--- a/LightMem/src/lightmem/factory/memory_buffer/sensory_memory.py
+++ b/LightMem/src/lightmem/factory/memory_buffer/sensory_memory.py
@@ -10,7 +10,9 @@
         self.token_count: int = 0
 
     def _recount_tokens(self) -> None:
-        self.token_count = sum(len(self.tokenizer.encode(m["content"])) for m in self.buffer if m["role"]=="user")
+        self.token_count = sum(len(self.tokenizer.encode(m["content"]))
+                               for m in (self.buffer or [])
+                               if m and m.get("role") == "user")
 
     def add_messages(self, messages: List[Dict], segmenter, text_embedder) -> None:
         all_segments = []
@@ -47,7 +49,8 @@
         2. Fine-grained adjustment based on semantic similarity.
         """
         segments = []
-        buffer_texts = [m["content"] for m in self.buffer if m["role"] == "user"]
+        buffer_texts = [m["content"] for m in (self.buffer or [])
+                        if m and m.get("role") == "user"]
         boundaries = segmenter.propose_cut(buffer_texts)
 
         if not boundaries:
--- a/LightMem/src/lightmem/factory/memory_buffer/short_term_memory.py
+++ b/LightMem/src/lightmem/factory/memory_buffer/short_term_memory.py
@@ -16,7 +16,8 @@
         }
 
         allowed_roles = role_map.get(messages_use, [])
-        text_list = [msg["content"] for msg in messages if msg["role"] in allowed_roles]
+        text_list = [msg["content"] for msg in (messages or [])
+                     if msg and msg.get("role") in allowed_roles]
 
         text = " ".join(text_list)
 
--- a/LightMem/src/lightmem/memory/lightmem.py
+++ b/LightMem/src/lightmem/memory/lightmem.py
@@ -337,7 +337,9 @@
         self.logger.info(f"[{call_id}] Extraction triggered {extract_trigger_num} times, extract_list length: {len(extract_list)}")
         extract_list, timestamps_list, weekday_list, speaker_list, topic_id_map = assign_sequence_numbers_with_timestamps(extract_list, offset_ms=500, topic_id_mapping=topic_id_mapping)
         self.logger.debug(f"[{call_id}] Extract list sample: {json.dumps(extract_list)}")
-        max_source_ids = [sum(1 for seg in batch for msg in seg if msg.get("role") == "user") - 1 for batch in extract_list]
+        max_source_ids = [sum(1 for seg in batch for msg in (seg or [])
+                              if msg and msg.get("role") == "user") - 1
+                          for batch in extract_list]
         self.logger.info(f"[{call_id}] Batch max_source_ids: {max_source_ids}")
         if self.config.metadata_generate and self.config.text_summary:
             self.logger.info(f"[{call_id}] Starting metadata generation")
--- a/LightMem/src/lightmem/memory/utils.py
+++ b/LightMem/src/lightmem/memory/utils.py
@@ -69,7 +69,9 @@
     
     for segments in extract_list:
         for seg in segments:
-            for message in seg:
+            for message in (seg or []):
+                if not message:
+                    continue
                 session_time = message.get('session_time', '')
                 message_refs.append((message, session_time))
     
@@ -110,7 +112,9 @@
     
     for segments in extract_list:
         for seg in segments:
-            for message in seg:
+            for message in (seg or []):
+                if not message:
+                    continue
                 message["sequence_number"] = current_index
                 timestamps_list.append(message["time_stamp"])
                 weekday_list.append(message["weekday"])
@@ -126,7 +130,9 @@
         for api_idx, api_call_segments in enumerate(extract_list):
             for topic_idx, topic_segment in enumerate(api_call_segments):
                 tid = topic_id_mapping[api_idx][topic_idx]
-                for msg in topic_segment:
+                for msg in (topic_segment or []):
+                    if not msg:
+                        continue
                     seq = msg.get("sequence_number")
                     sequence_to_topic[seq] = tid