EverMemOS

EverMemOS is an open source “memory operating system” from EverMind that adds durable, structured long term memory to AI agents. It focuses on letting LLM based assistants remember past interactions, build evolving user profiles, and reason with context instead of treating every prompt as a clean slate. Under the hood it uses a four layer architecture that mirrors aspects of human cognition: an agentic layer for planning, a memory layer for storage and recall, an index layer for embeddings and key value search, and an API / MCP interface layer that plugs into external systems.

What is EverMemOS? EverMemOS is an open source “memory operating system” from EverMind that adds durable, structured long term memory to AI agents. It focuses on letting LLM based assistants remember past interactions, build evolving user profiles, and reason with context instead of treating every prompt as a clean slate. Under the hood it uses a four layer architecture that mirrors aspects of human cognition: an agentic layer for planning, a memory layer for storage and recall, an index layer for embeddings and key value search, and an API / MCP interface layer that plugs into external systems. Key Features: Four-Layer Memory Design: Separates agent behavior, long term storage, indexing, and integration, so teams can drop EverMemOS in as a shared memory backbone across multiple agents and applications. Structured MemCells and Multi-Level Memories: Converts raw conversations into atomic MemCell units, then builds episodes, profiles, preferences, semantic knowledge, and more, giving agents rich, queryable memories instead of loose text blobs. Hybrid Retrieval and Agentic Recall: Combines BM25 keyword search via Elasticsearch, vector retrieval via Milvus, reciprocal-rank-fusion (RRF), and optional LLM-guided multi round retrieval, so agents can recall what matters without dragging in irrelevant context.