📚 MemOS: Self-Evolving Memory System for LLMs and Agents

The direction here is highly specific: solving the "amnesia" problem in AI agents. Positioned as a "memory OS" for LLMs and agents, it focuses on ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. In short, it tries to capture and persist the context and experience an agent accumulates over historical interactions. By optimizing how memory is stored and retrieved, it claims to save around 35% in token consumption for subsequent tasks. It also supports the MCP protocol, making it easier to integrate into existing development ecosystems. As agents handle increasingly long and complex tasks, relying solely on the LLM's native context window is neither economical nor reliable for retaining details. MemOS is exploring an external, structured long-term memory management approach, allowing AI to actually learn from past executions.