feat(responses): improve streaming for function calls

llama_stack/providers/inline/agents/meta_reference/openai_responses.py

@@ -33,6 +33,10 @@ from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseObjectStream,
     OpenAIResponseObjectStreamResponseCompleted,
     OpenAIResponseObjectStreamResponseCreated,
+    OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta,
+    OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone,
+    OpenAIResponseObjectStreamResponseOutputItemAdded,
+    OpenAIResponseObjectStreamResponseOutputItemDone,
     OpenAIResponseObjectStreamResponseOutputTextDelta,
     OpenAIResponseOutput,
     OpenAIResponseOutputMessageContent,
@@ -73,7 +77,9 @@ from llama_stack.apis.tools import ToolGroups, ToolInvocationResult, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.log import get_logger
 from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
-from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool
+from llama_stack.providers.utils.inference.openai_compat import (
+    convert_tooldef_to_openai_tool,
+)
 from llama_stack.providers.utils.responses.responses_store import ResponsesStore
 
 logger = get_logger(name=__name__, category="openai_responses")
@@ -82,7 +88,7 @@ OPENAI_RESPONSES_PREFIX = "openai_responses:"
 
 
 async def _convert_response_content_to_chat_content(
-    content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent],
+    content: (str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]),
 ) -> str | list[OpenAIChatCompletionContentPartParam]:
     """
     Convert the content parts from an OpenAI Response API request into OpenAI Chat Completion content parts.
@@ -150,7 +156,9 @@ async def _convert_response_input_to_chat_messages(
     return messages
 
 
-async def _convert_chat_choice_to_response_message(choice: OpenAIChoice) -> OpenAIResponseMessage:
+async def _convert_chat_choice_to_response_message(
+    choice: OpenAIChoice,
+) -> OpenAIResponseMessage:
     """
     Convert an OpenAI Chat Completion choice into an OpenAI Response output message.
     """
@@ -172,7 +180,9 @@ async def _convert_chat_choice_to_response_message(choice: OpenAIChoice) -> Open
     )
 
 
-async def _convert_response_text_to_chat_response_format(text: OpenAIResponseText) -> OpenAIResponseFormatParam:
+async def _convert_response_text_to_chat_response_format(
+    text: OpenAIResponseText,
+) -> OpenAIResponseFormatParam:
     """
     Convert an OpenAI Response text parameter into an OpenAI Chat Completion response format.
     """
@@ -228,7 +238,9 @@ class OpenAIResponsesImpl:
         self.vector_io_api = vector_io_api
 
     async def _prepend_previous_response(
-        self, input: str | list[OpenAIResponseInput], previous_response_id: str | None = None
+        self,
+        input: str | list[OpenAIResponseInput],
+        previous_response_id: str | None = None,
     ):
         if previous_response_id:
             previous_response_with_input = await self.responses_store.get_response_object(previous_response_id)
@@ -446,6 +458,8 @@ class OpenAIResponsesImpl:
 
             # Create a placeholder message item for delta events
             message_item_id = f"msg_{uuid.uuid4()}"
+            # Track tool call items for streaming events
+            tool_call_item_ids: dict[int, str] = {}
 
             async for chunk in completion_result:
                 chat_response_id = chunk.id
@@ -472,18 +486,61 @@ class OpenAIResponsesImpl:
                     if chunk_choice.delta.tool_calls:
                         for tool_call in chunk_choice.delta.tool_calls:
                             response_tool_call = chat_response_tool_calls.get(tool_call.index, None)
-                            if response_tool_call:
+                            # Create new tool call entry if this is the first chunk for this index
-                                # Don't attempt to concatenate arguments if we don't have any new argumentsAdd commentMore actions
+                            if response_tool_call is None:
-                                if tool_call.function.arguments:
-                                    # Guard against an initial None argument before we concatenate
-                                    response_tool_call.function.arguments = (
-                                        response_tool_call.function.arguments or ""
-                                    ) + tool_call.function.arguments
-                            else:
                                 tool_call_dict: dict[str, Any] = tool_call.model_dump()
                                 tool_call_dict.pop("type", None)
                                 response_tool_call = OpenAIChatCompletionToolCall(**tool_call_dict)
-                            chat_response_tool_calls[tool_call.index] = response_tool_call
+                                chat_response_tool_calls[tool_call.index] = response_tool_call
+
+                                # Create item ID for this tool call for streaming events
+                                tool_call_item_id = f"fc_{uuid.uuid4()}"
+                                tool_call_item_ids[tool_call.index] = tool_call_item_id
+
+                                # Emit output_item.added event for the new function call
+                                sequence_number += 1
+                                function_call_item = OpenAIResponseOutputMessageFunctionToolCall(
+                                    arguments="",  # Will be filled incrementally via delta events
+                                    call_id=tool_call.id or "",
+                                    name=tool_call.function.name if tool_call.function else "",
+                                    id=tool_call_item_id,
+                                    status="in_progress",
+                                )
+                                yield OpenAIResponseObjectStreamResponseOutputItemAdded(
+                                    response_id=response_id,
+                                    item=function_call_item,
+                                    output_index=len(output_messages),
+                                    sequence_number=sequence_number,
+                                )
+
+                            # Stream function call arguments as they arrive
+                            if tool_call.function and tool_call.function.arguments:
+                                tool_call_item_id = tool_call_item_ids[tool_call.index]
+                                sequence_number += 1
+                                yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta(
+                                    delta=tool_call.function.arguments,
+                                    item_id=tool_call_item_id,
+                                    output_index=len(output_messages),
+                                    sequence_number=sequence_number,
+                                )
+
+                                # Accumulate arguments for final response
+                                response_tool_call.function.arguments = (
+                                    response_tool_call.function.arguments or ""
+                                ) + tool_call.function.arguments
+
+            # Emit function_call_arguments.done events for completed tool calls
+            for tool_call_index in sorted(chat_response_tool_calls.keys()):
+                if tool_call_index in tool_call_item_ids:
+                    tool_call_item_id = tool_call_item_ids[tool_call_index]
+                    final_arguments = chat_response_tool_calls[tool_call_index].function.arguments or ""
+                    sequence_number += 1
+                    yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone(
+                        arguments=final_arguments,
+                        item_id=tool_call_item_id,
+                        output_index=len(output_messages),
+                        sequence_number=sequence_number,
+                    )
 
             # Convert collected chunks to complete response
             if chat_response_tool_calls:
@@ -532,18 +589,56 @@ class OpenAIResponsesImpl:
                 tool_call_log, tool_response_message = await self._execute_tool_call(tool_call, ctx)
                 if tool_call_log:
                     output_messages.append(tool_call_log)
+
+                    # Emit output_item.done event for completed non-function tool call
+                    # Find the item_id for this tool call
+                    matching_item_id = None
+                    for index, item_id in tool_call_item_ids.items():
+                        response_tool_call = chat_response_tool_calls.get(index)
+                        if response_tool_call and response_tool_call.id == tool_call.id:
+                            matching_item_id = item_id
+                            break
+
+                    if matching_item_id:
+                        sequence_number += 1
+                        yield OpenAIResponseObjectStreamResponseOutputItemDone(
+                            response_id=response_id,
+                            item=tool_call_log,
+                            output_index=len(output_messages) - 1,
+                            sequence_number=sequence_number,
+                        )
+
                 if tool_response_message:
                     next_turn_messages.append(tool_response_message)
 
             for tool_call in function_tool_calls:
-                output_messages.append(
+                # Find the item_id for this tool call from our tracking dictionary
-                    OpenAIResponseOutputMessageFunctionToolCall(
+                matching_item_id = None
-                        arguments=tool_call.function.arguments or "",
+                for index, item_id in tool_call_item_ids.items():
-                        call_id=tool_call.id,
+                    response_tool_call = chat_response_tool_calls.get(index)
-                        name=tool_call.function.name or "",
+                    if response_tool_call and response_tool_call.id == tool_call.id:
-                        id=f"fc_{uuid.uuid4()}",
+                        matching_item_id = item_id
-                        status="completed",
+                        break
-                    )
+
+                # Use existing item_id or create new one if not found
+                final_item_id = matching_item_id or f"fc_{uuid.uuid4()}"
+
+                function_call_item = OpenAIResponseOutputMessageFunctionToolCall(
+                    arguments=tool_call.function.arguments or "",
+                    call_id=tool_call.id,
+                    name=tool_call.function.name or "",
+                    id=final_item_id,
+                    status="completed",
+                )
+                output_messages.append(function_call_item)
+
+                # Emit output_item.done event for completed function call
+                sequence_number += 1
+                yield OpenAIResponseObjectStreamResponseOutputItemDone(
+                    response_id=response_id,
+                    item=function_call_item,
+                    output_index=len(output_messages) - 1,
+                    sequence_number=sequence_number,
                 )
 
             if not function_tool_calls and not non_function_tool_calls:
@@ -779,7 +874,8 @@ class OpenAIResponsesImpl:
                 )
             elif function.name == "knowledge_search":
                 response_file_search_tool = next(
-                    (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)), None
+                    (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)),
+                    None,
                 )
                 if response_file_search_tool:
                     # Use vector_stores.search API instead of knowledge_search tool
@@ -798,7 +894,9 @@ class OpenAIResponsesImpl:
             error_exc = e
 
         if function.name in ctx.mcp_tool_to_server:
-            from llama_stack.apis.agents.openai_responses import OpenAIResponseOutputMessageMCPCall
+            from llama_stack.apis.agents.openai_responses import (
+                OpenAIResponseOutputMessageMCPCall,
+            )
 
             message = OpenAIResponseOutputMessageMCPCall(
                 id=tool_call_id,
@@ -850,7 +948,10 @@ class OpenAIResponsesImpl:
             if isinstance(result.content, str):
                 content = result.content
             elif isinstance(result.content, list):
-                from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem
+                from llama_stack.apis.common.content_types import (
+                    ImageContentItem,
+                    TextContentItem,
+                )
 
                 content = []
                 for item in result.content:

tests/integration/non_ci/responses/test_responses.py

@@ -384,12 +384,18 @@ def test_response_non_streaming_mcp_tool(request, compat_client, text_model_id,
         assert list_tools.type == "mcp_list_tools"
         assert list_tools.server_label == "localmcp"
         assert len(list_tools.tools) == 2
-        assert {t.name for t in list_tools.tools} == {"get_boiling_point", "greet_everyone"}
+        assert {t.name for t in list_tools.tools} == {
+            "get_boiling_point",
+            "greet_everyone",
+        }
 
         call = response.output[1]
         assert call.type == "mcp_call"
         assert call.name == "get_boiling_point"
-        assert json.loads(call.arguments) == {"liquid_name": "myawesomeliquid", "celsius": True}
+        assert json.loads(call.arguments) == {
+            "liquid_name": "myawesomeliquid",
+            "celsius": True,
+        }
         assert call.error is None
         assert "-100" in call.output
 
@@ -581,6 +587,105 @@ def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_
             f"Last chunk should be response.completed, got {chunks[-1].type}"
         )
 
+        # Verify tool call streaming events are present
+        chunk_types = [chunk.type for chunk in chunks]
+
+        # Should have function call arguments delta events for tool calls
+        delta_events = [chunk for chunk in chunks if chunk.type == "response.function_call_arguments.delta"]
+        done_events = [chunk for chunk in chunks if chunk.type == "response.function_call_arguments.done"]
+
+        # Should have output item events for tool calls
+        item_added_events = [chunk for chunk in chunks if chunk.type == "response.output_item.added"]
+        item_done_events = [chunk for chunk in chunks if chunk.type == "response.output_item.done"]
+
+        # Verify we have substantial streaming activity (not just batch events)
+        assert len(chunks) > 10, f"Expected rich streaming with many events, got only {len(chunks)} chunks"
+
+        # Since this test involves MCP tool calls, we should see streaming events
+        assert len(delta_events) > 0, f"Expected function_call_arguments.delta events, got chunk types: {chunk_types}"
+        assert len(done_events) > 0, f"Expected function_call_arguments.done events, got chunk types: {chunk_types}"
+
+        # Should have output item events for function calls
+        assert len(item_added_events) > 0, f"Expected response.output_item.added events, got chunk types: {chunk_types}"
+        assert len(item_done_events) > 0, f"Expected response.output_item.done events, got chunk types: {chunk_types}"
+
+        # Verify delta events have proper structure
+        for delta_event in delta_events:
+            assert hasattr(delta_event, "delta"), "Delta event should have 'delta' field"
+            assert hasattr(delta_event, "item_id"), "Delta event should have 'item_id' field"
+            assert hasattr(delta_event, "sequence_number"), "Delta event should have 'sequence_number' field"
+            assert delta_event.delta, "Delta should not be empty"
+
+        # Verify done events have proper structure
+        for done_event in done_events:
+            assert hasattr(done_event, "arguments"), "Done event should have 'arguments' field"
+            assert hasattr(done_event, "item_id"), "Done event should have 'item_id' field"
+            assert done_event.arguments, "Final arguments should not be empty"
+
+        # Verify output item added events have proper structure
+        for added_event in item_added_events:
+            assert hasattr(added_event, "item"), "Added event should have 'item' field"
+            assert hasattr(added_event, "output_index"), "Added event should have 'output_index' field"
+            assert hasattr(added_event, "sequence_number"), "Added event should have 'sequence_number' field"
+            assert hasattr(added_event, "response_id"), "Added event should have 'response_id' field"
+            assert added_event.item.type in ["function_call", "mcp_call"], "Added item should be a tool call"
+            assert added_event.item.status == "in_progress", "Added item should be in progress"
+            assert added_event.response_id, "Response ID should not be empty"
+            assert isinstance(added_event.output_index, int), "Output index should be integer"
+            assert added_event.output_index >= 0, "Output index should be non-negative"
+
+        # Verify output item done events have proper structure
+        for done_event in item_done_events:
+            assert hasattr(done_event, "item"), "Done event should have 'item' field"
+            assert hasattr(done_event, "output_index"), "Done event should have 'output_index' field"
+            assert hasattr(done_event, "sequence_number"), "Done event should have 'sequence_number' field"
+            assert hasattr(done_event, "response_id"), "Done event should have 'response_id' field"
+            assert done_event.item.type in ["function_call", "mcp_call"], "Done item should be a tool call"
+            # Note: MCP calls don't have a status field, only function calls do
+            if done_event.item.type == "function_call":
+                assert done_event.item.status == "completed", "Function call should be completed"
+            assert done_event.response_id, "Response ID should not be empty"
+            assert isinstance(done_event.output_index, int), "Output index should be integer"
+            assert done_event.output_index >= 0, "Output index should be non-negative"
+
+        # Group function call argument events by item_id (these should have proper tracking)
+        function_call_events_by_item_id = {}
+        for chunk in chunks:
+            if hasattr(chunk, "item_id") and chunk.type in [
+                "response.function_call_arguments.delta",
+                "response.function_call_arguments.done",
+            ]:
+                item_id = chunk.item_id
+                if item_id not in function_call_events_by_item_id:
+                    function_call_events_by_item_id[item_id] = []
+                function_call_events_by_item_id[item_id].append(chunk)
+
+        for item_id, related_events in function_call_events_by_item_id.items():
+            # Should have at least one delta and one done event for a complete function call
+            delta_events = [e for e in related_events if e.type == "response.function_call_arguments.delta"]
+            done_events = [e for e in related_events if e.type == "response.function_call_arguments.done"]
+
+            assert len(delta_events) > 0, f"Item {item_id} should have at least one delta event"
+            assert len(done_events) == 1, f"Item {item_id} should have exactly one done event"
+
+            # Verify all events have the same item_id
+            for event in related_events:
+                assert event.item_id == item_id, f"Event should have consistent item_id {item_id}, got {event.item_id}"
+
+        # Basic pairing check: each output_item.added should be followed by some activity
+        # (but we can't enforce strict 1:1 pairing due to the complexity of multi-turn scenarios)
+        assert len(item_added_events) > 0, "Should have at least one output_item.added event"
+
+        # Verify response_id consistency across all events
+        response_ids = set()
+        for chunk in chunks:
+            if hasattr(chunk, "response_id"):
+                response_ids.add(chunk.response_id)
+            elif hasattr(chunk, "response") and hasattr(chunk.response, "id"):
+                response_ids.add(chunk.response.id)
+
+        assert len(response_ids) == 1, f"All events should reference the same response_id, found: {response_ids}"
+
         # Get the final response from the last chunk
         final_chunk = chunks[-1]
         if hasattr(final_chunk, "response"):
@@ -722,7 +827,9 @@ def vector_store_with_filtered_files(compat_client, text_model_id, tmp_path_fact
 
         # Attach file to vector store with attributes
         file_attach_response = compat_client.vector_stores.files.create(
-            vector_store_id=vector_store.id, file_id=file_response.id, attributes=file_data["attributes"]
+            vector_store_id=vector_store.id,
+            file_id=file_response.id,
+            attributes=file_data["attributes"],
         )
 
         # Wait for attachment