fix: agent generate config error (#1450)

src/google/adk/models/lite_llm.py

@@ -23,6 +23,7 @@ from typing import cast
 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
@@ -481,16 +482,22 @@ def _message_to_generate_content_response(
 
 def _get_completion_inputs(
     llm_request: LlmRequest,
-) -> tuple[Iterable[Message], Iterable[dict]]:
-  """Converts an LlmRequest to litellm inputs.
+) -> Tuple[
+    List[Message],
+    Optional[List[dict]],
+    Optional[types.SchemaUnion],
+    Optional[Dict],
+]:
+  """Converts an LlmRequest to litellm inputs and extracts generation params.
 
   Args:
     llm_request: The LlmRequest to convert.
 
   Returns:
-    The litellm inputs (message list, tool dictionary and response format).
+    The litellm inputs (message list, tool dictionary, response format and generation params).
   """
-  messages = []
+  # 1. Construct messages
+  messages: List[Message] = []
   for content in llm_request.contents or []:
     message_param_or_list = _content_to_message_param(content)
     if isinstance(message_param_or_list, list):
@@ -507,7 +514,8 @@ def _get_completion_inputs(
         ),
     )
 
-  tools = None
+  # 2. Convert tool declarations
+  tools: Optional[List[Dict]] = None
   if (
       llm_request.config
       and llm_request.config.tools
@@ -518,12 +526,39 @@ def _get_completion_inputs(
         for tool in llm_request.config.tools[0].function_declarations
     ]
 
-  response_format = None
-
-  if llm_request.config.response_schema:
+  # 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
 
-  return messages, tools, response_format
+  # 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]
+
+      if not generation_params:
+        generation_params = None
+
+  return messages, tools, response_format, generation_params
 
 
 def _build_function_declaration_log(
@@ -660,7 +695,9 @@ class LiteLlm(BaseLlm):
     self._maybe_append_user_content(llm_request)
     logger.debug(_build_request_log(llm_request))
 
-    messages, tools, response_format = _get_completion_inputs(llm_request)
+    messages, tools, response_format, generation_params = (
+        _get_completion_inputs(llm_request)
+    )
 
     completion_args = {
         "model": self.model,
@@ -670,6 +707,9 @@ class LiteLlm(BaseLlm):
     }
     completion_args.update(self._additional_args)
 
+    if generation_params:
+      completion_args.update(generation_params)
+
     if stream:
       text = ""
       # Track function calls by index

tests/unittests/models/test_litellm.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 
-import json
 from unittest.mock import AsyncMock
 from unittest.mock import Mock
 
@@ -1430,3 +1429,35 @@ 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