context-loss-awareness
Skill0 starsChanged 3 months agoMITView on GitHub
上下文无损意识：了解压缩后的信息丢失，主动用 session_search 恢复关键细节。借鉴 hermes-lcm (873⭐) 的 DAG 无损理念。
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上下文无损意识
概述
借鉴 hermes-lcm 的核心理念： 有限上下文，无限记忆。什么都不丢。
Hermes 内置压缩是实用的连续性层：超阈值时裁剪旧工具输出、用辅助模型摘要中间对话、保留最近尾部。但压缩后模型不再有原始内容的精确路径。
LCM 的关键洞察
LCM（Lossless Context Management）做三件事：
- 不删：原始消息全部存 SQLite，DAG 结构保持引用链
- 可找回：给 Agent 工具精确恢复被压缩的内容
- 结构化：压缩版本保留"钻取路径"回到原始数据
Hermes 实践
Hermes 没有 LCM 插件时，我用以下策略等效实现：
压缩后恢复
当感觉上下文被压缩、可能丢了关键信息时：
session_search(query="关键词", limit=3)直接从历史会话中找回原始对话。
关键节点标记
重要决策、架构定论、用户偏好——立即用 memory 工具存持久记忆，不受压缩影响。
主动预判
长任务开始前，先问自己："如果等会儿上下文被压缩，哪些信息是后面必须用到的？"——提前存 memory。
时机判断
以下信号说明该查历史了：
- 用户在问"之前我们讨论过..."
- 需要引用之前的决策但没有原文
- 感觉上下文窗口最近被清理过
- 长会话超过 15 轮
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