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[EMNLP 2025 Oral] MemoryOS is designed to provide a memory operating system for personalized AI agents.
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MemoryOS is designed to provide a memory operating system for personalized AI agents, enabling more coherent, personalized, and context-aware interactions. Drawing inspiration from memory management principles in operating systems, it adopts a hierarchical storage architecture with four core modules: Storage, Updating, Retrieval, and Generation, to achieve comprehensive and efficient memory management. On the LoCoMo benchmark, the model achieved average improvements of 49.11% and 46.18% in F1 and BLEU-1 scores.
🏆 TOP Performance in Memory Management The SOTA results in long-term memory benchmarks, boosting F1 scores by 49.11% and BLEU-1 by 46.18% on the LoCoMo benchmark.
🧠 Plug-and-Play Memory Management Architecture Enables seamless integration of pluggable memory modules—including storage engines, update strategies, and retrieval algorithms.
✨ Agent Workflow Create with Ease (MemoryOS-MCP) Inject long-term memory capabilities into various AI applications by calling modular tools provided by the MCP Server.
🌐 Universal LLM Support MemoryOS seamlessly integrates with a wide range of LLMs (e.g., OpenAI, Deepseek, Qwen ...)
Welcome to our Memory Family, a research line dedicated to exploring AI Memory.
Survey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends
TL;DR: Provides a unified theoretical framework for AI Memory, introducing a comprehensive taxonomy and systematically analyzing memory mechanisms, applications, and evaluation methods.
📄 Paper: http://github.com/BAI-LAB/Survey-on-AI-Memory/blob/main/Survey%20on%20AI%20Memory.pdf
LightSearcher: Efficient DeepSearch via Experiential Memory
TL;DR: Introduces experiential memory into deep search systems, enabling models to learn from successful reasoning trajectories and improve search efficiency.
📄 Paper: https://arxiv.org/abs/2512.06653
Memory OS of AI Agent
TL;DR: Proposes a memory operating system for AI agents that manages short-term, mid-term, and long-term personal memory through hierarchical storage, dynamic updating, retrieval, and generation, improving coherence and personalization in long conversations.
📄 Paper: https://arxiv.org/abs/2506.06326
memoryos/
├── __init__.py # Initializes the MemoryOS package
├── __pycache__/ # Python cache directory (auto-generated)
├── long_term.py # Manages long-term persona memory (user profile, knowledge)
├── memoryos.py # Main class for MemoryOS, orchestrating all components
├── mid_term.py # Manages mid-term memory, consolidating short-term interactions
├── prompts.py # Contains prompts used for LLM interactions (e.g., summarization, analysis)
├── retriever.py # Retrieves relevant information from all memory layers
├── short_term.py # Manages short-term memory for recent interactions
├── updater.py # Processes memory updates, including promoting information between layers
└── utils.py # Utility functions used across the library