MLP Storage Layer

To store encrypted AI agent memory across platforms, use a portable storage layer that encrypts memory blobs end-to-end and writes them to decentralized storage like IPFS or local disk. This ensures memories survive session resets and travel with the agent across environments.

Persisting AI agent context between sessions is best handled by generating a ContextPack that bundles relevant semantic, episodic, and reflection memories with access policies, then loading it to bootstrap the next session with full provenance.

IPFS works with Node.js for storing encrypted memory blobs through the ipfs-http-client dependency. Memories are encrypted with tweetnacl before being pinned to IPFS or written locally, providing verifiable integrity and decentralized access.

Cryptographic attestations for AI memory provenance work by signing each memory envelope with a portable IdentityKernel using tweetnacl. This binds the memory blob to its origin agent identity, creating verifiable proof of ownership and integrity across storage backends.

Decentralized storage supports sovereign AI agent identity by coupling an IdentityKernel with encrypted memory blobs on IPFS. The agent retains ownership of its cryptographic keys and memory attestations, enabling portable identity independent of any single platform.

Local storage for AI agent memory limits portability to a single machine, whereas IPFS enables decentralized access and verification across environments. Both options maintain encryption and attestations, but IPFS provides verifiable integrity without relying on a central server.