MemOS - Open Source AI Memory Management and Scheduling Platform for Sharing Long-Term Memories

What is MemOS?

MemOS is an open source memory management and scheduling framework for large language models (LLM) such as MemTensor. Memory is regarded as a resource as important as arithmetic power, and through the standardized MemCube memory unit, plaintext, activation state and parameter memories are managed in a unified way.MemOS adopts a three-tier architecture design, including API and application interface layer, memory scheduling and management layer, and memory storage and infrastructure layer, which builds a closed loop of full-link memory management from user interaction to the bottom layer storage. MemOS supports Model Context Protocol (MCP), which enables models to have standardized "read/write external memory" capability, and any Agent, IDE or model that supports MCP can directly access MemOS. MemOS optimizes the organization of plaintext memories in multi-round conversations and introduces the KV Cache reuse mechanism, which significantly improves inference performance and consistency. It also optimizes the interface performance for cloud platforms to bring memory writing into the millisecond era, while the open source version introduces an asynchronous mechanism to make memory operations lighter and faster.

Features of MemOS

• Unified Memory Management: Through MemCube encapsulation, plaintext, activation state and parameter memory are managed in a unified way, supporting flexible scheduling and storage optimization of hot and cold memories.
• Supports MCP protocol: Compatible with Model Context Protocol, enabling models, Agents and IDEs to read and write external memories in a standardized way for seamless integration.
• High Performance Optimization: Optimize interface performance for cloud platforms and introduce asynchronous mechanism to bring memory writing into the millisecond era and improve overall efficiency.
• Multi-Round Dialogue Optimization: Improve the organization of memories in multi-round conversations and introduce a KV Cache reuse mechanism to significantly improve inference performance and conversation consistency.
• Three-tier architecture design: Build a closed loop of full-link memory management from user interaction to underlying storage, covering application interfaces, scheduling management and storage infrastructure.
• Wide range of scenarios: Suitable for personalized intelligences, research and knowledge management, high-reliability domains (finance, law) and enterprise-level RAG applications.

Core Benefits of MemOS

• Efficient Memory Management: Unified management and flexible scheduling of different types of memories (plaintext, activation state, parameter memories) is achieved through standardized MemCube encapsulation, which improves the utilization efficiency of memory resources.
• Supports MCP protocol: Compatible with Model Context Protocol (MCP), providing standardized external memory read/write capabilities for models, Agents and IDEs, enabling seamless integration and lowering the development threshold.
• High Performance Optimization: Optimize interface performance for cloud platforms and introduce asynchronous mechanism to bring memory writing into the millisecond era, significantly improving the speed and efficiency of memory operations.
• Multiple rounds of dialog enhancement: Optimize the organization of memories in multi-round conversations, introduce KV Cache reuse mechanism, significantly improve inference performance and conversation consistency, and enhance user experience.
• Flexible Architecture DesignIt adopts a three-tier architecture covering application interface, memory scheduling management and storage infrastructure, building a closed loop of full-link memory management and adapting to a variety of application scenarios.

What is the official MemOS website

• Project website:: https://memos.openmem.net/
• Github repository:: https://github.com/MemTensor/MemOS

Who is MemOS for?

• AI developers: Developers who need to build efficient memory management systems for large language models (LLMs) can quickly implement memory scheduling and optimization with MemOS.
• Corporate Technical Team: For teams that need to improve model performance, optimize the multi-round conversation experience, or build a knowledge management system in enterprise applications, MemOS can provide a standardized memory management solution.
• (scientific) researcher: Researchers working in the fields of artificial intelligence and natural language processing can use MemOS for experiments and research on model optimization and memory management.
• Smart Body Developer: Teams developing personalized intelligences or Agents, the MCP protocol supported by MemOS can help them quickly integrate external memories and enhance the interaction capabilities of their intelligences.
• system architect: Architects responsible for designing and deploying large-scale AI systems, MemOS' three-tier architectural design and high-performance optimizations meet the memory management needs of complex systems.
• data scientist: Data scientists who need to analyze and optimize model memories can better manage and utilize model memory resources with MemOS.