SimpleMem

Why we included this project

An agent that needs to stay coherent across days of conversation usually comes down to one hard problem: what to keep, and how to find it again. SimpleMem tackles that directly by turning dialogues and facts into structured atomic memories, indexed by meaning and retrieved through semantic similarity rather than keyword matches. Its compression scheme is the more interesting piece: it trims redundant history while trying to stay faithful enough that recalled context remains accurate, which keeps long-running agents from ballooning in cost and latency. The same store also handles images, audio, and video, so a single memory layer can serve a multimodal agent workflow. Teams on MCP-compatible clients like Claude Desktop or Cursor can attach it as a memory server, and the Python integration gives full multimodal access for tighter control.