High-quality schematic diagrams, which provide a conceptual overview of the research, play a crucial role in summarizing and clarifying a study’s core ideas. However, creating these diagrams is time-consuming for authors and remains challenging for current AI systems, as it requires both a deep understanding of the paper’s content and a strong sense of visual design. To address this, we introduce SCISKETCH, an open-source framework that supports two automated workflows for schematic diagram generation using foundation models, shown in Figure 1. 1) In the graphic-code-based workflow, SCISKETCH follows a two-stage pipeline: it first produces a layout plan expressed in a graphical code language with a self-refinement and self-verification mechanism. It then integrates empirical images and symbolic icons to create a visually coherent, informative diagram. 2) In the image-based workflow, SCISKETCH directly synthesizes the diagram image through image generation with a self-refinement mechanism. Through both automatic and human evaluations, we show that SCISKETCH outperforms several state-of-the-art foundation models, including GPT-4o, and Gemini-2.5-Pro, in generating schematic diagrams for scientific papers. We make SCISKETCH fully open-sourced, providing researchers with an accessible, extensible tool for high-quality schematic diagram generation in scientific fields.