diff --git a/autorag/evaluate/metric/__init__.py b/autorag/evaluate/metric/__init__.py
index eea0a44da..4d52144a1 100644
--- a/autorag/evaluate/metric/__init__.py
+++ b/autorag/evaluate/metric/__init__.py
@@ -1,3 +1,3 @@
 from .generation import bleu, meteor, rouge, sem_score, g_eval, bert_score
-from .retrieval import retrieval_f1, retrieval_recall, retrieval_precision, retrieval_mrr, retrieval_ndcg
+from .retrieval import retrieval_f1, retrieval_recall, retrieval_precision, retrieval_mrr, retrieval_ndcg, retrieval_map
 from .retrieval_contents import retrieval_token_f1, retrieval_token_precision, retrieval_token_recall
diff --git a/autorag/evaluate/metric/retrieval.py b/autorag/evaluate/metric/retrieval.py
index 6ca460585..fa3b4798c 100644
--- a/autorag/evaluate/metric/retrieval.py
+++ b/autorag/evaluate/metric/retrieval.py
@@ -87,3 +87,20 @@ def retrieval_mrr(gt: List[List[str]], pred: List[str]) -> float:
                 rr_list.append(1.0 / (i + 1))
                 break
     return sum(rr_list) / len(gt_sets) if rr_list else 0.0
+
+
+@retrieval_metric
+def retrieval_map(gt: List[List[str]], pred: List[str]) -> float:
+    """
+    Mean Average Precision (MAP) is the mean of Average Precision (AP) for all queries.
+    """
+    gt_sets = [frozenset(g) for g in gt]
+
+    ap_list = []
+
+    for gt_set in gt_sets:
+        pred_hits = [1 if pred_id in gt_set else 0 for pred_id in pred]
+        precision_list = [sum(pred_hits[:i + 1]) / (i + 1) for i, hit in enumerate(pred_hits) if hit == 1]
+        ap_list.append(sum(precision_list) / len(precision_list) if precision_list else 0.0)
+
+    return sum(ap_list) / len(gt_sets) if ap_list else 0.0
diff --git a/autorag/evaluate/retrieval.py b/autorag/evaluate/retrieval.py
index 6a9fab205..98874e522 100644
--- a/autorag/evaluate/retrieval.py
+++ b/autorag/evaluate/retrieval.py
@@ -4,7 +4,8 @@
 
 import pandas as pd
 
-from autorag.evaluate.metric import retrieval_recall, retrieval_precision, retrieval_f1, retrieval_ndcg, retrieval_mrr
+from autorag.evaluate.metric import (retrieval_recall, retrieval_precision, retrieval_f1, retrieval_ndcg, retrieval_mrr,
+                                     retrieval_map)
 
 
 def evaluate_retrieval(retrieval_gt: List[List[List[str]]], metrics: List[str]):
@@ -28,6 +29,7 @@ def wrapper(*args, **kwargs) -> pd.DataFrame:
                 retrieval_f1.__name__: retrieval_f1,
                 retrieval_ndcg.__name__: retrieval_ndcg,
                 retrieval_mrr.__name__: retrieval_mrr,
+                retrieval_map.__name__: retrieval_map,
             }
 
             metric_scores = {}
diff --git a/tests/autorag/evaluate/metric/test_retrieval_metric.py b/tests/autorag/evaluate/metric/test_retrieval_metric.py
index 7e3a246c5..5f3dc14f9 100644
--- a/tests/autorag/evaluate/metric/test_retrieval_metric.py
+++ b/tests/autorag/evaluate/metric/test_retrieval_metric.py
@@ -1,6 +1,7 @@
 import pytest
 
-from autorag.evaluate.metric import retrieval_f1, retrieval_precision, retrieval_recall, retrieval_ndcg, retrieval_mrr
+from autorag.evaluate.metric import (retrieval_f1, retrieval_precision, retrieval_recall, retrieval_ndcg, retrieval_mrr,
+                                     retrieval_map)
 
 retrieval_gt = [
     [['test-1', 'test-2'], ['test-3']],
@@ -58,3 +59,10 @@ def test_retrieval_mrr():
     result = retrieval_mrr(retrieval_gt=retrieval_gt, pred_ids=pred)
     for gt, res in zip(solution, result):
         assert gt == pytest.approx(res, rel=1e-4)
+
+
+def test_retrieval_map():
+    solution = [5 / 12, 1 / 3, 1, 1 / 2, 1, None, None, 1 / 3]
+    result = retrieval_map(retrieval_gt=retrieval_gt, pred_ids=pred)
+    for gt, res in zip(solution, result):
+        assert gt == pytest.approx(res, rel=1e-4)