Python: make tool call view optional in DevUI + other link fixes (#2243)

* make tool call view optional in devui + other link fixes

* fix #2310, ensure correct port is shown in command

* fix dialog bug

* ensure executor ids are tracked per items, fix bug where data from concurrent executors where not seperated properly fix #2351

* fix: Enable multi-round human-in-the-loop (HIL) in DevUI workflows

- Backend: Enrich RequestInfoEvents with response schemas in send_responses_streaming path
- Frontend: Replace old HIL requests with new ones instead of accumulating them
- Frontend: Fix HIL response state management to prevent sending stale request responses

This allows workflows to properly handle sequential HIL requests, showing only the
current request to users and progressing through multiple input rounds correctly.

fixes #2334

* fix bug to ensure in memory entities cannot be reloaded in ui

python/packages/devui/agent_framework_devui/_discovery.py

@@ -229,6 +229,15 @@ class EntityDiscovery:
         Args:
             entity_id: Entity identifier to invalidate
         """
+        # Check if entity is in-memory - these cannot be invalidated
+        entity_info = self._entities.get(entity_id)
+        if entity_info and entity_info.source == "in_memory":
+            logger.warning(
+                f"Attempted to invalidate in-memory entity {entity_id} - ignoring "
+                f"(in-memory entities cannot be reloaded)"
+            )
+            return
+
         # Remove from loaded objects cache
         if entity_id in self._loaded_objects:
             del self._loaded_objects[entity_id]
@@ -366,6 +375,7 @@ class EntityDiscovery:
             description=description,
             type=entity_type,
             framework="agent_framework",
+            source=source,  # IMPORTANT: Pass the source parameter
             tools=[str(tool) for tool in (tools_list or [])],
             instructions=instructions,
             model_id=model,

python/packages/devui/agent_framework_devui/_executor.py

@@ -464,8 +464,11 @@ class AgentFrameworkExecutor:
                     except Exception as e:
                         logger.warning(f"Could not convert HIL responses to proper types: {e}")
 
-                    # Step 2: Now send responses to the in-memory workflow
                     async for event in workflow.send_responses_streaming(hil_responses):
+                        # Enrich new RequestInfoEvents that may come from subsequent HIL requests
+                        if isinstance(event, RequestInfoEvent):
+                            self._enrich_request_info_event_with_response_schema(event, workflow)
+
                         for trace_event in trace_collector.get_pending_events():
                             yield trace_event
                         yield event

python/packages/devui/agent_framework_devui/_mapper.py

@@ -29,7 +29,6 @@ from .models import (
     InputTokensDetails,
     OpenAIResponse,
     OutputTokensDetails,
-    ResponseCompletedEvent,
     ResponseErrorEvent,
     ResponseFunctionCallArgumentsDeltaEvent,
     ResponseFunctionResultComplete,
@@ -186,6 +185,8 @@ class MessageMapper:
             if isinstance(raw_event, AgentRunUpdateEvent):
                 # Extract the AgentRunResponseUpdate from the event's data attribute
                 if raw_event.data and isinstance(raw_event.data, AgentRunResponseUpdate):
+                    # Preserve executor_id in context for proper output routing
+                    context["current_executor_id"] = raw_event.executor_id
                     return await self._convert_agent_update(raw_event.data, context)
                 # If no data, treat as generic workflow event
                 return await self._convert_workflow_event(raw_event, context)
@@ -502,8 +503,17 @@ class MessageMapper:
             # Check if we're streaming text content
             has_text_content = any(content.__class__.__name__ == "TextContent" for content in update.contents)
 
-            # If we have text content and haven't created a message yet, create one
-            if has_text_content and "current_message_id" not in context:
+            # Check if we're in an executor context with an existing item
+            executor_id = context.get("current_executor_id")
+            executor_item_key = f"exec_item_{executor_id}" if executor_id else None
+
+            # If we have an executor item, use it for deltas instead of creating a message
+            if has_text_content and executor_item_key and executor_item_key in context:
+                # Use the executor's item ID for this agent's output
+                context["current_message_id"] = context[executor_item_key]
+                # Note: We don't create a new message item here since the executor item already exists
+            # Otherwise, create a message item if we haven't yet (for non-executor contexts)
+            elif has_text_content and "current_message_id" not in context:
                 message_id = f"msg_{uuid4().hex[:8]}"
                 context["current_message_id"] = message_id
                 context["output_index"] = context.get("output_index", -1) + 1
@@ -671,25 +681,9 @@ class MessageMapper:
                 ]
 
             if isinstance(event, AgentCompletedEvent):
-                execution_id = context.get("execution_id", f"agent_{uuid4().hex[:12]}")
-
-                response_obj = Response(
-                    id=f"resp_{execution_id}",
-                    object="response",
-                    created_at=float(time.time()),
-                    model=model_name,
-                    output=[],
-                    status="completed",
-                    parallel_tool_calls=False,
-                    tool_choice="none",
-                    tools=[],
-                )
-
-                return [
-                    ResponseCompletedEvent(
-                        type="response.completed", sequence_number=self._next_sequence(context), response=response_obj
-                    )
-                ]
+                # Don't emit response.completed here - the server will emit a proper one
+                # with usage data after aggregating all events
+                return []
 
             if isinstance(event, AgentFailedEvent):
                 execution_id = context.get("execution_id", f"agent_{uuid4().hex[:12]}")
@@ -839,35 +833,10 @@ class MessageMapper:
                         )
                     ]
 
-            # Handle WorkflowCompletedEvent - emit response.completed
+            # Handle WorkflowCompletedEvent - Don't emit response.completed here
+            # The server will emit a proper one with usage data after aggregating all events
             if event_class == "WorkflowCompletedEvent":
-                workflow_id = context.get("workflow_id", str(uuid4()))
-
-                # Import Response type for proper construction
-                from openai.types.responses import Response
-
-                # Get model name from request or use 'devui' as default
-                request_obj = context.get("request")
-                model_name = request_obj.model if request_obj and request_obj.model else "devui"
-
-                # Create a full Response object for completed state
-                response_obj = Response(
-                    id=f"resp_{workflow_id}",
-                    object="response",
-                    created_at=float(time.time()),
-                    model=model_name,
-                    output=[],  # Output items already sent via output_item.added events
-                    status="completed",
-                    parallel_tool_calls=False,
-                    tool_choice="none",
-                    tools=[],
-                )
-
-                return [
-                    ResponseCompletedEvent(
-                        type="response.completed", sequence_number=self._next_sequence(context), response=response_obj
-                    )
-                ]
+                return []
 
             if event_class == "WorkflowFailedEvent":
                 workflow_id = context.get("workflow_id", str(uuid4()))
@@ -1103,7 +1072,7 @@ class MessageMapper:
                         context[magentic_key] = message_id
                         context["output_index"] = context.get("output_index", -1) + 1
 
-                        # Import required types
+                        # Import required types for creating message containers
                         from openai.types.responses import ResponseOutputMessage, ResponseOutputText
                         from openai.types.responses.response_content_part_added_event import (
                             ResponseContentPartAddedEvent,

python/packages/devui/agent_framework_devui/_server.py

@@ -142,7 +142,7 @@ class DevServer:
                 discovery = self.executor.entity_discovery
                 for entity in self._pending_entities:
                     try:
-                        entity_info = await discovery.create_entity_info_from_object(entity, source="in-memory")
+                        entity_info = await discovery.create_entity_info_from_object(entity, source="in_memory")
                         discovery.register_entity(entity_info.id, entity_info, entity)
                         logger.info(f"Registered in-memory entity: {entity_info.id}")
                     except Exception as e:
@@ -552,6 +552,14 @@ class DevServer:
                 if not entity_info:
                     raise HTTPException(status_code=404, detail=f"Entity {entity_id} not found")
 
+                # Check if entity is in-memory (cannot be reloaded)
+                if entity_info.source == "in_memory":
+                    raise HTTPException(
+                        status_code=400,
+                        detail="In-memory entities cannot be reloaded. "
+                        "They only exist in memory and have no source files to reload from.",
+                    )
+
                 # Invalidate cache
                 executor.entity_discovery.invalidate_entity(entity_id)
 
@@ -1049,10 +1057,20 @@ class DevServer:
             from .models import ResponseCompletedEvent
 
             final_response = await executor.message_mapper.aggregate_to_response(events, request)
+
+            # The sequence number for response.completed should be the next number after all events
+            # The last event in the list should have the highest sequence number so far
+            # We need to increment from that
+            last_seq = 0
+            for event in reversed(events):
+                if hasattr(event, "sequence_number") and event.sequence_number is not None:
+                    last_seq = event.sequence_number
+                    break
+
             completed_event = ResponseCompletedEvent(
                 type="response.completed",
                 response=final_response,
-                sequence_number=len(events),
+                sequence_number=last_seq + 1,
             )
             yield f"data: {completed_event.model_dump_json()}\n\n"