Let me help you with this issue. The "cannot be resolved into an exception" error typically occurs when you're trying to return data alongside a failure state without properly structuring it. Here's how to properly handle this: 1. Using Exception Handling (Recommended Approach):

python
from prefect import flow, task
from prefect.states import Failed

@task
def my_task():
    try:
        # Your task logic here
        result = "some calculation"
        if something_went_wrong:
            raise ValueError("Something went wrong")
        return result
    except Exception as e:
        # You can include both the error and additional data
        return Failed(
            message="Task failed with error",
            data={"error": str(e), "additional_data": "some context"}
        )

@flow
def my_flow():
    result = my_task()
    return result

2. Using State Functions:

python
from prefect import flow, task
from prefect.states import Failed

@task
def my_task():
    try:
        # Your task logic here
        raise ValueError("Something went wrong")
    except Exception as e:
        # Create a Failed state with both the exception and data
        return Failed(
            message="Task failed",
            data={"my_data": "some data"},
            exception=e
        )

Key points to remember: - Always include the actual exception in the Failed state if you have one - Structure your data as a dictionary when including additional information - Use try/except blocks to handle the failure gracefully - Make sure your data is serializable (can be converted to JSON) If you're still getting the error, it would be helpful if you could share: 1. The specific code that's causing the error 2. The exact error message you're receiving 3. What kind of data you're trying to return This would allow me to provide more targeted assistance for your specific case.

I understand now - you're trying to pass a custom object (

MainFragSwapFlowState

) directly as data in a Failed state. The error occurs because Prefect needs the data to be serializable (typically JSON-serializable). Here's how you can fix this: 1. Convert the object to a dictionary:

python
from prefect.states import Failed

# Convert your object to a dictionary with only the relevant data
fs_dict = {
    "attribute1": fs.attribute1,
    "attribute2": fs.attribute2,
    # ... other relevant attributes
}

return Failed(
    message=failure_msg + msg + "\n" + flow_link_msg,
    data=fs_dict
)

2. Add a serialization method to your class:

python
class MainFragSwapFlowState:
    def to_dict(self):
        return {
            "attribute1": self.attribute1,
            "attribute2": self.attribute2,
            # ... other relevant attributes
        }

# Then in your flow:
return Failed(
    message=failure_msg + msg + "\n" + flow_link_msg,
    data=fs.to_dict()
)

3. If you need to preserve specific object attributes:

python
return Failed(
    message=failure_msg + msg + "\n" + flow_link_msg,
    data={
        "state_type": type(fs).__name__,
        "attributes": vars(fs),  # This gets all instance variables
        # Add any other specific data you need
    }
)

The key point is that the

data

parameter in a Failed state must be serializable (typically to JSON). Complex objects need to be converted to basic Python types (dictionaries, lists, strings, numbers, etc.) before being passed to the Failed state. If you need help with the specific attributes of your

MainFragSwapFlowState

object that you want to preserve, let me know and I can provide more targeted guidance.

Ah, this is an interesting architectural decision in Prefect! The difference comes down to how Prefect handles different states internally: 1. Completed States: - Can handle non-serializable data because the data typically stays within the same process - Only needs to be serialized if you explicitly configure result storage or persistence - Primarily used for passing data between tasks in memory 2. Failed States: - Must be serializable because Prefect needs to: - Store failure information for debugging - Pass error information through the orchestration layer - Support retry mechanisms - Enable proper error reporting and logging - May need to be transmitted between different systems or stored for later analysis This is why you can do:

python
@task
def my_task():
    complex_object = MyComplexClass()
    return complex_object  # This works fine and returns a Completed state

But need to serialize for failures:

python
@task
def my_task():
    try:
        complex_object = MyComplexClass()
        raise ValueError("Something went wrong")
    except Exception as e:
        return Failed(
            message="Task failed",
            data={"object_data": complex_object.to_dict()}  # Need to serialize
        )

It's a bit like the difference between returning a value from a function (which can be anything) versus logging an error (which needs to be in a format that can be written to logs and transmitted). If you need to preserve the full state of your complex object in a Failed state, you might want to: 1. Implement proper serialization methods for your class 2. Store the complex object elsewhere (like a database) and only include a reference in the Failed state 3. Or extract only the essential information needed for error handling

Ah, I see the issue now. When creating a Failed state, we need to include both the

data

and the actual

exception

. Here's how to fix it:

python
from prefect.states import Failed

# Option 1: Include the actual exception
exception = NotImplementedError("'MOVE_FRAG_ACCOUNTS' is not implemented")
return Failed(
    message="Flow execution failed",
    data={'error': str(exception)},
    exception=exception  # This is the key part we were missing!
)

# Option 2: Use exception_to_failed_state
from prefect.states import exception_to_failed_state

exception = NotImplementedError("'MOVE_FRAG_ACCOUNTS' is not implemented")
failed_state = exception_to_failed_state(
    exc=exception,
    message="Flow execution failed",
    data={'error': str(exception)}
)
return failed_state

The error you're seeing occurs because Prefect expects an actual exception object to be present when creating a Failed state. Simply passing a dictionary with error information isn't enough - Prefect needs to know what type of exception caused the failure. This is a bit counterintuitive, but it's designed this way to ensure proper error tracking and handling in Prefect's orchestration layer. The

exception

parameter is used to maintain the full exception context, while the

data

parameter can contain additional information you want to store alongside the error. Let me know if you need help modifying your specific code to include the proper exception handling!

❌ Error: status_code: 529, model_name: claude-3-5-sonnet-latest, body: {'type': 'error', 'error': {'type': 'overloaded_error', 'message': 'Overloaded'}}