Environment-free Synthetic Data Generation for API-Calling Agents

Researchers from Apple propose an environment-free synthetic data generation method for training API-calling LLM agents, using LLMs as digital world models to generate trajectories without executable environments. Fine-tuning models on synthetic data from this approach yields significant performance gains on AppWorld and OfficeBench benchmarks, establishing LLM-based API simulation as a scalable solution for training agents across diverse API ecosystems.

content type paper /research/ published July 2026 Environment-free Synthetic Data Generation for API-Calling Agents AuthorsSeanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli Environment-free Synthetic Data Generation for API-Calling Agents AuthorsSeanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli 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 generates trajectories mimicking interactions between an agent and a stateful environment. Specifically, an LLM first generates diverse tasks solvable with the provided APIs. A teacher agent then iteratively solves each task while an LLM simulator generates coherent synthetic API responses conditioned on the task context and simulation history. Finally, an LLM judge filters the trajectories to ensure the quality of the resulting dataset. We evaluate our approach on the challenging AppWorld and OfficeBench benchmarks, which include both information-retrieval and state-changing tasks. Fine-tuning models on our synthetic data yields significant performance gains, demonstrating that effective supervision for API-calling agents can be generated without any executable environment. Our results establish LLM-based API simulation as a practical, scalable solution for training agents across diverse API ecosystems. Reinforcement Learning for Long-Horizon Interactive LLM Agents February 5, 2025 research area Methods and Algorithms /research/?domain=Methods%20and%20Algorithms Interactive digital agents IDAs leverage APIs of stateful digital environments to perform tasks in response to user requests. While IDAs powered by instruction-tuned large language models LLMs can react to feedback from interface invocations in multi-step exchanges, they have not been trained in their respective digital environments. Prior methods accomplish less than half of tasks in sophisticated benchmarks such as AppWorld. We present a… Hierarchical and Dynamic Prompt Compression for Efficient Zero-shot API Usage April 15, 2024 research area Speech and Natural Language Processing /research/?domain=Speech%20and%20Natural%20Language%20Processing conference EACL /research/?event=EACL Long prompts present a significant challenge for practical LLM-based systems that need to operate with low latency and limited resources. We investigate prompt compression for zero-shot dialogue systems that learn to use unseen APIs directly in-context from their documentation, which may take up hundreds of prompt tokens per API. We start from a recently introduced approach Mu et al., 2023 that learns to compress the prompt into a few “gist…