Fix #968 by upgrading openai package to the latest

pyproject.toml

@@ -7,7 +7,7 @@ requires-python = ">=3.9"
 license = "MIT"
 authors = [{ name = "OpenAI", email = "[email protected]" }]
 dependencies = [
-    "openai>=1.87.0",
+    "openai>=1.93.1, <2",
     "pydantic>=2.10, <3",
     "griffe>=1.5.6, <2",
     "typing-extensions>=4.12.2, <5",

src/agents/model_settings.py

@@ -42,11 +42,14 @@ def validate_from_none(value: None) -> _Omit:
             serialization=core_schema.plain_serializer_function_ser_schema(lambda instance: None),
         )
 
+@dataclass
+class MCPToolChoice:
+    server_label: str
+    name: str
 
 Omit = Annotated[_Omit, _OmitTypeAnnotation]
 Headers: TypeAlias = Mapping[str, Union[str, Omit]]
-ToolChoice: TypeAlias = Union[Literal["auto", "required", "none"], str, None]
-
+ToolChoice: TypeAlias = Union[Literal["auto", "required", "none"], str, MCPToolChoice, None]
 
 @dataclass
 class ModelSettings:

src/agents/models/chatcmpl_converter.py

@@ -44,17 +44,20 @@
 from ..exceptions import AgentsException, UserError
 from ..handoffs import Handoff
 from ..items import TResponseInputItem, TResponseOutputItem
+from ..model_settings import MCPToolChoice
 from ..tool import FunctionTool, Tool
 from .fake_id import FAKE_RESPONSES_ID
 
 
 class Converter:
     @classmethod
     def convert_tool_choice(
-        cls, tool_choice: Literal["auto", "required", "none"] | str | None
+        cls, tool_choice: Literal["auto", "required", "none"] | str | MCPToolChoice | None
     ) -> ChatCompletionToolChoiceOptionParam | NotGiven:
         if tool_choice is None:
             return NOT_GIVEN
+        elif isinstance(tool_choice, MCPToolChoice):
+            raise UserError("MCPToolChoice is not supported for Chat Completions models")
         elif tool_choice == "auto":
             return "auto"
         elif tool_choice == "required":

src/agents/models/openai_responses.py

@@ -25,6 +25,7 @@
 from ..handoffs import Handoff
 from ..items import ItemHelpers, ModelResponse, TResponseInputItem
 from ..logger import logger
+from ..model_settings import MCPToolChoice
 from ..tool import (
     CodeInterpreterTool,
     ComputerTool,
@@ -303,10 +304,16 @@ class ConvertedTools:
 class Converter:
     @classmethod
     def convert_tool_choice(
-        cls, tool_choice: Literal["auto", "required", "none"] | str | None
+        cls, tool_choice: Literal["auto", "required", "none"] | str | MCPToolChoice | None
     ) -> response_create_params.ToolChoice | NotGiven:
         if tool_choice is None:
             return NOT_GIVEN
+        elif isinstance(tool_choice, MCPToolChoice):
+            return {
+                "server_label": tool_choice.server_label,
+                "type": "mcp",
+                "name": tool_choice.name,
+            }
         elif tool_choice == "required":
             return "required"
         elif tool_choice == "auto":
@@ -334,9 +341,9 @@ def convert_tool_choice(
                 "type": "code_interpreter",
             }
         elif tool_choice == "mcp":
-            return {
-                "type": "mcp",
-            }
+            # Note that this is still here for backwards compatibility,
+            # but migrating to MCPToolChoice is recommended.
+            return { "type": "mcp" }  # type: ignore [typeddict-item]
         else:
             return {
                 "type": "function",

tests/model_settings/test_serialization.py

@@ -5,7 +5,7 @@
 from pydantic import TypeAdapter
 from pydantic_core import to_json
 
-from agents.model_settings import ModelSettings
+from agents.model_settings import MCPToolChoice, ModelSettings
 
 
 def verify_serialization(model_settings: ModelSettings) -> None:
@@ -29,6 +29,17 @@ def test_basic_serialization() -> None:
     verify_serialization(model_settings)
 
 
+def test_mcp_tool_choice_serialization() -> None:
+    """Tests whether ModelSettings with MCPToolChoice can be serialized to a JSON string."""
+    # First, lets create a ModelSettings instance
+    model_settings = ModelSettings(
+        temperature=0.5,
+        tool_choice=MCPToolChoice(server_label="mcp", name="mcp_tool"),
+    )
+    # Now, lets serialize the ModelSettings instance to a JSON string
+    verify_serialization(model_settings)
+
+
 def test_all_fields_serialization() -> None:
     """Tests whether ModelSettings can be serialized to a JSON string."""
 

tests/test_items_helpers.py

@@ -11,7 +11,10 @@
 )
 from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
 from openai.types.responses.response_function_tool_call_param import ResponseFunctionToolCallParam
-from openai.types.responses.response_function_web_search import ResponseFunctionWebSearch
+from openai.types.responses.response_function_web_search import (
+    ActionSearch,
+    ResponseFunctionWebSearch,
+)
 from openai.types.responses.response_function_web_search_param import ResponseFunctionWebSearchParam
 from openai.types.responses.response_output_message import ResponseOutputMessage
 from openai.types.responses.response_output_message_param import ResponseOutputMessageParam
@@ -225,14 +228,20 @@ def test_to_input_items_for_file_search_call() -> None:
 
 def test_to_input_items_for_web_search_call() -> None:
     """A web search tool call output should produce the same dict as a web search input."""
-    ws_call = ResponseFunctionWebSearch(id="w1", status="completed", type="web_search_call")
+    ws_call = ResponseFunctionWebSearch(
+        id="w1",
+        action=ActionSearch(type="search", query="query"),
+        status="completed",
+        type="web_search_call",
+    )
     resp = ModelResponse(output=[ws_call], usage=Usage(), response_id=None)
     input_items = resp.to_input_items()
     assert isinstance(input_items, list) and len(input_items) == 1
     expected: ResponseFunctionWebSearchParam = {
         "id": "w1",
         "status": "completed",
         "type": "web_search_call",
+        "action": {"type": "search", "query": "query"},
     }
     assert input_items[0] == expected
 

tests/test_run_step_processing.py

@@ -7,6 +7,7 @@
     ResponseFunctionWebSearch,
 )
 from openai.types.responses.response_computer_tool_call import ActionClick
+from openai.types.responses.response_function_web_search import ActionSearch
 from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary
 from pydantic import BaseModel
 
@@ -306,7 +307,12 @@ async def test_file_search_tool_call_parsed_correctly():
 @pytest.mark.asyncio
 async def test_function_web_search_tool_call_parsed_correctly():
     agent = Agent(name="test")
-    web_search_call = ResponseFunctionWebSearch(id="w1", status="completed", type="web_search_call")
+    web_search_call = ResponseFunctionWebSearch(
+        id="w1",
+        action=ActionSearch(type="search", query="query"),
+        status="completed",
+        type="web_search_call",
+    )
     response = ModelResponse(
         output=[get_text_message("hello"), web_search_call],
         usage=Usage(),