Given the steep learning curve of professional 3D software and the time-consuming process of managing large 3D assets, language-guided 3D scene editing has significant potential in fields such as virtual reality, augmented reality, andgaming. However, recent approaches to language-guided 3D scene editing eitherrequire manual interventions or focus only on appearance modifications withoutsupporting comprehensive scene layout changes. In response, we propose EditRoom, a unified framework capable of executing a variety of layout edits throughnatural language commands, without requiring manual intervention. Specifically,EditRoom leverages Large Language Models (LLMs) for command planning andgenerates target scenes using a diffusion-based method, enabling six types of edits: rotate, translate, scale, replace, add, and remove. To addressthe lack of data for language-guided 3D scene editing, we have developed an automatic pipeline to augment existing 3D scene synthesis datasets and introducedEditRoom-DB, a large-scale dataset with 83k editing pairs, for training and evaluation. Our experiments demonstrate that our approach consistently outperformsother baselines across all metrics, indicating higher accuracy and coherence inlanguage-guided scene layout editing.