diff --git a/src/google/adk/models/lite_llm.py b/src/google/adk/models/lite_llm.py
index 7d13696c96..b6f90a1f5a 100644
--- a/src/google/adk/models/lite_llm.py
+++ b/src/google/adk/models/lite_llm.py
@@ -833,7 +833,7 @@ async def _content_to_message_param(
     for part in content.parts:
       if part.function_call:
         tool_call_id = part.function_call.id or ""
-        tool_call_dict: ChatCompletionAssistantToolCall = {
+        tool_call_dict: Dict[str, Any] = {
             "type": "function",
             "id": tool_call_id,
             "function": {
@@ -1688,13 +1688,33 @@ def _message_to_generate_content_response(
 
   if tool_calls:
     for tool_call in tool_calls:
-      if tool_call.type == "function":
+      if (
+          isinstance(tool_call, dict) and tool_call.get("type") == "function"
+      ) or (hasattr(tool_call, "type") and tool_call.type == "function"):
         thought_signature = _extract_thought_signature_from_tool_call(tool_call)
+
+        func_name = ""
+        func_args_str = "{}"
+        call_id = ""
+
+        if isinstance(tool_call, dict):
+          call_id = tool_call.get("id", "")
+          func = tool_call.get("function")
+          if isinstance(func, dict):
+            func_name = func.get("name", "")
+            func_args_str = func.get("arguments", "{}")
+        else:
+          call_id = getattr(tool_call, "id", "")
+          func = getattr(tool_call, "function", None)
+          if func:
+            func_name = getattr(func, "name", "")
+            func_args_str = getattr(func, "arguments", "{}")
+
         part = types.Part.from_function_call(
-            name=tool_call.function.name,
-            args=json.loads(tool_call.function.arguments or "{}"),
+            name=func_name,
+            args=json.loads(func_args_str or "{}"),
         )
-        part.function_call.id = tool_call.id
+        part.function_call.id = call_id
         if thought_signature:
           part.thought_signature = thought_signature
         parts.append(part)
@@ -2235,6 +2255,7 @@ async def generate_content_async(
       aggregated_llm_response_with_tool_call = None
       usage_metadata = None
       fallback_index = 0
+      last_model_version = effective_model
 
       def _finalize_tool_call_response(
           *, model_version: str, finish_reason: str
@@ -2249,17 +2270,15 @@ def _finalize_tool_call_response(
               except json.JSONDecodeError:
                 has_incomplete_tool_call_args = True
                 continue
-            tool_calls.append(
-                ChatCompletionMessageToolCall(
-                    type="function",
-                    id=func_data["id"],
-                    function=Function(
-                        name=func_data["name"],
-                        arguments=func_data["args"],
-                        index=index,
-                    ),
-                )
-            )
+            tool_calls.append({
+                "type": "function",
+                "id": func_data["id"],
+                "function": {
+                    "name": func_data["name"],
+                    "arguments": func_data["args"],
+                    "index": index,
+                },
+            })
 
         if has_incomplete_tool_call_args:
           return LlmResponse(
@@ -2319,6 +2338,7 @@ def _reset_stream_buffers() -> None:
         function_calls.clear()
 
       async for part in await self.llm_client.acompletion(**completion_args):
+        last_model_version = getattr(part, "model", effective_model)
         for chunk, finish_reason in _model_response_to_chunk(part):
           if isinstance(chunk, FunctionChunk):
             index = chunk.index or fallback_index
@@ -2349,7 +2369,7 @@ def _reset_stream_buffers() -> None:
                     content=chunk.text,
                 ),
                 is_partial=True,
-                model_version=part.model,
+                model_version=last_model_version,
             )
           elif isinstance(chunk, ReasoningChunk):
             if chunk.parts:
@@ -2357,7 +2377,7 @@ def _reset_stream_buffers() -> None:
               yield LlmResponse(
                   content=types.Content(role="model", parts=list(chunk.parts)),
                   partial=True,
-                  model_version=part.model,
+                  model_version=last_model_version,
               )
           elif isinstance(chunk, UsageMetadataChunk):
             usage_metadata = types.GenerateContentResponseUsageMetadata(
@@ -2380,7 +2400,7 @@ def _reset_stream_buffers() -> None:
           ):
             aggregated_llm_response_with_tool_call = (
                 _finalize_tool_call_response(
-                    model_version=part.model,
+                    model_version=last_model_version,
                     finish_reason=finish_reason,
                 )
             )
@@ -2394,21 +2414,21 @@ def _reset_stream_buffers() -> None:
               )
           ):
             aggregated_llm_response = _finalize_text_response(
-                model_version=part.model,
+                model_version=last_model_version,
                 finish_reason=finish_reason,
             )
             _reset_stream_buffers()
 
       if function_calls and not aggregated_llm_response_with_tool_call:
         aggregated_llm_response_with_tool_call = _finalize_tool_call_response(
-            model_version=part.model,
+            model_version=last_model_version,
             finish_reason="tool_calls",
         )
         _reset_stream_buffers()
 
       if (text or reasoning_parts) and not aggregated_llm_response:
         aggregated_llm_response = _finalize_text_response(
-            model_version=part.model,
+            model_version=last_model_version,
             finish_reason="stop",
         )
         _reset_stream_buffers()