fix(eval): make _EvalMetricResultWithInvocation.expected_invocation optional

conversation_scenario eval cases intentionally pass expected_invocation=None
from local_eval_service (matching the public EvalMetricResultPerInvocation
model), but the private _EvalMetricResultWithInvocation required a non-None
Invocation, causing a pydantic ValidationError.

Changes:
- Make expected_invocation Optional[Invocation] with default None
- Guard attribute access in _print_details when expected_invocation is None

Fixes #5214

Author: giulio-leone

src/google/adk/evaluation/agent_evaluator.py

@@ -90,7 +90,7 @@ class _EvalMetricResultWithInvocation(BaseModel):
   """
 
   actual_invocation: Invocation
-  expected_invocation: Invocation
+  expected_invocation: Optional[Invocation] = None
   eval_metric_result: EvalMetricResult
 
 
@@ -438,15 +438,21 @@ def _print_details(
           "threshold": threshold,
           "prompt": AgentEvaluator._convert_content_to_text(
               per_invocation_result.expected_invocation.user_content
+              if per_invocation_result.expected_invocation
+              else None
           ),
           "expected_response": AgentEvaluator._convert_content_to_text(
               per_invocation_result.expected_invocation.final_response
+              if per_invocation_result.expected_invocation
+              else None
           ),
           "actual_response": AgentEvaluator._convert_content_to_text(
               per_invocation_result.actual_invocation.final_response
           ),
           "expected_tool_calls": AgentEvaluator._convert_tool_calls_to_text(
               per_invocation_result.expected_invocation.intermediate_data
+              if per_invocation_result.expected_invocation
+              else None
           ),
           "actual_tool_calls": AgentEvaluator._convert_tool_calls_to_text(
               per_invocation_result.actual_invocation.intermediate_data

tests/unittests/evaluation/test_eval_metric_result_with_invocation.py

@@ -0,0 +1,167 @@
+# Copyright 2026 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.
+
+"""Regression tests for _EvalMetricResultWithInvocation None-handling.
+
+Covers the bug described in https://github.com/google/adk-python/issues/5214
+where passing expected_invocation=None (the normal path for
+conversation_scenario eval cases) caused a pydantic ValidationError.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import pytest
+
+from google.genai import types as genai_types
+
+from google.adk.evaluation.agent_evaluator import AgentEvaluator
+from google.adk.evaluation.agent_evaluator import (
+    _EvalMetricResultWithInvocation,
+)
+from google.adk.evaluation.eval_case import Invocation
+from google.adk.evaluation.eval_metrics import EvalMetricResult
+from google.adk.evaluation.eval_metrics import EvalMetricResultPerInvocation
+from google.adk.evaluation.eval_metrics import EvalStatus
+from google.adk.evaluation.eval_result import EvalCaseResult
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_invocation(**overrides) -> Invocation:
+    """Return a minimal Invocation instance."""
+    defaults = {
+        "user_content": genai_types.Content(
+            role="user", parts=[genai_types.Part(text="hello")]
+        ),
+    }
+    defaults.update(overrides)
+    return Invocation(**defaults)
+
+
+def _make_eval_metric_result(
+    metric_name: str = "test_metric",
+    score: float = 1.0,
+    status: EvalStatus = EvalStatus.PASSED,
+) -> EvalMetricResult:
+    return EvalMetricResult(
+        metric_name=metric_name,
+        score=score,
+        eval_status=status,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Tests: _EvalMetricResultWithInvocation accepts None
+# ---------------------------------------------------------------------------
+
+class TestEvalMetricResultWithInvocationNone:
+    """Regression: expected_invocation=None must be accepted (issue #5214)."""
+
+    def test_construction_with_none_expected_invocation(self):
+        """_EvalMetricResultWithInvocation should accept None for expected_invocation."""
+        result = _EvalMetricResultWithInvocation(
+            actual_invocation=_make_invocation(),
+            expected_invocation=None,
+            eval_metric_result=_make_eval_metric_result(),
+        )
+        assert result.expected_invocation is None
+
+    def test_construction_with_omitted_expected_invocation(self):
+        """expected_invocation should default to None when omitted."""
+        result = _EvalMetricResultWithInvocation(
+            actual_invocation=_make_invocation(),
+            eval_metric_result=_make_eval_metric_result(),
+        )
+        assert result.expected_invocation is None
+
+    def test_construction_with_real_expected_invocation(self):
+        """Normal case: providing a real Invocation should still work."""
+        inv = _make_invocation()
+        result = _EvalMetricResultWithInvocation(
+            actual_invocation=_make_invocation(),
+            expected_invocation=inv,
+            eval_metric_result=_make_eval_metric_result(),
+        )
+        assert result.expected_invocation is inv
+
+
+# ---------------------------------------------------------------------------
+# Tests: _get_eval_metric_results_with_invocation passes None through
+# ---------------------------------------------------------------------------
+
+class TestGetEvalMetricResultsWithNone:
+    """_get_eval_metric_results_with_invocation must propagate None."""
+
+    def test_none_expected_invocation_propagated(self):
+        actual = _make_invocation()
+        metric_result = _make_eval_metric_result(metric_name="m1")
+
+        eval_case_result = EvalCaseResult(
+            eval_set_id="test_set",
+            eval_id="scenario_1",
+            final_eval_status=EvalStatus.PASSED,
+            overall_eval_metric_results=[metric_result],
+            eval_metric_result_per_invocation=[
+                EvalMetricResultPerInvocation(
+                    actual_invocation=actual,
+                    expected_invocation=None,
+                    eval_metric_results=[metric_result],
+                )
+            ],
+            session_id="sess-1",
+        )
+
+        grouped = AgentEvaluator._get_eval_metric_results_with_invocation(
+            [eval_case_result]
+        )
+
+        assert "m1" in grouped
+        assert len(grouped["m1"]) == 1
+        assert grouped["m1"][0].expected_invocation is None
+        assert grouped["m1"][0].actual_invocation is actual
+
+
+# ---------------------------------------------------------------------------
+# Tests: _print_details does not crash when expected_invocation is None
+# ---------------------------------------------------------------------------
+
+class TestPrintDetailsNoneExpected:
+    """_print_details must handle None expected_invocation gracefully."""
+
+    def test_print_details_with_none_expected(self):
+        actual = _make_invocation()
+        metric_result = _make_eval_metric_result(score=0.9)
+
+        items = [
+            _EvalMetricResultWithInvocation(
+                actual_invocation=actual,
+                expected_invocation=None,
+                eval_metric_result=metric_result,
+            )
+        ]
+
+        # _print_details prints to stdout via tabulate/pandas — we just
+        # verify it doesn't raise.
+        with patch("builtins.print"):
+            AgentEvaluator._print_details(
+                eval_metric_result_with_invocations=items,
+                overall_eval_status=EvalStatus.PASSED,
+                overall_score=0.9,
+                metric_name="test_metric",
+                threshold=0.5,
+            )