Best practices for Flow state initialization with required fields

[visalvo]

Following up on #3744, I'd like to start a discussion about the design patterns and best practices for initializing Flow state with Pydantic models, particularly when dealing with required fields.

The Challenge

Currently, there's a tension between type safety and Flow initialization. When using Pydantic models with required fields as Flow state, developers face validation errors unless all fields are optional or have defaults.

class MyState(BaseModel):
    name: str  # Required field
    age: int     # Required field

class MyFlow(Flow[MyState]):
    def __init__(self, **kwargs):
        # implementation

# Currently fails with ValidationError
flow = MyFlow()

Questions for the Community

1. Architecture: Should Flow state initialization happen at __init__ time or at kickoff() time?
   • Pros/cons of early vs. late initialization
   • Impact on type safety and developer experience
2. API Design: What's the ideal developer experience for initializing flows with required state fields?

 # Option A: Pass state values to __init__
 flow = MyFlow(name="John", age=30)
 
 # Option B: Pass complete state object
 flow = MyFlow(state=MyState(name="John", age=30))
 
 # Option C: Defer initialization to kickoff
 flow = MyFlow()
 flow.kickoff(inputs={"name": "John", "age": 30})

4. Type Safety vs. Flexibility: How do we balance:
   • Strong typing with required fields
   • Optional fields for flexible state updates
   • Backward compatibility with existing flows

Related Work

A PR (#3743) has been submitted to address the immediate technical issue, but I think there's value in discussing the broader design implications before finalizing the approach.

Goal

Let's align on the recommended patterns for Flow state management, in order to provide clear guidance in documentation and ensure the API supports common use cases elegantly.

What are your thoughts and experiences with Flow state initialization?