SWE-Universe introduces a framework designed to create scalable, verifiable software engineering environments using GitHub pull requests. The main issue addressed is the low production yield and inefficiencies in automatic building processes. The authors developed a building agent that relies on a custom-trained model, which enhances the reliability of task generation through iterative self-verification and in-loop hacking detection. This method led to the successful scaling of real-world, multilingual software engineering environments, reaching a total of 807,693. The framework's effectiveness shines through its application in large-scale mid-training and reinforcement learning scenarios. The study highlights its application with Qwen3-Max-Thinking, achieving a notable score of 75.3% on the SWE-Bench Verified benchmark. This accomplishment underscores the framework's potential to significantly advance the capabilities of coding agents by providing a robust methodology and a critical resource for developers.