MemoryOps AI treats AI memory as governed state — policy-before-storage, auditable deletion, typed memories
Fit_Fortune953 · reddit · 2026-09-14
Developer FitFortune953 built MemoryOps AI as a side project arguing AI memory shouldn't be "embed → vector DB → retrieve" but a governed state system addressing enterprise questions: tenant isolation, real deletion guarantees, and tracing which memories influenced an answer.
Key design choices:
- Policy-before-storage: the LLM extractor only proposes memories; a deterministic policy broker is authoritative and blocks secrets/PII before storage.
- Deletion as an invariant: soft-delete keeps a governance tombstone + append-only audit trail, with background compaction clearing content/vectors — "auditable compaction + retrieval-exclusion," not crypto-shred.
- Typed memory (episodic/semantic/procedural/project) instead of one blob.
- Runs offline by default with deterministic stubs; OpenAI/Anthropic/Gemini adapters optional.
Python/FastAPI + Next.js, pre-1.0. The author asks whether governance or retrieval quality is the real production pain point.
Related event: MemoryOps AI: Turning AI Memory Into a Governed System(2 posts)→
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