diff --git a/expo/MCTS.py b/expo/MCTS.py
index aa4ade944..4564cd682 100644
--- a/expo/MCTS.py
+++ b/expo/MCTS.py
@@ -1,3 +1,4 @@
+import json
 import math
 import os
 import pickle
@@ -240,6 +241,7 @@ class MCTS:
     max_depth: int = 5
     c_explore: float = 1.4
     c_unvisited: float = 0.8
+    node_order: list = []
 
     def __init__(self, root_node, max_depth, use_fixed_insights):
         self.root_node = root_node
@@ -306,11 +308,32 @@ class MCTS:
         _, global_best_child = bfs(root, global_best_score, best_child, "test_score")
         _, dev_best_child = bfs(root, dev_best_score, best_child, "dev_score")
 
-        return {"dev_best": dev_best_child, "global_best": global_best_child}
+        return {"dev_best": dev_best_child, "global_best": global_best_child, "scores": self.get_score_order_dict()}
 
     def get_num_simulations(self):
         return self.root_node.visited
 
+    def save_node_order(self, node_id):
+        self.node_order.append(node_id)
+        with open(os.path.join(self.root_node.state["node_dir"], "node_order.json"), "w") as f:
+            json.dump(self.node_order, f)
+
+    def load_node_order(self):
+        with open(os.path.join(self.root_node.state["node_dir"], "node_order.json"), "r") as f:
+            self.node_order = json.load(f)
+
+    def get_score_order_dict(self):
+        scores = {"dev": [], "test": [], "dev_raw": [], "test_raw": []}
+        for node_id in self.node_order:
+            node = Node(parent=None, state=self.root_node.state, action=None, value=0)
+            node.id = node_id
+            node = node.load_node()
+            scores["dev"].append(node.normalized_reward["dev_score"])
+            scores["test"].append(node.normalized_reward["test_score"])
+            scores["dev_raw"].append(node.raw_reward["dev_score"])
+            scores["test_raw"].append(node.raw_reward["test_score"])
+        return scores
+
     async def search(self, state, rollouts, load_tree=False, reflection=False):
         role, root = initialize_di_root_node(state, reflection=reflection)
         self.root_node = root
@@ -329,8 +352,12 @@ class MCTS:
             self.backpropagate(root, reward)
             node, reward = await self.expand_and_simulate(root)
             # self.backpropagate(node, reward)
+            self.save_node_order(root.id)
+            self.save_node_order(node.id)
         else:
             root = self.root_node
+            self.load_node_order()
+
         for _ in range(rollouts):  # number of rollouts
             mcts_logger.log("MCTS", f"Start the next rollout {_+1}")
             node = self.select(root)
@@ -344,6 +371,7 @@ class MCTS:
             else:
                 node, reward = await self.expand_and_simulate(node)
                 # self.backpropagate(node, reward)
+            self.save_node_order(node.id)
         return self.best_path(root)
 
     async def expand_and_simulate(self, node):
@@ -373,6 +401,7 @@ class MCTS:
                 self.root_node = pickle.load(f)
             self.children[self.root_node] = self.root_node.children
             load_children_node(self.root_node)
+
             if self.children:
                 return True
         return False
diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py
index 5fb00ca8d..bd803bff1 100644
--- a/expo/experimenter/mcts.py
+++ b/expo/experimenter/mcts.py
@@ -29,6 +29,7 @@ class MCTSExperimenter(Experimenter):
         )
         best_node = best_nodes["global_best"]
         dev_best_node = best_nodes["dev_best"]
+        score_dict = best_nodes["scores"]
 
         self.copy_notebook(best_node, "best")
         self.copy_notebook(dev_best_node, "dev_best")
@@ -50,6 +51,7 @@ class MCTSExperimenter(Experimenter):
                 "user_requirement": best_node.state["requirement"],
                 "tree_text": text,
                 "args": vars(self.args),
+                "scores": score_dict,
             }
         ]
         self.save_result(results)