Mark some dataclasses as pydantic dataclasses (#1131)

This is the set of top level types used by Temporal for serialization
across activity boundaries. In order to ensure that the models contained
in these dataclasses are built prior to use, the dataclasses need to be
`pydantic.dataclasses.dataclass` rather than `dataclasses.dataclass`

This fixes issues where the types cannot be serialized if the contained
types happen not to have been built. This happens particularly often
when model logging is disabled, which happened to build the pydantic
models as a side effect.

Author: tconley1428

src/agents/items.py

@@ -5,6 +5,7 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar, Union
 
+import pydantic
 from openai.types.responses import (
     Response,
     ResponseComputerToolCall,
@@ -212,7 +213,7 @@ class MCPApprovalResponseItem(RunItemBase[McpApprovalResponse]):
 """An item generated by an agent."""
 
 
-@dataclass
+@pydantic.dataclasses.dataclass
 class ModelResponse:
     output: list[TResponseOutputItem]
     """A list of outputs (messages, tool calls, etc) generated by the model"""

src/agents/model_settings.py

@@ -2,14 +2,15 @@
 
 import dataclasses
 from collections.abc import Mapping
-from dataclasses import dataclass, fields, replace
+from dataclasses import fields, replace
 from typing import Annotated, Any, Literal, Union
 
 from openai import Omit as _Omit
 from openai._types import Body, Query
 from openai.types.responses import ResponseIncludable
 from openai.types.shared import Reasoning
 from pydantic import BaseModel, GetCoreSchemaHandler
+from pydantic.dataclasses import dataclass
 from pydantic_core import core_schema
 from typing_extensions import TypeAlias
 

src/agents/usage.py

@@ -1,6 +1,7 @@
-from dataclasses import dataclass, field
+from dataclasses import field
 
 from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails
+from pydantic.dataclasses import dataclass
 
 
 @dataclass