openai[patch]: support multi-turn computer use (#30410)

Here we accept ToolMessages of the form
```python
ToolMessage(
    content=<representation of screenshot> (see below),
    tool_call_id="abc123",
    additional_kwargs={"type": "computer_call_output"},
)
```
and translate them to `computer_call_output` items for the Responses
API.

We also propagate `reasoning_content` items from AIMessages.

## Example

### Load screenshots
```python
import base64

def load_png_as_base64(file_path):
    with open(file_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
        return encoded_string.decode('utf-8')

screenshot_1_base64 = load_png_as_base64("/path/to/screenshot/of/application.png")
screenshot_2_base64 = load_png_as_base64("/path/to/screenshot/of/desktop.png")
```

### Initial message and response
```python
from langchain_core.messages import HumanMessage, ToolMessage
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="computer-use-preview",
    model_kwargs={"truncation": "auto"},
)

tool = {
    "type": "computer_use_preview",
    "display_width": 1024,
    "display_height": 768,
    "environment": "browser"
}
llm_with_tools = llm.bind_tools([tool])

input_message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": (
                "Click the red X to close and reveal my Desktop. "
                "Proceed, no confirmation needed."
            )
        },
        {
            "type": "input_image",
            "image_url": f"data:image/png;base64,{screenshot_1_base64}",
        }
    ]
)

response = llm_with_tools.invoke(
    [input_message],
    reasoning={
        "generate_summary": "concise",
    },
)
response.additional_kwargs["tool_outputs"]
```

### Construct ToolMessage
```python
tool_call_id = response.additional_kwargs["tool_outputs"][0]["call_id"]

tool_message = ToolMessage(
    content=[
        {
            "type": "input_image",
            "image_url": f"data:image/png;base64,{screenshot_2_base64}"
        }
    ],
    #  content=f"data:image/png;base64,{screenshot_2_base64}",  # <-- also acceptable
    tool_call_id=tool_call_id,
    additional_kwargs={"type": "computer_call_output"},
)
```

### Invoke again
```python
messages = [
    input_message,
    response,
    tool_message,
]

response_2 = llm_with_tools.invoke(
    messages,
    reasoning={
        "generate_summary": "concise",
    },
)
```

docs/docs/integrations/chat/openai.ipynb

@@ -655,6 +655,266 @@
     "response.additional_kwargs"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "82b2cfbe-a019-4c6b-a323-a5d7c158cb0d",
+   "metadata": {},
+   "source": [
+    "#### Computer use\n",
+    "\n",
+    "`ChatOpenAI` supports the `\"computer-use-preview\"` model, which is a specialized model for the built-in computer use tool. To enable, pass a [computer use tool](https://platform.openai.com/docs/guides/tools-computer-use) as you would pass another tool.\n",
+    "\n",
+    "Currently, tool outputs for computer use are present in `AIMessage.additional_kwargs[\"tool_outputs\"]`. To reply to the computer use tool call, construct a `ToolMessage` with `{\"type\": \"computer_call_output\"}` in its `additional_kwargs`. The content of the message will be a screenshot. Below, we demonstrate a simple example.\n",
+    "\n",
+    "First, load two screenshots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0fab26a6-f041-4d40-8d7c-51ae8a1ad698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "\n",
+    "def load_png_as_base64(file_path):\n",
+    "    with open(file_path, \"rb\") as image_file:\n",
+    "        encoded_string = base64.b64encode(image_file.read())\n",
+    "        return encoded_string.decode(\"utf-8\")\n",
+    "\n",
+    "\n",
+    "screenshot_1_base64 = load_png_as_base64(\n",
+    "    \"/path/to/screenshot_1.png\"\n",
+    ")  # perhaps a screenshot of an application\n",
+    "screenshot_2_base64 = load_png_as_base64(\n",
+    "    \"/path/to/screenshot_2.png\"\n",
+    ")  # perhaps a screenshot of the Desktop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff26e977-1bf2-467d-a853-719c1132bb43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# Initialize model\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"computer-use-preview\",\n",
+    "    model_kwargs={\"truncation\": \"auto\"},\n",
+    ")\n",
+    "\n",
+    "# Bind computer-use tool\n",
+    "tool = {\n",
+    "    \"type\": \"computer_use_preview\",\n",
+    "    \"display_width\": 1024,\n",
+    "    \"display_height\": 768,\n",
+    "    \"environment\": \"browser\",\n",
+    "}\n",
+    "llm_with_tools = llm.bind_tools([tool])\n",
+    "\n",
+    "# Construct input message\n",
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": (\n",
+    "                \"Click the red X to close and reveal my Desktop. \"\n",
+    "                \"Proceed, no confirmation needed.\"\n",
+    "            ),\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"input_image\",\n",
+    "            \"image_url\": f\"data:image/png;base64,{screenshot_1_base64}\",\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "# Invoke model\n",
+    "response = llm_with_tools.invoke(\n",
+    "    [input_message],\n",
+    "    reasoning={\n",
+    "        \"generate_summary\": \"concise\",\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "714bce19-6360-4c09-ba44-59034050527f",
+   "metadata": {},
+   "source": [
+    "The response will include a call to the computer-use tool in its `additional_kwargs`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e4a12d04-d1ab-4bd5-b93d-7028f9c818fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'reasoning': {'id': 'rs_67ddb381c85081919c46e3e544a161e8051ff325ba1bad35',\n",
+       "  'summary': [{'text': 'Closing Visual Studio Code application',\n",
+       "    'type': 'summary_text'}],\n",
+       "  'type': 'reasoning'},\n",
+       " 'tool_outputs': [{'id': 'cu_67ddb385358c8191bf1a127b71bcf1ea051ff325ba1bad35',\n",
+       "   'action': {'button': 'left', 'type': 'click', 'x': 17, 'y': 38},\n",
+       "   'call_id': 'call_Ae3Ghz8xdqZQ01mosYhXXMho',\n",
+       "   'pending_safety_checks': [],\n",
+       "   'status': 'completed',\n",
+       "   'type': 'computer_call'}]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.additional_kwargs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f54e95aa-715e-4ebe-acbd-681ea832abb0",
+   "metadata": {},
+   "source": [
+    "We next construct a ToolMessage with these properties:\n",
+    "\n",
+    "1. It has a `tool_call_id` matching the `call_id` from the computer-call.\n",
+    "2. It has `{\"type\": \"computer_call_output\"}` in its `additional_kwargs`.\n",
+    "3. Its content is either an `image_url` or an `input_image` output block (see [OpenAI docs](https://platform.openai.com/docs/guides/tools-computer-use#5-repeat) for formatting)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "003626d2-82d9-41c2-995e-e9f8c1520d30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import ToolMessage\n",
+    "\n",
+    "tool_call_id = response.additional_kwargs[\"tool_outputs\"][0][\"call_id\"]\n",
+    "\n",
+    "tool_message = ToolMessage(\n",
+    "    content=[\n",
+    "        {\n",
+    "            \"type\": \"input_image\",\n",
+    "            \"image_url\": f\"data:image/png;base64,{screenshot_2_base64}\",\n",
+    "        }\n",
+    "    ],\n",
+    "    # content=f\"data:image/png;base64,{screenshot_2_base64}\",  # <-- also acceptable\n",
+    "    tool_call_id=tool_call_id,\n",
+    "    additional_kwargs={\"type\": \"computer_call_output\"},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffa2bc27-389d-4c3a-b646-a9c7eedc2cb7",
+   "metadata": {},
+   "source": [
+    "We can now invoke the model again using the message history:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ad10a31a-9b81-4dde-8a37-1a656543345a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    input_message,\n",
+    "    response,\n",
+    "    tool_message,\n",
+    "]\n",
+    "\n",
+    "response_2 = llm_with_tools.invoke(\n",
+    "    messages,\n",
+    "    reasoning={\n",
+    "        \"generate_summary\": \"concise\",\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fb3a7251-890a-467c-ab59-ae0331221964",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Done! The Desktop is now visible.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response_2.text()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2759df1-317c-4dd9-823a-4aab65e41939",
+   "metadata": {},
+   "source": [
+    "Instead of passing back the entire sequence, we can also use the [previous_response_id](#passing-previous_response_id):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6a40d11b-2426-48ec-bb5e-19e0b36fd74c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "previous_response_id = response.response_metadata[\"id\"]\n",
+    "\n",
+    "response_2 = llm_with_tools.invoke(\n",
+    "    [tool_message],\n",
+    "    previous_response_id=previous_response_id,\n",
+    "    reasoning={\n",
+    "        \"generate_summary\": \"concise\",\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "687d2f05-38b7-42a5-b640-bfd6b4753719",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The Visual Studio Code terminal has been closed and your desktop is now visible.'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response_2.text()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6fda05f0-4b81-4709-9407-f316d760ad50",

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -2291,7 +2291,7 @@ class ChatOpenAI(BaseChatOpenAI):  # type: ignore[override]
         self, *args: Any, stream_usage: Optional[bool] = None, **kwargs: Any
     ) -> Iterator[ChatGenerationChunk]:
         """Set default stream_options."""
-        if self._use_responses_api(kwargs):
+        if self._use_responses_api({**kwargs, **self.model_kwargs}):
             return super()._stream_responses(*args, **kwargs)
         else:
             stream_usage = self._should_stream_usage(stream_usage, **kwargs)
@@ -2309,7 +2309,7 @@ class ChatOpenAI(BaseChatOpenAI):  # type: ignore[override]
         self, *args: Any, stream_usage: Optional[bool] = None, **kwargs: Any
     ) -> AsyncIterator[ChatGenerationChunk]:
         """Set default stream_options."""
-        if self._use_responses_api(kwargs):
+        if self._use_responses_api({**kwargs, **self.model_kwargs}):
             async for chunk in super()._astream_responses(*args, **kwargs):
                 yield chunk
         else:
@@ -2942,6 +2942,25 @@ def _construct_responses_api_payload(
     return payload
 
 
+def _make_computer_call_output_from_message(message: ToolMessage) -> dict:
+    computer_call_output: dict = {
+        "call_id": message.tool_call_id,
+        "type": "computer_call_output",
+    }
+    if isinstance(message.content, list):
+        # Use first input_image block
+        output = next(
+            block
+            for block in message.content
+            if cast(dict, block)["type"] == "input_image"
+        )
+    else:
+        # string, assume image_url
+        output = {"type": "input_image", "image_url": message.content}
+    computer_call_output["output"] = output
+    return computer_call_output
+
+
 def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
     input_ = []
     for lc_msg in messages:
@@ -2951,15 +2970,26 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
             msg.pop("name")
         if msg["role"] == "tool":
             tool_output = msg["content"]
-            if not isinstance(tool_output, str):
+            if lc_msg.additional_kwargs.get("type") == "computer_call_output":
-                tool_output = _stringify(tool_output)
+                computer_call_output = _make_computer_call_output_from_message(
-            function_call_output = {
+                    cast(ToolMessage, lc_msg)
-                "type": "function_call_output",
+                )
-                "output": tool_output,
+                input_.append(computer_call_output)
-                "call_id": msg["tool_call_id"],
+            else:
-            }
+                if not isinstance(tool_output, str):
-            input_.append(function_call_output)
+                    tool_output = _stringify(tool_output)
+                function_call_output = {
+                    "type": "function_call_output",
+                    "output": tool_output,
+                    "call_id": msg["tool_call_id"],
+                }
+                input_.append(function_call_output)
         elif msg["role"] == "assistant":
+            # Reasoning items
+            reasoning_items = []
+            if reasoning := lc_msg.additional_kwargs.get("reasoning"):
+                reasoning_items.append(reasoning)
+            # Function calls
             function_calls = []
             if tool_calls := msg.pop("tool_calls", None):
                 # TODO: should you be able to preserve the function call object id on
@@ -2979,7 +3009,12 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
                     ):
                         function_call["id"] = _id
                     function_calls.append(function_call)
-
+            # Computer calls
+            computer_calls = []
+            tool_outputs = lc_msg.additional_kwargs.get("tool_outputs", [])
+            for tool_output in tool_outputs:
+                if tool_output.get("type") == "computer_call":
+                    computer_calls.append(tool_output)
             msg["content"] = msg.get("content") or []
             if lc_msg.additional_kwargs.get("refusal"):
                 if isinstance(msg["content"], str):
@@ -3013,7 +3048,9 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
                 msg["content"] = new_blocks
             if msg["content"]:
                 input_.append(msg)
+            input_.extend(reasoning_items)
             input_.extend(function_calls)
+            input_.extend(computer_calls)
         elif msg["role"] == "user":
             if isinstance(msg["content"], list):
                 new_blocks = []
@@ -3220,6 +3257,8 @@ def _convert_responses_chunk_to_generation_chunk(
         )
         if parsed := msg.additional_kwargs.get("parsed"):
             additional_kwargs["parsed"] = parsed
+        if reasoning := msg.additional_kwargs.get("reasoning"):
+            additional_kwargs["reasoning"] = reasoning
         usage_metadata = msg.usage_metadata
         response_metadata = {
             k: v for k, v in msg.response_metadata.items() if k != "id"
@@ -3245,6 +3284,7 @@ def _convert_responses_chunk_to_generation_chunk(
     elif chunk.type == "response.output_item.done" and chunk.item.type in (
         "web_search_call",
         "file_search_call",
+        "computer_call",
     ):
         additional_kwargs["tool_outputs"] = [
             chunk.item.model_dump(exclude_none=True, mode="json")

libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py

@@ -286,10 +286,14 @@ def test_reasoning() -> None:
     assert isinstance(response, AIMessage)
     assert response.additional_kwargs["reasoning"]
 
+    # Test init params + streaming
     llm = ChatOpenAI(model="o3-mini", reasoning_effort="low", use_responses_api=True)
-    response = llm.invoke("Hello")
+    full: Optional[BaseMessageChunk] = None
-    assert isinstance(response, AIMessage)
+    for chunk in llm.stream("Hello"):
-    assert response.additional_kwargs["reasoning"]
+        assert isinstance(chunk, AIMessageChunk)
+        full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessage)
+    assert full.additional_kwargs["reasoning"]
 
 
 def test_stateful_api() -> None:
@@ -304,6 +308,24 @@ def test_stateful_api() -> None:
     assert "bobo" in second_response.content[0]["text"].lower()  # type: ignore
 
 
+def test_route_from_model_kwargs() -> None:
+    llm = ChatOpenAI(model=MODEL_NAME, model_kwargs={"truncation": "auto"})
+    _ = next(llm.stream("Hello"))
+
+
+def test_computer_calls() -> None:
+    llm = ChatOpenAI(model="computer-use-preview", model_kwargs={"truncation": "auto"})
+    tool = {
+        "type": "computer_use_preview",
+        "display_width": 1024,
+        "display_height": 768,
+        "environment": "browser",
+    }
+    llm_with_tools = llm.bind_tools([tool], tool_choice="any")
+    response = llm_with_tools.invoke("Please wait a moment.")
+    assert response.additional_kwargs["tool_outputs"]
+
+
 def test_file_search() -> None:
     pytest.skip()  # TODO: set up infra
     llm = ChatOpenAI(model=MODEL_NAME)