I built an open-source memory governance layer for AI assistants - looking for technical feedback [P]

I’ve been working on a project called MemoryOps AI. The problem I’m trying to solve is context debt in AI agents. Most memory demos look like this: chat message → vector database → retrieve later That works for demos, but I think production agents need more than retrieval. They need rules for what memory is allowed to survive, what should expire, what should be blocked, what can be updated, and what must be audited. MemoryOps AI treats memory as governed state. The lifecycle is: Capture → Evaluate → Store → Retrieve → Rank → Compose → Update → Forget → Audit Some things I built into it: Policy-before-storage, so sensitive/secret-like content is filtered before memory is saved Typed memories instead of one generic memory bucket Tenant isolation Deletion guarantees Provenance for stored memory Append-only audit logs Retention policies Legal hold Consent-aware memory Background workers for lifecycle tasks A small playground/demo to test memory behavior I’m not posting this as a polished company launch. I’m mainly looking for feedback from people building agents, RAG systems, evals, or AI infrastructure. The questions I’m trying to answer are: What should an AI memory system be allowed to remember? How should old memory expire or get overwritten? How would you test that deleted memory never influences future output? What invariants would you expect before trusting memory in a real assistant? GitHub: https://github.com/patibandlavenkatamanideep/memoryops-ai Demo: memoryops-ai-production.up.railway.app Would appreciate any technical feedback, especially around memory lifecycle design, governance, and evals.