fix: Add resource cleanup mechanism in CodeAgent

[kingdomad]

Fix Docker Resource Leaks in CodeAgent

Added cleanup() and __del__() methods to CodeAgent class to properly release Docker container resources when the agent is destroyed. Otherwise, each time CodeAgent is used, Docker resources would need to be manually released.

[albertvillanova]

Thanks. A comment below.

Do you think a test could be easily implemented?

[albertvillanova]

I think the first condition is always True: self.python_executor is defined at instantiation.

[kingdomad]

You're right, I'll remove the first condition.

[kingdomad]

I found that the first condition is necessary because there are some cases where the program exits before self.python_executor is fully instantiated, and in those cases, CodeAgent doesn't have the python_executor attribute.

[albertvillanova]

I'm not sure of understanding your argument: if the program exits during instantiation, then the instance is not created, so self does not exist, so you cannot delete a non-existing instance. 🤔

Am I missing something?

[kingdomad]

No. If CodeAgent encounters an error during instantiation, the instance will still exist in an incomplete form and will still execute the logic in the __del__ method and cleanup method. For example, if an error occurs when instantiating the docker executor, the CodeAgent won't have a python_executor attribute, but it will still execute CodeAgent.cleanup. Without the if hasattr(self, "python_executor") check, it would raise another error. I've already tested this.

[kingdomad]

Even if, in 99% of cases, the __del__ method will not be executed and only in 1% of cases it will, we should still take this 1% into account and add the condition if hasattr(self, "python_executor") to enhance the robustness of the program.

[kingdomad]

In an interactive console, several factors can affect the execution of the __del__ method:

1. The Python interpreter may retain references to recently created objects, especially in an interactive environment.
2. Even if the variable a goes out of scope, Python's garbage collector might not immediately reclaim the object. Garbage collection is typically triggered when the system needs more memory or after specific time intervals.
3. In an interactive environment, the interpreter maintains special variables (such as _) that reference the results of recent expressions, potentially extending the object's lifecycle.

[albertvillanova]

I see your point, @kingdomad, and I appreciate the discussion.

• I agree that we should not ignore the 1% of cases where __del__ is reliably called. See my suggestion below.
• At the same time, we also need to account for the other 99% of cases where __del__ may not be called, ensuring proper resource cleanup in those scenarios. How do you think we could best handle this?

For the case where __del__ is called, I would suggest following the "Easier to ask for forgiveness than permission" approach, by wrapping the cleanup in a try block:

try:
    self.python_executor.delete()
except AttributeError:
    pass

Let me know what you think!

[kingdomad]

You make a valid point, but I think you're making this more complicated than it needs to be. I've consulted both Claude 3.7 and GPT-4o, and they both agree with my approach to modifying the code! Which approach do you prefer? I'm happy to implement your suggestion if that's what you'd like!

[kingdomad]

@albertvillanova I've reconsidered, and your approach makes sense. I'll implement the change using the try-except pattern as you suggested.