I struggled a bit with context sharing, knowledge sharing, memories sharing between AI agents (I use two or three on a daily basis). Each of them has its own memory, they dont share it or its a bit cumbersome to do memory curation and improve it (especially if there are some API AI calls that run occasionally from different models)Memoars is an attempt to solve it - one memory that belongs to you (no storage vendor lock) that any assistant can read and write through MCP.How it works:- Memory content is encrypted on your machine (XChaCha20-Poly1305, key derived with Argon2id) and written directly to storage you own - R2, S3, MinIO, Supabase etc)- A coordinator handles the metadata plane: sequence numbers, versions, grants, conflict resolution. It never receives the workspace content key, so it can't read memory content. It does see operational metadata - org, workspace, identity, version, usage- Every change lands in an append-only, hash-chained log with compare-and-swap on writes, so t...