diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..d8cd1df
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,38 @@
+import json
+import hashlib
+from pathlib import Path
+from functools import wraps
+
+def with_disk_cache(subdir_name: str):
+    """
+    A decorator to cache function results to a local JSON file.
+    The cache file is stored in `data/<subdir_name>/<key>.json`.
+    """
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            if not args or not isinstance(args[0], str):
+                key = hashlib.md5(str(args).encode()).hexdigest()
+            else:
+                val = args[0]
+                # If it's a simple ID, use it for readability. Otherwise hash it to avoid invalid filename characters.
+                if '/' not in val and '\\' not in val and len(val) < 50:
+                    key = val
+                else:
+                    key = hashlib.md5(val.encode()).hexdigest()
+                    
+            cache_p = Path(__file__).parent / 'data' / subdir_name / f"{key}.json"
+            
+            if cache_p.is_file():
+                try:
+                    return json.loads(cache_p.read_text(encoding="utf-8"))
+                except Exception:
+                    pass
+                
+            result = func(*args, **kwargs)
+            
+            cache_p.parent.mkdir(parents=True, exist_ok=True)
+            cache_p.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+            return result
+        return wrapper
+    return decorator
diff --git a/utils_ai.py b/utils_ai.py
index b51249f..dfe5e81 100644
--- a/utils_ai.py
+++ b/utils_ai.py
@@ -2,6 +2,8 @@ import os
 from pathlib import Path
 from openai import OpenAI
 
+from utils import with_disk_cache
+
 client = OpenAI()
 
 def _call_openai_with_prompt(prompt_id: str, prompt_version: str, input_text: str) -> str:
@@ -49,6 +51,7 @@ def _call_openai_with_prompt(prompt_id: str, prompt_version: str, input_text: st
         return str(response)
 
 
+@with_disk_cache('select_best_torrents')
 def select_best_torrents(torrents_text: str) -> str:
     """
     Calls the OpenAI API to select the best torrent IDs using a predefined prompt.
@@ -63,6 +66,7 @@ def select_best_torrents(torrents_text: str) -> str:
     )
 
 
+@with_disk_cache('generate_rename_mapping')
 def generate_rename_mapping(directory_text: str) -> dict[str, str]:
     """
     Calls the OpenAI API to generate a renaming mapping for files 
diff --git a/utils_mteam.py b/utils_mteam.py
index fc6e88a..14671a6 100644
--- a/utils_mteam.py
+++ b/utils_mteam.py
@@ -1,7 +1,10 @@
+import json
 import requests
 import tomllib
 from pathlib import Path
 
+from utils import with_disk_cache
+
 config = tomllib.loads(Path("config.toml").read_text())
 
 def _get_mteam_headers() -> dict:
@@ -19,6 +22,7 @@ def _get_mteam_headers() -> dict:
     }
 
 
+@with_disk_cache('search_mteam_torrents')
 def search_mteam_torrents(imdb_url: str, page_number: int = 1, page_size: int = 100) -> dict:
     """
     Search M-Team for torrents using IMDb URL.
@@ -47,6 +51,7 @@ def search_mteam_torrents(imdb_url: str, page_number: int = 1, page_size: int =
     return response.json()
 
 
+@with_disk_cache('mteam_imdb_info')
 def mteam_imdb_info(id: str) -> dict:
     """
     Search M-Team for torrents using IMDb URL.
diff --git a/workflow.py b/workflow.py
index e6a2677..7f5dde4 100644
--- a/workflow.py
+++ b/workflow.py
@@ -47,12 +47,6 @@ def process_imdb_workflow(imdb_id: str, dl_dir: str = "/data/qb", jellyfin_dir:
     print(f"\n=== [1] Searching Torrents for {imdb_id} ===")
     imdb_url = f"https://www.imdb.com/title/{imdb_id}/"
     search_res = search_mteam_torrents(imdb_url)
-    
-    # Save the raw JSON
-    json_path = f"{imdb_id}.json"
-    print(f"Saving search results to {json_path}...")
-    with open(json_path, "w", encoding="utf-8") as f:
-        json.dump(search_res, f, ensure_ascii=False, indent=2)
 
     # Extract the torrent list
     if "data" in search_res and isinstance(search_res["data"], dict) and "data" in search_res["data"]:
@@ -90,6 +84,8 @@ def process_imdb_workflow(imdb_id: str, dl_dir: str = "/data/qb", jellyfin_dir:
     for tid in selected_ids:
         print(f"\n=== [3] Downloading .torrent for ID: {tid} ===")
         torrent_bytes = generate_mteam_download_token(tid)
+        
+        # Save straight to local directory
         torrent_path = f"{tid}.torrent"
         with open(torrent_path, "wb") as f:
             f.write(torrent_bytes)