fix: ensure assistant message is followed by tool call message as expected by openai

Signed-off-by: Gordon Sim <gsim@redhat.com>

llama_stack/providers/inline/agents/meta_reference/responses/utils.py

@@ -101,14 +101,22 @@ async def convert_response_input_to_chat_messages(
     """
     messages: list[OpenAIMessageParam] = []
     if isinstance(input, list):
+        # extract all OpenAIResponseInputFunctionToolCallOutput items
+        # so their corresponding OpenAIToolMessageParam instances can
+        # be added immediately following the corresponding
+        # OpenAIAssistantMessageParam
+        tool_call_results = {}
         for input_item in input:
             if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
-                messages.append(
+                tool_call_results[input_item.call_id] = OpenAIToolMessageParam(
-                    OpenAIToolMessageParam(
+                    content=input_item.output,
-                        content=input_item.output,
+                    tool_call_id=input_item.call_id,
-                        tool_call_id=input_item.call_id,
-                    )
                 )
+
+        for input_item in input:
+            if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
+                # skip as these have been extracted and inserted in order
+                pass
             elif isinstance(input_item, OpenAIResponseOutputMessageFunctionToolCall):
                 tool_call = OpenAIChatCompletionToolCall(
                     index=0,
@@ -119,6 +127,9 @@ async def convert_response_input_to_chat_messages(
                     ),
                 )
                 messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
+                if input_item.call_id in tool_call_results:
+                    messages.append(tool_call_results[input_item.call_id])
+                    del tool_call_results[input_item.call_id]
             elif isinstance(input_item, OpenAIResponseOutputMessageMCPCall):
                 tool_call = OpenAIChatCompletionToolCall(
                     index=0,
@@ -146,6 +157,8 @@ async def convert_response_input_to_chat_messages(
                         f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
                     )
                 messages.append(message_type(content=content))
+        for result in tool_call_results.values():
+            messages.append(result)
     else:
         messages.append(OpenAIUserMessageParam(content=input))
     return messages

tests/integration/non_ci/responses/test_tool_responses.py

@@ -260,6 +260,94 @@ def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
     assert response.output[0].name == "get_weather"
 
 
+@pytest.mark.parametrize("case", custom_tool_test_cases)
+def test_response_function_call_ordering_1(compat_client, text_model_id, case):
+    response = compat_client.responses.create(
+        model=text_model_id,
+        input=case.input,
+        tools=case.tools,
+        stream=False,
+    )
+    assert len(response.output) == 1
+    assert response.output[0].type == "function_call"
+    assert response.output[0].status == "completed"
+    assert response.output[0].name == "get_weather"
+    inputs = []
+    inputs.append(
+        {
+            "role": "user",
+            "content": case.input,
+        }
+    )
+    inputs.append(
+        {
+            "type": "function_call_output",
+            "output": "It is raining.",
+            "call_id": response.output[0].call_id,
+        }
+    )
+    response = compat_client.responses.create(
+        model=text_model_id, input=inputs, tools=case.tools, stream=False, previous_response_id=response.id
+    )
+    assert len(response.output) == 1
+
+
+def test_response_function_call_ordering_2(compat_client, text_model_id):
+    tools = [
+        {
+            "type": "function",
+            "name": "get_weather",
+            "description": "Get current temperature for a given location.",
+            "parameters": {
+                "additionalProperties": False,
+                "properties": {
+                    "location": {
+                        "description": "City and country e.g. Bogotá, Colombia",
+                        "type": "string",
+                    }
+                },
+                "required": ["location"],
+                "type": "object",
+            },
+        }
+    ]
+    inputs = [
+        {
+            "role": "user",
+            "content": "Is the weather better in San Francisco or Los Angeles?",
+        }
+    ]
+    response = compat_client.responses.create(
+        model=text_model_id,
+        input=inputs,
+        tools=tools,
+        stream=False,
+    )
+    for output in response.output:
+        if output.type == "function_call" and output.status == "completed" and output.name == "get_weather":
+            inputs.append(output)
+    for output in response.output:
+        if output.type == "function_call" and output.status == "completed" and output.name == "get_weather":
+            weather = "It is raining."
+            if "Los Angeles" in output.arguments:
+                weather = "It is cloudy."
+            inputs.append(
+                {
+                    "type": "function_call_output",
+                    "output": weather,
+                    "call_id": output.call_id,
+                }
+            )
+    response = compat_client.responses.create(
+        model=text_model_id,
+        input=inputs,
+        tools=tools,
+        stream=False,
+    )
+    assert len(response.output) == 1
+    assert "Los Angeles" in response.output_text
+
+
 @pytest.mark.parametrize("case", multi_turn_tool_execution_test_cases)
 def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
     """Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""

tests/unit/providers/agents/meta_reference/test_response_conversion_utils.py

@@ -146,6 +146,47 @@ class TestConvertResponseInputToChatMessages:
         assert result[0].tool_calls[0].function.name == "test_function"
         assert result[0].tool_calls[0].function.arguments == '{"param": "value"}'
 
+    async def test_convert_function_call_ordering(self):
+        input_items = [
+            OpenAIResponseOutputMessageFunctionToolCall(
+                call_id="call_123",
+                name="test_function_a",
+                arguments='{"param": "value"}',
+            ),
+            OpenAIResponseOutputMessageFunctionToolCall(
+                call_id="call_456",
+                name="test_function_b",
+                arguments='{"param": "value"}',
+            ),
+            OpenAIResponseInputFunctionToolCallOutput(
+                output="AAA",
+                call_id="call_123",
+            ),
+            OpenAIResponseInputFunctionToolCallOutput(
+                output="BBB",
+                call_id="call_456",
+            ),
+        ]
+
+        result = await convert_response_input_to_chat_messages(input_items)
+        assert len(result) == 4
+        assert isinstance(result[0], OpenAIAssistantMessageParam)
+        assert len(result[0].tool_calls) == 1
+        assert result[0].tool_calls[0].id == "call_123"
+        assert result[0].tool_calls[0].function.name == "test_function_a"
+        assert result[0].tool_calls[0].function.arguments == '{"param": "value"}'
+        assert isinstance(result[1], OpenAIToolMessageParam)
+        assert result[1].content == "AAA"
+        assert result[1].tool_call_id == "call_123"
+        assert isinstance(result[2], OpenAIAssistantMessageParam)
+        assert len(result[2].tool_calls) == 1
+        assert result[2].tool_calls[0].id == "call_456"
+        assert result[2].tool_calls[0].function.name == "test_function_b"
+        assert result[2].tool_calls[0].function.arguments == '{"param": "value"}'
+        assert isinstance(result[3], OpenAIToolMessageParam)
+        assert result[3].content == "BBB"
+        assert result[3].tool_call_id == "call_456"
+
     async def test_convert_response_message(self):
         input_items = [
             OpenAIResponseMessage(