MemTensor/MemOS

Health v2 · maintenance only

Adoption not part of the score

11012 stars · 1009 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MemOS is a memory operating system for LLMs and AI agents that unifies storing, retrieving, and managing long-term, multi-modal memory through a single API. It supports graph-structured, editable memory, multi-cube knowledge bases, hybrid retrieval, and cross-task skill reuse, with self-hosted and cloud deployment options.

Use cases

• add persistent long-term memory to my AI agent
• reduce token usage in LLM chat applications
• give my chatbot personalized memory across sessions
• manage multiple knowledge bases for different users and projects
• self-host a memory backend for LLM apps
• connect memory to agent frameworks via MCP
• store and retrieve tool traces and multi-modal memories

When to choose

• you need production-grade, persistent, multi-modal memory for LLM apps or agents
• you want an editable, inspectable memory graph rather than a black-box embedding store
• you need self-hosted deployment with data control or a managed cloud option
• you want token savings and hybrid retrieval across long conversations

When to avoid

• you only need simple in-process caching or short-lived context windows
• you want a fully turnkey product with no integration work
• your project has no LLM or agent component

Facets

framework · maturity active

rag vector-database agent-framework mcp llm-inference search-engine sdk large-language-models databases developer-tools self-hosted python self-hosted cross-platform cloud long-term-memory memory-management memory-os token-savings agentic-ai hybrid-retrieval knowledge-base multi-modal-memory personalization deepseek-harness ai-agents retrieval-augmented-generation typescript docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="MemTensor/MemOS")