> ## 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.

# AccuracyEval

> AccuracyEval parameters, run methods, and AccuracyResult fields.

`AccuracyEval` runs an agent or team against an input, then scores the response against an expected output with an evaluator agent on a 1-10 scale.

```python theme={null}
from agno.eval import AccuracyEval, AccuracyResult
```

## Parameters

| Parameter                   | Type                                   | Default            | Description                                                                                                    |
| --------------------------- | -------------------------------------- | ------------------ | -------------------------------------------------------------------------------------------------------------- |
| `input`                     | `Union[str, Callable]`                 | Required           | Input sent to the agent or team. A callable must return the input string.                                      |
| `expected_output`           | `Union[str, Callable]`                 | Required           | Expected answer the evaluator scores against. A callable must return the expected string.                      |
| `agent`                     | `Optional[Agent]`                      | `None`             | Agent to evaluate. Provide exactly one of `agent` or `team`.                                                   |
| `team`                      | `Optional[Team]`                       | `None`             | Team to evaluate. Provide exactly one of `agent` or `team`.                                                    |
| `name`                      | `Optional[str]`                        | `None`             | Evaluation name.                                                                                               |
| `eval_id`                   | `str`                                  | Autogenerated UUID | Evaluation ID.                                                                                                 |
| `num_iterations`            | `int`                                  | `1`                | Number of times the input is run and scored.                                                                   |
| `model`                     | `Optional[Model]`                      | `None`             | Model for the default evaluator agent. Falls back to OpenAI `o4-mini` when unset.                              |
| `evaluator_agent`           | `Optional[Agent]`                      | `None`             | Custom agent that scores the response. Replaces the default evaluator.                                         |
| `additional_guidelines`     | `Optional[Union[str, List[str]]]`      | `None`             | Guidelines appended to the default evaluator's instructions.                                                   |
| `additional_context`        | `Optional[str]`                        | `None`             | Context appended to the default evaluator's instructions.                                                      |
| `print_summary`             | `bool`                                 | `False`            | Print the summary table with score statistics after the run.                                                   |
| `print_results`             | `bool`                                 | `False`            | Print the per-iteration results table 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.                                                                        |
| `result`                    | `Optional[AccuracyResult]`             | `None`             | Result of the evaluation. Set after a run completes.                                                           |

## Methods

| Method                                                                | Return type                | Description                                                                                                                |
| --------------------------------------------------------------------- | -------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `run(*, print_summary=True, print_results=True)`                      | `Optional[AccuracyResult]` | Run the agent or team `num_iterations` times and score each response. Raises with an async database; use `arun()` instead. |
| `arun(*, print_summary=True, print_results=True)`                     | `Optional[AccuracyResult]` | Async version of `run()`.                                                                                                  |
| `run_with_output(*, output, print_summary=True, print_results=True)`  | `Optional[AccuracyResult]` | Score the given `output` string instead of running the agent or team.                                                      |
| `arun_with_output(*, output, print_summary=True, print_results=True)` | `Optional[AccuracyResult]` | Async version of `run_with_output()`.                                                                                      |

## AccuracyResult

| Field           | Type                       | Description                                                        |
| --------------- | -------------------------- | ------------------------------------------------------------------ |
| `results`       | `List[AccuracyEvaluation]` | One entry per scored iteration.                                    |
| `avg_score`     | `Optional[float]`          | Average score across iterations. `None` when there are no results. |
| `mean_score`    | `Optional[float]`          | Mean score across iterations. Same value as `avg_score`.           |
| `min_score`     | `Optional[float]`          | Lowest score across iterations.                                    |
| `max_score`     | `Optional[float]`          | Highest score across iterations.                                   |
| `std_dev_score` | `Optional[float]`          | Standard deviation of scores. `0` with a single result.            |

`AccuracyResult` also exposes `print_summary(console=None)` and `print_results(console=None)` to render the statistics and per-iteration tables.

## AccuracyEvaluation

Each entry in `AccuracyResult.results` is an `AccuracyEvaluation`:

| Field             | Type  | Description                                      |
| ----------------- | ----- | ------------------------------------------------ |
| `input`           | `str` | Input sent to the agent or team.                 |
| `output`          | `str` | Response produced by the agent or team.          |
| `expected_output` | `str` | Expected answer the response was scored against. |
| `score`           | `int` | Accuracy score from 1 to 10.                     |
| `reason`          | `str` | The evaluator's reasoning for the score.         |