fix(gemini): propagate thoughtSignature in tool call results for Gemini 3

Gemini 3 thinking models require thoughtSignature fields on tool
result parts to maintain the thinking chain across turns. Without
this, multi-turn conversations with tool use fail on Gemini 3.

Changes:
- Add model parameter to convert_to_gemini_tool_call_result
- Extract thought_signature from tool calls via _get_thought_signature_from_tool
- Add thoughtSignature to function_response parts
- For Gemini 3, nest inline_data inside functionResponse to avoid
  leaking internal transition tokens into user-visible text
- Pass model= through from _gemini_convert_messages_with_history

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -1468,6 +1468,7 @@ def convert_to_gemini_tool_call_invoke(
 def convert_to_gemini_tool_call_result(  # noqa: PLR0915
     message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
     last_message_with_tool_calls: Optional[dict],
+    model: Optional[str] = None,
 ) -> Union[VertexPartType, List[VertexPartType]]:
     """
     OpenAI message with a tool result looks like:
@@ -1596,8 +1597,9 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
                                 f"Failed to process file in tool response: {e}"
                             )
     name: Optional[str] = message.get("name", "")  # type: ignore
+    thought_signature: Optional[str] = None
 
-    # Recover name from last message with tool calls
+    # Recover name and thought signature from last message with tool calls
     if last_message_with_tool_calls:
         tools = last_message_with_tool_calls.get("tool_calls", [])
         msg_tool_call_id = message.get("tool_call_id", None)
@@ -1609,6 +1611,7 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
                 and msg_tool_call_id == prev_tool_call_id
             ):
                 name = tool.get("function", {}).get("name", "")
+                thought_signature = _get_thought_signature_from_tool(dict(tool), model=model)
 
     if not name:
         raise Exception(
@@ -1642,13 +1645,28 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
 
     # Create part with function_response, and optionally inline_data for images (Computer Use)
     _part: VertexPartType = {"function_response": _function_response}
+    if thought_signature:
+        _part["thoughtSignature"] = thought_signature
 
     # For Computer Use, if we have images/files, we need separate parts:
     # - One part with function_response
     # - One part per inline_data item
     # Gemini's PartType is a oneof, so we can't have both in the same part
     if inline_data_list:
-        return [_part] + [{"inline_data": d} for d in inline_data_list]
+        # Gemini 3 "Thinking" models require nesting multimodal content inside functionResponse
+        # to avoid leaking internal transition tokens into the user-visible text.
+        is_gemini_3 = model is not None and "gemini-3" in model
+        if is_gemini_3:
+            _part["function_response"]["parts"] = [{"inline_data": d} for d in inline_data_list]  # type: ignore
+            return _part
+
+        # Backwards-compatible behavior for older Gemini models:
+        # Gemini's PartType is a oneof, so we can't have both function_response + inline_data in one part.
+        extra_parts: List[VertexPartType] = [{"inline_data": d} for d in inline_data_list]
+        for ep in extra_parts:
+            if thought_signature:
+                ep["thoughtSignature"] = thought_signature
+        return [_part] + extra_parts
 
     return _part
 

litellm/llms/vertex_ai/gemini/transformation.py

@@ -587,7 +587,7 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                 and messages[msg_i]["role"] in tool_call_message_roles
             ):
                 _part = convert_to_gemini_tool_call_result(
-                    messages[msg_i], last_message_with_tool_calls  # type: ignore
+                    messages[msg_i], last_message_with_tool_calls, model=model  # type: ignore
                 )
                 msg_i += 1
                 # Handle both single part and list of parts (for Computer Use with images)

tests/test_litellm/litellm_core_utils/prompt_templates/test_gemini_thought_signature.py

@@ -0,0 +1,125 @@
+def test_convert_gemini_tool_call_result_with_thought_signature():
+    """
+    Test that thought_signature from tool calls is propagated to the
+    function_response part when converting tool results for Gemini.
+
+    Gemini 3 thinking models require thoughtSignature fields on tool
+    result parts to maintain the thinking chain.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_gemini_tool_call_result,
+    )
+    from litellm.types.llms.openai import ChatCompletionToolMessage
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_abc",
+        content='{"result": "ok"}',
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_abc",
+                "type": "function",
+                "function": {
+                    "name": "get_data",
+                    "arguments": "{}",
+                    "provider_specific_fields": {
+                        "thought_signature": "dGhvdWdodA==",
+                    },
+                },
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+        model="gemini-3-flash-preview",
+    )
+    assert isinstance(result, dict)
+    assert "thoughtSignature" in result
+    assert result["thoughtSignature"] == "dGhvdWdodA=="
+
+
+def test_convert_gemini_tool_call_result_without_thought_signature():
+    """
+    Test that tool results without thought_signature still work correctly
+    and do not include the thoughtSignature field.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_gemini_tool_call_result,
+    )
+    from litellm.types.llms.openai import ChatCompletionToolMessage
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_xyz",
+        content='{"value": 42}',
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_xyz",
+                "type": "function",
+                "function": {"name": "compute", "arguments": "{}"},
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+        model="gemini-2.5-flash",
+    )
+    assert isinstance(result, dict)
+    assert "thoughtSignature" not in result
+
+
+def test_convert_gemini_tool_call_result_gemini3_inline_data_nested():
+    """
+    Test that for Gemini 3 models, inline_data (images) in tool results
+    are nested inside functionResponse.parts instead of being returned
+    as separate parts.
+
+    This prevents leaking internal transition tokens into user-visible text.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_gemini_tool_call_result,
+    )
+    from litellm.types.llms.openai import ChatCompletionToolMessage
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_img",
+        content=[
+            {"type": "text", "text": "screenshot captured"},
+            {"type": "image_url", "image_url": "data:image/jpeg;base64,/9j/4AAQ"},
+        ],
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_img",
+                "type": "function",
+                "function": {"name": "take_screenshot", "arguments": "{}"},
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+        model="gemini-3-flash-preview",
+    )
+    # For Gemini 3, should return a single part with nested parts inside functionResponse
+    assert isinstance(result, dict)
+    assert "function_response" in result
+    assert "parts" in result["function_response"]
+    assert any("inline_data" in p for p in result["function_response"]["parts"])