feat: Add implementation of BaseEvalService that runs evals locally

This change:
- Introduces the LocalEvalService Class.
- Implements only the "perform_inference" method. Evaluate method will be implemented in the next CL.
- Adds required test coverage.

PiperOrigin-RevId: 771151722

Author: ankursharmas

src/google/adk/evaluation/agent_creator.py

@@ -0,0 +1,35 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing_extensions import override
+
+from ..agents import BaseAgent
+
+
+class IdentityAgentCreator:
+  """An implementation of the AgentCreator interface that always returns a copy of the root agent."""
+
+  def __init__(self, root_agent: BaseAgent):
+    self._root_agent = root_agent
+
+  @override
+  def get_agent(
+      self,
+  ) -> BaseAgent:
+    """Returns a deep copy of the root agent."""
+    # TODO: Use Agent.clone() when the PR is merged.
+    # return self._root_agent.model_copy(deep=True)
+    return self._root_agent.clone()

src/google/adk/evaluation/base_eval_service.py

@@ -16,6 +16,7 @@
 
 from abc import ABC
 from abc import abstractmethod
+from enum import Enum
 from typing import AsyncGenerator
 from typing import Optional
 
@@ -56,6 +57,19 @@ class InferenceConfig(BaseModel):
 charges.""",
   )
 
+  parallelism: int = Field(
+      default=4,
+      description="""Number of parallel inferences to run during an Eval. Few
+factors to consider while changing this value:
+
+1) Your available quota with the model. Models tend to enforce per-minute or
+per-second SLAs. Using a larger value could result in the eval quickly consuming
+the quota.
+
+2) The tools used by the Agent could also have their SLA. Using a larger value
+could also overwhelm those tools.""",
+  )
+
 
 class InferenceRequest(BaseModel):
   """Represent a request to perform inferences for the eval cases in an eval set."""
@@ -88,6 +102,14 @@ class InferenceRequest(BaseModel):
   )
 
 
+class InferenceStatus(Enum):
+  """Status of the inference."""
+
+  UNKNOWN = 0
+  SUCCESS = 1
+  FAILURE = 2
+
+
 class InferenceResult(BaseModel):
   """Contains inference results for a single eval case."""
 
@@ -106,14 +128,25 @@ class InferenceResult(BaseModel):
       description="""Id of the eval case for which inferences were generated.""",
   )
 
-  inferences: list[Invocation] = Field(
-      description="""Inferences obtained from the Agent for the eval case."""
+  inferences: Optional[list[Invocation]] = Field(
+      default=None,
+      description="""Inferences obtained from the Agent for the eval case.""",
   )
 
   session_id: Optional[str] = Field(
       description="""Id of the inference session."""
   )
 
+  status: InferenceStatus = Field(
+      default=InferenceStatus.UNKNOWN,
+      description="""Status of the inference.""",
+  )
+
+  error_message: Optional[str] = Field(
+      default=None,
+      description="""Error message if the inference failed.""",
+  )
+
 
 class EvaluateRequest(BaseModel):
   model_config = ConfigDict(

src/google/adk/evaluation/evaluation_generator.py

@@ -137,7 +137,7 @@ async def _process_query(
   async def _generate_inferences_from_root_agent(
       invocations: list[Invocation],
       root_agent: Agent,
-      reset_func: Any,
+      reset_func: Optional[Any] = None,
       initial_session: Optional[SessionInput] = None,
       session_id: Optional[str] = None,
       session_service: Optional[BaseSessionService] = None,

src/google/adk/evaluation/local_eval_service.py

@@ -0,0 +1,183 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import AsyncGenerator
+from typing import Callable
+from typing import Optional
+import uuid
+
+from typing_extensions import override
+
+from ..agents import BaseAgent
+from ..artifacts.base_artifact_service import BaseArtifactService
+from ..artifacts.in_memory_artifact_service import InMemoryArtifactService
+from ..errors.not_found_error import NotFoundError
+from ..sessions.base_session_service import BaseSessionService
+from ..sessions.in_memory_session_service import InMemorySessionService
+from ..utils.feature_decorator import working_in_progress
+from .base_eval_service import BaseEvalService
+from .base_eval_service import EvaluateRequest
+from .base_eval_service import InferenceRequest
+from .base_eval_service import InferenceResult
+from .base_eval_service import InferenceStatus
+from .eval_result import EvalCaseResult
+from .eval_set import EvalCase
+from .eval_set_results_manager import EvalSetResultsManager
+from .eval_sets_manager import EvalSetsManager
+from .evaluation_generator import EvaluationGenerator
+from .metric_evaluator_registry import DEFAULT_METRIC_EVALUATOR_REGISTRY
+from .metric_evaluator_registry import MetricEvaluatorRegistry
+
+logger = logging.getLogger('google_adk.' + __name__)
+
+EVAL_SESSION_ID_PREFIX = '___eval___session___'
+
+
+def _get_session_id() -> str:
+  return f'{EVAL_SESSION_ID_PREFIX}{str(uuid.uuid4())}'
+
+
+@working_in_progress("Incomplete feature, don't use yet")
+class LocalEvalService(BaseEvalService):
+  """An implementation of BaseEvalService, that runs the evals locally."""
+
+  def __init__(
+      self,
+      root_agent: BaseAgent,
+      eval_sets_manager: EvalSetsManager,
+      metric_evaluator_registry: MetricEvaluatorRegistry = DEFAULT_METRIC_EVALUATOR_REGISTRY,
+      session_service: BaseSessionService = InMemorySessionService(),
+      artifact_service: BaseArtifactService = InMemoryArtifactService(),
+      eval_set_results_manager: Optional[EvalSetResultsManager] = None,
+      session_id_supplier: Callable[[], str] = _get_session_id,
+  ):
+    self._root_agent = root_agent
+    self._eval_sets_manager = eval_sets_manager
+    self._metric_evaluator_registry = metric_evaluator_registry
+    self._session_service = session_service
+    self._artifact_service = artifact_service
+    self._eval_set_results_manager = eval_set_results_manager
+    self._session_id_supplier = session_id_supplier
+
+  @override
+  async def perform_inference(
+      self,
+      inference_request: InferenceRequest,
+  ) -> AsyncGenerator[InferenceResult, None]:
+    """Returns InferenceResult obtained from the Agent as and when they are available.
+
+    Args:
+      inference_request: The request for generating inferences.
+    """
+    # Get the eval set from the storage.
+    eval_set = self._eval_sets_manager.get_eval_set(
+        app_name=inference_request.app_name,
+        eval_set_id=inference_request.eval_set_id,
+    )
+
+    if not eval_set:
+      raise NotFoundError(
+          f'Eval set with id {inference_request.eval_set_id} not found for app'
+          f' {inference_request.app_name}'
+      )
+
+    # Select eval cases for which we need to run inferencing. If the inference
+    # request specified eval cases, then we use only those.
+    eval_cases = eval_set.eval_cases
+    if inference_request.eval_case_ids:
+      eval_cases = [
+          eval_case
+          for eval_case in eval_cases
+          if eval_case.eval_id in inference_request.eval_case_ids
+      ]
+
+    root_agent = self._root_agent.clone()
+
+    semaphore = asyncio.Semaphore(
+        value=inference_request.inference_config.parallelism
+    )
+
+    async def run_inference(eval_case):
+      async with semaphore:
+        return await self._perform_inference_sigle_eval_item(
+            app_name=inference_request.app_name,
+            eval_set_id=inference_request.eval_set_id,
+            eval_case=eval_case,
+            root_agent=root_agent,
+        )
+
+    inference_results = [run_inference(eval_case) for eval_case in eval_cases]
+    for inference_result in asyncio.as_completed(inference_results):
+      yield await inference_result
+
+  @override
+  async def evaluate(
+      self,
+      evaluate_request: EvaluateRequest,
+  ) -> AsyncGenerator[EvalCaseResult, None]:
+    """Returns EvalCaseResult for each item as and when they are available.
+
+    Args:
+      evaluate_request: The request to perform metric evaluations on the
+        inferences.
+    """
+    raise NotImplementedError()
+
+  async def _perform_inference_sigle_eval_item(
+      self,
+      app_name: str,
+      eval_set_id: str,
+      eval_case: EvalCase,
+      root_agent: BaseAgent,
+  ) -> InferenceResult:
+    initial_session = eval_case.session_input
+    session_id = self._session_id_supplier()
+    inference_result = InferenceResult(
+        app_name=app_name,
+        eval_set_id=eval_set_id,
+        eval_case_id=eval_case.eval_id,
+        session_id=session_id,
+    )
+
+    try:
+      inferences = (
+          await EvaluationGenerator._generate_inferences_from_root_agent(
+              invocations=eval_case.conversation,
+              root_agent=root_agent,
+              initial_session=initial_session,
+              session_id=session_id,
+              session_service=self._session_service,
+              artifact_service=self._artifact_service,
+          )
+      )
+
+      inference_result.inferences = inferences
+      inference_result.status = InferenceStatus.SUCCESS
+
+      return inference_result
+    except Exception as e:
+      # We intentionally catch the Exception as we don't failures to affect
+      # other inferences.
+      logger.error(
+          'Inference failed for eval case `%s` with error %s',
+          eval_case.eval_id,
+          e,
+      )
+      inference_result.status = InferenceStatus.FAILURE
+      inference_result.error_message = str(e)
+      return inference_result