SimpleMem is a persistent memory system for AI assistants designed to maintain context across different user chat sessions. It functions as a memory server and multimodal vector database that stores and retrieves information from text, images, audio, and video.
The project features a context compression engine that distills interaction histories into compact units to reduce token consumption. It utilizes a distributed memory orchestrator and worker-thread parallel processing to reduce latency when querying large-scale dialogue datasets.
The system implements a hybrid indexing approach combining semantic and keyword search for multimodal retrieval. It also includes a diagnostic framework for retrieval optimization that identifies failures and adjusts configurations to improve search precision.