debug-gym: A Text-Based Environment for Interactive Debugging

Large Language Models (LLMs) are increasingly relied upon for coding tasks, yet in most scenarios it is assumed that all relevant information can be either accessed in context or matches their training data. We posit that LLMs can benefit from the ability to interactively explore a codebase to gather the information relevant to their task. To achieve this, we present a textual environment, namely debug-gym, for developing LLM-based agents in an interactive coding setting. Our environment is lightweight and provides a preset of useful tools, such as a Python debugger (pdb), designed to facilitate an LLM-based agent's interactive debugging. Beyond coding and debugging tasks, this approach can be generalized to other tasks that would benefit from information-seeking behavior by an LLM agent.

@article{yuan2025_2503.21557,
  title={ debug-gym: A Text-Based Environment for Interactive Debugging },
  author={ Xingdi Yuan and Morgane M Moss and Charbel El Feghali and Chinmay Singh and Darya Moldavskaya and Drew MacPhee and Lucas Caccia and Matheus Pereira and Minseon Kim and Alessandro Sordoni and Marc-Alexandre Côté },
  journal={arXiv preprint arXiv:2503.21557},
  year={ 2025 }
}