EverMemOS - AI Memory System

How much faster is EverMemOS retrieval compared to loading full context windows?

EverMemOS retrieves relevant memories in under 200 milliseconds and uses roughly 1/10 the tokens of a full context window approach. This speed matters if you are running multi-agent systems or high-frequency interactions where latency compounds; a stateless LLM loading your entire conversation history on every call burns tokens and slows response time.

Can EverMemOS ingest PDFs, spreadsheets, and images in a single workflow?

Yes. EverMemOS accepts multimodal data, PDFs, images, documents, spreadsheets, and emails, through a unified API for retrieval-augmented generation. You do not have to build separate pipelines for each file type; the system structures them into MemCells and MemScenes for semantic matching across formats.

Does EverMemOS store my data on its servers, or do I control where memory lives?

EverMemOS maintains data sovereignty; you control where the memory is stored rather than uploading everything to a vendor's infrastructure. This is important for sensitive use cases like therapeutic chatbots or customer support where privacy and compliance matter.

How does EverMemOS handle consistency when the same agent talks to multiple users or multiple agents coordinate with each other?

EverMemOS tracks temporal knowledge and coordinates group memory across agents, so facts stay consistent across sessions and platforms. Each interaction is recorded in the agent trajectory, and the system distills patterns from those interactions so the agent learns and improves over time instead of forgetting between conversations.