SwapAnything is a novel framework that allows for the swapping of any object in an image based on personalized concepts provided as references, while preserving the context. Compared to existing personalized theme-swapping methods, SwapAnything boasts three unique advantages: (1) precise control over any object and portions, rather than just the theme, (2) more faithful preservation of contextual pixels, and (3) a better adaptation of personalized concepts to imagery. It achieves this through targeted variable swapping on latent feature maps, swapping masked variables to maintain faithful context and initial semantic concept swapping. Afterwards, through appearance adjustments, the semantic concept is seamlessly integrated into the original image, encompassing target location, shape, style, and content. Extensive results on both manual and automatic evaluations demonstrate our method's significant improvement over baseline methods in personalized swapping. Furthermore, SwapAnything showcases precise and faithful swapping capabilities across tasks involving single objects, multiple objects, partial objects, and cross-domain swapping. SwapAnything also excels in text-based swapping and beyond swapping tasks, such as object insertion.