Summary

• _EvalMetricResultWithInvocation.expected_invocation is typed as Invocation (required), but local_eval_service.py:285-287 intentionally sets it to None when eval_case.conversation is None (i.e., conversation_scenario user-simulation cases)
• The public model EvalMetricResultPerInvocation in eval_metrics.py:323 already types this field as Optional[Invocation] = None
• This mismatch causes a pydantic ValidationError during post-processing in _get_eval_metric_results_with_invocation, after all metrics have been computed

Changes

• Make expected_invocation Optional[Invocation] = None in _EvalMetricResultWithInvocation
• Guard the three attribute accesses in _print_details to handle None (fall back to actual_invocation.user_content for the prompt column, None for expected response/tool calls)
• Both _convert_content_to_text and _convert_tool_calls_to_text already accept Optional parameters

Testing Plan

Verified with a pytest-based evaluation using AgentEvaluator.evaluate() against an evalset containing conversation_scenario cases (LLM-backed user simulation, no explicit conversation arrays).

Before fix — crashes after ~33 minutes of metric computation during post-processing:

pydantic_core._pydantic_core.ValidationError: 1 validation error for _EvalMetricResultWithInvocation
expected_invocation
  Input should be a valid dictionary or instance of Invocation [type=model_type, input_value=None, input_type=NoneType]

.venv/lib/python3.11/site-packages/google/adk/evaluation/agent_evaluator.py:639: ValidationError

After fix — the ValidationError is eliminated. The None expected_invocation flows through correctly because:

1. The field now accepts Optional[Invocation], matching the upstream EvalMetricResultPerInvocation model
2. _print_details gracefully handles None by falling back to actual_invocation.user_content for the prompt column and passing None to _convert_content_to_text/_convert_tool_calls_to_text (both already accept Optional inputs)

Reproduction evalset (any evalset with conversation_scenario triggers this):

{
  "eval_set_id": "test",
  "eval_cases": [{
    "eval_id": "scenario_1",
    "conversation_scenario": {
      "starting_prompt": "Hello",
      "conversation_plan": "Ask the agent a question and accept the answer."
    },
    "session_input": {"app_name": "my_agent", "user_id": "user1", "state": {}}
  }]
}

@pytest.mark.asyncio
async def test_scenario():
    await AgentEvaluator.evaluate("my_agent", "path/to/evalset.json", num_runs=1)

Fixes #5214