MemOS
by MemTensor
Self-evolving long-term memory OS for agents with hybrid retrieval and token savings
Overview
Provides an ultra-persistent memory OS for LLMs and agentic systems to enable cross-task skill reuse cross-task skill reuse and hybrid retrieval. Implements self-evolving memory structures and indexing strategies that reduce token usage and speed up retrieval, delivering reported token savings. Designed as a developer-facing TypeScript library with adapters for common LLMs and retrieval patterns retrieval patterns.
The Value Proposition
Ideal For
Teams building multi-agent systems or agent frameworks that need long-term memory, cross-task skill reuse, and lower RAG/token costs. This fits well with long-term memory workflows enabled by long-term memory and helps manage the cost of tokens through token costs.
How It's Used
- Persisting agent interactions and decisions to build an auditable agent track record
- Reducing RAG token costs by compressing and reusing skills across tasks
- Enabling agents to recall and reuse earlier outputs for multi-step, multi-agent workflows