EverOS

Agent-native memory infrastructure that turns agent execution and conversation into structured, persistent state with an LLM-agnostic memory layer, MCP integration, and Python/TypeScript dual SDK support.

A MemAgent framework that can extrapolate to 3.5M context tokens, along with a training framework for RL training of any agent workflow.

An LLM-based intelligent agent as a digital lifeform that values warmth, authenticity and genuine connection, with long-term memory and personalized conversation.

Universal memory layer for AI Agents providing scalable, extensible, and interoperable memory storage and retrieval to streamline agent state management for autonomous systems.