Memora's Serializable Memory Asset Protocol

LLM & AI Agent architectures represent a paradigm shift in digital interaction and knowledge creation. However, current frameworks lack standardized mechanisms for managing, owning, and exchanging the valuable intellectual property generated and consumed by these agents—their “memory.” This AI Agent Memory, encompassing interaction logs, learned representations (such as vector embeddings for retrieval-augmented generation), and defined personas, remains largely siloed and lacks portability and verifiable ownership. To address this gap, we propose the Serializable Memory Asset Protocol (SMAP), version 0.1, a comprehensive protocol specifically designed for the assetization of AI Agent Memory as Non-Fungible Tokens on high-performance blockchains, with an initial focus on Solana. SMAP introduces a standardized data model, a rich metadata schema optimized for discovery and utility (particularly for retrieval-augmented generation systems), and recommendations for secure off-chain data packaging using Apache Parquet. By serializing AI Agent Memory into verifiable, transferable SMAP NFTs, the protocol establishes clear ownership, enables transparent provenance, fosters interoperability, and provides the foundation for novel AI knowledge marketplaces. Leveraging Solana’s low transaction fees, high throughput, scalable Compressed NFTs, and robust NFT ecosystem, SMAP aims to unlock the economic potential of AI-generated intellectual property and drive a more open, interconnected, and efficient agentic economy. This document details the protocol’s rationale, core concepts, technical specifications, workflows, security considerations, and future development roadmap.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-05
DOI
https://doi.org/10.5281/zenodo.15341981
Primary Topic
Bayesian Methods and Mixture Models
Type
article
Field-Weighted Citation Impact
0.00
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article

Memora's Serializable Memory Asset Protocol

Zenodo (CERN European Organization for Nuclear Research)
Bayesian Methods and Mixture Models
article

Memora's Serializable Memory Asset Protocol

article en

Abstract

LLM & AI Agent architectures represent a paradigm shift in digital interaction and knowledge creation. However, current frameworks lack standardized mechanisms for managing, owning, and exchanging the valuable intellectual property generated and consumed by these agents—their “memory.” This AI Agent Memory, encompassing interaction logs, learned representations (such as vector embeddings for retrieval-augmented generation), and defined personas, remains largely siloed and lacks portability and verifiable ownership. To address this gap, we propose the Serializable Memory Asset Protocol (SMAP), version 0.1, a comprehensive protocol specifically designed for the assetization of AI Agent Memory as Non-Fungible Tokens on high-performance blockchains, with an initial focus on Solana. SMAP introduces a standardized data model, a rich metadata schema optimized for discovery and utility (particularly for retrieval-augmented generation systems), and recommendations for secure off-chain data packaging using Apache Parquet. By serializing AI Agent Memory into verifiable, transferable SMAP NFTs, the protocol establishes clear ownership, enables transparent provenance, fosters interoperability, and provides the foundation for novel AI knowledge marketplaces. Leveraging Solana’s low transaction fees, high throughput, scalable Compressed NFTs, and robust NFT ecosystem, SMAP aims to unlock the economic potential of AI-generated intellectual property and drive a more open, interconnected, and efficient agentic economy. This document details the protocol’s rationale, core concepts, technical specifications, workflows, security considerations, and future development roadmap.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 75%
Bayesian Methods and Mixture Models
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