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  * [ Overview  ](https://ai.pydantic.dev/evals/how-to/multi-run/#overview)
  * [ Basic Usage  ](https://ai.pydantic.dev/evals/how-to/multi-run/#basic-usage)
  * [ Accessing Grouped Results  ](https://ai.pydantic.dev/evals/how-to/multi-run/#accessing-grouped-results)
  * [ Aggregation  ](https://ai.pydantic.dev/evals/how-to/multi-run/#aggregation)
  * [ Default Behavior  ](https://ai.pydantic.dev/evals/how-to/multi-run/#default-behavior)
  * [ Next Steps  ](https://ai.pydantic.dev/evals/how-to/multi-run/#next-steps)


  1. [ Pydantic AI  ](https://ai.pydantic.dev/)
  2. [ Pydantic Evals  ](https://ai.pydantic.dev/evals/)
  3. [ How-To Guides  ](https://ai.pydantic.dev/evals/how-to/logfire-integration/)


# Multi-Run Evaluation
Run each case multiple times to measure variability and get more reliable aggregate results.
## Overview
AI systems are inherently stochastic — the same input can produce different outputs across runs. The `repeat` parameter lets you run each case multiple times and automatically aggregates the results, giving you a clearer picture of your system's typical behavior.
## Basic Usage
Pass `repeat` to [`evaluate()`](https://ai.pydantic.dev/api/pydantic_evals/dataset/#pydantic_evals.dataset.Dataset.evaluate "evaluate



      async
  ") or [`evaluate_sync()`](https://ai.pydantic.dev/api/pydantic_evals/dataset/#pydantic_evals.dataset.Dataset.evaluate_sync "evaluate_sync"):
```
from pydantic_evals import Case, Dataset

dataset = Dataset(
    cases=[
        Case(name='greeting', inputs='Say hello'),
        Case(name='farewell', inputs='Say goodbye'),
    ]
)


def task(inputs: str) -> str:
    return inputs.upper()


# Run each case 5 times
report = dataset.evaluate_sync(task, repeat=5)

# 2 cases × 5 repeats = 10 total runs
print(len(report.cases))
#> 10

```

When `repeat > 1`, each run gets an indexed name like `greeting [1/5]`, `greeting [2/5]`, etc., while the original case name is preserved in [`source_case_name`](https://ai.pydantic.dev/api/pydantic_evals/reporting/#pydantic_evals.reporting.ReportCase.source_case_name "source_case_name



      class-attribute
      instance-attribute
  ") for grouping.
## Accessing Grouped Results
Use [`case_groups()`](https://ai.pydantic.dev/api/pydantic_evals/reporting/#pydantic_evals.reporting.EvaluationReport.case_groups "case_groups") to access runs organized by original case, with per-group aggregated statistics:
```
from pydantic_evals import Case, Dataset

dataset = Dataset(
    cases=[
        Case(name='greeting', inputs='Say hello'),
        Case(name='farewell', inputs='Say goodbye'),
    ]
)


def task(inputs: str) -> str:
    return inputs.upper()


report = dataset.evaluate_sync(task, repeat=3)

groups = report.case_groups()
assert groups is not None  # None for single-run (repeat=1)

print(len(groups))
#> 2

group_names = [g.name for g in groups]
print(group_names)
#> ['greeting', 'farewell']

# Each group has 3 runs and aggregated statistics
for group in groups:
    assert len(group.runs) == 3
    assert len(group.failures) == 0
    assert group.summary.task_duration > 0

```

Each [`ReportCaseGroup`](https://ai.pydantic.dev/api/pydantic_evals/reporting/#pydantic_evals.reporting.ReportCaseGroup "ReportCaseGroup



      dataclass
  ") contains:
  * `name` — the original case name
  * `runs` — the individual [`ReportCase`](https://ai.pydantic.dev/api/pydantic_evals/reporting/#pydantic_evals.reporting.ReportCase "ReportCase



      dataclass
  ") results
  * `failures` — any runs that raised exceptions
  * `summary` — a [`ReportCaseAggregate`](https://ai.pydantic.dev/api/pydantic_evals/reporting/#pydantic_evals.reporting.ReportCaseAggregate "ReportCaseAggregate") with averaged scores, metrics, labels, assertions, and durations


## Aggregation
With `repeat > 1`, the report's [`averages()`][pydantic_evals.reporting.EvaluationReport.averages] uses a two-level aggregation strategy:
  1. **Per-group averages** : Each case's runs are averaged into a group summary
  2. **Cross-group averages** : The group summaries are averaged to produce the final result


This ensures each original case contributes equally to the overall averages, regardless of how many runs succeeded or failed.
```
from pydantic_evals import Case, Dataset
from pydantic_evals.evaluators import EqualsExpected

dataset = Dataset(
    cases=[
        Case(name='easy', inputs='hello', expected_output='HELLO'),
        Case(name='hard', inputs='world', expected_output='WORLD'),
    ],
    evaluators=[EqualsExpected()],
)


def task(inputs: str) -> str:
    return inputs.upper()


report = dataset.evaluate_sync(task, repeat=3)

averages = report.averages()
assert averages is not None
print(f'Overall assertion rate: {averages.assertions}')
#> Overall assertion rate: 1.0

```

## Default Behavior
When `repeat=1` (the default), behavior is identical to a standard evaluation — no run indexing, no `source_case_name`, and `case_groups()` returns `None`:
```
from pydantic_evals import Case, Dataset

dataset = Dataset(cases=[Case(name='test', inputs='hello')])


def task(inputs: str) -> str:
    return inputs.upper()


report = dataset.evaluate_sync(task)  # repeat=1 by default

assert report.case_groups() is None
assert all(c.source_case_name is None for c in report.cases)

```

## Next Steps
  * **[Concurrency& Performance](https://ai.pydantic.dev/evals/how-to/concurrency/)** — Control parallel execution with `max_concurrency`
  * **[Metrics& Attributes](https://ai.pydantic.dev/evals/how-to/metrics-attributes/)** — Track custom metrics across runs
  * **[Logfire Integration](https://ai.pydantic.dev/evals/how-to/logfire-integration/)** — Visualize multi-run results


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