sovseal — Private AI Memory

sovseal — Private AI Memory

Give Claude, Cursor, and every MCP client one shared memory that never leaves your machine. On-device vector search via LanceDB + Transformers.js (384-dim embeddings), AES-256-GCM client-side encryption, and write-behind ciphertext sync. ### What you can do 🧠 **Store memory** — embed and persist any context fact on-device. Automatic de-duplication, PII redaction, and write-behind encrypted sync. 🔍 **Recall memory** — semantic search over stored context. 10 ms warm recall, 0 RTT reads. Network is never on the read path. 📋 **Briefing context** — auto-summarized, reinforcement-aware briefing of procedural and semantic memories for bootstrapping new conversations. ### Tools - `store_memory` — embed content on-device (384-dim ONNX) → write local LanceDB → return. Ciphertext sync runs write-behind; nothing blocks. - `recall_memory` — embed query (LRU-cached) → vector search local LanceDB → top-K by L2 distance. 0 RTT. ### Resources - `sovseal://context/briefing` — summarized, reinfo