Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient framework to unlock their full potential. Existing audio agent frameworks often suffer from complex environment configurations and inefficient tool collaboration. To address these limitations, we introduce AudioFab, an open-source agent framework aimed at establishing an open and intelligent audio-processing ecosystem. Compared to existing solutions, AudioFab's modular design resolves dependency conflicts, simplifying tool integration and extension. It also optimizes tool learning through intelligent selection and few-shot learning, improving efficiency and accuracy in complex audio tasks. Furthermore, AudioFab provides a user-friendly natural language interface tailored for non-expert users. As a foundational framework, AudioFab's core contribution lies in offering a stable and extensible platform for future research and development in audio and multimodal AI. The code is available at https://github.com/SmileHnu/AudioFab.

翻译：当前，人工智能正在深刻变革音频领域；然而，众多先进算法与工具仍处于碎片化状态，缺乏统一高效的框架以释放其全部潜力。现有的音频智能体框架常受限于复杂的环境配置与低效的工具协作。为应对这些局限，我们提出了AudioFab——一个旨在建立开放智能音频处理生态系统的开源智能体框架。与现有方案相比，AudioFab的模块化设计解决了依赖冲突，简化了工具集成与扩展。该框架还通过智能选择与小样本学习优化工具学习过程，提升了复杂音频任务的执行效率与准确性。此外，AudioFab为普通用户提供了易用的自然语言交互界面。作为基础性框架，AudioFab的核心贡献在于为未来音频及多模态人工智能的研究与开发提供了稳定且可扩展的平台。代码已发布于https://github.com/SmileHnu/AudioFab。