diff --git a/examples/artifacts_toy.py b/examples/artifacts_toy.py
index da6f8e8e..b9558f32 100644
--- a/examples/artifacts_toy.py
+++ b/examples/artifacts_toy.py
@@ -31,7 +31,8 @@ task.upload_artifact('Numpy Eye', np.eye(100, 100))
 # add and upload Image (stored as .png file)
 im = Image.open('samples/dancing.jpg')
 task.upload_artifact('pillow_image', im)
-
+# add and upload a folder, artifact_object should be the folder path
+task.upload_artifact('local folder', artifact_object='samples/')
 
 # do something
 sleep(1.)
diff --git a/trains/binding/artifacts.py b/trains/binding/artifacts.py
index 318dafec..92eb38f8 100644
--- a/trains/binding/artifacts.py
+++ b/trains/binding/artifacts.py
@@ -114,17 +114,36 @@ class Artifact(object):
         self._metadata = dict(artifact_api_object.display_data) if artifact_api_object.display_data else {}
         self._preview = artifact_api_object.type_data.preview if artifact_api_object.type_data else None
 
-    def get_local_copy(self):
+    def get_local_copy(self, extract_archive=True):
         """
+        :param bool extract_archive: If True and artifact is of type 'archive' (compressed folder)
+            The returned path will be a temporary folder containing the archive content
         :return: a local path to a downloaded copy of the artifact
         """
         from trains.storage.helper import StorageHelper
-        return StorageHelper.get_local_copy(self.url)
+        local_path = StorageHelper.get_local_copy(self.url)
+        if local_path and extract_archive and self.type == 'archive':
+            try:
+                temp_folder = mkdtemp(prefix='artifact_', suffix='.archive_'+self.name)
+                ZipFile(local_path).extractall(path=temp_folder)
+            except Exception:
+                try:
+                    Path(temp_folder).rmdir()
+                except Exception:
+                    pass
+                return local_path
+            try:
+                Path(local_path).unlink()
+            except Exception:
+                pass
+            return temp_folder
+
+        return local_path
 
     def __repr__(self):
         return str({'name': self.name, 'size': self.size, 'type': self.type, 'mode': self.mode, 'url': self.url,
                     'hash': self.hash, 'timestamp': self.timestamp,
-                    'metadata': self.metadata, 'preview': self.preview,})
+                    'metadata': self.metadata, 'preview': self.preview, })
 
 
 class Artifacts(object):
@@ -294,7 +313,7 @@ class Artifacts(object):
                     os.close(fd)
 
                 artifact_object = zip_file
-                artifact_type = 'zip'
+                artifact_type = 'archive'
                 artifact_type_data.content_type = mimetypes.guess_type(artifact_object)[0]
                 local_filename = artifact_object
                 delete_after_upload = True
diff --git a/trains/storage/helper.py b/trains/storage/helper.py
index 9cb5b848..5881f071 100644
--- a/trains/storage/helper.py
+++ b/trains/storage/helper.py
@@ -569,6 +569,8 @@ class StorageHelper(object):
             # we won't think we have the entire file
             temp_local_path = '{}_{}{}'.format(local_path, time(), self._temp_download_suffix)
             obj = self._get_object(remote_path)
+            if not obj:
+                return None
 
             # object size in bytes
             total_size_mb = -1
diff --git a/trains/task.py b/trains/task.py
index fe27529f..c91a9b7e 100644
--- a/trains/task.py
+++ b/trains/task.py
@@ -686,10 +686,10 @@ class Task(_Task):
         :param str name: Artifact name. Notice! it will override previous artifact if name already exists
         :param object artifact_object: Artifact object to upload. Currently supports:
             - string / pathlib2.Path are treated as path to artifact file to upload
-                If wildcard or a folder is passed, zip file containing the local files will be created and uploaded.
-            - dict will be stored as .json,
-            - pandas.DataFrame will be stored as .csv.gz (compressed CSV file),
-            - numpy.ndarray will be stored as .npz,
+                If wildcard or a folder is passed, zip file containing the local files will be created and uploaded
+            - dict will be stored as .json file and uploaded
+            - pandas.DataFrame will be stored as .csv.gz (compressed CSV file) and uploaded
+            - numpy.ndarray will be stored as .npz and uploaded
             - PIL.Image will be stored to .png file and uploaded
         :param dict metadata: Simple key/value dictionary to store on the artifact
         :param bool delete_after_upload: If True local artifact will be deleted