diff --git a/sdmetrics/timeseries/sequence.py b/sdmetrics/timeseries/sequence.py
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+++ b/sdmetrics/timeseries/sequence.py
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+from sdmetrics.goal import Goal
+from sdmetrics.timeseries.base import TimeSeriesMetric
+from sdmetrics.single_column.statistical import KSComplement
+
+class SequenceLengthSimilarity(TimeSeriesMetric):
+    """Sequence Length test based on Kolmogorov-Smirnov statistic based metric.
+    This function uses the two-sample Kolmogorov–Smirnov test to compare
+    the distributions of the event sequence length (i.e. number of rows of events linked to each entity) 
+    using the empirical CDF between real and synthetic data.
+    
+    It returns 1 minus the KS Test D statistic, which indicates the maximum
+    distance between the expected CDF and the observed CDF values.
+    
+    As a result, the output value is 1.0 if the distributions are identical
+    and 0.0 if they are completely different.
+    Attributes:
+        name (str):
+            Name to use when reports about this metric are printed.
+        goal (sdmetrics.goal.Goal):
+            The goal of this metric.
+        min_value (Union[float, tuple[float]]):
+            Minimum value or values that this metric can take.
+        max_value (Union[float, tuple[float]]):
+            Maximum value or values that this metric can take.
+    """
+
+    name = 'Sequence Length Similarity'
+    goal = Goal.MAXIMIZE
+    min_value = 0.0
+    max_value = 1.0
+       
+    @classmethod
+    def compute(cls,real_data, synthetic_data, metadata=None, entity_columns=None):
+        """Compare event sequence length per entity using a Kolmogorov–Smirnov test.
+        Args:
+            real_data (Union[pandas.Series]):
+                The real dataset.
+            synthetic_data (Union[pandas.Series]):
+                The synthetic dataset.
+        Returns:
+            float:
+                1 minus the Kolmogorov–Smirnov D statistic.
+        """
+        _, entity_columns = super()._validate_inputs(
+            real_data, synthetic_data, metadata, entity_columns)
+        
+        
+        real_data = real_data.groupby(entity_columns).size()
+        synthetic_data = synthetic_data.groupby(entity_columns).size()
+        output = KSComplement.compute(real_data, synthetic_data)
+        return output
+    
+    @classmethod
+    def normalize(cls, raw_score):
+        """Return the `raw_score` as is, since it is already normalized.
+        Args:
+            raw_score (float):
+                The value of the metric from `compute`.
+        Returns:
+            float:
+                The normalized value of the metric
+        """
+        normalized = KSComplement.normalize(raw_score)
+        
+        return normalized
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