chore: import upstream snapshot with attribution

runtime/node/executor/human_executor.py

@@ -0,0 +1,56 @@
+"""Executor for Human nodes.
+
+Runs the human-in-the-loop interaction nodes.
+"""
+
+from typing import List
+
+from entity.configs import Node
+from entity.configs.node.human import HumanConfig
+from entity.messages import Message, MessageRole
+from runtime.node.executor.base import NodeExecutor
+
+
+class HumanNodeExecutor(NodeExecutor):
+    """Executor used for human interaction nodes."""
+    
+    def execute(self, node: Node, inputs: List[Message]) -> List[Message]:
+        """Execute a human node.
+        
+        Args:
+            node: Human node definition
+            inputs: Input messages
+            
+        Returns:
+            Result supplied by the human reviewer
+        """
+        self._ensure_not_cancelled()
+        if node.node_type != "human":
+            raise ValueError(f"Node {node.id} is not a human node")
+        
+        human_config = node.as_config(HumanConfig)
+        if not human_config:
+            raise ValueError(f"Node {node.id} has no human configuration")
+        
+        human_task_description = human_config.description
+        # Use prompt-style preview so humans see the same flattened text format
+        # instead of raw message JSON.
+        input_data = self._inputs_to_text(inputs)
+
+        prompt_service = self.context.get_human_prompt_service()
+        if prompt_service is None:
+            raise RuntimeError("HumanPromptService is not configured; cannot execute human node")
+
+        prompt_result = prompt_service.request(
+            node.id,
+            human_task_description or "",
+            inputs=input_data,
+            metadata={"node_type": "human"},
+        )
+
+        return [self._build_message(
+            MessageRole.USER,
+            prompt_result.as_message_content(),
+            source=node.id,
+        )]
+