Normalize integer output enums for Google ADK

Author: brianstrauch

CHANGELOG.md

@@ -69,6 +69,7 @@ to include examples, links to docs, or any other relevant information.
   the workflow sandbox.
 - `contrib.google_adk_agents`: agents with an `output_schema` no longer fail every workflow task
   when calling the model. The schema type is now sent to the model activity as its JSON schema.
+  Integer-valued output enums are normalized to strings to match Google GenAI.
 - `GoogleAdkPlugin` now passes OpenTelemetry modules through the workflow sandbox so ADK 2.9
   graph workflows can load their context support during execution.
 - `contrib.deepagents`: prevent duplicate input messages after continue-as-new.

temporalio/contrib/google_adk_agents/_model.py

@@ -1,6 +1,7 @@
 from collections.abc import AsyncGenerator, Callable
 from dataclasses import dataclass
 from datetime import timedelta
+from enum import Enum
 
 from google.adk.models import BaseLlm, LLMRegistry
 from google.adk.models.llm_request import LlmRequest
@@ -100,14 +101,23 @@ def _with_serializable_response_schema(llm_request: LlmRequest) -> LlmRequest:
     (for example a Pydantic model class), which the payload converter cannot
     serialize. google-genai and ADK's LiteLlm both turn such a type into its
     JSON schema before calling the model, so sending the JSON schema instead
-    is equivalent.
+    is equivalent. Integer-valued enums are normalized to string enums to
+    match google-genai's enum handling.
     """
     schema = llm_request.config.response_schema
     if schema is None or isinstance(schema, (dict, types.Schema)):
         return llm_request
+    response_schema = TypeAdapter(schema).json_schema()
+    if (
+        isinstance(schema, type)
+        and issubclass(schema, Enum)
+        and any(isinstance(member.value, int) for member in schema)
+    ):
+        response_schema["type"] = "string"
+        response_schema["enum"] = [str(member.value) for member in schema]
     request = llm_request.model_copy()
     request.config = llm_request.config.model_copy(
-        update={"response_schema": TypeAdapter(schema).json_schema()}
+        update={"response_schema": response_schema}
     )
     return request
 

tests/contrib/google_adk_agents/test_google_adk_agents.py

@@ -22,6 +22,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import AsyncGenerator
 from datetime import timedelta
+from enum import Enum, IntEnum
 from typing import Any
 
 import pytest
@@ -1179,6 +1180,26 @@ class CityWeather(BaseModel):
     temperature_c: float
 
 
+class NumericChoice(IntEnum):
+    FIRST = 10
+    SECOND = 20
+
+
+class IntegerChoice(Enum):
+    FIRST = 1
+    SECOND = 2
+
+
+class MixedChoice(Enum):
+    FIRST = 1
+    SECOND = "other"
+
+
+class StringChoice(str, Enum):
+    FIRST = "first"
+    SECOND = "second"
+
+
 class OutputSchemaModel(TestModel):
     def responses(self) -> list[LlmResponse]:
         return [
@@ -1250,7 +1271,7 @@ async def test_agent_with_output_schema(client: Client):
     assert result == {"city": "Paris", "temperature_c": 17.5}
 
 
-@pytest.mark.parametrize("schema", [CityWeather, list[CityWeather]])
+@pytest.mark.parametrize("schema", [CityWeather, list[CityWeather], StringChoice])
 def test_output_schema_type_sent_as_json_schema(schema: Any) -> None:
     request = LlmRequest(
         model="gemini-2.0-flash",
@@ -1269,6 +1290,34 @@ def test_output_schema_type_sent_as_json_schema(schema: Any) -> None:
     assert serialized["config"]["response_schema"] == TypeAdapter(schema).json_schema()
 
 
+@pytest.mark.parametrize(
+    ("schema", "expected_values"),
+    [
+        (NumericChoice, ["10", "20"]),
+        (IntegerChoice, ["1", "2"]),
+        (MixedChoice, ["1", "other"]),
+    ],
+)
+def test_output_schema_integer_enum_is_serializable(
+    schema: type[Enum], expected_values: list[str]
+) -> None:
+    request = LlmRequest(
+        model="gemini-2.0-flash",
+        config=types.GenerateContentConfig(),
+    )
+    request.set_output_schema(schema)
+
+    converted = _with_serializable_response_schema(request)
+
+    assert request.config.response_schema is schema
+    converter = GoogleAdkPlugin()._configure_data_converter(None).payload_converter
+    payloads = converter.to_payloads([converted])
+    restored = converter.from_payloads(payloads, [LlmRequest])[0]
+    response_schema = types.Schema.model_validate(restored.config.response_schema)
+    assert response_schema.type == types.Type.STRING
+    assert response_schema.enum == expected_values
+
+
 def test_json_output_schema_left_unchanged() -> None:
     request = LlmRequest(
         model="gemini-2.0-flash",