OpenClaw agents lose conversation history between sessions despite files existing on disk

OpenClaw agents lose their entire conversation history between sessions even when the files exist on disk. Silent daily session resets wipe agent memory without warning. Meanwhile, every frontier LLM degrades past 50K tokens (proven by Chroma across 18 models), meaning even within a session, agents progressively forget earlier context. ByteDance's OpenViking (19K stars in two weeks) proves massive demand for agent memory infrastructure. This tool gives OpenClaw agents a persistent, queryable memory layer that survives crashes, session boundaries, and context window limits by storing structured knowledge externally and injecting only relevant memories per turn.

Operators who run OpenClaw at scale report a recurring silent failure mode. The memory layer drops context unpredictably, there is no built in observability to tell you when it happens, and the only way to detect it is when a user complains that the agent forgot something. Third party memory plugins like hmem and MemOS Cloud replace the stack entirely but none solve the detection problem. This runtime middleware hooks into OpenClaw memory reads and writes, fingerprints every stored fact, runs continuous integrity checks against the filesystem, and alerts operators in real time when memory degrades or truncates so they can recover before user trust breaks.