Overview
MemOS is an open-source memory operating system designed for Large Language Models (LLMs) and AI agents, enabling persistent skill memory for cross-task reuse and evolution. Developed by MemTensor, it unifies store, retrieve, and manage operations for long-term memory, providing context-aware and personalized interactions across applications.
Built for developers and AI researchers, MemOS addresses a critical challenge in AI systems: maintaining coherent, long-term memory that persists across conversations and tasks. According to official benchmarks, the system delivers +43.70% accuracy improvement on LoCoMo evaluation compared to OpenAI Memory, while reducing memory token usage by 35.24%, making it both more effective and efficient than traditional AI chatbots.
With native support for text, images, tool traces, and personas, MemOS provides a comprehensive memory infrastructure that can be deployed via cloud API or self-hosted environments. Its graph-based architecture makes memory inspectable and editable by design, not a black-box embedding store.
Key Features
Unified Memory API — Single API to add, retrieve, edit, and delete memory structured as an inspectable graph, providing transparent control over how AI agents store and access information.
Multi-Modal Memory Support — Natively supports text, images, tool traces, and personas retrieved together in one memory system, enabling richer context awareness across different data types.
Multi-Cube Knowledge Base Management — Manage multiple knowledge bases as composable memory cubes with isolation, controlled sharing, and dynamic composition across users, projects, and agents.
Asynchronous Ingestion via MemScheduler — Run memory operations asynchronously with Redis Streams scheduling, delivering low-latency performance under high concurrency according to official specifications.
Memory Feedback & Correction — Refine memory with natural-language feedback, allowing you to correct, supplement, or replace existing memories over time as information evolves.
Enhanced Agent Integration — Official reports show up to 72% lower token usage in OpenClaw plugin scenarios through intelligent memory retrieval instead of loading full chat history, with multi-agent memory sharing via shared user IDs for seamless context handoff.
Pricing & Plans
MemOS offers two deployment options to match different needs:
Self-Hosted (Free)
- Complete access to all features
- Deploy via Docker Compose or CLI
- No usage limits or restrictions
- Full control over data and infrastructure
- Apache 2.0 License
- Ideal for developers, researchers, and organizations requiring on-premises deployment
Cloud API (Hosted)
- Free tier available with API key
- Managed infrastructure by OpenMem team
- Quick setup without self-hosting complexity
- Access via MemOS Dashboard at memos-dashboard.openmem.net
- Suitable for rapid prototyping and production deployments
- Paid plans include Starter, Pro, and Enterprise tiers with pricing available on official website
Both deployment options provide core memory capabilities (storage, retrieval, knowledge base management), though the Cloud version offers more out-of-the-box automation while self-hosted deployments provide greater customization and control.
Pros & Cons
Pros
- Fully open-source with Apache 2.0 License, providing complete transparency and customization freedom
- Superior performance with +43.70% accuracy improvement over OpenAI Memory and 35.24% token savings
- Comprehensive multi-modal support for text, images, and tool traces in a unified memory system
- Production-ready with asynchronous scheduling, millisecond-level latency, and high concurrency support
- Active community with 5,100+ GitHub stars and extensive documentation
- Flexible deployment options supporting both cloud-hosted and self-hosted environments
Cons
- Self-hosted deployment requires technical expertise with Docker, Neo4j, and Qdrant setup
- Relatively new project with first stable release in 2025, may have evolving APIs
- Documentation primarily in English with some Chinese resources, which may present language barriers
- May require external API keys for LLM inference, embedding, and memory extraction components (such as OpenAI or BaiLian), though alternative providers and local models can be configured based on deployment needs
Best For
- AI researchers developing long-term memory systems and studying memory-augmented generation for LLMs
- Developer teams building multi-agent AI systems that require shared memory and context persistence across conversations
- Organizations requiring on-premises AI memory infrastructure with full data sovereignty and control
- AI product builders integrating memory capabilities into chatbots, virtual assistants, or AI assistant applications
- Open-source contributors interested in advancing AI memory systems and contributing to cutting-edge research
FAQ
What is MemOS and how does it differ from other memory systems?
MemOS is a memory operating system that provides a unified API for long-term memory management in LLMs and AI agents. Unlike black-box embedding stores, MemOS structures memory as an inspectable graph with explicit editing capabilities, offering transparency and control. It also provides superior performance with +43.70% accuracy over OpenAI Memory while using 35.24% fewer tokens.
Is MemOS completely free to use?
Yes, MemOS is fully open-source under the Apache 2.0 License for self-hosted deployments, with no cost and complete access to all features. OpenMem also offers a cloud-hosted option with a free tier available, plus paid Starter, Pro, and Enterprise plans for higher usage requirements and additional support.
What technical requirements are needed for self-hosting MemOS?
Self-hosting requires Docker Compose, Neo4j database, and Qdrant vector database. You'll also need API keys for embeddings (OpenAI API or BaiLian). The project includes Docker configurations and detailed documentation for both Docker and CLI-based deployment methods.
Does MemOS support multiple programming languages?
Yes, MemOS provides a REST API that can be accessed from any programming language. The official examples use Python with requests, but you can integrate it with JavaScript, Go, Java, or any language that supports HTTP requests.
How does MemOS handle data privacy and security?
When self-hosted, you have complete control over data privacy as all information remains within your infrastructure. For cloud deployments, data is managed by OpenMem with security measures in place. Organizations with strict data sovereignty requirements should opt for self-hosted deployment.
Can MemOS integrate with existing AI agent frameworks?
Yes, MemOS includes official plugins for OpenClaw and supports integration with various AI agent frameworks through its REST API. The memory system can be plugged into LangChain, AutoGPT, and other agent architectures that support external memory interfaces.
What is the Multi-Cube Knowledge Base Management feature?
Multi-Cube allows you to create isolated knowledge bases (memory cubes) that can be composed dynamically. This enables you to maintain separate memory contexts for different projects, users, or applications while optionally sharing specific memory cubes across boundaries when needed.
How actively maintained is the MemOS project?
MemOS is actively maintained by the MemTensor team with regular updates. Version 2.0 (Stardust) was released in December 2025, adding knowledge base support, natural language feedback, multi-modal memory, and significant infrastructure improvements. The project has strong community engagement with 5,100+ GitHub stars.
