Memory Technical Architecture

GM Agents’ memory system is designed as a four-layer architecture. The pipeline captures raw interactions, converts them into structured vector memory, retrieves them intelligently through RAG for agent reasoning and ultimately anchors each memory shard onchain as a cryptographically owned commitment.

Memory Capture Layer

The Memory Capture Layer is responsible for collecting meaningful signals generated across a user’s interactions. It ingests both structured and unstructured data, such as conversations, notes, documents, behavioral patterns, preferences, corrections, JSON files and app interactions. This layer does more than simply log data, it identifies intent, habit formation and long-term behavioral signatures that represent the user’s personal intelligence.

Captured signals pass through a preprocessing pipeline that includes normalization, metadata extraction, timestamping and semantic segmentation. By shaping raw signals into coherent units of meaning, this layer establishes the foundation upon which persistent memory can be built. Nothing is recorded as “memory” until it is interpreted and transformed into a durable representation owned by the user.

Vector Memory Layer

After capture, the system transforms signals into vector embeddings through a multi-model embedding pipeline optimized for personalization. Each memory shard is encoded to preserve meaning, context and the user’s unique style or reasoning patterns.

These vectors are stored in decentralized storage networks such as Greenfield. This ensures immutability rather than centralized platform ownership.

On top of raw storage, the ZEP Engine constructs higher-level representations: preference graphs, identity embeddings, habit vectors and thematic clusters of user knowledge.

This transforms scattered experiences into a long-term, high-resolution memory graph, optimized for RAG retrieval and agent-level reasoning.

Context Retrieval Layer

The Context Retrieval Layer turns stored memory into active intelligence for AI agents. Through a Retrieval-Augmented Generation (RAG) pipeline, the system searches, ranks, filters, and assembles the most relevant memory shards for each user query.

This isn’t simple keyword retrieval, it involves semantic similarity scoring, temporal weighting, intent matching and cross-session continuity reconstruction.

This layer enables agents to maintain long-running narratives with users, understand evolving preferences, and perform deeper personalization. In practice, this layer is what allows agents to behave like digital companions.

Onchain Ownership Layer

The Onchain Ownership Layer anchors each memory shard as a hash commitment onchain.

Every access, retrieval or usage event can generate an onchain footprint, enabling transparent settlement and reward distribution.

It is also what unlocks memory as a digital asset class.