fix: propagate finish_reason from LiteLLM responses

src/google/adk/models/lite_llm.py

@@ -64,6 +64,19 @@
 _NEW_LINE = "\n"
 _EXCLUDED_PART_FIELD = {"inline_data": {"data"}}
 
+# Mapping of LiteLLM finish_reason strings to FinishReason enum values
+# Note: tool_calls/function_call map to STOP because:
+# 1. FinishReason.TOOL_CALL enum does not exist (as of google-genai 0.8.0)
+# 2. Tool calls represent normal completion (model stopped to invoke tools)
+# 3. Gemini native responses use STOP for tool calls (see lite_llm.py:910)
+_FINISH_REASON_MAPPING = {
+    "length": types.FinishReason.MAX_TOKENS,
+    "stop": types.FinishReason.STOP,
+    "tool_calls": types.FinishReason.STOP,  # Normal completion with tool invocation
+    "function_call": types.FinishReason.STOP,  # Legacy function call variant
+    "content_filter": types.FinishReason.SAFETY,
+}
+
 
 class ChatCompletionFileUrlObject(TypedDict, total=False):
   file_data: str
@@ -494,13 +507,23 @@ def _model_response_to_generate_content_response(
   """
 
   message = None
-  if response.get("choices", None):
-    message = response["choices"][0].get("message", None)
+  finish_reason = None
+  if choices := response.get("choices"):
+    first_choice = choices[0]
+    message = first_choice.get("message", None)
+    finish_reason = first_choice.get("finish_reason", None)
 
   if not message:
     raise ValueError("No message in response")
 
   llm_response = _message_to_generate_content_response(message)
+  if finish_reason:
+    # Map LiteLLM finish_reason strings to FinishReason enum
+    # This provides type consistency with Gemini native responses and avoids warnings
+    finish_reason_str = str(finish_reason).lower()
+    llm_response.finish_reason = _FINISH_REASON_MAPPING.get(
+        finish_reason_str, types.FinishReason.OTHER
+    )
   if response.get("usage", None):
     llm_response.usage_metadata = types.GenerateContentResponseUsageMetadata(
         prompt_token_count=response["usage"].get("prompt_tokens", 0),

src/google/adk/models/llm_response.py

@@ -16,6 +16,7 @@
 
 from typing import Any
 from typing import Optional
+from typing import Union
 
 from google.genai import types
 from pydantic import alias_generators
@@ -78,7 +79,11 @@ class LlmResponse(BaseModel):
   """
 
   finish_reason: Optional[types.FinishReason] = None
-  """The finish reason of the response."""
+  """The finish reason of the response.
+
+  Always a types.FinishReason enum. String values from underlying model providers
+  are mapped to corresponding enum values (with fallback to OTHER for unknown values).
+  """
 
   error_code: Optional[str] = None
   """Error code if the response is an error. Code varies by model."""

src/google/adk/telemetry/tracing.py

@@ -281,9 +281,11 @@ def trace_call_llm(
           llm_response.usage_metadata.candidates_token_count,
       )
   if llm_response.finish_reason:
+    # finish_reason is always FinishReason enum
+    finish_reason_str = llm_response.finish_reason.name.lower()
     span.set_attribute(
         'gen_ai.response.finish_reasons',
-        [llm_response.finish_reason.value.lower()],
+        [finish_reason_str],
     )
 
 

tests/unittests/models/test_litellm.py

@@ -1903,3 +1903,121 @@ def test_non_gemini_litellm_no_warning():
     # Test with non-Gemini model
     LiteLlm(model="openai/gpt-4o")
     assert len(w) == 0
+
+
+@pytest.mark.parametrize(
+    "finish_reason,response_content,expected_content,has_tool_calls",
+    [
+        ("length", "Test response", "Test response", False),
+        ("stop", "Complete response", "Complete response", False),
+        (
+            "tool_calls",
+            "",
+            "",
+            True,
+        ),
+        ("content_filter", "", "", False),
+    ],
+    ids=["length", "stop", "tool_calls", "content_filter"],
+)
+@pytest.mark.asyncio
+async def test_finish_reason_propagation(
+    mock_acompletion,
+    lite_llm_instance,
+    finish_reason,
+    response_content,
+    expected_content,
+    has_tool_calls,
+):
+  """Test that finish_reason is properly propagated from LiteLLM response."""
+  tool_calls = None
+  if has_tool_calls:
+    tool_calls = [
+        ChatCompletionMessageToolCall(
+            type="function",
+            id="test_id",
+            function=Function(
+                name="test_function",
+                arguments='{"arg": "value"}',
+            ),
+        )
+    ]
+
+  mock_response = ModelResponse(
+      choices=[
+          Choices(
+              message=ChatCompletionAssistantMessage(
+                  role="assistant",
+                  content=response_content,
+                  tool_calls=tool_calls,
+              ),
+              finish_reason=finish_reason,
+          )
+      ]
+  )
+  mock_acompletion.return_value = mock_response
+
+  llm_request = LlmRequest(
+      contents=[
+          types.Content(
+              role="user", parts=[types.Part.from_text(text="Test prompt")]
+          )
+      ],
+  )
+
+  async for response in lite_llm_instance.generate_content_async(llm_request):
+    assert response.content.role == "model"
+    # Verify finish_reason is mapped to FinishReason enum
+    assert isinstance(response.finish_reason, types.FinishReason)
+    # Verify correct enum mapping using dictionary
+    expected_mapping = {
+        "length": types.FinishReason.MAX_TOKENS,
+        "stop": types.FinishReason.STOP,
+        "tool_calls": types.FinishReason.STOP,
+        "content_filter": types.FinishReason.SAFETY,
+    }
+    assert response.finish_reason == expected_mapping[finish_reason]
+    if expected_content:
+      assert response.content.parts[0].text == expected_content
+    if has_tool_calls:
+      assert len(response.content.parts) > 0
+      assert response.content.parts[-1].function_call.name == "test_function"
+
+  mock_acompletion.assert_called_once()
+
+
+
+@pytest.mark.asyncio
+async def test_finish_reason_unknown_maps_to_other(
+    mock_acompletion, lite_llm_instance
+):
+  """Test that unknown finish_reason values map to FinishReason.OTHER."""
+  mock_response = ModelResponse(
+      choices=[
+          Choices(
+              message=ChatCompletionAssistantMessage(
+                  role="assistant",
+                  content="Test response",
+              ),
+              finish_reason="unknown_reason_type",
+          )
+      ]
+  )
+  mock_acompletion.return_value = mock_response
+
+  llm_request = LlmRequest(
+      contents=[
+          types.Content(
+              role="user", parts=[types.Part.from_text(text="Test prompt")]
+          )
+      ],
+  )
+
+  async for response in lite_llm_instance.generate_content_async(llm_request):
+    assert response.content.role == "model"
+    # Unknown finish_reason should map to OTHER
+    assert isinstance(response.finish_reason, types.FinishReason)
+    assert response.finish_reason == types.FinishReason.OTHER
+
+  mock_acompletion.assert_called_once()
+