Remove max_execution_time and max_execution_steps from ExecutionContext and GraphEngine since these limits are now handled by ExecutionLimitsLayer. This follows the separation of concerns principle by keeping execution limits as a cross-cutting concern handled by layers rather than embedded in core engine components.
Changes:
- Remove max_execution_time and max_execution_steps from ExecutionContext
- Remove these parameters from GraphEngine.__init__()
- Remove max_execution_time from Dispatcher
- Update workflow_entry.py to no longer pass these parameters
- Update all tests to remove these parameters
- Replace direct field access with private attributes and property decorators
- Implement deep copy protection for mutable objects (dict, LLMUsage)
- Add helper methods: set_output(), get_output(), update_outputs()
- Add increment_node_run_steps() and add_tokens() convenience methods
- Update loop_node and event_handlers to use new accessor methods
- Add comprehensive unit tests for immutability and validation
- Ensure backward compatibility with existing property access patterns
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)
This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization.
Key changes:
- Introduce distinct `value_type` tags for Integer and Float segments/variables
- Add `VariableUnion` and `SegmentUnion` types for proper type discrimination
- Leverage Pydantic's discriminated union feature for seamless serialization/deserialization
- Enable accurate serialization of data structures containing these types
Closes#22024.
This pull request introduces a feature aimed at improving the debugging experience during workflow editing. With the addition of variable persistence, the system will automatically retain the output variables from previously executed nodes. These persisted variables can then be reused when debugging subsequent nodes, eliminating the need for repetitive manual input.
By streamlining this aspect of the workflow, the feature minimizes user errors and significantly reduces debugging effort, offering a smoother and more efficient experience.
Key highlights of this change:
- Automatic persistence of output variables for executed nodes.
- Reuse of persisted variables to simplify input steps for nodes requiring them (e.g., `code`, `template`, `variable_assigner`).
- Enhanced debugging experience with reduced friction.
Closes#19735.
jyong