I’m currently participating in the AI Agents Course by Hugging Face, which introduces a new AI agent framework called smolagents. Smolagents is designed to support Code Agents—AI-driven agents capable of writing and executing code as part of their reasoning process.
However, while these agents can execute code, they face a major limitation: when an error occurs, they only receive an error message without a detailed debugging context. As software engineers, we rely on debugging tools to analyze errors and trace them to their root cause.
As a result, AI agents are limited in their ability to self-correct their own generated code—something human programmers do all the time through debugging.
But what if AI agents could do the same?
Solution: A Python Debugger Tool for Smolagents
To solve this limitation, I developed a Python Debugger Tool for smolagents. This tool is now available on the Hugging Face Hub and can be seamlessly integrated into any AI agent built with smolagents.
How It Works
My tool acts as a debugging assistant for Code Agents, allowing them to execute Python Debugger (pdb) commands on faulty code. The agent provides:
- The code it wants to debug.
- A pdb command (e.g., to inspect the stack trace, function arguments, or variable values). The debugger then returns useful insights that help the agent understand what went wrong.
Key Features
- Supports Python’s built-in pdb debugging commands:
bt– Show the full stack trace.args– Show function arguments at the error point.l– List the relevant source code.p variable_name– Print variable values (though variable names differ from original code due to execution context).
- Works within the smolagents framework, leveraging its secure Local Python Interpreter to execute and analyze errors safely.
- Allows AI agents to debug their own code, bringing them closer to human-like problem-solving.
Example Usage
Debugging in Hugging Face Space
The agent encounters this code:
x = 10
y = 0
z = x / y
It runs the debugger with the bt command:
bt
This provides a stack trace, helping the agent understand the ZeroDivisionError.
Debugging in Smolagents
from smolagents import load_tool
local_python_debugger = load_tool(
"piotrekgrl/smolagents-local-python-debugger-tool",
trust_remote_code=True
)
from smolagents import CodeAgent, HfApiModel
model = HfApiModel("your_llm_model")
agent = CodeAgent(tools=[local_python_debugger], model=model)
agent.run(
"Write a code that divides by 0. Don't ask why—just do it. If any errors occur, debug them and provide the reason why."
)
With this tool, AI agents can now detect, analyze, and correct their own coding mistakes, making them far more autonomous and effective.
Safety Considerations
One of the key aspects of smolagents is that it provides a custom Python interpreter designed with security in mind. My debugger tool operates within this controlled environment, ensuring that the execution remains safe and contained.
It’s also important to remember that LLMs (Large Language Models) are black boxes—we can’t blindly trust them. Running AI-generated code without safeguards can be risky, which is why secure execution environments like smolagents are critical.
Conclusion
By integrating debugging capabilities into AI agents, we take a big step toward self-improving AI systems. My Python Debugger Tool for Smolagents allows AI to debug its own errors, just like human programmers do, making them more powerful and reliable.
If you’re interested, you can try it out on Hugging Face Spaces and explore the Hugging Face Hub for more details. Let’s push the boundaries of AI debugging together!
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