AI memory is starting to behave less like a static notes file and more like a runtime dependency. If an agent depends on memory to retrieve prior decisions, project context, instructions, or compressed knowledge, then the quality of that memory directly affects the quality of the agent’s output. The problem is that memory systems often do not fail loudly. They degrade quietly through stale entries, duplicate memories, broken sync with instruction files like CLAUDE.md, missing logs, weak key structure, or oversized context that reduces retrieval quality.

This release came from a simple systems question: if we monitor infrastructure, logs, APIs, and databases, should memory also have observability? I wanted to experiment with a health check layer that treats memory as something inspectable and maintainable rather than a black box. The goal is not just to store context, but to detect when memory becomes unreliable, noisy, or inefficient before that degradation starts affecting the agent.

This resonates. We have been running into the silent degradation problem too. Stale entries accumulating, duplicate memories from retries, context drift over long sessions. One thing we found useful: separating facts, append-only and curated, from context, ephemeral and aggressively compacted. Makes health checks easier because you know which layer to validate. Have you looked at cryptographic integrity checks? We are experimenting with hashing memory artifacts to detect tampering or corruption, but it is overkill for most use cases. Curious if you are planning open source release. Would love to compare approaches.