多模态推理的最新进展使得智能体能够解析图像、将其与语言关联并执行结构化分析任务。然而，将此类能力扩展至遥感领域仍具挑战性，因为模型需在空间尺度、地理结构及多光谱指数等维度上进行推理，同时保持连贯的多步逻辑。为弥合这一差距，OpenEarthAgent提出了一种统一框架，用于开发基于卫星影像、自然语言查询及详细推理轨迹训练的工具增强型地理空间智能体。该训练流程依赖于结构化推理轨迹上的监督微调，使模型在多样分析场景中与经验证的多步工具交互对齐。配套语料库包含14,538个训练实例和1,169个评估实例，训练集包含超过10万次推理步骤，评估集超过7,000次推理步骤。数据覆盖城市、环境、灾害及基础设施领域，并整合了基于GIS的操作以及NDVI、NBR和NDBI等指数分析。基于显式的推理轨迹，所学智能体展现出结构化推理能力、稳定的空间理解力以及在不同条件下通过工具驱动的地理空间交互所呈现的可解释行为。实验结果表明，该方法在强基线基础上实现了持续改进，并在性能上与近期开源及闭源模型相当。

Recent progress in multimodal reasoning has enabled agents that can interpret imagery, connect it with language, and perform structured analytical tasks. Extending such capabilities to the remote sensing domain remains challenging, as models must reason over spatial scale, geographic structures, and multispectral indices while maintaining coherent multi-step logic. To bridge this gap, OpenEarthAgent introduces a unified framework for developing tool-augmented geospatial agents trained on satellite imagery, natural-language queries, and detailed reasoning traces. The training pipeline relies on supervised fine-tuning over structured reasoning trajectories, aligning the model with verified multistep tool interactions across diverse analytical contexts. The accompanying corpus comprises 14,538 training and 1,169 evaluation instances, with more than 100K reasoning steps in the training split and over 7K reasoning steps in the evaluation split. It spans urban, environmental, disaster, and infrastructure domains, and incorporates GIS-based operations alongside index analyses such as NDVI, NBR, and NDBI. Grounded in explicit reasoning traces, the learned agent demonstrates structured reasoning, stable spatial understanding, and interpretable behaviour through tool-driven geospatial interactions across diverse conditions. We report consistent improvements over a strong baseline and competitive performance relative to recent open and closed-source models.