google · copybara-service · Jun 17, 2025 · Jun 17, 2025
diff --git a/contributing/samples/quickstart/agent.py b/contributing/samples/quickstart/agent.py
@@ -29,7 +29,7 @@ def get_weather(city: str) -> dict:
         "status": "success",
         "report": (
             "The weather in New York is sunny with a temperature of 25 degrees"
-            " Celsius (41 degrees Fahrenheit)."
+            " Celsius (77 degrees Fahrenheit)."
         ),
     }
   else:

diff --git a/src/google/adk/models/lite_llm.py b/src/google/adk/models/lite_llm.py
@@ -23,7 +23,6 @@
 from typing import Dict
 from typing import Generator
 from typing import Iterable
-from typing import List
 from typing import Literal
 from typing import Optional
 from typing import Tuple
@@ -482,22 +481,16 @@ def _message_to_generate_content_response(
 
 def _get_completion_inputs(
     llm_request: LlmRequest,
-) -> Tuple[
-    List[Message],
-    Optional[List[dict]],
-    Optional[types.SchemaUnion],
-    Optional[Dict],
-]:
-  """Converts an LlmRequest to litellm inputs and extracts generation params.
+) -> tuple[Iterable[Message], Iterable[dict]]:
+  """Converts an LlmRequest to litellm inputs.
 
   Args:
     llm_request: The LlmRequest to convert.
 
   Returns:
-    The litellm inputs (message list, tool dictionary, response format and generation params).
+    The litellm inputs (message list, tool dictionary and response format).
   """
-  # 1. Construct messages
-  messages: List[Message] = []
+  messages = []
   for content in llm_request.contents or []:
     message_param_or_list = _content_to_message_param(content)
     if isinstance(message_param_or_list, list):
@@ -514,8 +507,7 @@ def _get_completion_inputs(
         ),
     )
 
-  # 2. Convert tool declarations
-  tools: Optional[List[Dict]] = None
+  tools = None
   if (
       llm_request.config
       and llm_request.config.tools
@@ -526,39 +518,12 @@ def _get_completion_inputs(
         for tool in llm_request.config.tools[0].function_declarations
     ]
 
-  # 3. Handle response format
-  response_format: Optional[types.SchemaUnion] = None
-  if llm_request.config and llm_request.config.response_schema:
-    response_format = llm_request.config.response_schema
-
-  # 4. Extract generation parameters
-  generation_params: Optional[Dict] = None
-  if llm_request.config:
-    config_dict = llm_request.config.model_dump(exclude_none=True)
-    # Generate LiteLlm parameters here,
-    # Following https://docs.litellm.ai/docs/completion/input.
-    generation_params = {}
-    param_mapping = {
-        "max_output_tokens": "max_completion_tokens",
-        "stop_sequences": "stop",
-    }
-    for key in (
-        "temperature",
-        "max_output_tokens",
-        "top_p",
-        "top_k",
-        "stop_sequences",
-        "presence_penalty",
-        "frequency_penalty",
-    ):
-      if key in config_dict:
-        mapped_key = param_mapping.get(key, key)
-        generation_params[mapped_key] = config_dict[key]
+  response_format = None
 
-      if not generation_params:
-        generation_params = None
+  if llm_request.config.response_schema:
+    response_format = llm_request.config.response_schema
 
-  return messages, tools, response_format, generation_params
+  return messages, tools, response_format
 
 
 def _build_function_declaration_log(
@@ -695,9 +660,7 @@ async def generate_content_async(
     self._maybe_append_user_content(llm_request)
     logger.debug(_build_request_log(llm_request))
 
-    messages, tools, response_format, generation_params = (
-        _get_completion_inputs(llm_request)
-    )
+    messages, tools, response_format = _get_completion_inputs(llm_request)
 
     completion_args = {
         "model": self.model,
@@ -707,9 +670,6 @@ async def generate_content_async(
     }
     completion_args.update(self._additional_args)
 
-    if generation_params:
-      completion_args.update(generation_params)
-
     if stream:
       text = ""
       # Track function calls by index

diff --git a/tests/unittests/models/test_litellm.py b/tests/unittests/models/test_litellm.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 
+import json
 from unittest.mock import AsyncMock
 from unittest.mock import Mock
 
@@ -1429,35 +1430,3 @@ async def test_generate_content_async_non_compliant_multiple_function_calls(
   assert final_response.content.parts[1].function_call.name == "function_2"
   assert final_response.content.parts[1].function_call.id == "1"
   assert final_response.content.parts[1].function_call.args == {"arg": "value2"}
-
-
-@pytest.mark.asyncio
-def test_get_completion_inputs_generation_params():
-  # Test that generation_params are extracted and mapped correctly
-  req = LlmRequest(
-      contents=[
-          types.Content(role="user", parts=[types.Part.from_text(text="hi")]),
-      ],
-      config=types.GenerateContentConfig(
-          temperature=0.33,
-          max_output_tokens=123,
-          top_p=0.88,
-          top_k=7,
-          stop_sequences=["foo", "bar"],
-          presence_penalty=0.1,
-          frequency_penalty=0.2,
-      ),
-  )
-  from google.adk.models.lite_llm import _get_completion_inputs
-
-  _, _, _, generation_params = _get_completion_inputs(req)
-  assert generation_params["temperature"] == 0.33
-  assert generation_params["max_completion_tokens"] == 123
-  assert generation_params["top_p"] == 0.88
-  assert generation_params["top_k"] == 7
-  assert generation_params["stop"] == ["foo", "bar"]
-  assert generation_params["presence_penalty"] == 0.1
-  assert generation_params["frequency_penalty"] == 0.2
-  # Should not include max_output_tokens
-  assert "max_output_tokens" not in generation_params
-  assert "stop_sequences" not in generation_params