data should confirm how conversations, long-term records, and derived indexes are handled respectively. Logs should also not retain their original content without boundaries. The article gives design checkpoints, and the specific retention period and data processing rules must be confirmed based on actual business.

Extended reading: AI Entrepreneurship.

Sources and editorial notes

AI assists in organizing and writing, and editors check public sources. The main text is an independent analysis by ChinaOPC, with source facts and simulation plans separated; no interviews, financial audits or actual product testing have been conducted. Official documents are based on the original text of the source and have not been adapted or replaced. English translation of the Chinese edition, prepared with machine assistance and editorial checks. Source verification dates refer to the original Chinese research.

Source titles and publisher names are retained in their original language. Refer to the linked originals for official documents and current requirements.

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