Debug-gym is a framework for developing and evaluating AI agents that perform interactive debugging of software. It provides a Gymnasium-inspired environment where agents can use tools like a debugger (pdb), file viewer, editor, and shell to inspect and modify code in order to fix bugs. The framework supports multiple execution backends (local, Docker, Kubernetes) to ensure isolation and reproducibility, and integrates with various LLM providers through a unified interface. The system is designed around a loop of observation, action, and feedback: the agent observes the current state (e.g., code, test output), selects a tool to apply (e.g., set a breakpoint, edit a file), and receives the outcome from the environment. This enables the creation of agents that can learn to debug or be evaluated on debugging benchmarks such as SWE-Bench and Mini-Nightmare. The framework also includes analysis tools for visualizing agent trajectories and logging experiment results, supporting both development and research use cases. Key architectural features include a plugin-based tool system, abstract terminal and LLM interfaces, and a secure workspace for code manipulation. These components are designed to be modular and extensible, allowing researchers and developers to plug in new tools, environments, or language models with minimal changes to the core loop.

The LLM identified 3 files worth highlighting for reuse: