fix(models): handle mixed tool responses and text/media in LiteLLM ad…

src/google/adk/models/lite_llm.py

@@ -444,24 +444,33 @@ def _extract_cached_prompt_tokens(usage: Any) -> int:
 
 
 async def _content_to_message_param(
-    content: types.Content,
-    *,
-    provider: str = "",
+  content: types.Content,
+  *,
+  provider: str = "",
 ) -> Union[Message, list[Message]]:
   """Converts a types.Content to a litellm Message or list of Messages.
 
-  Handles multipart function responses by returning a list of
-  ChatCompletionToolMessage objects if multiple function_response parts exist.
+  This function processes a `types.Content` object, which may contain multiple
+  parts, and converts them into a format suitable for LiteLLM. It handles
+  mixed content, such as tool responses alongside text or other media, by
+  generating a list of messages.
+
+  - `function_response` parts are converted into `tool` role messages.
+  - Other parts (text, images, etc.) are grouped and converted into a
+    single `user` or `assistant` message.
 
   Args:
     content: The content to convert.
     provider: The LLM provider name (e.g., "openai", "azure").
 
   Returns:
-    A litellm Message, a list of litellm Messages.
+    A single litellm Message, a list of litellm Messages, or an empty list if
+    the content is empty.
   """
-
   tool_messages = []
+  other_parts = []
+
+  # 1. Separate function responses from other content
   for part in content.parts:
     if part.function_response:
       response = part.function_response.response
@@ -477,11 +486,68 @@ async def _content_to_message_param(
               content=response_content,
           )
       )
-  if tool_messages:
+    else:
+      other_parts.append(part)
+
+  # 2. If ONLY tools are present, return them immediately
+  if tool_messages and not other_parts:
     return tool_messages if len(tool_messages) > 1 else tool_messages[0]
 
-  # Handle user or assistant messages
-  role = _to_litellm_role(content.role)
+  # 3. Handle user or assistant messages for the remaining parts
+  extra_message = None
+  if other_parts:
+    role = _to_litellm_role(content.role)
+
+    if role == "user":
+      user_parts = [part for part in other_parts if not part.thought]
+      message_content = await _get_content(user_parts, provider=provider) or None
+      if message_content:
+        extra_message = ChatCompletionUserMessage(role="user", content=message_content)
+
+    else:  # assistant/model
+      tool_calls = []
+      content_parts: list[types.Part] = []
+      reasoning_parts: list[types.Part] = []
+      for part in other_parts:
+        if part.function_call:
+          tool_calls.append(
+              ChatCompletionAssistantToolCall(
+                  type="function",
+                  id=part.function_call.id,
+                  function=Function(
+                      name=part.function_call.name,
+                      arguments=_safe_json_serialize(part.function_call.args),
+                  ),
+              )
+          )
+        elif part.thought:
+          reasoning_parts.append(part)
+        else:
+          content_parts.append(part)
+
+      message_content = await _get_content(content_parts, provider=provider) or None
+      # Using p.text as requested by the code review
+      reasoning_content = (
+          "\n".join([p.text for p in reasoning_parts if p.text])
+          if reasoning_parts
+          else None
+      )
+
+      extra_message = ChatCompletionAssistantMessage(
+          role="assistant",
+          content=message_content,
+          tool_calls=tool_calls if tool_calls else None,
+          thought=reasoning_content,
+      )
+
+  # 4. Combine and Return
+  final_messages = tool_messages + ([extra_message] if extra_message else [])
+
+  if not final_messages:
+    return []
+
+  return final_messages if len(final_messages) > 1 else final_messages[0]
+
 
   if role == "user":
     user_parts = [part for part in content.parts if not part.thought]