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

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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",
    },
)
```
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260
docs/docs/integrations/chat/openai.ipynb
View File

@ -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",

62
libs/partners/openai/langchain_openai/chat_models/base.py
View File

@ -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):
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)
if lc_msg.additional_kwargs.get("type") == "computer_call_output":
computer_call_output = _make_computer_call_output_from_message(
cast(ToolMessage, lc_msg)
)
input_.append(computer_call_output)
else:
if not isinstance(tool_output, str):
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")

28
libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py
View File

@ -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")
assert isinstance(response, AIMessage)
assert response.additional_kwargs["reasoning"]
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream("Hello"):
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)