feat(openai): custom tools (#32449)

libs/partners/openai/langchain_openai/__init__.py

@@ -1,6 +1,7 @@
 from langchain_openai.chat_models import AzureChatOpenAI, ChatOpenAI
 from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
 from langchain_openai.llms import AzureOpenAI, OpenAI
+from langchain_openai.tools import custom_tool
 
 __all__ = [
     "OpenAI",
@@ -9,4 +10,5 @@ __all__ = [
     "AzureOpenAI",
     "AzureChatOpenAI",
     "AzureOpenAIEmbeddings",
+    "custom_tool",
 ]

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

@@ -3582,6 +3582,20 @@ def _make_computer_call_output_from_message(message: ToolMessage) -> dict:
     return computer_call_output
 
 
+def _make_custom_tool_output_from_message(message: ToolMessage) -> Optional[dict]:
+    custom_tool_output = None
+    for block in message.content:
+        if isinstance(block, dict) and block.get("type") == "custom_tool_call_output":
+            custom_tool_output = {
+                "type": "custom_tool_call_output",
+                "call_id": message.tool_call_id,
+                "output": block.get("output") or "",
+            }
+            break
+
+    return custom_tool_output
+
+
 def _pop_index_and_sub_index(block: dict) -> dict:
     """When streaming, langchain-core uses the ``index`` key to aggregate
     text blocks. OpenAI API does not support this key, so we need to remove it.
@@ -3608,7 +3622,10 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
             msg.pop("name")
         if msg["role"] == "tool":
             tool_output = msg["content"]
-            if lc_msg.additional_kwargs.get("type") == "computer_call_output":
+            custom_tool_output = _make_custom_tool_output_from_message(lc_msg)  # type: ignore[arg-type]
+            if custom_tool_output:
+                input_.append(custom_tool_output)
+            elif lc_msg.additional_kwargs.get("type") == "computer_call_output":
                 computer_call_output = _make_computer_call_output_from_message(
                     cast(ToolMessage, lc_msg)
                 )
@@ -3663,6 +3680,7 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
                             "file_search_call",
                             "function_call",
                             "computer_call",
+                            "custom_tool_call",
                             "code_interpreter_call",
                             "mcp_call",
                             "mcp_list_tools",
@@ -3690,7 +3708,8 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
                 content_call_ids = {
                     block["call_id"]
                     for block in input_
-                    if block.get("type") == "function_call" and "call_id" in block
+                    if block.get("type") in ("function_call", "custom_tool_call")
+                    and "call_id" in block
                 }
                 for tool_call in tool_calls:
                     if tool_call["id"] not in content_call_ids:
@@ -3841,6 +3860,15 @@ def _construct_lc_result_from_responses_api(
                     "error": error,
                 }
                 invalid_tool_calls.append(tool_call)
+        elif output.type == "custom_tool_call":
+            content_blocks.append(output.model_dump(exclude_none=True, mode="json"))
+            tool_call = {
+                "type": "tool_call",
+                "name": output.name,
+                "args": {"__arg1": output.input},
+                "id": output.call_id,
+            }
+            tool_calls.append(tool_call)
         elif output.type in (
             "reasoning",
             "web_search_call",
@@ -4044,6 +4072,23 @@ def _convert_responses_chunk_to_generation_chunk(
         tool_output = chunk.item.model_dump(exclude_none=True, mode="json")
         tool_output["index"] = current_index
         content.append(tool_output)
+    elif (
+        chunk.type == "response.output_item.done"
+        and chunk.item.type == "custom_tool_call"
+    ):
+        _advance(chunk.output_index)
+        tool_output = chunk.item.model_dump(exclude_none=True, mode="json")
+        tool_output["index"] = current_index
+        content.append(tool_output)
+        tool_call_chunks.append(
+            {
+                "type": "tool_call_chunk",
+                "name": chunk.item.name,
+                "args": json.dumps({"__arg1": chunk.item.input}),
+                "id": chunk.item.call_id,
+                "index": current_index,
+            }
+        )
     elif chunk.type == "response.function_call_arguments.delta":
         _advance(chunk.output_index)
         tool_call_chunks.append(

libs/partners/openai/langchain_openai/tools/__init__.py

@@ -0,0 +1,3 @@
+from langchain_openai.tools.custom_tool import custom_tool
+
+__all__ = ["custom_tool"]

libs/partners/openai/langchain_openai/tools/custom_tool.py

@@ -0,0 +1,109 @@
+import inspect
+from collections.abc import Awaitable
+from typing import Any, Callable
+
+from langchain_core.tools import tool
+
+
+def _make_wrapped_func(func: Callable[..., str]) -> Callable[..., list[dict[str, Any]]]:
+    def wrapped(x: str) -> list[dict[str, Any]]:
+        return [{"type": "custom_tool_call_output", "output": func(x)}]
+
+    return wrapped
+
+
+def _make_wrapped_coroutine(
+    coroutine: Callable[..., Awaitable[str]],
+) -> Callable[..., Awaitable[list[dict[str, Any]]]]:
+    async def wrapped(*args: Any, **kwargs: Any) -> list[dict[str, Any]]:
+        result = await coroutine(*args, **kwargs)
+        return [{"type": "custom_tool_call_output", "output": result}]
+
+    return wrapped
+
+
+def custom_tool(*args: Any, **kwargs: Any) -> Any:
+    """Decorator to create an OpenAI custom tool.
+
+    Custom tools allow for tools with (potentially long) freeform string inputs.
+
+    See below for an example using LangGraph:
+
+    .. code-block:: python
+
+        @custom_tool
+        def execute_code(code: str) -> str:
+            \"\"\"Execute python code.\"\"\"
+            return "27"
+
+
+        llm = ChatOpenAI(model="gpt-5", output_version="responses/v1")
+
+        agent = create_react_agent(llm, [execute_code])
+
+        input_message = {"role": "user", "content": "Use the tool to calculate 3^3."}
+        for step in agent.stream(
+            {"messages": [input_message]},
+            stream_mode="values",
+        ):
+            step["messages"][-1].pretty_print()
+
+    You can also specify a format for a corresponding context-free grammar using the
+    ``format`` kwarg:
+
+    .. code-block:: python
+
+        from langchain_openai import ChatOpenAI, custom_tool
+        from langgraph.prebuilt import create_react_agent
+
+        grammar = \"\"\"
+        start: expr
+        expr: term (SP ADD SP term)* -> add
+        | term
+        term: factor (SP MUL SP factor)* -> mul
+        | factor
+        factor: INT
+        SP: " "
+        ADD: "+"
+        MUL: "*"
+        %import common.INT
+        \"\"\"
+
+        format = {"type": "grammar", "syntax": "lark", "definition": grammar}
+
+        # highlight-next-line
+        @custom_tool(format=format)
+        def do_math(input_string: str) -> str:
+            \"\"\"Do a mathematical operation.\"\"\"
+            return "27"
+
+
+        llm = ChatOpenAI(model="gpt-5", output_version="responses/v1")
+
+        agent = create_react_agent(llm, [do_math])
+
+        input_message = {"role": "user", "content": "Use the tool to calculate 3^3."}
+        for step in agent.stream(
+            {"messages": [input_message]},
+            stream_mode="values",
+        ):
+            step["messages"][-1].pretty_print()
+    """
+
+    def decorator(func: Callable[..., Any]) -> Any:
+        metadata = {"type": "custom_tool"}
+        if "format" in kwargs:
+            metadata["format"] = kwargs.pop("format")
+        tool_obj = tool(infer_schema=False, **kwargs)(func)
+        tool_obj.metadata = metadata
+        tool_obj.description = func.__doc__
+        if inspect.iscoroutinefunction(func):
+            tool_obj.coroutine = _make_wrapped_coroutine(func)
+        else:
+            tool_obj.func = _make_wrapped_func(func)
+        return tool_obj
+
+    if args and callable(args[0]) and not kwargs:
+        return decorator(args[0])
+
+    return decorator