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EverMemOS is an open-source memory operating system built to give AI agents long-term, structured memory. It captures conversations, transforms them into organized memory units, and enables agents to recall past interactions with context and meaning. Instead of treating each prompt independently, it builds evolving user profiles, tracks preferences, and connects related events into coherent narratives. Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions.
Features
- Long-term memory system that stores, structures, and retrieves conversational data for AI agents
- MemCell-based architecture that converts raw interactions into structured, queryable memory units
- Hybrid retrieval combining vector search, keyword search, and agent-driven recall
- Dynamic user profiling that continuously updates preferences, habits, and context
- Multi-layer architecture separating memory, indexing, agent logic, and integrations
- Developer-ready infrastructure with Python API and integrated services like MongoDB and Redis
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