> ## Documentation Index > Fetch the complete documentation index at: https://docs.agno.com/llms.txt > Use this file to discover all available pages before exploring further. # AgentAsJudgeEval > AgentAsJudgeEval parameters, binary and numeric scoring, and AgentAsJudgeResult fields. `AgentAsJudgeEval` grades an input/output pair against custom criteria with an LLM judge. Score recorded outputs directly with `run()`, or attach the eval as a `post_hook` to grade live runs. ```python theme={null} from agno.eval import AgentAsJudgeEval, AgentAsJudgeResult ``` ## Parameters | Parameter | Type | Default | Description | | --------------------------- | ---------------------------------------------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `criteria` | `str` | `""` | Criteria the judge grades the output against. | | `scoring_strategy` | `Literal["numeric", "binary"]` | `"binary"` | How the judge grades. `"binary"` returns a pass/fail verdict. `"numeric"` returns a 1-10 score that passes when it meets `threshold`. | | `threshold` | `int` | `7` | Minimum passing score from 1 to 10. Only used when `scoring_strategy` is `"numeric"`. | | `on_fail` | `Optional[Callable[[AgentAsJudgeEvaluation], None]]` | `None` | Callback invoked with the evaluation when it fails. Async callbacks require `arun()`. | | `additional_guidelines` | `Optional[Union[str, List[str]]]` | `None` | Guidelines appended to the default judge's instructions. | | `name` | `Optional[str]` | `None` | Evaluation name. | | `model` | `Optional[Model]` | `None` | Model for the default judge agent. Falls back to OpenAI `gpt-5-mini` when unset. | | `evaluator_agent` | `Optional[Agent]` | `None` | Custom judge agent. Its `output_schema` is replaced with the response schema matching `scoring_strategy`. | | `print_summary` | `bool` | `False` | Print the summary table with pass rate and score statistics after the run. | | `print_results` | `bool` | `False` | Print the per-evaluation results after the run. | | `show_spinner` | `bool` | `True` | Render the progress spinner. Set to `False` when the eval runs inside code that must not write to the console. | | `file_path_to_save_results` | `Optional[str]` | `None` | File path where the result is saved. | | `debug_mode` | `bool` | `False` | Enable debug logs. Defaults to `True` when the `AGNO_DEBUG` environment variable is `"true"`. | | `db` | `Optional[Union[BaseDb, AsyncBaseDb]]` | `None` | Database where the eval run is stored. | | `telemetry` | `bool` | `True` | Log minimal telemetry for the eval run. | | `run_in_background` | `bool` | `False` | Run the eval in the background when attached as a `post_hook`, without blocking the agent's response. Takes effect only when the run is served through AgentOS, which supplies the background task queue; direct `run()` and `arun()` calls on the agent execute the eval inline. | ## Methods ### `run()` and `arun()` Both accept the same keyword arguments and return `Optional[AgentAsJudgeResult]`. Provide either `input` and `output` for a single evaluation, or `cases` for batch evaluation. `run()` raises with an async database; use `arun()` instead. | Argument | Type | Default | Description | | --------------- | -------------------------------- | ------- | ------------------------------------------------------------------ | | `input` | `Optional[str]` | `None` | Input text for a single evaluation. | | `output` | `Optional[str]` | `None` | Output text for a single evaluation. | | `cases` | `Optional[List[Dict[str, str]]]` | `None` | Batch of dicts with `input` and `output` keys, evaluated in order. | | `print_summary` | `bool` | `False` | Print the summary table after the run. | | `print_results` | `bool` | `False` | Print the per-evaluation results after the run. | ### Hook methods `AgentAsJudgeEval` extends `BaseEval`, so an instance can be passed directly to an Agent's or Team's `post_hooks`. The `post_check()` and `async_post_check()` methods grade the run's input and output and log the result to `db` with the agent or team ID attached. Pre-hooks are not supported and raise `ValueError`. ## AgentAsJudgeResult | Field | Type | Description | | --------------- | ------------------------------ | ----------------------------------------------------- | | `run_id` | `str` | Unique ID for this evaluation run. | | `results` | `List[AgentAsJudgeEvaluation]` | One entry per evaluated input/output pair. | | `avg_score` | `Optional[float]` | Average score. `None` in binary mode. | | `min_score` | `Optional[float]` | Lowest score. `None` in binary mode. | | `max_score` | `Optional[float]` | Highest score. `None` in binary mode. | | `std_dev_score` | `Optional[float]` | Standard deviation of scores. `None` in binary mode. | | `pass_rate` | `float` | Percentage of evaluations that passed, from 0 to 100. | `AgentAsJudgeResult` also exposes `print_summary(console=None)` and `print_results(console=None)`. ## AgentAsJudgeEvaluation Each entry in `AgentAsJudgeResult.results` is an `AgentAsJudgeEvaluation`: | Field | Type | Description | | ---------- | --------------- | --------------------------------------------------------------------------------- | | `input` | `str` | Input text that was evaluated. | | `output` | `str` | Output text that was evaluated. | | `criteria` | `str` | Criteria the output was graded against. | | `score` | `Optional[int]` | Score from 1 to 10 in numeric mode. `None` in binary mode. | | `reason` | `str` | The judge's reasoning for the verdict. | | `passed` | `bool` | Whether the evaluation passed. In numeric mode, `True` when `score >= threshold`. |