LatentMAS is a training-free multi-agent framework where agents pass latent thoughts through their working memory rather than producing long textual reasoning traces. The approach achieves efficient multi-step reasoning with drastically fewer tokens and major speedups while maintaining superior performance. It is compatible with any HuggingFace model and optionally vLLM backends.