MemTensor/MemOS

Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings

What it solves

MemOS provides AI agents with persistent, long-term memory, solving the problem of agents forgetting context, user preferences, and past interactions over time. It transforms memory from a "black-box" embedding store into an inspectable and editable system that allows agents to grow and evolve their capabilities through experience.

How it works

MemOS operates as a "Memory Operating System" that unifies the storage, retrieval, and management of information. It uses a graph-based structure for memory, allowing for more complex relationships between data points. The system supports multi-modal memory (text, images, tool traces, and personas) and organizes knowledge bases into "memory cubes" for controlled sharing and isolation.

To ensure production stability, it includes a MemScheduler for asynchronous ingestion of memories with low latency. It also allows for memory correction via natural-language feedback, enabling the users or agents to refine existing memories.

Who it’s for

  • AI Agent Developers: Those building assistants, customer support bots, or personalized agents that require consistent, context-rich interactions.
  • Enterprise Users: Teams needing isolated or shared memory across multiple agents and projects.
  • Local-First Developers: Users of Hermes Agent or OpenClaw who want 100% on-device memory using SQLite.

Highlights

  • Unified Memory API: A single interface for adding, retrieving, editing, and deleting memory.
  • Multi-Modal Support: Natively handles text, images, and tool traces in one system.
  • Memory Cubes: Composable knowledge bases that enable dynamic composition and isolation.
  • Asynchronous Ingestion: High-concurrency support via MemScheduler.
  • Tiered Skill Evolution: Local plugins support evolving memory from L1 traces to L3 world models and crystallized skills.
  • Hybrid Retrieval: Combines FTS5 and vector search for more accurate recall.