[pull] main from google:main

contributing/samples/bigquery/agent.py

@@ -71,7 +71,7 @@
 # The variable name `root_agent` determines what your root agent is for the
 # debug CLI
 root_agent = LlmAgent(
-    model="gemini-2.0-flash",
+    model="gemini-2.5-flash",
     name=BIGQUERY_AGENT_NAME,
     description=(
         "Agent to answer questions about BigQuery data and models and execute"

src/google/adk/cli/cli_tools_click.py

@@ -36,7 +36,6 @@
 from . import cli_deploy
 from .. import version
 from ..evaluation.constants import MISSING_EVAL_DEPENDENCIES_MESSAGE
-from ..sessions.migration import migration_runner
 from .cli import run_cli
 from .fast_api import get_fast_api_app
 from .utils import envs
@@ -1589,6 +1588,8 @@ def cli_migrate_session(
   """Migrates a session database to the latest schema version."""
   logs.setup_adk_logger(getattr(logging, log_level.upper()))
   try:
+    from ..sessions.migration import migration_runner
+
     migration_runner.upgrade(source_db_url, dest_db_url)
     click.secho("Migration check and upgrade process finished.", fg="green")
   except Exception as e:

src/google/adk/evaluation/eval_config.py

@@ -23,7 +23,9 @@
 from pydantic import BaseModel
 from pydantic import ConfigDict
 from pydantic import Field
+from pydantic import model_validator
 
+from ..agents.common_configs import CodeConfig
 from ..evaluation.eval_metrics import EvalMetric
 from .eval_metrics import BaseCriterion
 from .eval_metrics import Threshold
@@ -72,11 +74,46 @@ class EvalConfig(BaseModel):
 """,
   )
 
+  custom_metrics: Optional[dict[str, CodeConfig]] = Field(
+      default=None,
+      description="""A dictionary mapping custom metric names to CodeConfig
+objects, which specify the path to the function for each custom metric.
+
+If a metric name in `criteria` is also present in `custom_metrics`, the
+corresponding `CodeConfig`'s `name` field will be used to locate the custom
+metric implementation. The `name` field should contain the fully qualified
+path to the custom metric function, e.g., `my.custom.metrics.metric_function`.
+
+Example:
+{
+  "criteria": {
+    "my_custom_metric": 0.5
+  },
+  "custom_metrics": {
+    "my_custom_metric": {
+      "name": "path.to.my.custom.metric.function"
+    }
+  }
+}
+""",
+  )
+
   user_simulator_config: Optional[BaseUserSimulatorConfig] = Field(
       default=None,
       description="Config to be used by the user simulator.",
   )
 
+  @model_validator(mode="after")
+  def check_custom_metrics_code_config_args(self) -> "EvalConfig":
+    if self.custom_metrics:
+      for metric_name, metric_config in self.custom_metrics.items():
+        if metric_config.args:
+          raise ValueError(
+              f"args field in CodeConfig for custom metric '{metric_name}' is"
+              " not supported."
+          )
+    return self
+
 
 _DEFAULT_EVAL_CONFIG = EvalConfig(
     criteria={"tool_trajectory_avg_score": 1.0, "response_match_score": 0.8}
@@ -106,12 +143,20 @@ def get_eval_metrics_from_config(eval_config: EvalConfig) -> list[EvalMetric]:
   eval_metric_list = []
   if eval_config.criteria:
     for metric_name, criterion in eval_config.criteria.items():
+      custom_function_path = None
+      if (
+          eval_config.custom_metrics
+          and metric_name in eval_config.custom_metrics
+      ):
+        custom_function_path = eval_config.custom_metrics[metric_name].name
+
       if isinstance(criterion, float):
         eval_metric_list.append(
             EvalMetric(
                 metric_name=metric_name,
                 threshold=criterion,
                 criterion=BaseCriterion(threshold=criterion),
+                custom_function_path=custom_function_path,
             )
         )
       elif isinstance(criterion, BaseCriterion):
@@ -120,6 +165,7 @@ def get_eval_metrics_from_config(eval_config: EvalConfig) -> list[EvalMetric]:
                 metric_name=metric_name,
                 threshold=criterion.threshold,
                 criterion=criterion,
+                custom_function_path=custom_function_path,
             )
         )
       else:

src/google/adk/evaluation/eval_metrics.py

@@ -279,6 +279,11 @@ class EvalMetric(EvalBaseModel):
       default=None, description="""Evaluation criterion used by the metric."""
   )
 
+  custom_function_path: Optional[str] = Field(
+      default=None,
+      description="""Path to custom function, if this is a custom metric.""",
+  )
+
 
 class EvalMetricResultDetails(EvalBaseModel):
   rubric_scores: Optional[list[RubricScore]] = Field(

tests/unittests/evaluation/test_eval_config.py

@@ -20,6 +20,7 @@
 from google.adk.evaluation.eval_config import get_evaluation_criteria_or_default
 from google.adk.evaluation.eval_rubrics import Rubric
 from google.adk.evaluation.eval_rubrics import RubricContent
+import pytest
 
 
 def test_get_evaluation_criteria_or_default_returns_default():
@@ -99,6 +100,42 @@ def test_get_eval_metrics_from_config():
   assert eval_metrics[3].criterion.rubrics[0] == rubric_1
 
 
+def test_get_eval_metrics_from_config_with_custom_metrics():
+  eval_config = EvalConfig(
+      criteria={
+          "custom_metric_1": 1.0,
+          "custom_metric_2": {
+              "threshold": 0.5,
+          },
+      },
+      custom_metrics={
+          "custom_metric_1": {"name": "path/to/custom/metric_1"},
+          "custom_metric_2": {"name": "path/to/custom/metric_2"},
+      },
+  )
+  eval_metrics = get_eval_metrics_from_config(eval_config)
+
+  assert len(eval_metrics) == 2
+  assert eval_metrics[0].metric_name == "custom_metric_1"
+  assert eval_metrics[0].threshold == 1.0
+  assert eval_metrics[0].criterion.threshold == 1.0
+  assert eval_metrics[0].custom_function_path == "path/to/custom/metric_1"
+  assert eval_metrics[1].metric_name == "custom_metric_2"
+  assert eval_metrics[1].threshold == 0.5
+  assert eval_metrics[1].criterion.threshold == 0.5
+  assert eval_metrics[1].custom_function_path == "path/to/custom/metric_2"
+
+
+def test_custom_metric_code_config_with_args_raises_error():
+  with pytest.raises(ValueError):
+    eval_config = EvalConfig(
+        criteria={"custom_metric": 1.0},
+        custom_metrics={
+            "custom_metric": {"name": "name", "args": [{"value": 1}]}
+        },
+    )
+
+
 def test_get_eval_metrics_from_config_empty_criteria():
   eval_config = EvalConfig(criteria={})
   eval_metrics = get_eval_metrics_from_config(eval_config)