MLP Storage Layer

Name: MLP Storage Layer
Availability: InStock
Author: riley-coyote

Store encrypted AI agent memories with cryptographic attestations across local or IPFS storage.

34|4|Updated Dec 21, 2025

Search Tags:#encryption #cryptography #decentralized #storage #context-pack #ipfs #mlp

Ready to run

npx skills add https://github.com/Riley-Coyote/memory-ledger-protocol-v0.2 --skill mlp-storage-layer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: MLP Storage Layer
Source: https://github.com/Riley-Coyote/memory-ledger-protocol-v0.2/tree/main/mlp-storage
Command: npx skills add https://github.com/Riley-Coyote/memory-ledger-protocol-v0.2 --skill mlp-storage-layer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dotenv, ipfs-http-client, tweetnacl, tweetnacl-util, yaml.

What problem does it solve?

Memory produced by AI agents is scattered across platforms and difficult to back up or migrate, risking loss of context and ownership. The MLP Storage Layer provides a portable, encrypted memory store with cryptographic provenance, enabling memories to travel with the agent across environments.

Core Features & Use Cases

Persistent, cross-platform memories that survive session resets and context changes
Portable IdentityKernel integration for secure, sovereign identity
End-to-end encryption of memory blobs and cryptographic attestations for provenance
Decentralized storage options (IPFS/Pinata or local) with verifiable integrity
ContextPack generation to bootstrap sessions with relevant memories and policies

Quick Start

Initialize MLP Storage in your project, store memories, and generate a ContextPack to bootstrap a session.

Frequently Asked Questions about MLP Storage Layer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I store encrypted AI agent memory across different platforms?▼

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.

What's the best way to persist AI agent context between sessions?▼

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.

Does IPFS work with Node.js for storing encrypted memory blobs?▼

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.

How do cryptographic attestations work for AI memory provenance?▼

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.

Can I use decentralized storage for sovereign AI agent identity?▼

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.

What are the limitations of local storage vs IPFS for AI agent memory?▼

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.