Statistics

Two collection methods return aggregated statistics across your organization’s test runs and test results, for building dashboards or reports:

TestRuns.stats() — status distribution, result breakdown, timeline, top executors
TestResults.stats() — pass rates by metric, behavior, category, topic, and timeline

Test Run Statistics

Use TestRuns.stats() to get aggregated data across all test runs in your organization.

Available Modes

Mode	Description
`ALL`	Complete dataset with all sections
`SUMMARY`	Overall summary and metadata only
`STATUS`	Status distribution (Completed, Partial, Failed, etc.)
`RESULTS`	Test result pass/fail/pending breakdown
`TEST_SETS`	Most frequently run test sets
`EXECUTORS`	Top test executors
`TIMELINE`	Monthly trend data

Usage

test_run_stats.py
from rhesis.sdk.entities import TestRuns, TestRun
from rhesis.sdk.entities.stats import TestRunStatsMode

# Get all stats for the last 6 months
stats = TestRuns.stats(mode=TestRunStatsMode.ALL, months=6, top=5)

# Access typed results
print(f"Total runs: {stats.metadata.total_test_runs}")

for item in stats.status_distribution:
    print(f"  {item.status}: {item.count} ({item.percentage}%)")

print(f"Pass rate: {stats.result_distribution.pass_rate}%")

# Stats for a single test run
run = TestRun(id="your-run-id")
run_stats = run.stats()

Test Result Statistics

Use TestResults.stats() to get pass/fail breakdowns across test results, grouped by different dimensions.

Available Modes

Mode	Description
`ALL`	Complete dataset with all sections
`SUMMARY`	Overall pass rates and metadata only
`METRICS`	Pass rates per evaluation metric
`BEHAVIOR`	Pass rates per behavior
`CATEGORY`	Pass rates per category
`TOPIC`	Pass rates per topic
`OVERALL`	Aggregate pass/fail rates
`TIMELINE`	Monthly trend data
`TEST_RUNS`	Per-run summary breakdown

Usage

test_result_stats.py
from rhesis.sdk.entities import TestResults
from rhesis.sdk.entities.stats import TestResultStatsMode

# Get topic breakdown
stats = TestResults.stats(mode=TestResultStatsMode.TOPIC, months=6)

for topic, rates in stats.topic_pass_rates.items():
    print(f"  {topic}: {rates.passed}/{rates.total} ({rates.pass_rate}%)")

# Get everything at once
all_stats = TestResults.stats(mode=TestResultStatsMode.ALL)

# Access different dimensions from the same response
all_stats.topic_pass_rates
all_stats.behavior_pass_rates
all_stats.category_pass_rates
all_stats.metric_pass_rates
all_stats.overall_pass_rates
all_stats.timeline

Filtering

Both methods accept filter parameters to narrow results by time range, test sets, users, and more.

Parameter	Type	Description
`months`	`int`	Months of historical data (default: 6)
`top`	`int`	Max items in ranked lists
`start_date`	`str`	ISO date, overrides months
`end_date`	`str`	ISO date, overrides months
`test_set_ids`	`list`	Filter by test set IDs
`test_run_ids`	`list`	Filter by test run IDs
`user_ids`	`list`	Filter by user IDs

filtered_stats.py
from rhesis.sdk.entities import TestResults
from rhesis.sdk.entities.stats import TestResultStatsMode

# Filter by test set and time range
stats = TestResults.stats(
    mode=TestResultStatsMode.ALL,
    months=3,
    test_set_ids=["test-set-id-1", "test-set-id-2"],
)

# Filter by specific topics
topic_stats = TestResults.stats(
    mode=TestResultStatsMode.TOPIC,
    topic_ids=["topic-id"],
)

Pandas Integration

Both TestRunStats and TestResultStats provide a to_dataframe() method for converting results to pandas DataFrames. This is useful for charting and further analysis in notebooks.

pandas is an optional dependency. Install it with pip install pandas if you want to use to_dataframe().

stats_dataframe.py
from rhesis.sdk.entities import TestRuns, TestResults
from rhesis.sdk.entities.stats import TestRunStatsMode, TestResultStatsMode

# Test run timeline as a DataFrame
run_stats = TestRuns.stats(mode=TestRunStatsMode.ALL, months=6)
timeline_df = run_stats.to_dataframe("timeline")
print(timeline_df)
#       date  total_runs                      result_breakdown
# 0  2026-01          10  {'passed': 8, 'failed': 2, 'pending': 0}
# 1  2026-02          15  {'passed': 12, 'failed': 3, 'pending': 0}

# Topic pass rates as a DataFrame (dict sections use the key as index)
result_stats = TestResults.stats(mode=TestResultStatsMode.TOPIC)
topic_df = result_stats.to_dataframe("topic_pass_rates")
print(topic_df)
#            total  passed  failed  pass_rate
# Safety        50      40      10      80.00
# Accuracy      30      25       5      83.33

Related — Test Runs & Results | SDK Metrics | Test Execution