fix(models): aggregate streaming thought parts for all LiteLlm providers

src/google/adk/models/lite_llm.py

@@ -3232,7 +3232,11 @@ def _finalize_tool_call_response(
                 tool_calls=tool_calls,
             ),
             model_version=model_version,
-            thought_parts=list(reasoning_parts) if reasoning_parts else None,
+            thought_parts=(
+                _aggregate_streaming_thought_parts(reasoning_parts)
+                if reasoning_parts
+                else None
+            ),
         )
         mapped_finish_reason = _map_finish_reason(finish_reason)
         llm_response.finish_reason = mapped_finish_reason
@@ -3256,7 +3260,11 @@ def _finalize_text_response(
                 content=message_content,
             ),
             model_version=model_version,
-            thought_parts=list(reasoning_parts) if reasoning_parts else None,
+            thought_parts=(
+                _aggregate_streaming_thought_parts(reasoning_parts)
+                if reasoning_parts
+                else None
+            ),
         )
         mapped_finish_reason = _map_finish_reason(finish_reason)
         llm_response.finish_reason = mapped_finish_reason

tests/unittests/models/test_litellm.py

@@ -4536,6 +4536,78 @@ async def test_generate_content_async_stream_with_reasoning_tokens(
   )
 
 
+@pytest.mark.asyncio
+async def test_generate_content_async_stream_aggregates_reasoning_deltas(
+    mock_completion, lite_llm_instance
+):
+  """Per-token reasoning deltas must collapse into one thought part.
+
+  Regression test: the streaming finalizers passed the raw per-delta thought
+  parts straight through, so a model that streams reasoning token by token
+  (e.g. xai/grok via LiteLLM) produced one `types.Part(thought=True)` per
+  delta in the final, non-partial response instead of one part per thinking
+  block -- the same shape the non-streaming path already produces.
+  """
+  streaming_reasoning_response = [
+      ModelResponseStream(
+          model="test_model",
+          choices=[
+              StreamingChoices(
+                  finish_reason=None,
+                  delta=Delta(role="assistant", reasoning_content="The"),
+              )
+          ],
+      ),
+      ModelResponseStream(
+          model="test_model",
+          choices=[
+              StreamingChoices(
+                  finish_reason=None,
+                  delta=Delta(role="assistant", reasoning_content=" user"),
+              )
+          ],
+      ),
+      ModelResponseStream(
+          model="test_model",
+          choices=[
+              StreamingChoices(
+                  finish_reason=None,
+                  delta=Delta(role="assistant", reasoning_content=" wants"),
+              )
+          ],
+      ),
+      ModelResponseStream(
+          model="test_model",
+          choices=[
+              StreamingChoices(
+                  finish_reason=None,
+                  delta=Delta(role="assistant", content="Hi!"),
+              )
+          ],
+      ),
+      ModelResponseStream(
+          model="test_model",
+          choices=[StreamingChoices(finish_reason="stop")],
+      ),
+  ]
+  mock_completion.return_value = iter(streaming_reasoning_response)
+
+  responses = [
+      response
+      async for response in lite_llm_instance.generate_content_async(
+          LLM_REQUEST_WITH_FUNCTION_DECLARATION, stream=True
+      )
+  ]
+
+  final_response = responses[-1]
+  assert final_response.partial is not True
+  thought_parts = [
+      part for part in final_response.content.parts if part.thought
+  ]
+  assert len(thought_parts) == 1
+  assert thought_parts[0].text == "The user wants"
+
+
 @pytest.mark.asyncio
 async def test_generate_content_async_stream_with_usage_metadata(
     mock_completion, lite_llm_instance