Feature request: selective memory checkpoints for very long ChatGPT and Codex threads

ChatGPT Feature requests
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1

We are frequent users in Brazil, using ChatGPT and Codex daily to operate and evolve a healthcare marketplace. In long-running operational chats spanning many work sessions, the bottleneck is no longer only whether the system remembers context. The system can appear to repeatedly process too much historical context, which may lead to latency, UI freezing, duplicated responses, fragmented answers, and gradual degradation over time.

Suggested approach: incremental, selectively retrievable memory checkpoints

  1. Keep the most recent conversation turns fully active.
  2. Compact older history into immutable checkpoints indexed by entities, topics, decisions, people, files, and keywords.
  3. Retrieve only the checkpoints relevant to the current request.
  4. Preserve access to the original conversation details when necessary.
  5. Give decisions and state changes higher priority than casual conversation.
  6. Index relationships and aliases, not only literal keyword matches.
  7. Let advanced users inspect and pin selected checkpoints as canonical memories.

The desired outcome is long context with selective retrieval, instead of long context with brute-force reprocessing.

This would be especially valuable for users maintaining a continuous working relationship with the same chat across product development, paid media, QA, automation, customer support, and marketplace operations.

Submitted as a practical product suggestion from a frequent Brazilian user.

— WBusiness / Life Center Shop

2

Thanks for the detailed suggestion. Codex has /compact for long conversations: https://learn.chatgpt.com/docs/developer-commands. We’ll pass along the selective-checkpoint and pinning ideas. We don’t have a timeline to share; the freezing and duplicate responses would also benefit from a specific example.