📄 中文摘要
在为 AI 代理构建持久记忆时，最关键的问题是如何确保在检索时最相关的记忆能够浮现。信息具有保质期，过时的调试日志、临时解决方案和架构决策的重要性各不相同。如果所有记忆被平等对待，检索结果将会被大量无关信息淹没。为了解决这一问题，作者分享了自己的经验和开源代码，尽管这种方法可能并非最佳，但希望能引发更多人的讨论和不同的解决方案。
为 AI 代理记忆添加生命周期
出处: Adding a Lifecycle to AI Agent Memory
发布: 2026年2月27日
📄 English Summary
Adding a Lifecycle to AI Agent Memory
When building persistent memory for an AI agent, a crucial challenge is ensuring that the most relevant memories surface during retrieval. Information has a shelf life; outdated debug logs, temporary workarounds, and architectural decisions all hold varying levels of importance. Treating every memory equally can lead to retrieval results being overwhelmed by irrelevant data. To address this issue, the author shares their experiences and open-source code, acknowledging that their approach may not be the best and inviting others to share their solutions.