diff --git a/.jules/bolt.md b/.jules/bolt.md
index 1de7202..bb9c3e9 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -8,3 +8,6 @@
 ## 2025-07-14 - [Aggregation for Activity Feeds]
 **Learning:** Replacing loop-based lookups (N+1 queries) with single aggregation pipelines in `get_recent_activity` reduces database roundtrips by over 90% (from 42 queries to 2). Using `$unwind` without `preserveNullAndEmptyArrays` effectively replicates `if tool and worker:` filtering logic in the database layer.
 **Action:** Apply similar aggregation patterns to other feed-like features (e.g., `manual_lending` in `app/routes/admin/system.py`).
+## 2025-01-24 - [N+1 Query Resolution in Manual Lending]
+**Learning:** The `manual_lending` route had a severe N+1 query problem where each active lending and recent consumable usage triggered two additional `find_one` calls. Replacing these with separate aggregation pipelines using `$lookup` and `$project` reduces the query count from O(N) to O(1).
+**Action:** Always check loop-based lookups in routes that list multiple items and prioritize aggregation even if local `mongomock` tests don't show immediate performance gains, as the real benefit is in reducing database roundtrips.
diff --git a/app/routes/admin/system.py b/app/routes/admin/system.py
index ed44480..1104b25 100644
--- a/app/routes/admin/system.py
+++ b/app/routes/admin/system.py
@@ -124,44 +124,83 @@ def manual_lending():
         # Hole aktuelle Ausleihen
         current_lendings = []
 
-        # Aktuelle Werkzeug-Ausleihen
-        active_tool_lendings = mongodb.find('lendings', {'returned_at': None})
-        for lending in active_tool_lendings:
-            tool = mongodb.find_one('tools', {'barcode': lending['tool_barcode']})
-            worker = mongodb.find_one('workers', {'barcode': lending['worker_barcode']})
-
-            if tool and worker:
-                current_lendings.append({
-                    'item_name': tool['name'],
-                    'item_barcode': tool['barcode'],
-                    'worker_name': f"{worker['firstname']} {worker['lastname']}",
-                    'worker_barcode': worker['barcode'],
-                    'action_date': lending['lent_at'],
-                    'category': 'Werkzeug',
-                    'amount': None
-                })
-
-        # Aktuelle Verbrauchsmaterial-Ausgaben (letzte 30 Tage)
+        # Aktuelle Werkzeug-Ausleihen (Optimiert mit Aggregation zur Vermeidung von N+1 Problemen)
+        active_tool_lendings_pipeline = [
+            {'$match': {'returned_at': None}},
+            {
+                '$lookup': {
+                    'from': 'tools',
+                    'localField': 'tool_barcode',
+                    'foreignField': 'barcode',
+                    'as': 'tool_info'
+                }
+            },
+            {'$unwind': '$tool_info'},
+            {
+                '$lookup': {
+                    'from': 'workers',
+                    'localField': 'worker_barcode',
+                    'foreignField': 'barcode',
+                    'as': 'worker_info'
+                }
+            },
+            {'$unwind': '$worker_info'},
+            {
+                '$project': {
+                    'item_name': '$tool_info.name',
+                    'item_barcode': '$tool_info.barcode',
+                    'worker_name': {'$concat': ['$worker_info.firstname', ' ', '$worker_info.lastname']},
+                    'worker_barcode': '$worker_info.barcode',
+                    'action_date': '$lent_at',
+                    'category': {'$literal': 'Werkzeug'},
+                    'amount': {'$literal': None}
+                }
+            }
+        ]
+
+        current_lendings.extend(list(mongodb.aggregate('lendings', active_tool_lendings_pipeline)))
+
+        # Aktuelle Verbrauchsmaterial-Ausgaben der letzten 30 Tage (Optimiert mit Aggregation)
         thirty_days_ago = datetime.now() - timedelta(days=30)
-        recent_consumable_usages = mongodb.find('consumable_usages', {
-            'used_at': {'$gte': thirty_days_ago},
-            'quantity': {'$lt': 0}  # Nur Ausgaben (negative Werte), nicht Entnahmen
-        })
+        recent_consumable_usages_pipeline = [
+            {
+                '$match': {
+                    'used_at': {'$gte': thirty_days_ago},
+                    'quantity': {'$lt': 0}  # Nur Ausgaben (negative Werte)
+                }
+            },
+            {
+                '$lookup': {
+                    'from': 'consumables',
+                    'localField': 'consumable_barcode',
+                    'foreignField': 'barcode',
+                    'as': 'consumable_info'
+                }
+            },
+            {'$unwind': '$consumable_info'},
+            {
+                '$lookup': {
+                    'from': 'workers',
+                    'localField': 'worker_barcode',
+                    'foreignField': 'barcode',
+                    'as': 'worker_info'
+                }
+            },
+            {'$unwind': '$worker_info'},
+            {
+                '$project': {
+                    'item_name': '$consumable_info.name',
+                    'item_barcode': '$consumable_info.barcode',
+                    'worker_name': {'$concat': ['$worker_info.firstname', ' ', '$worker_info.lastname']},
+                    'worker_barcode': '$worker_info.barcode',
+                    'action_date': '$used_at',
+                    'category': {'$literal': 'Verbrauchsmaterial'},
+                    'amount': '$quantity'
+                }
+            }
+        ]
 
-        for usage in recent_consumable_usages:
-            consumable = mongodb.find_one('consumables', {'barcode': usage['consumable_barcode']})
-            worker = mongodb.find_one('workers', {'barcode': usage['worker_barcode']})
-
-            if consumable and worker:
-                current_lendings.append({
-                    'item_name': consumable['name'],
-                    'item_barcode': consumable['barcode'],
-                    'worker_name': f"{worker['firstname']} {worker['lastname']}",
-                    'worker_barcode': worker['barcode'],
-                    'action_date': usage['used_at'],
-                    'category': 'Verbrauchsmaterial',
-                    'amount': usage['quantity']
-                })
+        current_lendings.extend(list(mongodb.aggregate('consumable_usages', recent_consumable_usages_pipeline)))
 
         # Sortiere nach Datum (neueste zuerst)
         def safe_date_key(lending):