EverMind - Long-Term Memory for AI Agents

How does EverMind handle context window limits when I'm working with large language models?

EverMind structures long-term interactions into MemCells and MemScenes, which organizes memory so you can retrieve relevant context without feeding the entire conversation history into your model's context window. This approach cuts token usage to about 1/10th of what a full context window would require while maintaining conversation coherence across sessions.

Can EverMind ingest different file types like PDFs and images in a single workflow?

Yes. EverMind supports multimodal retrieval through a single API, meaning you can feed PDFs, images, documents, spreadsheets, and emails into the same memory system. The system performs deep semantic understanding rather than keyword matching, so it retrieves the right context regardless of format.

Does EverMind work with multiple AI agents at once?

EverMind is built for multi-agent systems and includes group memory coordination features. This means multiple agents can share and reference a common memory bank while maintaining individual trajectory records, which is useful for orchestration tasks where agents need to coordinate or learn from each other's experiences.

What retrieval speed can I expect when EverMind looks up stored memories?

EverMind retrieves memories in under 200 milliseconds. This latency threshold matters for interactive use cases like real-time customer support or conversational AI companions, where delays break the sense of responsiveness.