Python: Refactor RequestInfoExecutor (#1403)

* Refactor RequestInfoExecutor

* Update AI script

* Fix formatting

* Address comments

* fix unit test

python/samples/getting_started/workflows/agents/workflow_as_agent_human_in_the_loop.py

@@ -5,7 +5,7 @@ import sys
 from collections.abc import Mapping
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, cast
+from typing import Any
 
 # Ensure local getting_started package can be imported when running as a script.
 _SAMPLES_ROOT = Path(__file__).resolve().parents[3]
@@ -150,23 +150,17 @@ async def main() -> None:
         else:
             raise TypeError("Unexpected argument type for human review function call.")
 
-        request_payload_obj: Any = request.data
-        if not isinstance(request_payload_obj, Mapping):
-            raise ValueError("Human review request payload must be a mapping.")
-        request_payload = cast(Mapping[str, Any], request_payload_obj)
+        request_payload: Any = request.data
+        if not isinstance(request_payload, HumanReviewRequest):
+            raise ValueError("Human review request payload must be a HumanReviewRequest.")
 
-        agent_request_obj = request_payload.get("agent_request")
-        if not isinstance(agent_request_obj, Mapping):
-            raise ValueError("Human review request must include agent_request mapping data.")
-        agent_request_data = cast(Mapping[str, Any], agent_request_obj)
-
-        request_id_obj = agent_request_data.get("request_id")
-        if not isinstance(request_id_obj, str):
-            raise ValueError("Human review request_id must be a string.")
-        request_id_value = request_id_obj
+        agent_request = request_payload.agent_request
+        if agent_request is None:
+            raise ValueError("Human review request must include agent_request.")
 
+        request_id = agent_request.request_id
         # Mock a human response approval for demonstration purposes.
-        human_response = ReviewResponse(request_id=request_id_value, feedback="Approved", approved=True)
+        human_response = ReviewResponse(request_id=request_id, feedback="Approved", approved=True)
 
         # Create the function call result object to send back to the agent.
         human_review_function_result = FunctionResultContent(

python/samples/getting_started/workflows/checkpoint/checkpoint_with_human_in_the_loop.py

@@ -23,6 +23,7 @@ from agent_framework import (
     WorkflowOutputEvent,
     WorkflowRunState,
     WorkflowStatusEvent,
+    get_checkpoint_summary,
     handler,
 )
 from agent_framework.azure import AzureOpenAIChatClient
@@ -246,14 +247,12 @@ def _render_checkpoint_summary(checkpoints: list["WorkflowCheckpoint"]) -> None:
     """Pretty-print saved checkpoints with the new framework summaries."""
 
     print("\nCheckpoint summary:")
-    for summary in [
-        RequestInfoExecutor.checkpoint_summary(cp) for cp in sorted(checkpoints, key=lambda c: c.timestamp)
-    ]:
+    for summary in [get_checkpoint_summary(cp) for cp in sorted(checkpoints, key=lambda c: c.timestamp)]:
         # Compose a single line per checkpoint so the user can scan the output
         # and pick the resume point that still has outstanding human work.
         line = (
             f"- {summary.checkpoint_id} | iter={summary.iteration_count} "
-            f"| targets={summary.targets} | states={summary.executor_states}"
+            f"| targets={summary.targets} | states={summary.executor_ids}"
         )
         if summary.status:
             line += f" | status={summary.status}"
@@ -312,7 +311,7 @@ def _prompt_for_responses(requests: list[tuple[str, HumanApprovalRequest]]) -> d
 def _maybe_pre_supply_responses(cp: "WorkflowCheckpoint") -> dict[str, str] | None:
     """Offer to collect responses before resuming a checkpoint."""
 
-    pending = RequestInfoExecutor.pending_requests_from_checkpoint(cp)
+    pending = get_checkpoint_summary(cp).pending_requests
     if not pending:
         return None
 
@@ -468,7 +467,7 @@ async def main() -> None:
         return
 
     chosen = sorted_cps[idx]
-    summary = RequestInfoExecutor.checkpoint_summary(chosen)
+    summary = get_checkpoint_summary(chosen)
     if summary.status == "completed":
         print("Selected checkpoint already reflects a completed workflow; nothing to resume.")
         return

python/samples/getting_started/workflows/checkpoint/checkpoint_with_resume.py

@@ -12,10 +12,10 @@ from agent_framework import (
     ChatMessage,
     Executor,
     FileCheckpointStorage,
-    RequestInfoExecutor,
     Role,
     WorkflowBuilder,
     WorkflowContext,
+    get_checkpoint_summary,
     handler,
 )
 from agent_framework.azure import AzureOpenAIChatClient
@@ -194,7 +194,7 @@ def _render_checkpoint_summary(checkpoints: list["WorkflowCheckpoint"]) -> None:
 
     print("\nCheckpoint summary:")
     for cp in sorted(checkpoints, key=lambda c: c.timestamp):
-        summary = RequestInfoExecutor.checkpoint_summary(cp)
+        summary = get_checkpoint_summary(cp)
         msg_count = sum(len(v) for v in cp.messages.values())
         state_keys = sorted(cp.executor_states.keys())
         orig = cp.shared_state.get("original_input")
@@ -241,7 +241,7 @@ async def main():
 
     print("\nAvailable checkpoints to resume from:")
     for idx, cp in enumerate(sorted_cps):
-        summary = RequestInfoExecutor.checkpoint_summary(cp)
+        summary = get_checkpoint_summary(cp)
         line = f"  [{idx}] id={summary.checkpoint_id} iter={summary.iteration_count}"
         if summary.status:
             line += f" status={summary.status}"