LightMem vs memory-os
ICLR 2026 memory framework for LLMs/agents: LLMLingua pre-compression, topic segmentation and offline memory updates — leading LoCoMo/LongMemEval results at lower token cost. — versus — A 7-layer memory operating system for Hermes Agent: Qdrant vectors, structured facts, fabric recall, an auto-curated wiki, and surgical context injection.
Layered memory operating systems from opposite cultures: memory-os is a 7-layer production system for one agent stack, LightMem is a modular research framework you assemble per experiment.
| LightMem | memory-os | |
|---|---|---|
| Stars | 1.0k | 1.3k |
| Forks | 92 | 119 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | 3 days ago | 1 months ago |
| Topics | memory | memory |
| Curated connections | 2 | 2 |
LightMem — the curator's take
Research-grade memory with receipts: reproduction scripts for LoCoMo/LongMemEval plus a baseline harness that benchmarks Mem0, A-MEM and LangMem side by side — useful even if you adopt none of them. The compression-first pipeline (LLMLingua before storage) is the differentiating idea. Expect paper-adjacent ergonomics: manual model downloads and config dicts, not a polished product.
memory-os — the curator's take
The most architecturally ambitious take on agent memory we've mapped: seven distinct layers from raw vectors to an auto-curated wiki, each with its own recall path, so the agent gets the RIGHT kind of memory injected rather than a similarity dump. The catch is coupling: it's built FOR Hermes Agent — adopting the architecture elsewhere means porting, not installing; and 7 layers is real operational surface for a ~1.3k-star project.