AILGSENov 11, 2025

SynthTools: A Framework for Scaling Synthetic Tools for Agent Development

arXiv:2511.09572v12 citationsh-index: 3Has Code
Originality Incremental advance
AI Analysis

This provides a scalable solution for researchers and developers working on tool-use AI agents, though it is incremental as it builds on prior synthetic tool generation methods.

The paper tackles the problem of limited real-world APIs for training and evaluating AI agents by introducing SynthTools, a framework for generating synthetic tool ecosystems, which produces toolsets with twice the domain coverage and tools per domain compared to prior work and achieves 94% and 99% accuracy in simulation and audit components.

AI agents increasingly rely on external tools to solve complex, long-horizon tasks. Advancing such agents requires reproducible evaluation and large-scale training in controllable, diverse, and realistic tool-use environments. However, real-world APIs are limited in availability, domain coverage, and stability, often requiring access keys and imposing rate limits, which render them impractical for stable evaluation or scalable training. To address these challenges, we introduce SynthTools, a flexible and scalable framework for generating synthetic tool ecosystems. Our framework consists of three core components: Tool Generation for automatic and scalable creation of diverse tools, Tool Simulation to emulate realistic tool behaviors, and Tool Audit to ensure correctness and consistency of tool simulation. To illustrate its scalability, we show that SynthTools can readily produce toolsets that span twice as many domains and twice as many tools per domain as prior work. Furthermore, the tool simulation and tool audit components demonstrate strong reliability, achieving $94\%$ and $99\%$ accuracy respectively. Finally, we construct downstream tasks from the generated tools that even state-of-the-art models struggle to complete. By enabling scalable, diverse, and reliable tool ecosystems, SynthTools provides a practical path toward large-scale training and stable evaluation of tool-use agents. Our code is available at https://github.com/namkoong-lab/SynthTools.

Foundations

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