diff --git a/src/llama_stack_client/lib/cli/eval/run_benchmark.py b/src/llama_stack_client/lib/cli/eval/run_benchmark.py
index d049c104..e088137e 100644
--- a/src/llama_stack_client/lib/cli/eval/run_benchmark.py
+++ b/src/llama_stack_client/lib/cli/eval/run_benchmark.py
@@ -13,7 +13,13 @@
 from tqdm.rich import tqdm
 
 from ..common.utils import create_bar_chart
-from .utils import aggregate_accuracy, aggregate_average, aggregate_categorical_count, aggregate_median
+from .utils import (
+    aggregate_accuracy,
+    aggregate_average,
+    aggregate_weighted_average,
+    aggregate_categorical_count,
+    aggregate_median,
+)
 
 
 @click.command("run-benchmark")
@@ -94,9 +100,7 @@ def run_benchmark(
         scoring_functions = benchmark.scoring_functions
         dataset_id = benchmark.dataset_id
 
-        results = client.datasets.iterrows(
-            dataset_id=dataset_id, limit=-1 if num_examples is None else num_examples
-        )
+        results = client.datasets.iterrows(dataset_id=dataset_id, limit=-1 if num_examples is None else num_examples)
 
         output_res = {}
 
@@ -146,6 +150,8 @@ def run_benchmark(
                             output_res[scoring_fn].append(aggregate_categorical_count(scoring_results))
                         elif aggregation_function == "average":
                             output_res[scoring_fn].append(aggregate_average(scoring_results))
+                        elif aggregation_function == "weighted_average":
+                            output_res[scoring_fn].append(aggregate_weighted_average(scoring_results))
                         elif aggregation_function == "median":
                             output_res[scoring_fn].append(aggregate_median(scoring_results))
                         elif aggregation_function == "accuracy":
diff --git a/src/llama_stack_client/lib/cli/eval/utils.py b/src/llama_stack_client/lib/cli/eval/utils.py
index 102b8817..96d8d54c 100644
--- a/src/llama_stack_client/lib/cli/eval/utils.py
+++ b/src/llama_stack_client/lib/cli/eval/utils.py
@@ -24,6 +24,19 @@ def aggregate_average(
     }
 
 
+def aggregate_weighted_average(
+    scoring_results: List[Dict[str, Union[bool, float, str, List[object], object, None]]],
+) -> Dict[str, Any]:
+    return {
+        "weighted_average": sum(
+            result["score"] * result["weight"]
+            for result in scoring_results
+            if result["score"] is not None and result["weight"] is not None
+        )
+        / sum(result["weight"] for result in scoring_results if result["weight"] is not None),
+    }
+
+
 def aggregate_median(
     scoring_results: List[Dict[str, Union[bool, float, str, List[object], object, None]]],
 ) -> Dict[str, Any]: