Memory Lifecycle

Memory inside GM Agents follows a clear lifecycle — from capture, to structuring, to onchain anchoring, to activation inside agents.

This lifecycle ensures that every piece of information a user chooses to store is transformed from raw data into a persistent, verifiable and reusable intelligence asset.

By standardizing how memory is generated, processed, secured and used, GM Agents creates a predictable and transparent system that supports personalization, agent training and future tokenization.

Capture

GM Agents captures memory through:

• Conversations and long-term chat history

• Docs、PDFs、.JSON files

• Behavioral traces (choices, corrections, workflows)

• Optional device-generated context

These inputs do not form memory by themselves.

Memory emerges after AI organizes them into meaningful, time-linked insights, the foundation of a persistent digital identity that only you own.

This captured intelligence becomes the seed for personalized agents.

Structure

Once captured, memory is transformed into a format AI can reliably use, store and reuse.

Step 1 — Embedding

Text, actions and metadata are converted into vector embeddings that encode meaning, intent and personal patterns.

Step 2 — Vector Memory Store

These vectorized shards are stored in a decentralized storage layer (e.g Greenfield) enabling:

• encryption

• access control

• retrieval across applications

• long-term persistence

Step 3 — Onchain Anchoring

Each memory shard is anchored onchain as a hash commitment, ensuring:

• User ownership

• Permission control

• Transparent usage accounting

• Composability

At this point, memory is no longer fleeting data, it becomes a verifiable, user-owned digital asset.

Application

With structured memory in place, users can create personal or public AI agents that inherit their long-term memory.

Agents can be trained or configured to use:

• your tone

• your decision patterns

• your domain knowledge

• your behaviors and routines

• your workflows and templates

Memory becomes the “model weight extension” unique to each user, turning a generic model into your model.

Memory creates the agent and access rights establish the market.

Tokenization

When an agent accumulates enough memory, utility and identity, it can function as its own economic entity.

GM Agents enables the tokenization of memory-backed agents, where memory serves as provenance for the value of the agent’s token.

This creates a new category of assets: Agent Tokens, whose value is tied to an agent’s capabilities and memory set.