fix: handle additionalProperties for openai models

contributing/samples/gepa/experiment.py

@@ -43,7 +43,6 @@
 from tau_bench.types import EnvRunResult
 from tau_bench.types import RunConfig
 import tau_bench_agent as tau_bench_agent_lib
-
 import utils
 
 

contributing/samples/gepa/run_experiment.py

@@ -25,7 +25,6 @@
 from absl import flags
 import experiment
 from google.genai import types
-
 import utils
 
 _OUTPUT_DIR = flags.DEFINE_string(

src/google/adk/models/lite_llm.py

@@ -1023,12 +1023,15 @@ def _model_response_to_chunk(
           if not func_name and not func_args:
             continue
 
-          yield FunctionChunk(
-              id=tool_call.id,
-              name=func_name,
-              args=func_args,
-              index=func_index,
-          ), finish_reason
+          yield (
+              FunctionChunk(
+                  id=tool_call.id,
+                  name=func_name,
+                  args=func_args,
+                  index=func_index,
+              ),
+              finish_reason,
+          )
 
     if finish_reason and not (message_content or tool_calls):
       yield None, finish_reason
@@ -1040,12 +1043,17 @@ def _model_response_to_chunk(
   # finish_reason set. But this is not the case we are observing from litellm.
   # So we are sending it as a separate chunk to be set on the llm_response.
   if response.get("usage", None):
-    yield UsageMetadataChunk(
-        prompt_tokens=response["usage"].get("prompt_tokens", 0),
-        completion_tokens=response["usage"].get("completion_tokens", 0),
-        total_tokens=response["usage"].get("total_tokens", 0),
-        cached_prompt_tokens=_extract_cached_prompt_tokens(response["usage"]),
-    ), None
+    yield (
+        UsageMetadataChunk(
+            prompt_tokens=response["usage"].get("prompt_tokens", 0),
+            completion_tokens=response["usage"].get("completion_tokens", 0),
+            total_tokens=response["usage"].get("total_tokens", 0),
+            cached_prompt_tokens=_extract_cached_prompt_tokens(
+                response["usage"]
+            ),
+        ),
+        None,
+    )
 
 
 def _model_response_to_generate_content_response(
@@ -1146,6 +1154,24 @@ def _message_to_generate_content_response(
   )
 
 
+def _enforce_closed_schema(schema: dict):
+  if not isinstance(schema, dict):
+    return
+
+  if schema.get("type") == "object":
+    schema.setdefault("additionalProperties", False)
+
+    for prop in schema.get("properties", {}).values():
+      _enforce_closed_schema(prop)
+
+  if "items" in schema:
+    _enforce_closed_schema(schema["items"])
+
+  if "$defs" in schema:
+    for def_schema in schema["$defs"].values():
+      _enforce_closed_schema(def_schema)
+
+
 def _to_litellm_response_format(
     response_schema: types.SchemaUnion,
     model: str,
@@ -1206,14 +1232,9 @@ def _to_litellm_response_format(
 
   # OpenAI-compatible format (default) per LiteLLM docs:
   # https://docs.litellm.ai/docs/completion/json_mode
-  if (
-      isinstance(schema_dict, dict)
-      and schema_dict.get("type") == "object"
-      and "additionalProperties" not in schema_dict
-  ):
+  if isinstance(schema_dict, dict):
     # OpenAI structured outputs require explicit additionalProperties: false.
-    schema_dict = dict(schema_dict)
-    schema_dict["additionalProperties"] = False
+    _enforce_closed_schema(schema_dict)
 
   return {
       "type": "json_schema",
@@ -1433,7 +1454,12 @@ def _warn_gemini_via_litellm(model_string: str) -> None:
   # Check if warning should be suppressed via environment variable
   if os.environ.get(
       "ADK_SUPPRESS_GEMINI_LITELLM_WARNINGS", ""
-  ).strip().lower() in ("1", "true", "yes", "on"):
+  ).strip().lower() in (
+      "1",
+      "true",
+      "yes",
+      "on",
+  ):
     return
 
   warnings.warn(
@@ -1541,9 +1567,12 @@ async def generate_content_async(
     logger.debug(_build_request_log(llm_request))
 
     effective_model = llm_request.model or self.model
-    messages, tools, response_format, generation_params = (
-        await _get_completion_inputs(llm_request, effective_model)
-    )
+    (
+        messages,
+        tools,
+        response_format,
+        generation_params,
+    ) = await _get_completion_inputs(llm_request, effective_model)
     normalized_messages = _normalize_ollama_chat_messages(
         messages,
         model=effective_model,