fix: use tool_responses role for gemma4 models in LiteLLM integration

src/google/adk/models/lite_llm.py

@@ -807,9 +807,14 @@ async def _content_to_message_param(
           if isinstance(response, str)
           else _safe_json_serialize(response)
       )
+      # gemma4 requires role='tool_responses' for recognizing function_response parts as responses
+      # from the tool call, instead of OpenAI-compatible 'tool' role used by other models.
+      # Earlier Gemma versions before version 4 do not support tool use,
+      # so this check is intentionally scoped to only look for "gemma4" in the model name.
+      tool_role = "tool_responses" if "gemma4" in model.lower() else "tool"
       tool_messages.append(
           ChatCompletionToolMessage(
-              role="tool",
+              role=tool_role,
               tool_call_id=part.function_response.id,
               content=response_content,
           )
@@ -824,6 +829,7 @@ async def _content_to_message_param(
     follow_up = await _content_to_message_param(
         types.Content(role=content.role, parts=non_tool_parts),
         provider=provider,
+        model=model,
     )
     follow_up_messages = (
         follow_up if isinstance(follow_up, list) else [follow_up]
@@ -934,12 +940,16 @@ async def _content_to_message_param(
     )
 
 
-def _ensure_tool_results(messages: List[Message]) -> List[Message]:
+def _ensure_tool_results(messages: List[Message], model: str) -> List[Message]:
   """Insert placeholder tool messages for missing tool results.
 
   LiteLLM-backed providers like OpenAI and Anthropic reject histories where an
   assistant tool call is not followed by tool responses before the next
   non-tool message. This helps recover from interrupted tool execution.
+
+  For models that expect a different tool response role (e.g. Gemma4 models,
+  which require 'tool_responses' instead of 'tool'), the role is adjusted
+  accordingly.
   """
   if not messages:
     return messages
@@ -948,17 +958,19 @@ def _ensure_tool_results(messages: List[Message]) -> List[Message]:
 
   healed_messages: List[Message] = []
   pending_tool_call_ids: List[str] = []
+  expected_tool_role = "tool_responses" if "gemma4" in model.lower() else "tool"
 
   for message in messages:
     role = message.get("role")
-    if pending_tool_call_ids and role != "tool":
+
+    if pending_tool_call_ids and role != expected_tool_role:
       logger.warning(
           "Missing tool results for tool_call_id(s): %s",
           pending_tool_call_ids,
       )
       healed_messages.extend(
           ChatCompletionToolMessage(
-              role="tool",
+              role=expected_tool_role,
               tool_call_id=tool_call_id,
               content=_MISSING_TOOL_RESULT_MESSAGE,
           )
@@ -971,21 +983,22 @@ def _ensure_tool_results(messages: List[Message]) -> List[Message]:
       pending_tool_call_ids = [
           tool_call.get("id") for tool_call in tool_calls if tool_call.get("id")
       ]
-    elif role == "tool":
+    elif role == expected_tool_role:
       tool_call_id = message.get("tool_call_id")
       if tool_call_id in pending_tool_call_ids:
         pending_tool_call_ids.remove(tool_call_id)
 
     healed_messages.append(message)
 
+  # Final block also uses expected_tool_role
   if pending_tool_call_ids:
     logger.warning(
         "Missing tool results for tool_call_id(s): %s",
         pending_tool_call_ids,
     )
     healed_messages.extend(
         ChatCompletionToolMessage(
-            role="tool",
+            role=expected_tool_role,
             tool_call_id=tool_call_id,
             content=_MISSING_TOOL_RESULT_MESSAGE,
         )
@@ -1905,7 +1918,7 @@ async def _get_completion_inputs(
             content=llm_request.config.system_instruction,
         ),
     )
-  messages = _ensure_tool_results(messages)
+  messages = _ensure_tool_results(messages, model)
 
   # 2. Convert tool declarations
   tools: Optional[List[Dict]] = None

tests/unittests/models/test_lite_llm_gemma_tool_role.py

@@ -0,0 +1,187 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for Gemma-specific tool role handling in _content_to_message_param.
+
+Gemma's chat template expects role='tool_responses' for tool result messages,
+while the OpenAI-compatible default is role='tool'. This module verifies that
+_content_to_message_param sets the correct role based on the model name.
+"""
+
+from google.adk.models.lite_llm import _content_to_message_param
+from google.genai import types
+import pytest
+
+# Auxiliar
+
+
+def _make_function_response_content(
+    function_name: str = "get_weather",
+    response_data: dict | None = None,
+    call_id: str = "call_001",
+) -> types.Content:
+  """Builds a types.Content with a single function_response part."""
+  if response_data is None:
+    response_data = {"city": "Santiago de Cuba", "condition": "sunny"}
+  return types.Content(
+      role="user",
+      parts=[
+          types.Part(
+              function_response=types.FunctionResponse(
+                  name=function_name,
+                  response=response_data,
+                  id=call_id,
+              )
+          )
+      ],
+  )
+
+
+def _make_multi_function_response_content(
+    call_ids: list[str] | None = None,
+) -> types.Content:
+  """Builds a types.Content with multiple function_response parts."""
+  if call_ids is None:
+    call_ids = ["call_001", "call_002"]
+  return types.Content(
+      role="user",
+      parts=[
+          types.Part(
+              function_response=types.FunctionResponse(
+                  name=f"tool_{i}",
+                  response={"result": f"value_{i}"},
+                  id=call_id,
+              )
+          )
+          for i, call_id in enumerate(call_ids)
+      ],
+  )
+
+
+def _extract_role(msg) -> str:
+  """Extracts role from a litellm message, whether dict or object."""
+  if isinstance(msg, dict):
+    return msg["role"]
+  return msg.role
+
+
+class TestToolRoleSingleResponse:
+  """_content_to_message_param with a single function_response part."""
+
+  @pytest.mark.asyncio
+  async def test_non_gemma_model_uses_tool_role(self):
+    """Non-Gemma models should get role='tool' (OpenAI-compatible default)."""
+    content = _make_function_response_content()
+
+    result = await _content_to_message_param(
+        content, model="ollama/qwen2.5-coder:3b"
+    )
+
+    assert _extract_role(result) == "tool"
+
+  @pytest.mark.asyncio
+  async def test_gemma4_model_uses_tool_responses_role(self):
+    """Models containing 'gemma4' should get role='tool_responses'."""
+    content = _make_function_response_content()
+
+    result = await _content_to_message_param(content, model="ollama/gemma4:e2b")
+
+    assert _extract_role(result) == "tool_responses", (
+        "Gemma models require role='tool_responses' to match their chat "
+        "template; role='tool' causes infinite tool-calling loops."
+    )
+
+  @pytest.mark.asyncio
+  async def test_gemma4_uppercase_model_name(self):
+    """Model name matching should be case-insensitive."""
+    content = _make_function_response_content()
+
+    result = await _content_to_message_param(content, model="ollama/Gemma4:31b")
+
+    assert _extract_role(result) == "tool_responses"
+
+  @pytest.mark.asyncio
+  async def test_tool_call_id_and_content_preserved(self):
+    """Fix must not alter tool_call_id or content — only role changes."""
+    content = _make_function_response_content(
+        response_data={"status": "ok"}, call_id="my_call_123"
+    )
+
+    result = await _content_to_message_param(content, model="ollama/gemma4:e2b")
+
+    if isinstance(result, dict):
+      assert result["tool_call_id"] == "my_call_123"
+      assert "ok" in result["content"]
+    else:
+      assert result.tool_call_id == "my_call_123"
+      assert "ok" in result.content
+
+  @pytest.mark.asyncio
+  async def test_empty_model_string_uses_tool_role(self):
+    """Empty model string should fall back to default role='tool'."""
+    content = _make_function_response_content()
+
+    result = await _content_to_message_param(content, model="")
+
+    assert _extract_role(result) == "tool"
+
+  @pytest.mark.asyncio
+  async def test_unrelated_models_use_tool_role(self):
+    """Models that do not contain 'gemma4' must not be affected."""
+    unaffected_models = [
+        "ollama/llama3:8b",
+        "anthropic/claude-3-opus",
+        "openai/gpt-4o",
+        "ollama/gemma3:4b",  # gemma3 != gemma4
+    ]
+    for model in unaffected_models:
+      content = _make_function_response_content()
+      result = await _content_to_message_param(content, model=model)
+      assert (
+          _extract_role(result) == "tool"
+      ), f"Model '{model}' should not be affected by the Gemma4 fix."
+
+
+class TestToolRoleMultipleResponses:
+  """_content_to_message_param with multiple function_response parts."""
+
+  @pytest.mark.asyncio
+  async def test_gemma4_all_messages_use_tool_responses_role(self):
+    """All messages in a multi-response must have role='tool_responses'."""
+    content = _make_multi_function_response_content(
+        call_ids=["call_a", "call_b", "call_c"]
+    )
+
+    result = await _content_to_message_param(content, model="ollama/gemma4:4b")
+
+    assert isinstance(result, list)
+    assert len(result) == 3
+    for msg in result:
+      assert _extract_role(msg) == "tool_responses", (
+          "Every tool message in a multi-response must use 'tool_responses' "
+          "for Gemma4 models."
+      )
+
+  @pytest.mark.asyncio
+  async def test_non_gemma_multi_response_uses_tool_role(self):
+    """Non-Gemma multi-response messages should all have role='tool'."""
+    content = _make_multi_function_response_content(
+        call_ids=["call_a", "call_b"]
+    )
+
+    result = await _content_to_message_param(content, model="openai/gpt-4o")
+
+    assert isinstance(result, list)
+    for msg in result:
+      assert _extract_role(msg) == "tool"