diff --git a/pyproject.toml b/pyproject.toml
index 115c7c4eb..38c6660c0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,7 +37,7 @@ Repository = "https://github.com/openai/openai-agents-python"
 [project.optional-dependencies]
 voice = ["numpy>=2.2.0, <3; python_version>='3.10'", "websockets>=15.0, <16"]
 viz = ["graphviz>=0.17"]
-litellm = ["litellm>=1.67.4.post1, <2"]
+litellm = ["litellm>=1.80.8, <2"]
 realtime = ["websockets>=15.0, <16"]
 sqlalchemy = ["SQLAlchemy>=2.0", "asyncpg>=0.29.0"]
 encrypt = ["cryptography>=45.0, <46"]
diff --git a/src/agents/extensions/models/litellm_model.py b/src/agents/extensions/models/litellm_model.py
index 439146c6c..498bfd25d 100644
--- a/src/agents/extensions/models/litellm_model.py
+++ b/src/agents/extensions/models/litellm_model.py
@@ -62,6 +62,15 @@ class InternalChatCompletionMessage(ChatCompletionMessage):
     thinking_blocks: list[dict[str, Any]] | None = None
 
 
+class InternalToolCall(ChatCompletionMessageFunctionToolCall):
+    """
+    An internal subclass to carry provider-specific metadata (e.g., Gemini thought signatures)
+    without modifying the original model.
+    """
+
+    extra_content: dict[str, Any] | None = None
+
+
 class LitellmModel(Model):
     """This class enables using any model via LiteLLM. LiteLLM allows you to acess OpenAPI,
     Anthropic, Gemini, Mistral, and many other models.
@@ -168,9 +177,15 @@ async def get_response(
                 "output_tokens": usage.output_tokens,
             }
 
+            # Build provider_data for provider specific fields
+            provider_data: dict[str, Any] = {"model": self.model}
+            if message is not None and hasattr(response, "id"):
+                provider_data["response_id"] = response.id
+
             items = (
                 Converter.message_to_output_items(
-                    LitellmConverter.convert_message_to_openai(message)
+                    LitellmConverter.convert_message_to_openai(message, model=self.model),
+                    provider_data=provider_data,
                 )
                 if message is not None
                 else []
@@ -215,7 +230,9 @@ async def stream_response(
             )
 
             final_response: Response | None = None
-            async for chunk in ChatCmplStreamHandler.handle_stream(response, stream):
+            async for chunk in ChatCmplStreamHandler.handle_stream(
+                response, stream, model=self.model
+            ):
                 yield chunk
 
                 if chunk.type == "response.completed":
@@ -280,13 +297,19 @@ async def _fetch_response(
         )
 
         converted_messages = Converter.items_to_messages(
-            input, preserve_thinking_blocks=preserve_thinking_blocks
+            input, model=self.model, preserve_thinking_blocks=preserve_thinking_blocks
         )
 
         # Fix for interleaved thinking bug: reorder messages to ensure tool_use comes before tool_result  # noqa: E501
         if "anthropic" in self.model.lower() or "claude" in self.model.lower():
             converted_messages = self._fix_tool_message_ordering(converted_messages)
 
+        # Convert Google's extra_content to litellm's provider_specific_fields format
+        if "gemini" in self.model.lower():
+            converted_messages = self._convert_gemini_extra_content_to_provider_specific_fields(
+                converted_messages
+            )
+
         if system_instructions:
             converted_messages.insert(
                 0,
@@ -436,6 +459,65 @@ async def _fetch_response(
         )
         return response, ret
 
+    def _convert_gemini_extra_content_to_provider_specific_fields(
+        self, messages: list[ChatCompletionMessageParam]
+    ) -> list[ChatCompletionMessageParam]:
+        """
+        Convert Gemini model's extra_content format to provider_specific_fields format for litellm.
+
+        Transforms tool calls from internal format:
+            extra_content={"google": {"thought_signature": "..."}}
+        To litellm format:
+            provider_specific_fields={"thought_signature": "..."}
+
+        Only processes tool_calls that appear after the last user message.
+        See: https://ai.google.dev/gemini-api/docs/thought-signatures
+        """
+
+        # Find the index of the last user message
+        last_user_index = -1
+        for i in range(len(messages) - 1, -1, -1):
+            if isinstance(messages[i], dict) and messages[i].get("role") == "user":
+                last_user_index = i
+                break
+
+        for i, message in enumerate(messages):
+            if not isinstance(message, dict):
+                continue
+
+            # Only process assistant messages that come after the last user message
+            # If no user message found (last_user_index == -1), process all messages
+            if last_user_index != -1 and i <= last_user_index:
+                continue
+
+            # Check if this is an assistant message with tool calls
+            if message.get("role") == "assistant" and message.get("tool_calls"):
+                tool_calls = message.get("tool_calls", [])
+
+                for tool_call in tool_calls:  # type: ignore[attr-defined]
+                    if not isinstance(tool_call, dict):
+                        continue
+
+                    # Default to skip validator, overridden if valid thought signature exists
+                    tool_call["provider_specific_fields"] = {
+                        "thought_signature": "skip_thought_signature_validator"
+                    }
+
+                    # Override with actual thought signature if extra_content exists
+                    if "extra_content" in tool_call:
+                        extra_content = tool_call.pop("extra_content")
+                        if isinstance(extra_content, dict):
+                            # Extract google-specific fields
+                            google_fields = extra_content.get("google")
+                            if google_fields and isinstance(google_fields, dict):
+                                thought_sig = google_fields.get("thought_signature")
+                                if thought_sig:
+                                    tool_call["provider_specific_fields"] = {
+                                        "thought_signature": thought_sig
+                                    }
+
+        return messages
+
     def _fix_tool_message_ordering(
         self, messages: list[ChatCompletionMessageParam]
     ) -> list[ChatCompletionMessageParam]:
@@ -563,15 +645,26 @@ def _merge_headers(self, model_settings: ModelSettings):
 class LitellmConverter:
     @classmethod
     def convert_message_to_openai(
-        cls, message: litellm.types.utils.Message
+        cls, message: litellm.types.utils.Message, model: str | None = None
     ) -> ChatCompletionMessage:
+        """
+        Convert a LiteLLM message to OpenAI ChatCompletionMessage format.
+
+        Args:
+            message: The LiteLLM message to convert
+            model: The target model to convert to. Used to handle provider-specific
+                transformations.
+        """
         if message.role != "assistant":
             raise ModelBehaviorError(f"Unsupported role: {message.role}")
 
         tool_calls: (
             list[ChatCompletionMessageFunctionToolCall | ChatCompletionMessageCustomToolCall] | None
         ) = (
-            [LitellmConverter.convert_tool_call_to_openai(tool) for tool in message.tool_calls]
+            [
+                LitellmConverter.convert_tool_call_to_openai(tool, model=model)
+                for tool in message.tool_calls
+            ]
             if message.tool_calls
             else None
         )
@@ -641,13 +734,43 @@ def convert_annotations_to_openai(
 
     @classmethod
     def convert_tool_call_to_openai(
-        cls, tool_call: litellm.types.utils.ChatCompletionMessageToolCall
+        cls, tool_call: litellm.types.utils.ChatCompletionMessageToolCall, model: str | None = None
     ) -> ChatCompletionMessageFunctionToolCall:
-        return ChatCompletionMessageFunctionToolCall(
-            id=tool_call.id,
+        # Clean up litellm's addition of __thought__ suffix to tool_call.id for
+        # Gemini models. See: https://github.com/BerriAI/litellm/pull/16895
+        # This suffix is redundant since we can get thought_signature from
+        # provider_specific_fields, and this hack causes validation errors when
+        # cross-model passing to other models.
+        tool_call_id = tool_call.id
+        if model and "gemini" in model.lower() and "__thought__" in tool_call_id:
+            tool_call_id = tool_call_id.split("__thought__")[0]
+
+        # Convert litellm's tool call format to chat completion message format
+        base_tool_call = ChatCompletionMessageFunctionToolCall(
+            id=tool_call_id,
             type="function",
             function=Function(
                 name=tool_call.function.name or "",
                 arguments=tool_call.function.arguments,
             ),
         )
+
+        # Preserve provider-specific fields if present (e.g., Gemini thought signatures)
+        if hasattr(tool_call, "provider_specific_fields") and tool_call.provider_specific_fields:
+            # Convert to nested extra_content structure
+            extra_content: dict[str, Any] = {}
+            provider_fields = tool_call.provider_specific_fields
+
+            # Check for thought_signature (Gemini specific)
+            if model and "gemini" in model.lower():
+                if "thought_signature" in provider_fields:
+                    extra_content["google"] = {
+                        "thought_signature": provider_fields["thought_signature"]
+                    }
+
+            return InternalToolCall(
+                **base_tool_call.model_dump(),
+                extra_content=extra_content if extra_content else None,
+            )
+
+        return base_tool_call
diff --git a/src/agents/handoffs/history.py b/src/agents/handoffs/history.py
index dc59547fb..e2623c471 100644
--- a/src/agents/handoffs/history.py
+++ b/src/agents/handoffs/history.py
@@ -144,7 +144,7 @@ def _format_transcript_item(item: TResponseInputItem) -> str:
         return f"{prefix}: {content_str}" if content_str else prefix
 
     item_type = item.get("type", "item")
-    rest = {k: v for k, v in item.items() if k != "type"}
+    rest = {k: v for k, v in item.items() if k not in ("type", "provider_data")}
     try:
         serialized = json.dumps(rest, ensure_ascii=False, default=str)
     except TypeError:
diff --git a/src/agents/models/chatcmpl_converter.py b/src/agents/models/chatcmpl_converter.py
index bc0304be0..b81dc2160 100644
--- a/src/agents/models/chatcmpl_converter.py
+++ b/src/agents/models/chatcmpl_converter.py
@@ -94,18 +94,37 @@ def convert_response_format(
         }
 
     @classmethod
-    def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]:
+    def message_to_output_items(
+        cls,
+        message: ChatCompletionMessage,
+        provider_data: dict[str, Any] | None = None,
+    ) -> list[TResponseOutputItem]:
+        """
+        Convert a ChatCompletionMessage to a list of response output items.
+
+        Args:
+            message: The chat completion message to convert
+            provider_data: Metadata indicating the source model that generated this message.
+                Contains provider-specific information like model name and response_id,
+                which is attached to output items.
+        """
         items: list[TResponseOutputItem] = []
 
         # Check if message is agents.extentions.models.litellm_model.InternalChatCompletionMessage
         # We can't actually import it here because litellm is an optional dependency
         # So we use hasattr to check for reasoning_content and thinking_blocks
         if hasattr(message, "reasoning_content") and message.reasoning_content:
-            reasoning_item = ResponseReasoningItem(
-                id=FAKE_RESPONSES_ID,
-                summary=[Summary(text=message.reasoning_content, type="summary_text")],
-                type="reasoning",
-            )
+            reasoning_kwargs: dict[str, Any] = {
+                "id": FAKE_RESPONSES_ID,
+                "summary": [Summary(text=message.reasoning_content, type="summary_text")],
+                "type": "reasoning",
+            }
+
+            # Add provider_data if available
+            if provider_data:
+                reasoning_kwargs["provider_data"] = provider_data
+
+            reasoning_item = ResponseReasoningItem(**reasoning_kwargs)
 
             # Store thinking blocks for Anthropic compatibility
             if hasattr(message, "thinking_blocks") and message.thinking_blocks:
@@ -129,13 +148,19 @@ def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TRespon
 
             items.append(reasoning_item)
 
-        message_item = ResponseOutputMessage(
-            id=FAKE_RESPONSES_ID,
-            content=[],
-            role="assistant",
-            type="message",
-            status="completed",
-        )
+        message_kwargs: dict[str, Any] = {
+            "id": FAKE_RESPONSES_ID,
+            "content": [],
+            "role": "assistant",
+            "type": "message",
+            "status": "completed",
+        }
+
+        # Add provider_data if available
+        if provider_data:
+            message_kwargs["provider_data"] = provider_data
+
+        message_item = ResponseOutputMessage(**message_kwargs)
         if message.content:
             message_item.content.append(
                 ResponseOutputText(
@@ -155,15 +180,35 @@ def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TRespon
         if message.tool_calls:
             for tool_call in message.tool_calls:
                 if tool_call.type == "function":
-                    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",
-                        )
-                    )
+                    # Create base function call item
+                    func_call_kwargs: dict[str, Any] = {
+                        "id": FAKE_RESPONSES_ID,
+                        "call_id": tool_call.id,
+                        "arguments": tool_call.function.arguments,
+                        "name": tool_call.function.name,
+                        "type": "function_call",
+                    }
+
+                    # Build provider_data for function call
+                    func_provider_data: dict[str, Any] = {}
+
+                    # Start with provider_data (if provided)
+                    if provider_data:
+                        func_provider_data.update(provider_data)
+
+                    # Convert Google's extra_content field data to item's provider_data field
+                    if hasattr(tool_call, "extra_content") and tool_call.extra_content:
+                        google_fields = tool_call.extra_content.get("google")
+                        if google_fields and isinstance(google_fields, dict):
+                            thought_sig = google_fields.get("thought_signature")
+                            if thought_sig:
+                                func_provider_data["thought_signature"] = thought_sig
+
+                    # Add provider_data if we have any
+                    if func_provider_data:
+                        func_call_kwargs["provider_data"] = func_provider_data
+
+                    items.append(ResponseFunctionToolCall(**func_call_kwargs))
                 elif tool_call.type == "custom":
                     pass
 
@@ -339,6 +384,7 @@ def extract_all_content(
     def items_to_messages(
         cls,
         items: str | Iterable[TResponseInputItem],
+        model: str | None = None,
         preserve_thinking_blocks: bool = False,
     ) -> list[ChatCompletionMessageParam]:
         """
@@ -346,6 +392,9 @@ def items_to_messages(
 
         Args:
             items: A string or iterable of response input items to convert
+            model: The target model to convert to. Used to restore provider-specific data
+                (e.g., Gemini thought signatures, Claude thinking blocks) when converting
+                items back to chat completion messages for the target model.
             preserve_thinking_blocks: Whether to preserve thinking blocks in tool calls
                 for reasoning models like Claude 4 Sonnet/Opus which support interleaved
                 thinking. When True, thinking blocks are reconstructed and included in
@@ -533,6 +582,20 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
                         "arguments": arguments,
                     },
                 )
+
+                # Restore provider_data back to chat completion message for non-OpenAI models
+                if "provider_data" in func_call:
+                    provider_fields = func_call["provider_data"]  # type: ignore[typeddict-item]
+                    if isinstance(provider_fields, dict):
+                        # Restore thought_signature for Gemini in Google's extra_content format
+                        if model and "gemini" in model.lower():
+                            thought_sig = provider_fields.get("thought_signature")
+
+                            if thought_sig:
+                                new_tool_call["extra_content"] = {  # type: ignore[typeddict-unknown-key]
+                                    "google": {"thought_signature": thought_sig}
+                                }
+
                 tool_calls.append(new_tool_call)
                 asst["tool_calls"] = tool_calls
             # 5) function call output => tool message
@@ -559,9 +622,21 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
                 # Reconstruct thinking blocks from content (text) and encrypted_content (signature)
                 content_items = reasoning_item.get("content", [])
                 encrypted_content = reasoning_item.get("encrypted_content")
-                signatures = encrypted_content.split("\n") if encrypted_content else []
 
-                if content_items and preserve_thinking_blocks:
+                item_provider_data: dict[str, Any] = reasoning_item.get("provider_data", {})  # type: ignore[assignment]
+                item_model = item_provider_data.get("model", "")
+
+                if (
+                    model
+                    and ("claude" in model.lower() or "anthropic" in model.lower())
+                    and content_items
+                    and preserve_thinking_blocks
+                    # Items may not all originate from Claude, so we need to check for model match.
+                    # For backward compatibility, if provider_data is missing, we ignore the check.
+                    and (model == item_model or item_provider_data == {})
+                ):
+                    signatures = encrypted_content.split("\n") if encrypted_content else []
+
                     # Reconstruct thinking blocks from content and signature
                     reconstructed_thinking_blocks = []
                     for content_item in content_items:
diff --git a/src/agents/models/chatcmpl_stream_handler.py b/src/agents/models/chatcmpl_stream_handler.py
index f1c504977..7b8f5ea31 100644
--- a/src/agents/models/chatcmpl_stream_handler.py
+++ b/src/agents/models/chatcmpl_stream_handler.py
@@ -2,6 +2,7 @@
 
 from collections.abc import AsyncIterator
 from dataclasses import dataclass, field
+from typing import Any
 
 from openai import AsyncStream
 from openai.types.chat import ChatCompletionChunk
@@ -65,6 +66,8 @@ class StreamingState:
     # Store accumulated thinking text and signature for Anthropic compatibility
     thinking_text: str = ""
     thinking_signature: str | None = None
+    # Store provider data for all output items
+    provider_data: dict[str, Any] = field(default_factory=dict)
 
 
 class SequenceNumber:
@@ -83,7 +86,17 @@ async def handle_stream(
         cls,
         response: Response,
         stream: AsyncStream[ChatCompletionChunk],
+        model: str | None = None,
     ) -> AsyncIterator[TResponseStreamEvent]:
+        """
+        Handle a streaming chat completion response and yield response events.
+
+        Args:
+            response: The initial Response object to populate with streamed data
+            stream: The async stream of chat completion chunks from the model
+            model: The source model that is generating this stream. Used to handle
+                provider-specific stream processing.
+        """
         usage: CompletionUsage | None = None
         state = StreamingState()
         sequence_number = SequenceNumber()
@@ -104,6 +117,15 @@ async def handle_stream(
             if not chunk.choices or not chunk.choices[0].delta:
                 continue
 
+            # Build provider_data for non-OpenAI Responses API endpoints format
+            if model:
+                state.provider_data["model"] = model
+            elif hasattr(chunk, "model") and chunk.model:
+                state.provider_data["model"] = chunk.model
+
+            if hasattr(chunk, "id") and chunk.id:
+                state.provider_data["response_id"] = chunk.id
+
             delta = chunk.choices[0].delta
 
             # Handle thinking blocks from Anthropic (for preserving signatures)
@@ -123,20 +145,16 @@ async def handle_stream(
             if hasattr(delta, "reasoning_content"):
                 reasoning_content = delta.reasoning_content
                 if reasoning_content and not state.reasoning_content_index_and_output:
-                    state.reasoning_content_index_and_output = (
-                        0,
-                        ResponseReasoningItem(
-                            id=FAKE_RESPONSES_ID,
-                            summary=[Summary(text="", type="summary_text")],
-                            type="reasoning",
-                        ),
+                    reasoning_item = ResponseReasoningItem(
+                        id=FAKE_RESPONSES_ID,
+                        summary=[Summary(text="", type="summary_text")],
+                        type="reasoning",
                     )
+                    if state.provider_data:
+                        reasoning_item.provider_data = state.provider_data.copy()  # type: ignore[attr-defined]
+                    state.reasoning_content_index_and_output = (0, reasoning_item)
                     yield ResponseOutputItemAddedEvent(
-                        item=ResponseReasoningItem(
-                            id=FAKE_RESPONSES_ID,
-                            summary=[Summary(text="", type="summary_text")],
-                            type="reasoning",
-                        ),
+                        item=reasoning_item,
                         output_index=0,
                         type="response.output_item.added",
                         sequence_number=sequence_number.get_and_increment(),
@@ -177,22 +195,17 @@ async def handle_stream(
             if hasattr(delta, "reasoning"):
                 reasoning_text = delta.reasoning
                 if reasoning_text and not state.reasoning_content_index_and_output:
-                    state.reasoning_content_index_and_output = (
-                        0,
-                        ResponseReasoningItem(
-                            id=FAKE_RESPONSES_ID,
-                            summary=[],
-                            content=[Content(text="", type="reasoning_text")],
-                            type="reasoning",
-                        ),
+                    reasoning_item = ResponseReasoningItem(
+                        id=FAKE_RESPONSES_ID,
+                        summary=[],
+                        content=[Content(text="", type="reasoning_text")],
+                        type="reasoning",
                     )
+                    if state.provider_data:
+                        reasoning_item.provider_data = state.provider_data.copy()  # type: ignore[attr-defined]
+                    state.reasoning_content_index_and_output = (0, reasoning_item)
                     yield ResponseOutputItemAddedEvent(
-                        item=ResponseReasoningItem(
-                            id=FAKE_RESPONSES_ID,
-                            summary=[],
-                            content=[Content(text="", type="reasoning_text")],
-                            type="reasoning",
-                        ),
+                        item=reasoning_item,
                         output_index=0,
                         type="response.output_item.added",
                         sequence_number=sequence_number.get_and_increment(),
@@ -244,6 +257,8 @@ async def handle_stream(
                         type="message",
                         status="in_progress",
                     )
+                    if state.provider_data:
+                        assistant_item.provider_data = state.provider_data.copy()  # type: ignore[attr-defined]
                     # Notify consumers of the start of a new output message + first content part
                     yield ResponseOutputItemAddedEvent(
                         item=assistant_item,
@@ -302,6 +317,8 @@ async def handle_stream(
                         type="message",
                         status="in_progress",
                     )
+                    if state.provider_data:
+                        assistant_item.provider_data = state.provider_data.copy()  # type: ignore[attr-defined]
                     # Notify downstream that assistant message + first content part are starting
                     yield ResponseOutputItemAddedEvent(
                         item=assistant_item,
@@ -359,7 +376,60 @@ async def handle_stream(
                         state.function_calls[tc_delta.index].name = tc_function.name
 
                     if tc_delta.id:
-                        state.function_calls[tc_delta.index].call_id = tc_delta.id
+                        # Clean up litellm's addition of __thought__ suffix to tool_call.id for
+                        # Gemini models. See: https://github.com/BerriAI/litellm/pull/16895
+                        # This suffix is redundant since we can get thought_signature from
+                        # provider_specific_fields, and this hack causes validation errors when
+                        # cross-model passing to other models.
+                        tool_call_id = tc_delta.id
+                        if model and "gemini" in model.lower() and "__thought__" in tool_call_id:
+                            tool_call_id = tool_call_id.split("__thought__")[0]
+
+                        state.function_calls[tc_delta.index].call_id = tool_call_id
+
+                    # Initialize provider_data for this function call from state.provider_data
+                    if not hasattr(state.function_calls[tc_delta.index], "provider_data"):
+                        if state.provider_data:
+                            state.function_calls[
+                                tc_delta.index
+                            ].provider_data = state.provider_data.copy()  # type: ignore[attr-defined]
+
+                    # Capture provider_specific_fields data from LiteLLM
+                    if (
+                        hasattr(tc_delta, "provider_specific_fields")
+                        and tc_delta.provider_specific_fields
+                    ):
+                        # Handle Gemini thought_signatures
+                        if model and "gemini" in model.lower():
+                            provider_specific_fields = tc_delta.provider_specific_fields
+                            if isinstance(provider_specific_fields, dict):
+                                thought_sig = provider_specific_fields.get("thought_signature")
+                                if thought_sig:
+                                    # Start with state.provider_data, then add thought_signature
+                                    func_provider_data = (
+                                        state.provider_data.copy() if state.provider_data else {}
+                                    )
+                                    func_provider_data["thought_signature"] = thought_sig
+                                    state.function_calls[
+                                        tc_delta.index
+                                    ].provider_data = func_provider_data  # type: ignore[attr-defined]
+
+                    # Capture extra_content data from Google's chatcmpl endpoint
+                    if hasattr(tc_delta, "extra_content") and tc_delta.extra_content:
+                        extra_content = tc_delta.extra_content
+                        if isinstance(extra_content, dict):
+                            google_fields = extra_content.get("google")
+                            if google_fields and isinstance(google_fields, dict):
+                                thought_sig = google_fields.get("thought_signature")
+                                if thought_sig:
+                                    # Start with state.provider_data, then add thought_signature
+                                    func_provider_data = (
+                                        state.provider_data.copy() if state.provider_data else {}
+                                    )
+                                    func_provider_data["thought_signature"] = thought_sig
+                                    state.function_calls[
+                                        tc_delta.index
+                                    ].provider_data = func_provider_data  # type: ignore[attr-defined]
 
                     function_call = state.function_calls[tc_delta.index]
 
@@ -390,14 +460,28 @@ async def handle_stream(
                         )
 
                         # Send initial function call added event
+                        func_call_item = ResponseFunctionToolCall(
+                            id=FAKE_RESPONSES_ID,
+                            call_id=function_call.call_id,
+                            arguments="",  # Start with empty arguments
+                            name=function_call.name,
+                            type="function_call",
+                        )
+                        # Merge provider_data from state and function_call (e.g. thought_signature)
+                        if state.provider_data or (
+                            hasattr(function_call, "provider_data") and function_call.provider_data
+                        ):
+                            merged_provider_data = (
+                                state.provider_data.copy() if state.provider_data else {}
+                            )
+                            if (
+                                hasattr(function_call, "provider_data")
+                                and function_call.provider_data
+                            ):
+                                merged_provider_data.update(function_call.provider_data)
+                            func_call_item.provider_data = merged_provider_data  # type: ignore[attr-defined]
                         yield ResponseOutputItemAddedEvent(
-                            item=ResponseFunctionToolCall(
-                                id=FAKE_RESPONSES_ID,
-                                call_id=function_call.call_id,
-                                arguments="",  # Start with empty arguments
-                                name=function_call.name,
-                                type="function_call",
-                            ),
+                            item=func_call_item,
                             output_index=function_call_starting_index,
                             type="response.output_item.added",
                             sequence_number=sequence_number.get_and_increment(),
@@ -485,14 +569,27 @@ async def handle_stream(
             if state.function_call_streaming.get(index, False):
                 # Function call was streamed, just send the completion event
                 output_index = state.function_call_output_idx[index]
+
+                # Build function call kwargs, include provider_data if present
+                func_call_kwargs: dict[str, Any] = {
+                    "id": FAKE_RESPONSES_ID,
+                    "call_id": function_call.call_id,
+                    "arguments": function_call.arguments,
+                    "name": function_call.name,
+                    "type": "function_call",
+                }
+
+                # Merge provider_data from state and function_call (e.g. thought_signature)
+                if state.provider_data or (
+                    hasattr(function_call, "provider_data") and function_call.provider_data
+                ):
+                    merged_provider_data = state.provider_data.copy() if state.provider_data else {}
+                    if hasattr(function_call, "provider_data") and function_call.provider_data:
+                        merged_provider_data.update(function_call.provider_data)
+                    func_call_kwargs["provider_data"] = merged_provider_data
+
                 yield ResponseOutputItemDoneEvent(
-                    item=ResponseFunctionToolCall(
-                        id=FAKE_RESPONSES_ID,
-                        call_id=function_call.call_id,
-                        arguments=function_call.arguments,
-                        name=function_call.name,
-                        type="function_call",
-                    ),
+                    item=ResponseFunctionToolCall(**func_call_kwargs),
                     output_index=output_index,
                     type="response.output_item.done",
                     sequence_number=sequence_number.get_and_increment(),
@@ -513,15 +610,27 @@ async def handle_stream(
                     1 for streaming in state.function_call_streaming.values() if streaming
                 )
 
+                # Build function call kwargs, include provider_data if present
+                fallback_func_call_kwargs: dict[str, Any] = {
+                    "id": FAKE_RESPONSES_ID,
+                    "call_id": function_call.call_id,
+                    "arguments": function_call.arguments,
+                    "name": function_call.name,
+                    "type": "function_call",
+                }
+
+                # Merge provider_data from state and function_call (e.g. thought_signature)
+                if state.provider_data or (
+                    hasattr(function_call, "provider_data") and function_call.provider_data
+                ):
+                    merged_provider_data = state.provider_data.copy() if state.provider_data else {}
+                    if hasattr(function_call, "provider_data") and function_call.provider_data:
+                        merged_provider_data.update(function_call.provider_data)
+                    fallback_func_call_kwargs["provider_data"] = merged_provider_data
+
                 # Send all events at once (backward compatibility)
                 yield ResponseOutputItemAddedEvent(
-                    item=ResponseFunctionToolCall(
-                        id=FAKE_RESPONSES_ID,
-                        call_id=function_call.call_id,
-                        arguments=function_call.arguments,
-                        name=function_call.name,
-                        type="function_call",
-                    ),
+                    item=ResponseFunctionToolCall(**fallback_func_call_kwargs),
                     output_index=fallback_starting_index,
                     type="response.output_item.added",
                     sequence_number=sequence_number.get_and_increment(),
@@ -534,13 +643,7 @@ async def handle_stream(
                     sequence_number=sequence_number.get_and_increment(),
                 )
                 yield ResponseOutputItemDoneEvent(
-                    item=ResponseFunctionToolCall(
-                        id=FAKE_RESPONSES_ID,
-                        call_id=function_call.call_id,
-                        arguments=function_call.arguments,
-                        name=function_call.name,
-                        type="function_call",
-                    ),
+                    item=ResponseFunctionToolCall(**fallback_func_call_kwargs),
                     output_index=fallback_starting_index,
                     type="response.output_item.done",
                     sequence_number=sequence_number.get_and_increment(),
@@ -574,6 +677,8 @@ async def handle_stream(
                 type="message",
                 status="completed",
             )
+            if state.provider_data:
+                assistant_msg.provider_data = state.provider_data.copy()  # type: ignore[attr-defined]
             if state.text_content_index_and_output:
                 assistant_msg.content.append(state.text_content_index_and_output[1])
             if state.refusal_content_index_and_output:
@@ -594,6 +699,7 @@ async def handle_stream(
 
         final_response = response.model_copy()
         final_response.output = outputs
+
         final_response.usage = (
             ResponseUsage(
                 input_tokens=usage.prompt_tokens or 0,
diff --git a/src/agents/models/openai_chatcompletions.py b/src/agents/models/openai_chatcompletions.py
index 76f36d86b..22f370c74 100644
--- a/src/agents/models/openai_chatcompletions.py
+++ b/src/agents/models/openai_chatcompletions.py
@@ -117,7 +117,16 @@ async def get_response(
                 "output_tokens": usage.output_tokens,
             }
 
-            items = Converter.message_to_output_items(message) if message is not None else []
+            # Build provider_data for provider_specific_fields
+            provider_data = {"model": self.model}
+            if message is not None and hasattr(response, "id"):
+                provider_data["response_id"] = response.id
+
+            items = (
+                Converter.message_to_output_items(message, provider_data=provider_data)
+                if message is not None
+                else []
+            )
 
             return ModelResponse(
                 output=items,
@@ -160,7 +169,9 @@ async def stream_response(
             )
 
             final_response: Response | None = None
-            async for chunk in ChatCmplStreamHandler.handle_stream(response, stream):
+            async for chunk in ChatCmplStreamHandler.handle_stream(
+                response, stream, model=self.model
+            ):
                 yield chunk
 
                 if chunk.type == "response.completed":
@@ -218,7 +229,7 @@ async def _fetch_response(
         stream: bool = False,
         prompt: ResponsePromptParam | None = None,
     ) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]:
-        converted_messages = Converter.items_to_messages(input)
+        converted_messages = Converter.items_to_messages(input, model=self.model)
 
         if system_instructions:
             converted_messages.insert(
diff --git a/src/agents/models/openai_responses.py b/src/agents/models/openai_responses.py
index a8695c89c..45bf73fc6 100644
--- a/src/agents/models/openai_responses.py
+++ b/src/agents/models/openai_responses.py
@@ -43,6 +43,7 @@
 from ..usage import Usage
 from ..util._json import _to_dump_compatible
 from ..version import __version__
+from .fake_id import FAKE_RESPONSES_ID
 from .interface import Model, ModelTracing
 
 if TYPE_CHECKING:
@@ -253,6 +254,7 @@ async def _fetch_response(
     ) -> Response | AsyncStream[ResponseStreamEvent]:
         list_input = ItemHelpers.input_to_new_input_list(input)
         list_input = _to_dump_compatible(list_input)
+        list_input = self._remove_openai_responses_api_incompatible_fields(list_input)
 
         if model_settings.parallel_tool_calls and tools:
             parallel_tool_calls: bool | Omit = True
@@ -342,6 +344,49 @@ async def _fetch_response(
         )
         return cast(Union[Response, AsyncStream[ResponseStreamEvent]], response)
 
+    def _remove_openai_responses_api_incompatible_fields(self, list_input: list[Any]) -> list[Any]:
+        """
+        Remove or transform input items that are incompatible with the OpenAI Responses API.
+
+        Only items with truthy provider_data are processed.
+        This function handles the following incompatibilities:
+        - provider_data: Removes fields specific to other providers (e.g., Gemini, Claude).
+        - Fake IDs: Removes temporary IDs (FAKE_RESPONSES_ID) that should not be sent to OpenAI.
+        - Reasoning items: Filters out provider-specific reasoning items entirely.
+        """
+        # Early return optimization: if no item has provider_data, return unchanged.
+        has_provider_data = any(
+            isinstance(item, dict) and item.get("provider_data") for item in list_input
+        )
+        if not has_provider_data:
+            return list_input
+
+        result = []
+        for item in list_input:
+            cleaned = self._clean_item_for_openai(item)
+            if cleaned is not None:
+                result.append(cleaned)
+        return result
+
+    def _clean_item_for_openai(self, item: Any) -> Any | None:
+        # Only process dict items
+        if not isinstance(item, dict):
+            return item
+
+        # Filter out reasoning items with provider_data (provider-specific reasoning).
+        if item.get("type") == "reasoning" and item.get("provider_data"):
+            return None
+
+        # Remove fake response ID.
+        if item.get("id") == FAKE_RESPONSES_ID:
+            del item["id"]
+
+        # Remove provider_data field.
+        if "provider_data" in item:
+            del item["provider_data"]
+
+        return item
+
     def _get_client(self) -> AsyncOpenAI:
         if self._client is None:
             self._client = AsyncOpenAI()
diff --git a/tests/test_anthropic_thinking_blocks.py b/tests/test_anthropic_thinking_blocks.py
index 8fbc59833..63b89ba6d 100644
--- a/tests/test_anthropic_thinking_blocks.py
+++ b/tests/test_anthropic_thinking_blocks.py
@@ -183,7 +183,11 @@ def test_anthropic_thinking_blocks_with_tool_calls():
         else:
             items_as_dicts.append(cast(dict[str, Any], item))
 
-    messages = Converter.items_to_messages(items_as_dicts, preserve_thinking_blocks=True)  # type: ignore[arg-type]
+    messages = Converter.items_to_messages(
+        items_as_dicts,  # type: ignore[arg-type]
+        model="anthropic/claude-4-opus",
+        preserve_thinking_blocks=True,
+    )
 
     # Find the assistant message with tool calls
     assistant_messages = [
diff --git a/tests/test_gemini_thought_signatures.py b/tests/test_gemini_thought_signatures.py
new file mode 100644
index 000000000..42975414e
--- /dev/null
+++ b/tests/test_gemini_thought_signatures.py
@@ -0,0 +1,126 @@
+"""
+Test for Gemini thought signatures in function calling.
+
+Validates that thought signatures are preserved through the bidirectional roundtrip:
+- Gemini chatcmpl message → response item → back to message
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from openai.types.chat.chat_completion_message_tool_call import Function
+
+from agents.extensions.models.litellm_model import InternalChatCompletionMessage, InternalToolCall
+from agents.models.chatcmpl_converter import Converter
+
+
+def test_gemini_thought_signature_roundtrip():
+    """Test that thought signatures are preserved from Gemini responses to messages."""
+
+    # Create mock Gemini response with thought signature in new extra_content structure
+    class MockToolCall(InternalToolCall):
+        def __init__(self):
+            super().__init__(
+                id="call_123",
+                type="function",
+                function=Function(name="get_weather", arguments='{"city": "Paris"}'),
+                extra_content={"google": {"thought_signature": "test_signature_abc"}},
+            )
+
+    message = InternalChatCompletionMessage(
+        role="assistant",
+        content="I'll check the weather.",
+        reasoning_content="",
+        tool_calls=[MockToolCall()],
+    )
+
+    # Step 1: Convert to items
+    provider_data = {"model": "gemini/gemini-3-pro", "response_id": "gemini-response-id-123"}
+
+    items = Converter.message_to_output_items(message, provider_data=provider_data)
+
+    func_calls = [item for item in items if hasattr(item, "type") and item.type == "function_call"]
+    assert len(func_calls) == 1
+
+    # Verify thought_signature is stored in items with our provider_data structure
+    func_call_dict = func_calls[0].model_dump()
+
+    assert func_call_dict["provider_data"]["model"] == "gemini/gemini-3-pro"
+    assert func_call_dict["provider_data"]["response_id"] == "gemini-response-id-123"
+    assert func_call_dict["provider_data"]["thought_signature"] == "test_signature_abc"
+
+    # Step 2: Convert back to messages
+    items_as_dicts = [item.model_dump() for item in items]
+    messages = Converter.items_to_messages(
+        [{"role": "user", "content": "test"}] + items_as_dicts,
+        model="gemini/gemini-3-pro",
+    )
+
+    # Verify thought_signature is restored in extra_content format
+    assistant_msg = [msg for msg in messages if msg.get("role") == "assistant"][0]
+    tool_call = assistant_msg["tool_calls"][0]  # type: ignore[index, typeddict-item]
+    assert tool_call["extra_content"]["google"]["thought_signature"] == "test_signature_abc"
+
+
+def test_gemini_multiple_tool_calls_with_thought_signatures():
+    """Test multiple tool calls each preserve their own thought signatures."""
+    tool_call_1 = InternalToolCall(
+        id="call_1",
+        type="function",
+        function=Function(name="func_a", arguments='{"x": 1}'),
+        extra_content={"google": {"thought_signature": "sig_aaa"}},
+    )
+    tool_call_2 = InternalToolCall(
+        id="call_2",
+        type="function",
+        function=Function(name="func_b", arguments='{"y": 2}'),
+        extra_content={"google": {"thought_signature": "sig_bbb"}},
+    )
+
+    message = InternalChatCompletionMessage(
+        role="assistant",
+        content="Calling two functions.",
+        reasoning_content="",
+        tool_calls=[tool_call_1, tool_call_2],
+    )
+
+    provider_data = {"model": "gemini/gemini-3-pro"}
+    items = Converter.message_to_output_items(message, provider_data=provider_data)
+
+    func_calls = [i for i in items if hasattr(i, "type") and i.type == "function_call"]
+    assert len(func_calls) == 2
+
+    assert func_calls[0].model_dump()["provider_data"]["thought_signature"] == "sig_aaa"
+    assert func_calls[1].model_dump()["provider_data"]["thought_signature"] == "sig_bbb"
+
+
+def test_gemini_thought_signature_items_to_messages():
+    """Test that items_to_messages restores extra_content from provider_data for Gemini."""
+
+    # Create a function call item with provider_data containing thought_signature
+    func_call_item = {
+        "id": "fake-id",
+        "call_id": "call_restore",
+        "name": "restore_func",
+        "arguments": '{"test": true}',
+        "type": "function_call",
+        "provider_data": {
+            "model": "gemini/gemini-3-pro",
+            "response_id": "gemini-response-id-123",
+            "thought_signature": "restored_sig_xyz",
+        },
+    }
+
+    items = [{"role": "user", "content": "test"}, func_call_item]
+    messages = Converter.items_to_messages(items, model="gemini/gemini-3-pro")  # type: ignore[arg-type]
+
+    # Find the assistant message with tool_calls
+    assistant_msgs = [m for m in messages if m.get("role") == "assistant"]
+    assert len(assistant_msgs) == 1
+
+    tool_calls: list[dict[str, Any]] = assistant_msgs[0].get("tool_calls", [])  # type: ignore[assignment]
+    assert len(tool_calls) == 1
+
+    # Verify extra_content is restored in Google format
+    assert tool_calls[0]["extra_content"]["google"]["thought_signature"] == "restored_sig_xyz"
diff --git a/tests/test_gemini_thought_signatures_stream.py b/tests/test_gemini_thought_signatures_stream.py
new file mode 100644
index 000000000..22b7763a5
--- /dev/null
+++ b/tests/test_gemini_thought_signatures_stream.py
@@ -0,0 +1,210 @@
+"""
+Test for Gemini thought signatures in streaming function calls.
+
+Validates that thought signatures are captured from streaming chunks
+and included in the final function call events.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+from typing import Any, cast
+
+import pytest
+from openai.types.chat import ChatCompletionChunk
+from openai.types.chat.chat_completion_chunk import (
+    Choice,
+    ChoiceDelta,
+    ChoiceDeltaToolCall,
+    ChoiceDeltaToolCallFunction,
+)
+from openai.types.responses import Response
+
+from agents.models.chatcmpl_stream_handler import ChatCmplStreamHandler
+
+# ========== Helper Functions ==========
+
+
+def create_tool_call_delta(
+    index: int,
+    tool_call_id: str | None = None,
+    function_name: str | None = None,
+    arguments: str | None = None,
+    provider_specific_fields: dict[str, Any] | None = None,
+    extra_content: dict[str, Any] | None = None,
+) -> ChoiceDeltaToolCall:
+    """Create a tool call delta for streaming."""
+    function = ChoiceDeltaToolCallFunction(
+        name=function_name,
+        arguments=arguments,
+    )
+
+    delta = ChoiceDeltaToolCall(
+        index=index,
+        id=tool_call_id,
+        type="function" if tool_call_id else None,
+        function=function,
+    )
+
+    # Add provider_specific_fields (litellm format)
+    if provider_specific_fields:
+        delta_any = cast(Any, delta)
+        delta_any.provider_specific_fields = provider_specific_fields
+
+    # Add extra_content (Google chatcmpl format)
+    if extra_content:
+        delta_any = cast(Any, delta)
+        delta_any.extra_content = extra_content
+
+    return delta
+
+
+def create_chunk(
+    tool_calls: list[ChoiceDeltaToolCall] | None = None,
+    content: str | None = None,
+    include_usage: bool = False,
+) -> ChatCompletionChunk:
+    """Create a ChatCompletionChunk for testing."""
+    delta = ChoiceDelta(
+        content=content,
+        role="assistant" if content or tool_calls else None,
+        tool_calls=tool_calls,
+    )
+
+    chunk = ChatCompletionChunk(
+        id="chunk-id-123",
+        created=1,
+        model="gemini/gemini-3-pro",
+        object="chat.completion.chunk",
+        choices=[Choice(index=0, delta=delta, finish_reason=None)],
+    )
+
+    if include_usage:
+        from openai.types.completion_usage import CompletionUsage
+
+        chunk.usage = CompletionUsage(
+            completion_tokens=10,
+            prompt_tokens=5,
+            total_tokens=15,
+        )
+
+    return chunk
+
+
+def create_final_chunk() -> ChatCompletionChunk:
+    """Create a final chunk with finish_reason='tool_calls'."""
+    return ChatCompletionChunk(
+        id="chunk-id-456",
+        created=1,
+        model="gemini/gemini-3-pro",
+        object="chat.completion.chunk",
+        choices=[Choice(index=0, delta=ChoiceDelta(), finish_reason="tool_calls")],
+    )
+
+
+async def create_fake_stream(
+    chunks: list[ChatCompletionChunk],
+) -> AsyncIterator[ChatCompletionChunk]:
+    """Create an async iterator from chunks."""
+    for chunk in chunks:
+        yield chunk
+
+
+def create_mock_response() -> Response:
+    """Create a mock Response object."""
+    return Response(
+        id="resp-id",
+        created_at=0,
+        model="gemini/gemini-3-pro",
+        object="response",
+        output=[],
+        tool_choice="auto",
+        tools=[],
+        parallel_tool_calls=False,
+    )
+
+
+# ========== Tests ==========
+
+
+@pytest.mark.asyncio
+async def test_stream_captures_litellmprovider_specific_fields_thought_signature():
+    """Test streaming captures thought_signature from litellm's provider_specific_fields."""
+    chunks = [
+        create_chunk(
+            tool_calls=[
+                create_tool_call_delta(
+                    index=0,
+                    tool_call_id="call_stream_1",
+                    function_name="get_weather",
+                    provider_specific_fields={"thought_signature": "litellm_sig_123"},
+                )
+            ]
+        ),
+        create_chunk(tool_calls=[create_tool_call_delta(index=0, arguments='{"city": "Tokyo"}')]),
+        create_final_chunk(),
+    ]
+
+    response = create_mock_response()
+    stream = create_fake_stream(chunks)
+
+    events = []
+    async for event in ChatCmplStreamHandler.handle_stream(
+        response,
+        stream,  # type: ignore[arg-type]
+        model="gemini/gemini-3-pro",
+    ):
+        events.append(event)
+
+    # Find function call done event
+    done_events = [e for e in events if e.type == "response.output_item.done"]
+    func_done = [
+        e for e in done_events if hasattr(e.item, "type") and e.item.type == "function_call"
+    ]
+    assert len(func_done) == 1
+
+    provider_data = func_done[0].item.model_dump().get("provider_data", {})
+    assert provider_data.get("thought_signature") == "litellm_sig_123"
+    assert provider_data["model"] == "gemini/gemini-3-pro"
+    assert provider_data["response_id"] == "chunk-id-123"
+
+
+@pytest.mark.asyncio
+async def test_stream_captures_google_extra_content_thought_signature():
+    """Test streaming captures thought_signature from Google's extra_content format."""
+    chunks = [
+        create_chunk(
+            tool_calls=[
+                create_tool_call_delta(
+                    index=0,
+                    tool_call_id="call_stream_2",
+                    function_name="search",
+                    extra_content={"google": {"thought_signature": "google_sig_456"}},
+                )
+            ]
+        ),
+        create_chunk(tool_calls=[create_tool_call_delta(index=0, arguments='{"query": "test"}')]),
+        create_final_chunk(),
+    ]
+
+    response = create_mock_response()
+    stream = create_fake_stream(chunks)
+
+    events = []
+    async for event in ChatCmplStreamHandler.handle_stream(
+        response,
+        stream,  # type: ignore[arg-type]
+        model="gemini/gemini-3-pro",
+    ):
+        events.append(event)
+
+    done_events = [e for e in events if e.type == "response.output_item.done"]
+    func_done = [
+        e for e in done_events if hasattr(e.item, "type") and e.item.type == "function_call"
+    ]
+    assert len(func_done) == 1
+
+    provider_data = func_done[0].item.model_dump().get("provider_data", {})
+    assert provider_data.get("thought_signature") == "google_sig_456"
+    assert provider_data["model"] == "gemini/gemini-3-pro"
+    assert provider_data["response_id"] == "chunk-id-123"
diff --git a/tests/test_model_payload_iterators.py b/tests/test_model_payload_iterators.py
index 3d7b9edc6..5c16db07c 100644
--- a/tests/test_model_payload_iterators.py
+++ b/tests/test_model_payload_iterators.py
@@ -60,7 +60,7 @@ async def test_chat_completions_materializes_iterator_payload(
     monkeypatch.setattr(
         chat_converter,
         "items_to_messages",
-        classmethod(lambda _cls, _input: [{"role": "user", "content": message_iter}]),
+        classmethod(lambda _cls, _input, **kwargs: [{"role": "user", "content": message_iter}]),
     )
     monkeypatch.setattr(
         chat_converter,
diff --git a/tests/test_remove_openai_responses_api_incompatible_fields.py b/tests/test_remove_openai_responses_api_incompatible_fields.py
new file mode 100644
index 000000000..87c91196b
--- /dev/null
+++ b/tests/test_remove_openai_responses_api_incompatible_fields.py
@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+from typing import Any
+from unittest.mock import MagicMock
+
+import pytest
+
+from agents.models.fake_id import FAKE_RESPONSES_ID
+from agents.models.openai_responses import OpenAIResponsesModel
+
+
+@pytest.fixture
+def model() -> OpenAIResponsesModel:
+    """Create a model instance for testing."""
+    mock_client = MagicMock()
+    return OpenAIResponsesModel(model="gpt-5", openai_client=mock_client)
+
+
+class TestRemoveOpenAIResponsesAPIIncompatibleFields:
+    """Tests for _remove_openai_responses_api_incompatible_fields method."""
+
+    def test_returns_unchanged_when_no_provider_data(self, model: OpenAIResponsesModel):
+        """When no items have provider_data, the input should be returned unchanged."""
+        list_input = [
+            {"type": "message", "content": "hello"},
+            {"type": "function_call", "call_id": "call_123", "name": "test"},
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert result is list_input  # Same object reference.
+
+    def test_removes_reasoning_items_with_provider_data(self, model: OpenAIResponsesModel):
+        """Reasoning items with provider_data should be completely removed."""
+        list_input = [
+            {"type": "message", "content": "hello"},
+            {"type": "reasoning", "provider_data": {"model": "gemini/gemini-3"}},
+            {"type": "function_call", "call_id": "call_123"},
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert len(result) == 2
+        assert result[0] == {"type": "message", "content": "hello"}
+        assert result[1] == {"type": "function_call", "call_id": "call_123"}
+
+    def test_keeps_reasoning_items_without_provider_data(self, model: OpenAIResponsesModel):
+        """Reasoning items without provider_data should be kept."""
+        list_input = [
+            {"type": "reasoning", "summary": []},
+            {"type": "message", "content": "hello", "provider_data": {"foo": "bar"}},
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert len(result) == 2
+        assert result[0] == {"type": "reasoning", "summary": []}
+        assert result[1] == {"type": "message", "content": "hello"}
+
+    def test_removes_provider_data_from_all_items(self, model: OpenAIResponsesModel):
+        """provider_data field should be removed from all dict items."""
+        list_input = [
+            {"type": "message", "content": "hello", "provider_data": {"model": "gemini/gemini-3"}},
+            {
+                "type": "function_call",
+                "call_id": "call_123",
+                "provider_data": {"model": "gemini/gemini-3"},
+            },
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert len(result) == 2
+        assert "provider_data" not in result[0]
+        assert "provider_data" not in result[1]
+
+    def test_removes_fake_responses_id(self, model: OpenAIResponsesModel):
+        """Items with id equal to FAKE_RESPONSES_ID should have their id removed."""
+        list_input = [
+            {
+                "type": "message",
+                "id": FAKE_RESPONSES_ID,
+                "content": "hello",
+                "provider_data": {"model": "gemini/gemini-3"},
+            },
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert len(result) == 1
+        assert "id" not in result[0]
+        assert result[0]["content"] == "hello"
+
+    def test_preserves_real_ids(self, model: OpenAIResponsesModel):
+        """Real IDs (not FAKE_RESPONSES_ID) should be preserved."""
+        list_input = [
+            {
+                "type": "message",
+                "id": "msg_real123",
+                "content": "hello",
+                "provider_data": {},
+            },
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert result[0]["id"] == "msg_real123"
+
+    def test_handles_empty_list(self, model: OpenAIResponsesModel):
+        """Empty list should be returned unchanged."""
+        list_input: list[dict[str, Any]] = []
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        assert result == []
+
+    def test_combined_scenario(self, model: OpenAIResponsesModel):
+        """Test a realistic scenario with multiple items needing different processing."""
+        list_input = [
+            {"type": "message", "content": "user input"},
+            {"type": "reasoning", "summary": [], "provider_data": {"model": "gemini/gemini-3"}},
+            {
+                "type": "function_call",
+                "call_id": "call_abc_123",
+                "name": "get_weather",
+                "provider_data": {"model": "gemini/gemini-3"},
+            },
+            {
+                "type": "function_call_output",
+                "call_id": "call_abc_123",
+                "output": '{"temp": 72}',
+            },
+            {
+                "type": "message",
+                "id": FAKE_RESPONSES_ID,
+                "content": "The weather is 72F",
+                "provider_data": {"model": "gemini/gemini-3"},
+            },
+        ]
+
+        result = model._remove_openai_responses_api_incompatible_fields(list_input)
+
+        # Should have 4 items (reasoning with provider_data removed).
+        assert len(result) == 4
+
+        # First item unchanged (no provider_data).
+        assert result[0] == {"type": "message", "content": "user input"}
+
+        # Function call: __thought__ suffix removed, provider_data removed.
+        assert result[1]["type"] == "function_call"
+        assert result[1]["call_id"] == "call_abc_123"
+        assert "provider_data" not in result[1]
+
+        # Function call output: __thought__ suffix removed, provider_data removed.
+        assert result[2]["type"] == "function_call_output"
+        assert result[2]["call_id"] == "call_abc_123"
+
+        # Last message: fake id removed, provider_data removed.
+        assert result[3]["type"] == "message"
+        assert result[3]["content"] == "The weather is 72F"
+        assert "id" not in result[3]
+        assert "provider_data" not in result[3]
diff --git a/uv.lock b/uv.lock
index 128a6451c..269ca2afa 100644
--- a/uv.lock
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