Environment-free Synthetic Data Generation for API-Calling Agents

Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overcome this, we propose an environment-free synthetic data generation approach that leverages LLMs as on-the-fly digital world models. Given only API specifications, our method genera

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ethics.ai (17 July 2026), “Environment-free Synthetic Data Generation for API-Calling Agents,” evidence record 11916, https://ethics.ai/record/11916 (originally published by HuggingFace Daily Papers).

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