diff --git a/graph_net/torch/extractor.py b/graph_net/torch/extractor.py
index 522701d8e..568ad995a 100644
--- a/graph_net/torch/extractor.py
+++ b/graph_net/torch/extractor.py
@@ -2,6 +2,7 @@
 import torch
 import json
 import shutil
+import glob
 from graph_net.torch import utils
 from graph_net.torch.fx_graph_serialize_util import serialize_graph_module_to_str
 
@@ -72,12 +73,28 @@ def move_files(self, source_dir, target_dir):
                 target_path = os.path.join(target_dir, item)
                 shutil.move(source_path, target_path)
 
+    def _cleanup_stale_data(self, model_path):
+        for stale_dir in glob.glob(os.path.join(model_path, "subgraph_*")):
+            shutil.rmtree(stale_dir)
+        for stale_file_name in (
+            "model.py",
+            "graph_net.json",
+            "input_meta.py",
+            "weight_meta.py",
+            "input_tensor_constraints.py",
+            "graph_hash.txt",
+        ):
+            stale_file = os.path.join(model_path, stale_file_name)
+            if os.path.isfile(stale_file):
+                os.remove(stale_file)
+
     def __call__(self, gm: torch.fx.GraphModule, sample_inputs):
         # 1. Get model path
         model_path = os.path.join(self.workspace_path, self.name)
         os.makedirs(model_path, exist_ok=True)
 
         if self.subgraph_counter == 0:
+            self._cleanup_stale_data(model_path)
             subgraph_path = model_path
         else:
             if self.subgraph_counter == 1:
@@ -124,17 +141,30 @@ def try_rename_placeholder(node):
                     gm.graph.erase_node(node)
 
         assert input_idx == len(sample_inputs)
+
+        # 3. Serialize graph
+        base_code = serialize_graph_module_to_str(gm)
+
         if self.mut_graph_codes is not None:
             assert isinstance(self.mut_graph_codes, list)
-            self.mut_graph_codes.append(serialize_graph_module_to_str(gm))
-        # 3. Generate and save model code
-        base_code = serialize_graph_module_to_str(gm)
-        # gm.graph.print_tabular()
+            self.mut_graph_codes.append(base_code)
+
+        # 4. Save tensor metadata
+        converted = utils.convert_state_and_inputs(params, [])
+        utils.save_converted_to_text(converted, file_path=subgraph_path)
+        utils.save_constraints_text(
+            converted,
+            file_path=os.path.join(subgraph_path, "input_tensor_constraints.py"),
+        )
+
+        # 5. Save model code
         write_code = utils.apply_templates(base_code)
         with open(os.path.join(subgraph_path, "model.py"), "w") as fp:
             fp.write(write_code)
 
-        # 4. Save metadata
+        # 6. Save metadata LAST — graph_net.json serves as the
+        #    completion marker: if it exists, all other files are guaranteed
+        #    to be fully written.
         metadata = {
             "framework": "torch",
             "num_devices_required": 1,
@@ -145,15 +175,6 @@ def try_rename_placeholder(node):
         with open(os.path.join(subgraph_path, "graph_net.json"), "w") as f:
             json.dump(metadata, f, indent=4)
 
-        # 5. Save tensor metadata
-        # Adapt to different input structures (e.g., single tensor vs. dict/tuple of tensors)
-        converted = utils.convert_state_and_inputs(params, [])
-        utils.save_converted_to_text(converted, file_path=subgraph_path)
-        utils.save_constraints_text(
-            converted,
-            file_path=os.path.join(subgraph_path, "input_tensor_constraints.py"),
-        )
-
         print(
             f"Graph and tensors for '{self.name}' extracted successfully to: {model_path}"
         )