
EverMemOS is an AI-native operating system for building agents and applications with true long-term memory, persistent identity, and evolving intelligence. Powered by the EverMind platform, it gives developers a unified layer for storing, retrieving, and reasoning over everything an agent has ever seen, done, or learned—across sessions, devices, and deployments. Instead of fragile, stateless prompts, EverMemOS lets you compose agents that remember users, refine their own behavior over time, and coordinate multiple open-source or proprietary models behind a single, coherent personality. Its memory-first architecture abstracts away vector stores, embeddings, and context management, so you can focus on product logic while EverMemOS automatically optimizes what to remember, forget, and surface. Running on the new EverMind cloud, EverMemOS ships with APIs, SDKs, and reference blueprints tailored to AI developer workflows: from copilots and customer support agents to research assistants and autonomous tools. It seamlessly connects to your existing data sources and model providers, allowing you to plug memory-aware intelligence into any stack with minimal changes. As EverMemOS enters beta, the Memory Genesis Competition 2026 invites developers to push the limits of lifelong agents, novel memory architectures, and emergent behaviors. Whether you're prototyping a single agent or designing an entire agentic platform, EverMemOS provides the foundation for durable, adaptive AI systems that improve continuously with every interaction.
Build AI copilots that remember project history, coding style, and past decisions to provide more precise suggestions over time.
Create customer support agents that persist user context, preferences, and previous tickets across channels and sessions.
Develop research and analysis agents that accumulate domain knowledge, sources, and prior conclusions for deeper reasoning.
Prototype multi-agent systems where specialized agents share a common memory space and coordinate through EverMemOS.
Instrument existing AI applications with a durable memory layer to reduce prompt complexity and improve relevance.