debug-gym

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

debug-gym is a text-based interactive debugging framework, designed for debugging Python programs.

[Technical Report] [Project Page]

1. Installation

conda create -n debug-gym python=3.12
conda activate debug-gym
pip install -e .

To install the development dependencies:

pip install -e '.[dev]'

Set your API information in llm.yaml

First, create an LLM config template by running the debug-gym-init-llm-config entrypoint:

python -m debug_gym.init_llm_config ~/.config/debug_gym

[!TIP] Run debug-gym-init-llm-config --help for more options. By default, the template is created at ~/.config/debug_gym/llm.yaml, but you can specify any directory.

Then, edit this file with your endpoint and credentials. You can choose one of these authentication methods:

• For authenticating with an API key, provide api_key.
• For az login or Managed Identity authentication on Azure, remove api_key and include scope instead.

[!WARNING] When using open-sourced LLMs, e.g., via vLLM, you need to correctly setup HF_TOKEN required by the tokenizer.

By default, debug-gym looks for the LLM config file at ~/.config/debug_gym/llm.yaml. You can change this behavior by exporting the environment variable LLM_CONFIG_FILE_PATH or by setting llm_config_file_path in your script config file (see Running Baselines).

2. System Design

The structure of debug-gym is as below:

debug_gym
├── gym
│   ├── envs
│   ├── terminal
│   └── tools
└── agents

debug_gym.gym is a simulation environment. Given a code repository, an agent can iteratively interact with a set of tools, such as pdb, that are designed for investigate the code. Once gathered enough information, the agent can propose a patch that rewrites certain lines of the code. The terminal will subsequently execute the new code against a set of test cases.

debug_gym.agents are LLM-based debugging agents that use debug_gym.gym to interact with code repositories to seek necessary information and thus fix potential bugs. At an interaction step, the agent takes a text observation that describes the environment states and tool states as input, it is expected to generate a command, subsequently, the environment will provide a new text observation in response, describing the state change caused by that command.

2.1. Environment and Tools

Our base environment, RepoEnv, is an interactive environment that follows the Gymnasium paradigm. Once the environment env is instantiated, one can use env.reset() to start an episode and receives initial informations. Then, one can interact with the environment using env.step(action), where action specifies one of the available tools (see below), doing so will return subsequent informations (e.g, error message, debugger stdout, etc.)

One of the core designs of debug-gym is the notion of tools. Users can dynamically import tools, or develop customized tools and utilize them in the environment. Tools are modules that augment an agent's action space, observation space, or provide additonal functionalities to the agent. Below are the set of tools we have implemented so far.

Tool name	Description
listdir	It returns the directory tree at a given subdirectory. This is particularly useful when dealing with a repository with multiple files.
view	It is used to change an agent's focus to a particular source code file. This is particularly useful when dealing with a repository with multiple files.
eval	It runs the current code repository using the provided entrypoint (e.g., pytest), and returns the terminal's output (e.g., error message).
pdb	Interactive debugger wrapping the Python pdb tool. In additon, users can choose to maintain a set of persistent breakpoints (as in some programming IDEs), which are not reset after every eval. With such feature, a new pdb debugging session is activated automatically, with all the breakpoints restored. Note such breakpoint can be cleared by pdb commands such as cl.
rewrite	It can be used to rewrite a certain piece of code to fix the bug. The inputs of this tool call include the file path, the start and end line numbers, and the new code.

Upon importing a tool, its action space and observation space will be automatically merged into debug-gym's action space and observation space; its instruction will also be merged into the overall instruction provided to the agent (e.g., as system prompt).

Users can include a .debugignore file in the repository to specify files and directories that are not visible to debug-gym, similarly, they can include a .debugreadonly to specify files and directories that are read only by the agents (e.g., the test files). Both files share the same syntax as .gitignore.

2.2. Agents

We provide the below LLM-based agents, they all have minimal design and serve the purpose of demonstrating the debug-gym APIs.

Agent name	Available Tools	Description
debug_agent	pdb, patcher, view, eval	A minimal agent that dumps all available information into its prompt and queries the LLM to generate a command.
rewrite_agent	patcher, view, eval	A debug_agent but pdb tool is disabled (an agent keeps rewriting).
debug_5_agent	pdb, patcher, view, eval	A debug_agent, but pdb tool is only enabled after certain amount of rewrites.

2.3. Benchmarks

To demonstrate how to integrate debug-gym with coding tasks and repositories, we provide example code importing two widely used benchmarks, namely aider and swebench, and a small set of minimal buggy code snippets, namely mini_nightmare.

Benchmark name	Link
aider	https://github.com/Aider-AI/aider
swebench	https://github.com/princeton-nlp/SWE-bench
mini_nightmare	A set of 10 hand-crafted minimal buggy code snippet where rewrite only agents have harder time to tackle. Read details here.

3. Running Baselines

We use .yaml files to specify configurations. Example config files can be found in scripts/. To run an agent:

python scripts/run.py scripts/config_<benchmark name>.yaml --agent <agent name>

Add -v, --debug to be verbose, or to enter debug mode.

[!WARNING] When using --debug, you will need to press c to continue after each reasoning step.

3.1. Overriding Values in Config

-p is a handy way to override values defined in config. For example, the below command will run rewrite_agent agent on Aider with human mode (while in config file it specifies gpt-4o).

python scripts/run.py scripts/config_aider.yaml --agent rewrite_agent -v -p rewrite_agent.llm_name="human"

3.2. Debugging a Custom Repository

Modify scripts/config.yaml, especially the env_kwargs to set the path and entrypoint of the custom repository. We assume there is a .debugignore file and a .debugreadonly within the repository that labels files/folders that are not seen or not editable, respectively.

As an example, we provide a buggy pytorch code repository in data/pytorch.

python scripts/run.py scripts/config.yaml --agent <agent name>

3.3. Design Your Own Tool

debug-gym's modular design makes it extensible. Users are encouraged to extend debug-gym to their specific usecases, for example by creating new tools that diversify an agent's action and observation spaces. For detailed instruction on designing new tools that are debug-gym-compatible, please refer to the Technical Report.

Citation

tbd

Contributing

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