feat(responses): add output_text delta events to responses (#2265)

This adds initial streaming support to the Responses API. 

This PR makes sure that the _first_ inference call made to chat
completions streams out.

There's more to be done:
 - tool call output tokens need to stream out when possible
- we need to loop through multiple rounds of inference and they all need
to stream out.

## Test Plan

Added a test. Executed as:

```
FIREWORKS_API_KEY=... \
  pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
  --provider=stack:fireworks --model meta-llama/Llama-4-Scout-17B-16E-Instruct
```

Then, started a llama stack fireworks distro and tested against it like
this:

```
OPENAI_API_KEY=blah \
   pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
   --base-url http://localhost:8321/v1/openai/v1 \
  --model meta-llama/Llama-4-Scout-17B-16E-Instruct 
```

docs/_static/llama-stack-spec.html

@@ -7540,6 +7540,9 @@
                     {
                         "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated"
                     },
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta"
+                    },
                     {
                         "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
                     }
@@ -7548,6 +7551,7 @@
                     "propertyName": "type",
                     "mapping": {
                         "response.created": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated",
+                        "response.output_text.delta": "#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta",
                         "response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
                     }
                 }
@@ -7590,6 +7594,41 @@
                 ],
                 "title": "OpenAIResponseObjectStreamResponseCreated"
             },
+            "OpenAIResponseObjectStreamResponseOutputTextDelta": {
+                "type": "object",
+                "properties": {
+                    "content_index": {
+                        "type": "integer"
+                    },
+                    "delta": {
+                        "type": "string"
+                    },
+                    "item_id": {
+                        "type": "string"
+                    },
+                    "output_index": {
+                        "type": "integer"
+                    },
+                    "sequence_number": {
+                        "type": "integer"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "response.output_text.delta",
+                        "default": "response.output_text.delta"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "content_index",
+                    "delta",
+                    "item_id",
+                    "output_index",
+                    "sequence_number",
+                    "type"
+                ],
+                "title": "OpenAIResponseObjectStreamResponseOutputTextDelta"
+            },
             "CreateUploadSessionRequest": {
                 "type": "object",
                 "properties": {

docs/_static/llama-stack-spec.yaml

@@ -5294,11 +5294,13 @@ components:
     OpenAIResponseObjectStream:
       oneOf:
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
+        - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta'
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
       discriminator:
         propertyName: type
         mapping:
           response.created: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
+          response.output_text.delta: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta'
           response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
     "OpenAIResponseObjectStreamResponseCompleted":
       type: object
@@ -5330,6 +5332,33 @@ components:
         - type
       title: >-
         OpenAIResponseObjectStreamResponseCreated
+    "OpenAIResponseObjectStreamResponseOutputTextDelta":
+      type: object
+      properties:
+        content_index:
+          type: integer
+        delta:
+          type: string
+        item_id:
+          type: string
+        output_index:
+          type: integer
+        sequence_number:
+          type: integer
+        type:
+          type: string
+          const: response.output_text.delta
+          default: response.output_text.delta
+      additionalProperties: false
+      required:
+        - content_index
+        - delta
+        - item_id
+        - output_index
+        - sequence_number
+        - type
+      title: >-
+        OpenAIResponseObjectStreamResponseOutputTextDelta
     CreateUploadSessionRequest:
       type: object
       properties:

llama_stack/apis/agents/openai_responses.py

@@ -149,6 +149,16 @@ class OpenAIResponseObjectStreamResponseCreated(BaseModel):
     type: Literal["response.created"] = "response.created"
 
 
+@json_schema_type
+class OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel):
+    content_index: int
+    delta: str
+    item_id: str
+    output_index: int
+    sequence_number: int
+    type: Literal["response.output_text.delta"] = "response.output_text.delta"
+
+
 @json_schema_type
 class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
     response: OpenAIResponseObject
@@ -156,7 +166,9 @@ class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
 
 
 OpenAIResponseObjectStream = Annotated[
-    OpenAIResponseObjectStreamResponseCreated | OpenAIResponseObjectStreamResponseCompleted,
+    OpenAIResponseObjectStreamResponseCreated
+    | OpenAIResponseObjectStreamResponseOutputTextDelta
+    | OpenAIResponseObjectStreamResponseCompleted,
     Field(discriminator="type"),
 ]
 register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")

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

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import json
+import time
 import uuid
 from collections.abc import AsyncIterator
 from typing import Any, cast
@@ -29,10 +30,12 @@ from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseObjectStream,
     OpenAIResponseObjectStreamResponseCompleted,
     OpenAIResponseObjectStreamResponseCreated,
+    OpenAIResponseObjectStreamResponseOutputTextDelta,
     OpenAIResponseOutput,
     OpenAIResponseOutputMessageContent,
     OpenAIResponseOutputMessageContentOutputText,
     OpenAIResponseOutputMessageFunctionToolCall,
+    OpenAIResponseOutputMessageMCPListTools,
     OpenAIResponseOutputMessageWebSearchToolCall,
 )
 from llama_stack.apis.inference.inference import (
@@ -255,110 +258,14 @@ class OpenAIResponsesImpl:
         """
         return await self.responses_store.list_response_input_items(response_id, after, before, include, limit, order)
 
-    async def create_openai_response(
+    async def _process_response_choices(
         self,
-        input: str | list[OpenAIResponseInput],
+        chat_response: OpenAIChatCompletion,
-        model: str,
+        ctx: ChatCompletionContext,
-        instructions: str | None = None,
+        tools: list[OpenAIResponseInputTool] | None,
-        previous_response_id: str | None = None,
+    ) -> list[OpenAIResponseOutput]:
-        store: bool | None = True,
+        """Handle tool execution and response message creation."""
-        stream: bool | None = False,
-        temperature: float | None = None,
-        tools: list[OpenAIResponseInputTool] | None = None,
-    ):
         output_messages: list[OpenAIResponseOutput] = []
-
-        stream = False if stream is None else stream
-
-        # Huge TODO: we need to run this in a loop, until morale improves
-
-        # Create context to run "chat completion"
-        input = await self._prepend_previous_response(input, previous_response_id)
-        messages = await _convert_response_input_to_chat_messages(input)
-        await self._prepend_instructions(messages, instructions)
-        chat_tools, mcp_tool_to_server, mcp_list_message = (
-            await self._convert_response_tools_to_chat_tools(tools) if tools else (None, {}, None)
-        )
-        if mcp_list_message:
-            output_messages.append(mcp_list_message)
-
-        ctx = ChatCompletionContext(
-            model=model,
-            messages=messages,
-            tools=chat_tools,
-            mcp_tool_to_server=mcp_tool_to_server,
-            stream=stream,
-            temperature=temperature,
-        )
-
-        # Run inference
-        chat_response = await self.inference_api.openai_chat_completion(
-            model=model,
-            messages=messages,
-            tools=chat_tools,
-            stream=stream,
-            temperature=temperature,
-        )
-
-        # Collect output
-        if stream:
-            # TODO: refactor this into a separate method that handles streaming
-            chat_response_id = ""
-            chat_response_content = []
-            chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {}
-            # TODO: these chunk_ fields are hacky and only take the last chunk into account
-            chunk_created = 0
-            chunk_model = ""
-            chunk_finish_reason = ""
-            async for chunk in chat_response:
-                chat_response_id = chunk.id
-                chunk_created = chunk.created
-                chunk_model = chunk.model
-                for chunk_choice in chunk.choices:
-                    # TODO: this only works for text content
-                    chat_response_content.append(chunk_choice.delta.content or "")
-                    if chunk_choice.finish_reason:
-                        chunk_finish_reason = chunk_choice.finish_reason
-
-                    # Aggregate tool call arguments across chunks, using their index as the aggregation key
-                    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:
-                                response_tool_call.function.arguments += tool_call.function.arguments
-                            else:
-                                tool_call_dict: dict[str, Any] = tool_call.model_dump()
-                                # Ensure we don't have any empty type field in the tool call dict.
-                                # The OpenAI client used by providers often returns a type=None here.
-                                tool_call_dict.pop("type", None)
-                                response_tool_call = OpenAIChatCompletionToolCall(**tool_call_dict)
-                            chat_response_tool_calls[tool_call.index] = response_tool_call
-
-            # Convert the dict of tool calls by index to a list of tool calls to pass back in our response
-            if chat_response_tool_calls:
-                tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
-            else:
-                tool_calls = None
-            assistant_message = OpenAIAssistantMessageParam(
-                content="".join(chat_response_content),
-                tool_calls=tool_calls,
-            )
-            chat_response = OpenAIChatCompletion(
-                id=chat_response_id,
-                choices=[
-                    OpenAIChoice(
-                        message=assistant_message,
-                        finish_reason=chunk_finish_reason,
-                        index=0,
-                    )
-                ],
-                created=chunk_created,
-                model=chunk_model,
-            )
-        else:
-            # dump and reload to map to our pydantic types
-            chat_response = OpenAIChatCompletion(**chat_response.model_dump())
-
         # Execute tool calls if any
         for choice in chat_response.choices:
             if choice.message.tool_calls and tools:
@@ -380,7 +287,128 @@ class OpenAIResponsesImpl:
             else:
                 output_messages.append(await _convert_chat_choice_to_response_message(choice))
 
-        # Create response object
+        return output_messages
+
+    async def _store_response(
+        self,
+        response: OpenAIResponseObject,
+        original_input: str | list[OpenAIResponseInput],
+    ) -> None:
+        new_input_id = f"msg_{uuid.uuid4()}"
+        if isinstance(original_input, str):
+            # synthesize a message from the input string
+            input_content = OpenAIResponseInputMessageContentText(text=original_input)
+            input_content_item = OpenAIResponseMessage(
+                role="user",
+                content=[input_content],
+                id=new_input_id,
+            )
+            input_items_data = [input_content_item]
+        else:
+            # we already have a list of messages
+            input_items_data = []
+            for input_item in original_input:
+                if isinstance(input_item, OpenAIResponseMessage):
+                    # These may or may not already have an id, so dump to dict, check for id, and add if missing
+                    input_item_dict = input_item.model_dump()
+                    if "id" not in input_item_dict:
+                        input_item_dict["id"] = new_input_id
+                    input_items_data.append(OpenAIResponseMessage(**input_item_dict))
+                else:
+                    input_items_data.append(input_item)
+
+        await self.responses_store.store_response_object(
+            response_object=response,
+            input=input_items_data,
+        )
+
+    async def create_openai_response(
+        self,
+        input: str | list[OpenAIResponseInput],
+        model: str,
+        instructions: str | None = None,
+        previous_response_id: str | None = None,
+        store: bool | None = True,
+        stream: bool | None = False,
+        temperature: float | None = None,
+        tools: list[OpenAIResponseInputTool] | None = None,
+    ):
+        stream = False if stream is None else stream
+        original_input = input  # Keep reference for storage
+
+        output_messages: list[OpenAIResponseOutput] = []
+
+        # Input preprocessing
+        input = await self._prepend_previous_response(input, previous_response_id)
+        messages = await _convert_response_input_to_chat_messages(input)
+        await self._prepend_instructions(messages, instructions)
+
+        # Tool setup
+        chat_tools, mcp_tool_to_server, mcp_list_message = (
+            await self._convert_response_tools_to_chat_tools(tools) if tools else (None, {}, None)
+        )
+        if mcp_list_message:
+            output_messages.append(mcp_list_message)
+
+        ctx = ChatCompletionContext(
+            model=model,
+            messages=messages,
+            tools=chat_tools,
+            mcp_tool_to_server=mcp_tool_to_server,
+            stream=stream,
+            temperature=temperature,
+        )
+
+        inference_result = await self.inference_api.openai_chat_completion(
+            model=model,
+            messages=messages,
+            tools=chat_tools,
+            stream=stream,
+            temperature=temperature,
+        )
+
+        if stream:
+            return self._create_streaming_response(
+                inference_result=inference_result,
+                ctx=ctx,
+                output_messages=output_messages,
+                original_input=original_input,
+                model=model,
+                store=store,
+                tools=tools,
+            )
+        else:
+            return await self._create_non_streaming_response(
+                inference_result=inference_result,
+                ctx=ctx,
+                output_messages=output_messages,
+                original_input=original_input,
+                model=model,
+                store=store,
+                tools=tools,
+            )
+
+    async def _create_non_streaming_response(
+        self,
+        inference_result: Any,
+        ctx: ChatCompletionContext,
+        output_messages: list[OpenAIResponseOutput],
+        original_input: str | list[OpenAIResponseInput],
+        model: str,
+        store: bool | None,
+        tools: list[OpenAIResponseInputTool] | None,
+    ) -> OpenAIResponseObject:
+        chat_response = OpenAIChatCompletion(**inference_result.model_dump())
+
+        # Process response choices (tool execution and message creation)
+        output_messages.extend(
+            await self._process_response_choices(
+                chat_response=chat_response,
+                ctx=ctx,
+                tools=tools,
+            )
+        )
+
         response = OpenAIResponseObject(
             created_at=chat_response.created,
             id=f"resp-{uuid.uuid4()}",
@@ -393,45 +421,135 @@ class OpenAIResponsesImpl:
 
         # Store response if requested
         if store:
-            new_input_id = f"msg_{uuid.uuid4()}"
+            await self._store_response(
-            if isinstance(input, str):
+                response=response,
-                # synthesize a message from the input string
+                original_input=original_input,
-                input_content = OpenAIResponseInputMessageContentText(text=input)
-                input_content_item = OpenAIResponseMessage(
-                    role="user",
-                    content=[input_content],
-                    id=new_input_id,
-                )
-                input_items_data = [input_content_item]
-            else:
-                # we already have a list of messages
-                input_items_data = []
-                for input_item in input:
-                    if isinstance(input_item, OpenAIResponseMessage):
-                        # These may or may not already have an id, so dump to dict, check for id, and add if missing
-                        input_item_dict = input_item.model_dump()
-                        if "id" not in input_item_dict:
-                            input_item_dict["id"] = new_input_id
-                        input_items_data.append(OpenAIResponseMessage(**input_item_dict))
-                    else:
-                        input_items_data.append(input_item)
-
-            await self.responses_store.store_response_object(
-                response_object=response,
-                input=input_items_data,
             )
 
-        if stream:
-
-            async def async_response() -> AsyncIterator[OpenAIResponseObjectStream]:
-                # TODO: response created should actually get emitted much earlier in the process
-                yield OpenAIResponseObjectStreamResponseCreated(response=response)
-                yield OpenAIResponseObjectStreamResponseCompleted(response=response)
-
-            return async_response()
-
         return response
 
+    async def _create_streaming_response(
+        self,
+        inference_result: Any,
+        ctx: ChatCompletionContext,
+        output_messages: list[OpenAIResponseOutput],
+        original_input: str | list[OpenAIResponseInput],
+        model: str,
+        store: bool | None,
+        tools: list[OpenAIResponseInputTool] | None,
+    ) -> AsyncIterator[OpenAIResponseObjectStream]:
+        # Create initial response and emit response.created immediately
+        response_id = f"resp-{uuid.uuid4()}"
+        created_at = int(time.time())
+
+        initial_response = OpenAIResponseObject(
+            created_at=created_at,
+            id=response_id,
+            model=model,
+            object="response",
+            status="in_progress",
+            output=output_messages.copy(),
+        )
+
+        # Emit response.created immediately
+        yield OpenAIResponseObjectStreamResponseCreated(response=initial_response)
+
+        # For streaming, inference_result is an async iterator of chunks
+        # Stream chunks and emit delta events as they arrive
+        chat_response_id = ""
+        chat_response_content = []
+        chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {}
+        chunk_created = 0
+        chunk_model = ""
+        chunk_finish_reason = ""
+        sequence_number = 0
+
+        # Create a placeholder message item for delta events
+        message_item_id = f"msg_{uuid.uuid4()}"
+
+        async for chunk in inference_result:
+            chat_response_id = chunk.id
+            chunk_created = chunk.created
+            chunk_model = chunk.model
+            for chunk_choice in chunk.choices:
+                # Emit incremental text content as delta events
+                if chunk_choice.delta.content:
+                    sequence_number += 1
+                    yield OpenAIResponseObjectStreamResponseOutputTextDelta(
+                        content_index=0,
+                        delta=chunk_choice.delta.content,
+                        item_id=message_item_id,
+                        output_index=0,
+                        sequence_number=sequence_number,
+                    )
+
+                # Collect content for final response
+                chat_response_content.append(chunk_choice.delta.content or "")
+                if chunk_choice.finish_reason:
+                    chunk_finish_reason = chunk_choice.finish_reason
+
+                # Aggregate tool call arguments across chunks, using their index as the aggregation key
+                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:
+                            response_tool_call.function.arguments += 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
+
+        # Convert collected chunks to complete response
+        if chat_response_tool_calls:
+            tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
+        else:
+            tool_calls = None
+        assistant_message = OpenAIAssistantMessageParam(
+            content="".join(chat_response_content),
+            tool_calls=tool_calls,
+        )
+        chat_response_obj = OpenAIChatCompletion(
+            id=chat_response_id,
+            choices=[
+                OpenAIChoice(
+                    message=assistant_message,
+                    finish_reason=chunk_finish_reason,
+                    index=0,
+                )
+            ],
+            created=chunk_created,
+            model=chunk_model,
+        )
+
+        # Process response choices (tool execution and message creation)
+        output_messages.extend(
+            await self._process_response_choices(
+                chat_response=chat_response_obj,
+                ctx=ctx,
+                tools=tools,
+            )
+        )
+
+        # Create final response
+        final_response = OpenAIResponseObject(
+            created_at=created_at,
+            id=response_id,
+            model=model,
+            object="response",
+            status="completed",
+            output=output_messages,
+        )
+
+        if store:
+            await self._store_response(
+                response=final_response,
+                original_input=original_input,
+            )
+
+        # Emit response.completed
+        yield OpenAIResponseObjectStreamResponseCompleted(response=final_response)
+
     async def _convert_response_tools_to_chat_tools(
         self, tools: list[OpenAIResponseInputTool]
     ) -> tuple[
@@ -441,7 +559,6 @@ class OpenAIResponsesImpl:
     ]:
         from llama_stack.apis.agents.openai_responses import (
             MCPListToolsTool,
-            OpenAIResponseOutputMessageMCPListTools,
         )
         from llama_stack.apis.tools.tools import Tool
 

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

@@ -232,9 +232,17 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
 
     # Check that we got the content from our mocked tool execution result
     chunks = [chunk async for chunk in result]
-    assert len(chunks) > 0
+    assert len(chunks) == 2  # Should have response.created and response.completed
-    assert chunks[0].response.output[0].type == "function_call"
+
-    assert chunks[0].response.output[0].name == "get_weather"
+    # Check response.created event (should have empty output)
+    assert chunks[0].type == "response.created"
+    assert len(chunks[0].response.output) == 0
+
+    # Check response.completed event (should have the tool call)
+    assert chunks[1].type == "response.completed"
+    assert len(chunks[1].response.output) == 1
+    assert chunks[1].response.output[0].type == "function_call"
+    assert chunks[1].response.output[0].name == "get_weather"
 
 
 @pytest.mark.asyncio

tests/verifications/openai_api/conftest.py

@@ -10,17 +10,17 @@ from tests.verifications.openai_api.fixtures.fixtures import _load_all_verificat
 def pytest_generate_tests(metafunc):
     """Dynamically parametrize tests based on the selected provider and config."""
     if "model" in metafunc.fixturenames:
+        model = metafunc.config.getoption("model")
+        if model:
+            metafunc.parametrize("model", [model])
+            return
+
         provider = metafunc.config.getoption("provider")
         if not provider:
             print("Warning: --provider not specified. Skipping model parametrization.")
             metafunc.parametrize("model", [])
             return
 
-        model = metafunc.config.getoption("model")
-        if model:
-            metafunc.parametrize("model", [model])
-            return
-
         try:
             config_data = _load_all_verification_configs()
         except (OSError, FileNotFoundError) as e:

tests/verifications/openai_api/fixtures/test_cases/responses.yaml

@@ -77,11 +77,12 @@ test_response_image:
           image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg"
       output: "llama"
 
+# the models are really poor at tool calling after seeing images :/
 test_response_multi_turn_image:
   test_name: test_response_multi_turn_image
   test_params:
     case:
-    - case_id: "llama_image_search"
+    - case_id: "llama_image_understanding"
       turns:
       - input:
         - role: user
@@ -91,7 +92,5 @@ test_response_multi_turn_image:
           - type: input_image
             image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg"
         output: "llama"
-      - input: "Search the web using the search tool for the animal from the previous response. Your search query should be a single phrase that includes the animal's name and the words 'maverick', 'scout' and 'llm'"
+      - input: "What country do you find this animal primarily in? What continent?"
-        tools:
+        output: "peru"
-        - type: web_search
-        output: "model"

tests/verifications/openai_api/test_responses.py

@@ -7,6 +7,7 @@
 import json
 
 import httpx
+import openai
 import pytest
 
 from llama_stack import LlamaStackAsLibraryClient
@@ -61,23 +62,151 @@ def test_response_streaming_basic(request, openai_client, model, provider, verif
     if should_skip_test(verification_config, provider, model, test_name_base):
         pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
 
+    import time
+
     response = openai_client.responses.create(
         model=model,
         input=case["input"],
         stream=True,
     )
-    streamed_content = []
+
+    # Track events and timing to verify proper streaming
+    events = []
+    event_times = []
     response_id = ""
+
+    start_time = time.time()
+
     for chunk in response:
-        if chunk.type == "response.completed":
+        current_time = time.time()
+        event_times.append(current_time - start_time)
+        events.append(chunk)
+
+        if chunk.type == "response.created":
+            # Verify response.created is emitted first and immediately
+            assert len(events) == 1, "response.created should be the first event"
+            assert event_times[0] < 0.1, "response.created should be emitted immediately"
+            assert chunk.response.status == "in_progress"
             response_id = chunk.response.id
-            streamed_content.append(chunk.response.output_text.strip())
 
-    assert len(streamed_content) > 0
+        elif chunk.type == "response.completed":
-    assert case["output"].lower() in "".join(streamed_content).lower()
+            # Verify response.completed comes after response.created
+            assert len(events) >= 2, "response.completed should come after response.created"
+            assert chunk.response.status == "completed"
+            assert chunk.response.id == response_id, "Response ID should be consistent"
 
+            # Verify content quality
+            output_text = chunk.response.output_text.lower().strip()
+            assert len(output_text) > 0, "Response should have content"
+            assert case["output"].lower() in output_text, f"Expected '{case['output']}' in response"
+
+    # Verify we got both required events
+    event_types = [event.type for event in events]
+    assert "response.created" in event_types, "Missing response.created event"
+    assert "response.completed" in event_types, "Missing response.completed event"
+
+    # Verify event order
+    created_index = event_types.index("response.created")
+    completed_index = event_types.index("response.completed")
+    assert created_index < completed_index, "response.created should come before response.completed"
+
+    # Verify stored response matches streamed response
     retrieved_response = openai_client.responses.retrieve(response_id=response_id)
-    assert retrieved_response.output_text == "".join(streamed_content)
+    final_event = events[-1]
+    assert retrieved_response.output_text == final_event.response.output_text
+
+
+@pytest.mark.parametrize(
+    "case",
+    responses_test_cases["test_response_basic"]["test_params"]["case"],
+    ids=case_id_generator,
+)
+def test_response_streaming_incremental_content(request, openai_client, model, provider, verification_config, case):
+    """Test that streaming actually delivers content incrementally, not just at the end."""
+    test_name_base = get_base_test_name(request)
+    if should_skip_test(verification_config, provider, model, test_name_base):
+        pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
+
+    import time
+
+    response = openai_client.responses.create(
+        model=model,
+        input=case["input"],
+        stream=True,
+    )
+
+    # Track all events and their content to verify incremental streaming
+    events = []
+    content_snapshots = []
+    event_times = []
+
+    start_time = time.time()
+
+    for chunk in response:
+        current_time = time.time()
+        event_times.append(current_time - start_time)
+        events.append(chunk)
+
+        # Track content at each event based on event type
+        if chunk.type == "response.output_text.delta":
+            # For delta events, track the delta content
+            content_snapshots.append(chunk.delta)
+        elif hasattr(chunk, "response") and hasattr(chunk.response, "output_text"):
+            # For response.created/completed events, track the full output_text
+            content_snapshots.append(chunk.response.output_text)
+        else:
+            content_snapshots.append("")
+
+    # Verify we have the expected events
+    event_types = [event.type for event in events]
+    assert "response.created" in event_types, "Missing response.created event"
+    assert "response.completed" in event_types, "Missing response.completed event"
+
+    # Check if we have incremental content updates
+    created_index = event_types.index("response.created")
+    completed_index = event_types.index("response.completed")
+
+    # The key test: verify content progression
+    created_content = content_snapshots[created_index]
+    completed_content = content_snapshots[completed_index]
+
+    # Verify that response.created has empty or minimal content
+    assert len(created_content) == 0, f"response.created should have empty content, got: {repr(created_content[:100])}"
+
+    # Verify that response.completed has the full content
+    assert len(completed_content) > 0, "response.completed should have content"
+    assert case["output"].lower() in completed_content.lower(), f"Expected '{case['output']}' in final content"
+
+    # Check for true incremental streaming by looking for delta events
+    delta_events = [i for i, event_type in enumerate(event_types) if event_type == "response.output_text.delta"]
+
+    # Assert that we have delta events (true incremental streaming)
+    assert len(delta_events) > 0, "Expected delta events for true incremental streaming, but found none"
+
+    # Verify delta events have content and accumulate to final content
+    delta_content_total = ""
+    non_empty_deltas = 0
+
+    for delta_idx in delta_events:
+        delta_content = content_snapshots[delta_idx]
+        if delta_content:
+            delta_content_total += delta_content
+            non_empty_deltas += 1
+
+    # Assert that we have meaningful delta content
+    assert non_empty_deltas > 0, "Delta events found but none contain content"
+    assert len(delta_content_total) > 0, "Delta events found but total delta content is empty"
+
+    # Verify that the accumulated delta content matches the final content
+    assert delta_content_total.strip() == completed_content.strip(), (
+        f"Delta content '{delta_content_total}' should match final content '{completed_content}'"
+    )
+
+    # Verify timing: delta events should come between created and completed
+    for delta_idx in delta_events:
+        assert created_index < delta_idx < completed_index, (
+            f"Delta event at index {delta_idx} should be between created ({created_index}) and completed ({completed_index})"
+        )
 
 
 @pytest.mark.parametrize(
@@ -178,7 +307,7 @@ def test_response_non_streaming_mcp_tool(request, openai_client, model, provider
         exc_type = (
             AuthenticationRequiredError
             if isinstance(openai_client, LlamaStackAsLibraryClient)
-            else httpx.HTTPStatusError
+            else (httpx.HTTPStatusError, openai.AuthenticationError)
         )
         with pytest.raises(exc_type):
             openai_client.responses.create(