Extract chat completions conversion code into helper (#522)

Small refactor for rest of stack.

---
[//]: # (BEGIN SAPLING FOOTER)
* #524
* #523
* __->__ #522

src/agents/models/chatcmpl_converter.py

@@ -0,0 +1,466 @@
+from __future__ import annotations
+
+import json
+from collections.abc import Iterable
+from typing import Any, Literal, cast
+
+from openai import NOT_GIVEN, NotGiven
+from openai.types.chat import (
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionContentPartImageParam,
+    ChatCompletionContentPartParam,
+    ChatCompletionContentPartTextParam,
+    ChatCompletionDeveloperMessageParam,
+    ChatCompletionMessage,
+    ChatCompletionMessageParam,
+    ChatCompletionMessageToolCallParam,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionToolChoiceOptionParam,
+    ChatCompletionToolMessageParam,
+    ChatCompletionUserMessageParam,
+)
+from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam
+from openai.types.chat.completion_create_params import ResponseFormat
+from openai.types.responses import (
+    EasyInputMessageParam,
+    ResponseFileSearchToolCallParam,
+    ResponseFunctionToolCall,
+    ResponseFunctionToolCallParam,
+    ResponseInputContentParam,
+    ResponseInputImageParam,
+    ResponseInputTextParam,
+    ResponseOutputMessage,
+    ResponseOutputMessageParam,
+    ResponseOutputRefusal,
+    ResponseOutputText,
+)
+from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message
+
+from ..agent_output import AgentOutputSchema
+from ..exceptions import AgentsException, UserError
+from ..handoffs import Handoff
+from ..items import TResponseInputItem, TResponseOutputItem
+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
+    ) -> ChatCompletionToolChoiceOptionParam | NotGiven:
+        if tool_choice is None:
+            return NOT_GIVEN
+        elif tool_choice == "auto":
+            return "auto"
+        elif tool_choice == "required":
+            return "required"
+        elif tool_choice == "none":
+            return "none"
+        else:
+            return {
+                "type": "function",
+                "function": {
+                    "name": tool_choice,
+                },
+            }
+
+    @classmethod
+    def convert_response_format(
+        cls, final_output_schema: AgentOutputSchema | None
+    ) -> ResponseFormat | NotGiven:
+        if not final_output_schema or final_output_schema.is_plain_text():
+            return NOT_GIVEN
+
+        return {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "final_output",
+                "strict": final_output_schema.strict_json_schema,
+                "schema": final_output_schema.json_schema(),
+            },
+        }
+
+    @classmethod
+    def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]:
+        items: list[TResponseOutputItem] = []
+
+        message_item = ResponseOutputMessage(
+            id=FAKE_RESPONSES_ID,
+            content=[],
+            role="assistant",
+            type="message",
+            status="completed",
+        )
+        if message.content:
+            message_item.content.append(
+                ResponseOutputText(text=message.content, type="output_text", annotations=[])
+            )
+        if message.refusal:
+            message_item.content.append(
+                ResponseOutputRefusal(refusal=message.refusal, type="refusal")
+            )
+        if message.audio:
+            raise AgentsException("Audio is not currently supported")
+
+        if message_item.content:
+            items.append(message_item)
+
+        if message.tool_calls:
+            for tool_call in message.tool_calls:
+                items.append(
+                    ResponseFunctionToolCall(
+                        id=FAKE_RESPONSES_ID,
+                        call_id=tool_call.id,
+                        arguments=tool_call.function.arguments,
+                        name=tool_call.function.name,
+                        type="function_call",
+                    )
+                )
+
+        return items
+
+    @classmethod
+    def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None:
+        if not isinstance(item, dict):
+            return None
+
+        keys = item.keys()
+        # EasyInputMessageParam only has these two keys
+        if keys != {"content", "role"}:
+            return None
+
+        role = item.get("role", None)
+        if role not in ("user", "assistant", "system", "developer"):
+            return None
+
+        if "content" not in item:
+            return None
+
+        return cast(EasyInputMessageParam, item)
+
+    @classmethod
+    def maybe_input_message(cls, item: Any) -> Message | None:
+        if (
+            isinstance(item, dict)
+            and item.get("type") == "message"
+            and item.get("role")
+            in (
+                "user",
+                "system",
+                "developer",
+            )
+        ):
+            return cast(Message, item)
+
+        return None
+
+    @classmethod
+    def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None:
+        if isinstance(item, dict) and item.get("type") == "file_search_call":
+            return cast(ResponseFileSearchToolCallParam, item)
+        return None
+
+    @classmethod
+    def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None:
+        if isinstance(item, dict) and item.get("type") == "function_call":
+            return cast(ResponseFunctionToolCallParam, item)
+        return None
+
+    @classmethod
+    def maybe_function_tool_call_output(
+        cls,
+        item: Any,
+    ) -> FunctionCallOutput | None:
+        if isinstance(item, dict) and item.get("type") == "function_call_output":
+            return cast(FunctionCallOutput, item)
+        return None
+
+    @classmethod
+    def maybe_item_reference(cls, item: Any) -> ItemReference | None:
+        if isinstance(item, dict) and item.get("type") == "item_reference":
+            return cast(ItemReference, item)
+        return None
+
+    @classmethod
+    def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None:
+        # ResponseOutputMessage is only used for messages with role assistant
+        if (
+            isinstance(item, dict)
+            and item.get("type") == "message"
+            and item.get("role") == "assistant"
+        ):
+            return cast(ResponseOutputMessageParam, item)
+        return None
+
+    @classmethod
+    def extract_text_content(
+        cls, content: str | Iterable[ResponseInputContentParam]
+    ) -> str | list[ChatCompletionContentPartTextParam]:
+        all_content = cls.extract_all_content(content)
+        if isinstance(all_content, str):
+            return all_content
+        out: list[ChatCompletionContentPartTextParam] = []
+        for c in all_content:
+            if c.get("type") == "text":
+                out.append(cast(ChatCompletionContentPartTextParam, c))
+        return out
+
+    @classmethod
+    def extract_all_content(
+        cls, content: str | Iterable[ResponseInputContentParam]
+    ) -> str | list[ChatCompletionContentPartParam]:
+        if isinstance(content, str):
+            return content
+        out: list[ChatCompletionContentPartParam] = []
+
+        for c in content:
+            if isinstance(c, dict) and c.get("type") == "input_text":
+                casted_text_param = cast(ResponseInputTextParam, c)
+                out.append(
+                    ChatCompletionContentPartTextParam(
+                        type="text",
+                        text=casted_text_param["text"],
+                    )
+                )
+            elif isinstance(c, dict) and c.get("type") == "input_image":
+                casted_image_param = cast(ResponseInputImageParam, c)
+                if "image_url" not in casted_image_param or not casted_image_param["image_url"]:
+                    raise UserError(
+                        f"Only image URLs are supported for input_image {casted_image_param}"
+                    )
+                out.append(
+                    ChatCompletionContentPartImageParam(
+                        type="image_url",
+                        image_url={
+                            "url": casted_image_param["image_url"],
+                            "detail": casted_image_param["detail"],
+                        },
+                    )
+                )
+            elif isinstance(c, dict) and c.get("type") == "input_file":
+                raise UserError(f"File uploads are not supported for chat completions {c}")
+            else:
+                raise UserError(f"Unknown content: {c}")
+        return out
+
+    @classmethod
+    def items_to_messages(
+        cls,
+        items: str | Iterable[TResponseInputItem],
+    ) -> list[ChatCompletionMessageParam]:
+        """
+        Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam.
+
+        Rules:
+        - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam
+        - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam
+        - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam
+        - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam
+        - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam
+        - tool calls get attached to the *current* assistant message, or create one if none.
+        - tool outputs => ChatCompletionToolMessageParam
+        """
+
+        if isinstance(items, str):
+            return [
+                ChatCompletionUserMessageParam(
+                    role="user",
+                    content=items,
+                )
+            ]
+
+        result: list[ChatCompletionMessageParam] = []
+        current_assistant_msg: ChatCompletionAssistantMessageParam | None = None
+
+        def flush_assistant_message() -> None:
+            nonlocal current_assistant_msg
+            if current_assistant_msg is not None:
+                # The API doesn't support empty arrays for tool_calls
+                if not current_assistant_msg.get("tool_calls"):
+                    del current_assistant_msg["tool_calls"]
+                result.append(current_assistant_msg)
+                current_assistant_msg = None
+
+        def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
+            nonlocal current_assistant_msg
+            if current_assistant_msg is None:
+                current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant")
+                current_assistant_msg["tool_calls"] = []
+            return current_assistant_msg
+
+        for item in items:
+            # 1) Check easy input message
+            if easy_msg := cls.maybe_easy_input_message(item):
+                role = easy_msg["role"]
+                content = easy_msg["content"]
+
+                if role == "user":
+                    flush_assistant_message()
+                    msg_user: ChatCompletionUserMessageParam = {
+                        "role": "user",
+                        "content": cls.extract_all_content(content),
+                    }
+                    result.append(msg_user)
+                elif role == "system":
+                    flush_assistant_message()
+                    msg_system: ChatCompletionSystemMessageParam = {
+                        "role": "system",
+                        "content": cls.extract_text_content(content),
+                    }
+                    result.append(msg_system)
+                elif role == "developer":
+                    flush_assistant_message()
+                    msg_developer: ChatCompletionDeveloperMessageParam = {
+                        "role": "developer",
+                        "content": cls.extract_text_content(content),
+                    }
+                    result.append(msg_developer)
+                elif role == "assistant":
+                    flush_assistant_message()
+                    msg_assistant: ChatCompletionAssistantMessageParam = {
+                        "role": "assistant",
+                        "content": cls.extract_text_content(content),
+                    }
+                    result.append(msg_assistant)
+                else:
+                    raise UserError(f"Unexpected role in easy_input_message: {role}")
+
+            # 2) Check input message
+            elif in_msg := cls.maybe_input_message(item):
+                role = in_msg["role"]
+                content = in_msg["content"]
+                flush_assistant_message()
+
+                if role == "user":
+                    msg_user = {
+                        "role": "user",
+                        "content": cls.extract_all_content(content),
+                    }
+                    result.append(msg_user)
+                elif role == "system":
+                    msg_system = {
+                        "role": "system",
+                        "content": cls.extract_text_content(content),
+                    }
+                    result.append(msg_system)
+                elif role == "developer":
+                    msg_developer = {
+                        "role": "developer",
+                        "content": cls.extract_text_content(content),
+                    }
+                    result.append(msg_developer)
+                else:
+                    raise UserError(f"Unexpected role in input_message: {role}")
+
+            # 3) response output message => assistant
+            elif resp_msg := cls.maybe_response_output_message(item):
+                flush_assistant_message()
+                new_asst = ChatCompletionAssistantMessageParam(role="assistant")
+                contents = resp_msg["content"]
+
+                text_segments = []
+                for c in contents:
+                    if c["type"] == "output_text":
+                        text_segments.append(c["text"])
+                    elif c["type"] == "refusal":
+                        new_asst["refusal"] = c["refusal"]
+                    elif c["type"] == "output_audio":
+                        # Can't handle this, b/c chat completions expects an ID which we dont have
+                        raise UserError(
+                            f"Only audio IDs are supported for chat completions, but got: {c}"
+                        )
+                    else:
+                        raise UserError(f"Unknown content type in ResponseOutputMessage: {c}")
+
+                if text_segments:
+                    combined = "\n".join(text_segments)
+                    new_asst["content"] = combined
+
+                new_asst["tool_calls"] = []
+                current_assistant_msg = new_asst
+
+            # 4) function/file-search calls => attach to assistant
+            elif file_search := cls.maybe_file_search_call(item):
+                asst = ensure_assistant_message()
+                tool_calls = list(asst.get("tool_calls", []))
+                new_tool_call = ChatCompletionMessageToolCallParam(
+                    id=file_search["id"],
+                    type="function",
+                    function={
+                        "name": "file_search_call",
+                        "arguments": json.dumps(
+                            {
+                                "queries": file_search.get("queries", []),
+                                "status": file_search.get("status"),
+                            }
+                        ),
+                    },
+                )
+                tool_calls.append(new_tool_call)
+                asst["tool_calls"] = tool_calls
+
+            elif func_call := cls.maybe_function_tool_call(item):
+                asst = ensure_assistant_message()
+                tool_calls = list(asst.get("tool_calls", []))
+                arguments = func_call["arguments"] if func_call["arguments"] else "{}"
+                new_tool_call = ChatCompletionMessageToolCallParam(
+                    id=func_call["call_id"],
+                    type="function",
+                    function={
+                        "name": func_call["name"],
+                        "arguments": arguments,
+                    },
+                )
+                tool_calls.append(new_tool_call)
+                asst["tool_calls"] = tool_calls
+            # 5) function call output => tool message
+            elif func_output := cls.maybe_function_tool_call_output(item):
+                flush_assistant_message()
+                msg: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": func_output["call_id"],
+                    "content": func_output["output"],
+                }
+                result.append(msg)
+
+            # 6) item reference => handle or raise
+            elif item_ref := cls.maybe_item_reference(item):
+                raise UserError(
+                    f"Encountered an item_reference, which is not supported: {item_ref}"
+                )
+
+            # 7) If we haven't recognized it => fail or ignore
+            else:
+                raise UserError(f"Unhandled item type or structure: {item}")
+
+        flush_assistant_message()
+        return result
+
+    @classmethod
+    def tool_to_openai(cls, tool: Tool) -> ChatCompletionToolParam:
+        if isinstance(tool, FunctionTool):
+            return {
+                "type": "function",
+                "function": {
+                    "name": tool.name,
+                    "description": tool.description or "",
+                    "parameters": tool.params_json_schema,
+                },
+            }
+
+        raise UserError(
+            f"Hosted tools are not supported with the ChatCompletions API. Got tool type: "
+            f"{type(tool)}, tool: {tool}"
+        )
+
+    @classmethod
+    def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam:
+        return {
+            "type": "function",
+            "function": {
+                "name": handoff.tool_name,
+                "description": handoff.tool_description,
+                "parameters": handoff.input_json_schema,
+            },
+        }

src/agents/models/openai_chatcompletions.py

@@ -3,71 +3,46 @@ from __future__ import annotations
 import dataclasses
 import json
 import time
-from collections.abc import AsyncIterator, Iterable
+from collections.abc import AsyncIterator
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Literal, cast, overload
 
-from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream, NotGiven
+from openai import NOT_GIVEN, AsyncOpenAI, AsyncStream
 from openai.types import ChatModel
-from openai.types.chat import (
+from openai.types.chat import ChatCompletion, ChatCompletionChunk
-    ChatCompletion,
-    ChatCompletionAssistantMessageParam,
-    ChatCompletionChunk,
-    ChatCompletionContentPartImageParam,
-    ChatCompletionContentPartParam,
-    ChatCompletionContentPartTextParam,
-    ChatCompletionDeveloperMessageParam,
-    ChatCompletionMessage,
-    ChatCompletionMessageParam,
-    ChatCompletionMessageToolCallParam,
-    ChatCompletionSystemMessageParam,
-    ChatCompletionToolChoiceOptionParam,
-    ChatCompletionToolMessageParam,
-    ChatCompletionUserMessageParam,
-)
-from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam
-from openai.types.chat.completion_create_params import ResponseFormat
 from openai.types.completion_usage import CompletionUsage
 from openai.types.responses import (
-    EasyInputMessageParam,
     Response,
     ResponseCompletedEvent,
     ResponseContentPartAddedEvent,
     ResponseContentPartDoneEvent,
     ResponseCreatedEvent,
-    ResponseFileSearchToolCallParam,
     ResponseFunctionCallArgumentsDeltaEvent,
     ResponseFunctionToolCall,
-    ResponseFunctionToolCallParam,
-    ResponseInputContentParam,
-    ResponseInputImageParam,
-    ResponseInputTextParam,
     ResponseOutputItem,
     ResponseOutputItemAddedEvent,
     ResponseOutputItemDoneEvent,
     ResponseOutputMessage,
-    ResponseOutputMessageParam,
     ResponseOutputRefusal,
     ResponseOutputText,
     ResponseRefusalDeltaEvent,
     ResponseTextDeltaEvent,
     ResponseUsage,
 )
-from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message
 from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails
 
 from .. import _debug
 from ..agent_output import AgentOutputSchema
-from ..exceptions import AgentsException, UserError
 from ..handoffs import Handoff
-from ..items import ModelResponse, TResponseInputItem, TResponseOutputItem, TResponseStreamEvent
+from ..items import ModelResponse, TResponseInputItem, TResponseStreamEvent
 from ..logger import logger
-from ..tool import FunctionTool, Tool
+from ..tool import Tool
 from ..tracing import generation_span
 from ..tracing.span_data import GenerationSpanData
 from ..tracing.spans import Span
 from ..usage import Usage
 from ..version import __version__
+from .chatcmpl_converter import Converter
 from .fake_id import FAKE_RESPONSES_ID
 from .interface import Model, ModelTracing
 
@@ -152,7 +127,7 @@ class OpenAIChatCompletionsModel(Model):
                 "output_tokens": usage.output_tokens,
             }
 
-            items = _Converter.message_to_output_items(response.choices[0].message)
+            items = Converter.message_to_output_items(response.choices[0].message)
 
             return ModelResponse(
                 output=items,
@@ -486,7 +461,7 @@ class OpenAIChatCompletionsModel(Model):
         tracing: ModelTracing,
         stream: bool = False,
     ) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]:
-        converted_messages = _Converter.items_to_messages(input)
+        converted_messages = Converter.items_to_messages(input)
 
         if system_instructions:
             converted_messages.insert(
@@ -506,13 +481,13 @@ class OpenAIChatCompletionsModel(Model):
             if model_settings.parallel_tool_calls is False
             else NOT_GIVEN
         )
-        tool_choice = _Converter.convert_tool_choice(model_settings.tool_choice)
+        tool_choice = Converter.convert_tool_choice(model_settings.tool_choice)
-        response_format = _Converter.convert_response_format(output_schema)
+        response_format = Converter.convert_response_format(output_schema)
 
-        converted_tools = [ToolConverter.to_openai(tool) for tool in tools] if tools else []
+        converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else []
 
         for handoff in handoffs:
-            converted_tools.append(ToolConverter.convert_handoff_tool(handoff))
+            converted_tools.append(Converter.convert_handoff_tool(handoff))
 
         if _debug.DONT_LOG_MODEL_DATA:
             logger.debug("Calling LLM")
@@ -526,9 +501,9 @@ class OpenAIChatCompletionsModel(Model):
             )
 
         reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None
-        store = _Converter.get_store_param(self._get_client(), model_settings)
+        store = _Helpers.get_store_param(self._get_client(), model_settings)
 
-        stream_options = _Converter.get_stream_options_param(
+        stream_options = _Helpers.get_stream_options_param(
             self._get_client(), model_settings, stream=stream
         )
 
@@ -580,7 +555,7 @@ class OpenAIChatCompletionsModel(Model):
         return self._client
 
 
-class _Converter:
+class _Helpers:
     @classmethod
     def is_openai(cls, client: AsyncOpenAI):
         return str(client.base_url).startswith("https://api.openai.com")
@@ -606,425 +581,3 @@ class _Converter:
         )
         stream_options = {"include_usage": include_usage} if include_usage is not None else None
         return stream_options
-
-    @classmethod
-    def convert_tool_choice(
-        cls, tool_choice: Literal["auto", "required", "none"] | str | None
-    ) -> ChatCompletionToolChoiceOptionParam | NotGiven:
-        if tool_choice is None:
-            return NOT_GIVEN
-        elif tool_choice == "auto":
-            return "auto"
-        elif tool_choice == "required":
-            return "required"
-        elif tool_choice == "none":
-            return "none"
-        else:
-            return {
-                "type": "function",
-                "function": {
-                    "name": tool_choice,
-                },
-            }
-
-    @classmethod
-    def convert_response_format(
-        cls, final_output_schema: AgentOutputSchema | None
-    ) -> ResponseFormat | NotGiven:
-        if not final_output_schema or final_output_schema.is_plain_text():
-            return NOT_GIVEN
-
-        return {
-            "type": "json_schema",
-            "json_schema": {
-                "name": "final_output",
-                "strict": final_output_schema.strict_json_schema,
-                "schema": final_output_schema.json_schema(),
-            },
-        }
-
-    @classmethod
-    def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]:
-        items: list[TResponseOutputItem] = []
-
-        message_item = ResponseOutputMessage(
-            id=FAKE_RESPONSES_ID,
-            content=[],
-            role="assistant",
-            type="message",
-            status="completed",
-        )
-        if message.content:
-            message_item.content.append(
-                ResponseOutputText(text=message.content, type="output_text", annotations=[])
-            )
-        if message.refusal:
-            message_item.content.append(
-                ResponseOutputRefusal(refusal=message.refusal, type="refusal")
-            )
-        if message.audio:
-            raise AgentsException("Audio is not currently supported")
-
-        if message_item.content:
-            items.append(message_item)
-
-        if message.tool_calls:
-            for tool_call in message.tool_calls:
-                items.append(
-                    ResponseFunctionToolCall(
-                        id=FAKE_RESPONSES_ID,
-                        call_id=tool_call.id,
-                        arguments=tool_call.function.arguments,
-                        name=tool_call.function.name,
-                        type="function_call",
-                    )
-                )
-
-        return items
-
-    @classmethod
-    def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None:
-        if not isinstance(item, dict):
-            return None
-
-        keys = item.keys()
-        # EasyInputMessageParam only has these two keys
-        if keys != {"content", "role"}:
-            return None
-
-        role = item.get("role", None)
-        if role not in ("user", "assistant", "system", "developer"):
-            return None
-
-        if "content" not in item:
-            return None
-
-        return cast(EasyInputMessageParam, item)
-
-    @classmethod
-    def maybe_input_message(cls, item: Any) -> Message | None:
-        if (
-            isinstance(item, dict)
-            and item.get("type") == "message"
-            and item.get("role")
-            in (
-                "user",
-                "system",
-                "developer",
-            )
-        ):
-            return cast(Message, item)
-
-        return None
-
-    @classmethod
-    def maybe_file_search_call(cls, item: Any) -> ResponseFileSearchToolCallParam | None:
-        if isinstance(item, dict) and item.get("type") == "file_search_call":
-            return cast(ResponseFileSearchToolCallParam, item)
-        return None
-
-    @classmethod
-    def maybe_function_tool_call(cls, item: Any) -> ResponseFunctionToolCallParam | None:
-        if isinstance(item, dict) and item.get("type") == "function_call":
-            return cast(ResponseFunctionToolCallParam, item)
-        return None
-
-    @classmethod
-    def maybe_function_tool_call_output(
-        cls,
-        item: Any,
-    ) -> FunctionCallOutput | None:
-        if isinstance(item, dict) and item.get("type") == "function_call_output":
-            return cast(FunctionCallOutput, item)
-        return None
-
-    @classmethod
-    def maybe_item_reference(cls, item: Any) -> ItemReference | None:
-        if isinstance(item, dict) and item.get("type") == "item_reference":
-            return cast(ItemReference, item)
-        return None
-
-    @classmethod
-    def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None:
-        # ResponseOutputMessage is only used for messages with role assistant
-        if (
-            isinstance(item, dict)
-            and item.get("type") == "message"
-            and item.get("role") == "assistant"
-        ):
-            return cast(ResponseOutputMessageParam, item)
-        return None
-
-    @classmethod
-    def extract_text_content(
-        cls, content: str | Iterable[ResponseInputContentParam]
-    ) -> str | list[ChatCompletionContentPartTextParam]:
-        all_content = cls.extract_all_content(content)
-        if isinstance(all_content, str):
-            return all_content
-        out: list[ChatCompletionContentPartTextParam] = []
-        for c in all_content:
-            if c.get("type") == "text":
-                out.append(cast(ChatCompletionContentPartTextParam, c))
-        return out
-
-    @classmethod
-    def extract_all_content(
-        cls, content: str | Iterable[ResponseInputContentParam]
-    ) -> str | list[ChatCompletionContentPartParam]:
-        if isinstance(content, str):
-            return content
-        out: list[ChatCompletionContentPartParam] = []
-
-        for c in content:
-            if isinstance(c, dict) and c.get("type") == "input_text":
-                casted_text_param = cast(ResponseInputTextParam, c)
-                out.append(
-                    ChatCompletionContentPartTextParam(
-                        type="text",
-                        text=casted_text_param["text"],
-                    )
-                )
-            elif isinstance(c, dict) and c.get("type") == "input_image":
-                casted_image_param = cast(ResponseInputImageParam, c)
-                if "image_url" not in casted_image_param or not casted_image_param["image_url"]:
-                    raise UserError(
-                        f"Only image URLs are supported for input_image {casted_image_param}"
-                    )
-                out.append(
-                    ChatCompletionContentPartImageParam(
-                        type="image_url",
-                        image_url={
-                            "url": casted_image_param["image_url"],
-                            "detail": casted_image_param["detail"],
-                        },
-                    )
-                )
-            elif isinstance(c, dict) and c.get("type") == "input_file":
-                raise UserError(f"File uploads are not supported for chat completions {c}")
-            else:
-                raise UserError(f"Unknown content: {c}")
-        return out
-
-    @classmethod
-    def items_to_messages(
-        cls,
-        items: str | Iterable[TResponseInputItem],
-    ) -> list[ChatCompletionMessageParam]:
-        """
-        Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam.
-
-        Rules:
-        - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam
-        - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam
-        - EasyInputMessage or InputMessage (role=developer) => ChatCompletionDeveloperMessageParam
-        - InputMessage (role=assistant) => Start or flush a ChatCompletionAssistantMessageParam
-        - response_output_message => Also produces/flushes a ChatCompletionAssistantMessageParam
-        - tool calls get attached to the *current* assistant message, or create one if none.
-        - tool outputs => ChatCompletionToolMessageParam
-        """
-
-        if isinstance(items, str):
-            return [
-                ChatCompletionUserMessageParam(
-                    role="user",
-                    content=items,
-                )
-            ]
-
-        result: list[ChatCompletionMessageParam] = []
-        current_assistant_msg: ChatCompletionAssistantMessageParam | None = None
-
-        def flush_assistant_message() -> None:
-            nonlocal current_assistant_msg
-            if current_assistant_msg is not None:
-                # The API doesn't support empty arrays for tool_calls
-                if not current_assistant_msg.get("tool_calls"):
-                    del current_assistant_msg["tool_calls"]
-                result.append(current_assistant_msg)
-                current_assistant_msg = None
-
-        def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
-            nonlocal current_assistant_msg
-            if current_assistant_msg is None:
-                current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant")
-                current_assistant_msg["tool_calls"] = []
-            return current_assistant_msg
-
-        for item in items:
-            # 1) Check easy input message
-            if easy_msg := cls.maybe_easy_input_message(item):
-                role = easy_msg["role"]
-                content = easy_msg["content"]
-
-                if role == "user":
-                    flush_assistant_message()
-                    msg_user: ChatCompletionUserMessageParam = {
-                        "role": "user",
-                        "content": cls.extract_all_content(content),
-                    }
-                    result.append(msg_user)
-                elif role == "system":
-                    flush_assistant_message()
-                    msg_system: ChatCompletionSystemMessageParam = {
-                        "role": "system",
-                        "content": cls.extract_text_content(content),
-                    }
-                    result.append(msg_system)
-                elif role == "developer":
-                    flush_assistant_message()
-                    msg_developer: ChatCompletionDeveloperMessageParam = {
-                        "role": "developer",
-                        "content": cls.extract_text_content(content),
-                    }
-                    result.append(msg_developer)
-                elif role == "assistant":
-                    flush_assistant_message()
-                    msg_assistant: ChatCompletionAssistantMessageParam = {
-                        "role": "assistant",
-                        "content": cls.extract_text_content(content),
-                    }
-                    result.append(msg_assistant)
-                else:
-                    raise UserError(f"Unexpected role in easy_input_message: {role}")
-
-            # 2) Check input message
-            elif in_msg := cls.maybe_input_message(item):
-                role = in_msg["role"]
-                content = in_msg["content"]
-                flush_assistant_message()
-
-                if role == "user":
-                    msg_user = {
-                        "role": "user",
-                        "content": cls.extract_all_content(content),
-                    }
-                    result.append(msg_user)
-                elif role == "system":
-                    msg_system = {
-                        "role": "system",
-                        "content": cls.extract_text_content(content),
-                    }
-                    result.append(msg_system)
-                elif role == "developer":
-                    msg_developer = {
-                        "role": "developer",
-                        "content": cls.extract_text_content(content),
-                    }
-                    result.append(msg_developer)
-                else:
-                    raise UserError(f"Unexpected role in input_message: {role}")
-
-            # 3) response output message => assistant
-            elif resp_msg := cls.maybe_response_output_message(item):
-                flush_assistant_message()
-                new_asst = ChatCompletionAssistantMessageParam(role="assistant")
-                contents = resp_msg["content"]
-
-                text_segments = []
-                for c in contents:
-                    if c["type"] == "output_text":
-                        text_segments.append(c["text"])
-                    elif c["type"] == "refusal":
-                        new_asst["refusal"] = c["refusal"]
-                    elif c["type"] == "output_audio":
-                        # Can't handle this, b/c chat completions expects an ID which we dont have
-                        raise UserError(
-                            f"Only audio IDs are supported for chat completions, but got: {c}"
-                        )
-                    else:
-                        raise UserError(f"Unknown content type in ResponseOutputMessage: {c}")
-
-                if text_segments:
-                    combined = "\n".join(text_segments)
-                    new_asst["content"] = combined
-
-                new_asst["tool_calls"] = []
-                current_assistant_msg = new_asst
-
-            # 4) function/file-search calls => attach to assistant
-            elif file_search := cls.maybe_file_search_call(item):
-                asst = ensure_assistant_message()
-                tool_calls = list(asst.get("tool_calls", []))
-                new_tool_call = ChatCompletionMessageToolCallParam(
-                    id=file_search["id"],
-                    type="function",
-                    function={
-                        "name": "file_search_call",
-                        "arguments": json.dumps(
-                            {
-                                "queries": file_search.get("queries", []),
-                                "status": file_search.get("status"),
-                            }
-                        ),
-                    },
-                )
-                tool_calls.append(new_tool_call)
-                asst["tool_calls"] = tool_calls
-
-            elif func_call := cls.maybe_function_tool_call(item):
-                asst = ensure_assistant_message()
-                tool_calls = list(asst.get("tool_calls", []))
-                arguments = func_call["arguments"] if func_call["arguments"] else "{}"
-                new_tool_call = ChatCompletionMessageToolCallParam(
-                    id=func_call["call_id"],
-                    type="function",
-                    function={
-                        "name": func_call["name"],
-                        "arguments": arguments,
-                    },
-                )
-                tool_calls.append(new_tool_call)
-                asst["tool_calls"] = tool_calls
-            # 5) function call output => tool message
-            elif func_output := cls.maybe_function_tool_call_output(item):
-                flush_assistant_message()
-                msg: ChatCompletionToolMessageParam = {
-                    "role": "tool",
-                    "tool_call_id": func_output["call_id"],
-                    "content": func_output["output"],
-                }
-                result.append(msg)
-
-            # 6) item reference => handle or raise
-            elif item_ref := cls.maybe_item_reference(item):
-                raise UserError(
-                    f"Encountered an item_reference, which is not supported: {item_ref}"
-                )
-
-            # 7) If we haven't recognized it => fail or ignore
-            else:
-                raise UserError(f"Unhandled item type or structure: {item}")
-
-        flush_assistant_message()
-        return result
-
-
-class ToolConverter:
-    @classmethod
-    def to_openai(cls, tool: Tool) -> ChatCompletionToolParam:
-        if isinstance(tool, FunctionTool):
-            return {
-                "type": "function",
-                "function": {
-                    "name": tool.name,
-                    "description": tool.description or "",
-                    "parameters": tool.params_json_schema,
-                },
-            }
-
-        raise UserError(
-            f"Hosted tools are not supported with the ChatCompletions API. Got tool type: "
-            f"{type(tool)}, tool: {tool}"
-        )
-
-    @classmethod
-    def convert_handoff_tool(cls, handoff: Handoff[Any]) -> ChatCompletionToolParam:
-        return {
-            "type": "function",
-            "function": {
-                "name": handoff.tool_name,
-                "description": handoff.tool_description,
-                "parameters": handoff.input_json_schema,
-            },
-        }

tests/test_openai_chatcompletions.py

@@ -31,7 +31,7 @@ from agents import (
     generation_span,
 )
 from agents.models.fake_id import FAKE_RESPONSES_ID
-from agents.models.openai_chatcompletions import _Converter
+from agents.models.openai_chatcompletions import _Helpers
 
 
 @pytest.mark.allow_call_model_methods
@@ -301,32 +301,32 @@ def test_store_param():
 
     model_settings = ModelSettings()
     client = AsyncOpenAI()
-    assert _Converter.get_store_param(client, model_settings) is True, (
+    assert _Helpers.get_store_param(client, model_settings) is True, (
         "Should default to True for OpenAI API calls"
     )
 
     model_settings = ModelSettings(store=False)
-    assert _Converter.get_store_param(client, model_settings) is False, (
+    assert _Helpers.get_store_param(client, model_settings) is False, (
         "Should respect explicitly set store=False"
     )
 
     model_settings = ModelSettings(store=True)
-    assert _Converter.get_store_param(client, model_settings) is True, (
+    assert _Helpers.get_store_param(client, model_settings) is True, (
         "Should respect explicitly set store=True"
     )
 
     client = AsyncOpenAI(base_url="http://www.notopenai.com")
     model_settings = ModelSettings()
-    assert _Converter.get_store_param(client, model_settings) is None, (
+    assert _Helpers.get_store_param(client, model_settings) is None, (
         "Should default to None for non-OpenAI API calls"
     )
 
     model_settings = ModelSettings(store=False)
-    assert _Converter.get_store_param(client, model_settings) is False, (
+    assert _Helpers.get_store_param(client, model_settings) is False, (
         "Should respect explicitly set store=False"
     )
 
     model_settings = ModelSettings(store=True)
-    assert _Converter.get_store_param(client, model_settings) is True, (
+    assert _Helpers.get_store_param(client, model_settings) is True, (
         "Should respect explicitly set store=True"
     )

tests/test_openai_chatcompletions_converter.py

@@ -4,7 +4,7 @@
 # See LICENSE file in the project root for full license information.
 
 """
-Unit tests for the internal `_Converter` class defined in
+Unit tests for the internal `Converter` class defined in
 `agents.models.openai_chatcompletions`. The converter is responsible for
 translating between internal "item" structures (e.g., `ResponseOutputMessage`
 and related types from `openai.types.responses`) and the ChatCompletion message
@@ -12,10 +12,10 @@ structures defined by the OpenAI client library.
 
 These tests exercise both conversion directions:
 
-- `_Converter.message_to_output_items` turns a `ChatCompletionMessage` (as
+- `Converter.message_to_output_items` turns a `ChatCompletionMessage` (as
   returned by the OpenAI API) into a list of `ResponseOutputItem` instances.
 
-- `_Converter.items_to_messages` takes in either a simple string prompt, or a
+- `Converter.items_to_messages` takes in either a simple string prompt, or a
   list of input/output items such as `ResponseOutputMessage` and
   `ResponseFunctionToolCallParam` dicts, and constructs a list of
   `ChatCompletionMessageParam` dicts suitable for sending back to the API.
@@ -41,8 +41,8 @@ from openai.types.responses.response_input_item_param import FunctionCallOutput
 from agents.agent_output import AgentOutputSchema
 from agents.exceptions import UserError
 from agents.items import TResponseInputItem
+from agents.models.chatcmpl_converter import Converter
 from agents.models.fake_id import FAKE_RESPONSES_ID
-from agents.models.openai_chatcompletions import _Converter
 
 
 def test_message_to_output_items_with_text_only():
@@ -51,7 +51,7 @@ def test_message_to_output_items_with_text_only():
     into a single ResponseOutputMessage containing one ResponseOutputText.
     """
     msg = ChatCompletionMessage(role="assistant", content="Hello")
-    items = _Converter.message_to_output_items(msg)
+    items = Converter.message_to_output_items(msg)
     # Expect exactly one output item (the message)
     assert len(items) == 1
     message_item = cast(ResponseOutputMessage, items[0])
@@ -72,7 +72,7 @@ def test_message_to_output_items_with_refusal():
     with a ResponseOutputRefusal content part.
     """
     msg = ChatCompletionMessage(role="assistant", refusal="I'm sorry")
-    items = _Converter.message_to_output_items(msg)
+    items = Converter.message_to_output_items(msg)
     assert len(items) == 1
     message_item = cast(ResponseOutputMessage, items[0])
     assert len(message_item.content) == 1
@@ -93,7 +93,7 @@ def test_message_to_output_items_with_tool_call():
         function=Function(name="myfn", arguments='{"x":1}'),
     )
     msg = ChatCompletionMessage(role="assistant", content="Hi", tool_calls=[tool_call])
-    items = _Converter.message_to_output_items(msg)
+    items = Converter.message_to_output_items(msg)
     # Should produce a message item followed by one function tool call item
     assert len(items) == 2
     message_item = cast(ResponseOutputMessage, items[0])
@@ -111,7 +111,7 @@ def test_items_to_messages_with_string_user_content():
     A simple string as the items argument should be converted into a user
     message param dict with the same content.
     """
-    result = _Converter.items_to_messages("Ask me anything")
+    result = Converter.items_to_messages("Ask me anything")
     assert isinstance(result, list)
     assert len(result) == 1
     msg = result[0]
@@ -130,7 +130,7 @@ def test_items_to_messages_with_easy_input_message():
             "content": "How are you?",
         }
     ]
-    messages = _Converter.items_to_messages(items)
+    messages = Converter.items_to_messages(items)
     assert len(messages) == 1
     out = messages[0]
     assert out["role"] == "user"
@@ -174,7 +174,7 @@ def test_items_to_messages_with_output_message_and_function_call():
         resp_msg.model_dump(),  # type:ignore
         func_item,
     ]
-    messages = _Converter.items_to_messages(items)
+    messages = Converter.items_to_messages(items)
     # Should return a single assistant message
     assert len(messages) == 1
     assistant = messages[0]
@@ -197,16 +197,16 @@ def test_items_to_messages_with_output_message_and_function_call():
 
 def test_convert_tool_choice_handles_standard_and_named_options() -> None:
     """
-    The `_Converter.convert_tool_choice` method should return NOT_GIVEN
+    The `Converter.convert_tool_choice` method should return NOT_GIVEN
     if no choice is provided, pass through values like "auto", "required",
     or "none" unchanged, and translate any other string into a function
     selection dict.
     """
-    assert _Converter.convert_tool_choice(None).__class__.__name__ == "NotGiven"
+    assert Converter.convert_tool_choice(None).__class__.__name__ == "NotGiven"
-    assert _Converter.convert_tool_choice("auto") == "auto"
+    assert Converter.convert_tool_choice("auto") == "auto"
-    assert _Converter.convert_tool_choice("required") == "required"
+    assert Converter.convert_tool_choice("required") == "required"
-    assert _Converter.convert_tool_choice("none") == "none"
+    assert Converter.convert_tool_choice("none") == "none"
-    tool_choice_dict = _Converter.convert_tool_choice("mytool")
+    tool_choice_dict = Converter.convert_tool_choice("mytool")
     assert isinstance(tool_choice_dict, dict)
     assert tool_choice_dict["type"] == "function"
     assert tool_choice_dict["function"]["name"] == "mytool"
@@ -214,20 +214,20 @@ def test_convert_tool_choice_handles_standard_and_named_options() -> None:
 
 def test_convert_response_format_returns_not_given_for_plain_text_and_dict_for_schemas() -> None:
     """
-    The `_Converter.convert_response_format` method should return NOT_GIVEN
+    The `Converter.convert_response_format` method should return NOT_GIVEN
     when no output schema is provided or if the output schema indicates
     plain text. For structured output schemas, it should return a dict
     with type `json_schema` and include the generated JSON schema and
     strict flag from the provided `AgentOutputSchema`.
     """
     # when output is plain text (schema None or output_type str), do not include response_format
-    assert _Converter.convert_response_format(None).__class__.__name__ == "NotGiven"
+    assert Converter.convert_response_format(None).__class__.__name__ == "NotGiven"
     assert (
-        _Converter.convert_response_format(AgentOutputSchema(str)).__class__.__name__ == "NotGiven"
+        Converter.convert_response_format(AgentOutputSchema(str)).__class__.__name__ == "NotGiven"
     )
     # For e.g. integer output, we expect a response_format dict
     schema = AgentOutputSchema(int)
-    resp_format = _Converter.convert_response_format(schema)
+    resp_format = Converter.convert_response_format(schema)
     assert isinstance(resp_format, dict)
     assert resp_format["type"] == "json_schema"
     assert resp_format["json_schema"]["name"] == "final_output"
@@ -247,7 +247,7 @@ def test_items_to_messages_with_function_output_item():
         "call_id": "somecall",
         "output": '{"foo": "bar"}',
     }
-    messages = _Converter.items_to_messages([func_output_item])
+    messages = Converter.items_to_messages([func_output_item])
     assert len(messages) == 1
     tool_msg = messages[0]
     assert tool_msg["role"] == "tool"
@@ -266,16 +266,16 @@ def test_extract_all_and_text_content_for_strings_and_lists():
     should filter to only the textual parts.
     """
     prompt = "just text"
-    assert _Converter.extract_all_content(prompt) == prompt
+    assert Converter.extract_all_content(prompt) == prompt
-    assert _Converter.extract_text_content(prompt) == prompt
+    assert Converter.extract_text_content(prompt) == prompt
     text1: ResponseInputTextParam = {"type": "input_text", "text": "one"}
     text2: ResponseInputTextParam = {"type": "input_text", "text": "two"}
-    all_parts = _Converter.extract_all_content([text1, text2])
+    all_parts = Converter.extract_all_content([text1, text2])
     assert isinstance(all_parts, list)
     assert len(all_parts) == 2
     assert all_parts[0]["type"] == "text" and all_parts[0]["text"] == "one"
     assert all_parts[1]["type"] == "text" and all_parts[1]["text"] == "two"
-    text_parts = _Converter.extract_text_content([text1, text2])
+    text_parts = Converter.extract_text_content([text1, text2])
     assert isinstance(text_parts, list)
     assert all(p["type"] == "text" for p in text_parts)
     assert [p["text"] for p in text_parts] == ["one", "two"]
@@ -288,12 +288,12 @@ def test_items_to_messages_handles_system_and_developer_roles():
     `message` typed dicts.
     """
     sys_items: list[TResponseInputItem] = [{"role": "system", "content": "setup"}]
-    sys_msgs = _Converter.items_to_messages(sys_items)
+    sys_msgs = Converter.items_to_messages(sys_items)
     assert len(sys_msgs) == 1
     assert sys_msgs[0]["role"] == "system"
     assert sys_msgs[0]["content"] == "setup"
     dev_items: list[TResponseInputItem] = [{"role": "developer", "content": "debug"}]
-    dev_msgs = _Converter.items_to_messages(dev_items)
+    dev_msgs = Converter.items_to_messages(dev_items)
     assert len(dev_msgs) == 1
     assert dev_msgs[0]["role"] == "developer"
     assert dev_msgs[0]["content"] == "debug"
@@ -301,7 +301,7 @@ def test_items_to_messages_handles_system_and_developer_roles():
 
 def test_maybe_input_message_allows_message_typed_dict():
     """
-    The `_Converter.maybe_input_message` should recognize a dict with
+    The `Converter.maybe_input_message` should recognize a dict with
     "type": "message" and a supported role as an input message. Ensure
     that such dicts are passed through by `items_to_messages`.
     """
@@ -311,9 +311,9 @@ def test_maybe_input_message_allows_message_typed_dict():
         "role": "user",
         "content": "hi",
     }
-    assert _Converter.maybe_input_message(message_dict) is not None
+    assert Converter.maybe_input_message(message_dict) is not None
     # items_to_messages should process this correctly
-    msgs = _Converter.items_to_messages([message_dict])
+    msgs = Converter.items_to_messages([message_dict])
     assert len(msgs) == 1
     assert msgs[0]["role"] == "user"
     assert msgs[0]["content"] == "hi"
@@ -331,7 +331,7 @@ def test_tool_call_conversion():
         type="function_call",
     )
 
-    messages = _Converter.items_to_messages([function_call])
+    messages = Converter.items_to_messages([function_call])
     assert len(messages) == 1
     tool_msg = messages[0]
     assert tool_msg["role"] == "assistant"
@@ -348,7 +348,7 @@ def test_tool_call_conversion():
 @pytest.mark.parametrize("role", ["user", "system", "developer"])
 def test_input_message_with_all_roles(role: str):
     """
-    The `_Converter.maybe_input_message` should recognize a dict with
+    The `Converter.maybe_input_message` should recognize a dict with
     "type": "message" and a supported role as an input message. Ensure
     that such dicts are passed through by `items_to_messages`.
     """
@@ -359,9 +359,9 @@ def test_input_message_with_all_roles(role: str):
         "role": casted_role,
         "content": "hi",
     }
-    assert _Converter.maybe_input_message(message_dict) is not None
+    assert Converter.maybe_input_message(message_dict) is not None
     # items_to_messages should process this correctly
-    msgs = _Converter.items_to_messages([message_dict])
+    msgs = Converter.items_to_messages([message_dict])
     assert len(msgs) == 1
     assert msgs[0]["role"] == casted_role
     assert msgs[0]["content"] == "hi"
@@ -372,7 +372,7 @@ def test_item_reference_errors():
     Test that item references are converted correctly.
     """
     with pytest.raises(UserError):
-        _Converter.items_to_messages(
+        Converter.items_to_messages(
             [
                 {
                     "type": "item_reference",
@@ -392,14 +392,14 @@ def test_unknown_object_errors():
     """
     with pytest.raises(UserError, match="Unhandled item type or structure"):
         # Purposely ignore the type error
-        _Converter.items_to_messages([TestObject()])  # type: ignore
+        Converter.items_to_messages([TestObject()])  # type: ignore
 
 
 def test_assistant_messages_in_history():
     """
     Test that assistant messages are added to the history.
     """
-    messages = _Converter.items_to_messages(
+    messages = Converter.items_to_messages(
         [
             {
                 "role": "user",

tests/test_tool_converter.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel
 
 from agents import Agent, Handoff, function_tool, handoff
 from agents.exceptions import UserError
-from agents.models.openai_chatcompletions import ToolConverter
+from agents.models.chatcmpl_converter import Converter
 from agents.tool import FileSearchTool, WebSearchTool
 
 
@@ -15,7 +15,7 @@ def test_to_openai_with_function_tool():
     some_function(a="foo", b=[1, 2, 3])
 
     tool = function_tool(some_function)
-    result = ToolConverter.to_openai(tool)
+    result = Converter.tool_to_openai(tool)
 
     assert result["type"] == "function"
     assert result["function"]["name"] == "some_function"
@@ -34,7 +34,7 @@ class Foo(BaseModel):
 def test_convert_handoff_tool():
     agent = Agent(name="test_1", handoff_description="test_2")
     handoff_obj = handoff(agent=agent)
-    result = ToolConverter.convert_handoff_tool(handoff_obj)
+    result = Converter.convert_handoff_tool(handoff_obj)
 
     assert result["type"] == "function"
     assert result["function"]["name"] == Handoff.default_tool_name(agent)
@@ -48,7 +48,7 @@ def test_convert_handoff_tool():
 
 def test_tool_converter_hosted_tools_errors():
     with pytest.raises(UserError):
-        ToolConverter.to_openai(WebSearchTool())
+        Converter.tool_to_openai(WebSearchTool())
 
     with pytest.raises(UserError):
-        ToolConverter.to_openai(FileSearchTool(vector_store_ids=["abc"], max_num_results=1))
+        Converter.tool_to_openai(FileSearchTool(vector_store_ids=["abc"], max_num_results=1))