Text2Mem: A Unified Memory Operation Language for Memory Operating System

2025-09-14

AI Summary

Existing LLM agent memory frameworks support only basic operations (e.g., encode, retrieve, delete) and lack advanced capabilities such as merging, promoting, or demoting memories; moreover, memory commands lack formal specification, leading to unpredictable behavior and poor cross-system interoperability. Method: We propose Text2Mem—the first standardized memory operation language—featuring a JSON Schema–defined instruction set and semantic invariants that establish an end-to-end path from natural language directives to deterministic execution. Its three-layer architecture (parsing–validation–adaptation) decouples instruction generation from execution, supporting the full spectrum of operations (encode, retrieve, merge, promote, etc.). A unified execution contract integrates embedding and summarization models, backed by an extensible SQL prototype. Contribution/Results: Text2Mem ensures safety, determinism, and portability across heterogeneous backends. We further introduce Text2Mem Bench, a benchmark suite enabling systematic evaluation of memory operation frameworks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language model agents increasingly depend on memory to sustain long horizon interaction, but existing frameworks remain limited. Most expose only a few basic primitives such as encode, retrieve, and delete, while higher order operations like merge, promote, demote, split, lock, and expire are missing or inconsistently supported. Moreover, there is no formal and executable specification for memory commands, leaving scope and lifecycle rules implicit and causing unpredictable behavior across systems. We introduce Text2Mem, a unified memory operation language that provides a standardized pathway from natural language to reliable execution. Text2Mem defines a compact yet expressive operation set aligned with encoding, storage, and retrieval. Each instruction is represented as a JSON based schema instance with required fields and semantic invariants, which a parser transforms into typed operation objects with normalized parameters. A validator ensures correctness before execution, while adapters map typed objects either to a SQL prototype backend or to real memory frameworks. Model based services such as embeddings or summarization are integrated when required. All results are returned through a unified execution contract. This design ensures safety, determinism, and portability across heterogeneous backends. We also outline Text2Mem Bench, a planned benchmark that separates schema generation from backend execution to enable systematic evaluation. Together, these components establish the first standardized foundation for memory control in agents.

Problem

• Standardizing memory operations for LLM agents
• Providing formal specification for memory commands
• Ensuring cross-system safety and portability

Innovation

• JSON-based schema for memory operations
• Validator ensures execution correctness
• Adapters map to SQL and frameworks