fix: propagate finish_reason from LiteLLM responses

src/google/adk/models/lite_llm.py

@@ -494,13 +494,32 @@ 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()
+    if finish_reason_str == "length":
+      llm_response.finish_reason = types.FinishReason.MAX_TOKENS
+    elif finish_reason_str == "stop":
+      llm_response.finish_reason = types.FinishReason.STOP
+    elif "tool" in finish_reason_str or "function" in finish_reason_str:
+      # Handle tool_calls, function_call variants
+      llm_response.finish_reason = types.FinishReason.STOP
+    elif finish_reason_str == "content_filter":
+      llm_response.finish_reason = types.FinishReason.SAFETY
+    else:
+      # For unknown reasons, use OTHER
+      llm_response.finish_reason = 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
@@ -77,8 +78,11 @@ class LlmResponse(BaseModel):
   Only used for streaming mode.
   """
 
-  finish_reason: Optional[types.FinishReason] = None
-  """The finish reason of the response."""
+  finish_reason: Optional[Union[types.FinishReason, str]] = None
+  """The finish reason of the response.
+
+  Can be either a types.FinishReason enum (from Gemini) or a string (from LiteLLM).
+  """
 
   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 +281,14 @@ def trace_call_llm(
           llm_response.usage_metadata.candidates_token_count,
       )
   if llm_response.finish_reason:
+    if isinstance(llm_response.finish_reason, types.FinishReason):
+      finish_reason_str = llm_response.finish_reason.name.lower()
+    else:
+      # Fallback for string values (should not occur with LiteLLM after enum mapping)
+      finish_reason_str = str(llm_response.finish_reason).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 +1903,122 @@ 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, not raw string
+    assert isinstance(response.finish_reason, types.FinishReason)
+    # Verify correct enum mapping
+    if finish_reason == "length":
+      assert response.finish_reason == types.FinishReason.MAX_TOKENS
+    elif finish_reason == "stop":
+      assert response.finish_reason == types.FinishReason.STOP
+    elif finish_reason == "tool_calls":
+      assert response.finish_reason == types.FinishReason.STOP
+    elif finish_reason == "content_filter":
+      assert response.finish_reason == types.FinishReason.SAFETY
+    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()
+