diff --git a/bot.py b/bot.py
index 358ef07..74cf0b7 100644
--- a/bot.py
+++ b/bot.py
@@ -387,6 +387,24 @@ async def on_message(message: discord.Message) -> None:
         cleaned_message: str = preprocess_message(formatted_message)
         if cleaned_message:
             memory.append(cleaned_message)
+            message_metadata = {
+                "user_id": str(message.author.id),
+                "user_name": str(message.author),
+                "guild_id": str(message.guild.id) if message.guild else "DM",
+                "guild_name": str(message.guild.name) if message.guild else "DM",
+                "channel_id": str(message.channel.id),
+                "channel_name": str(message.channel),
+                "message": message.content,
+                "timestamp": time.time()
+            }
+            try:
+                if isinstance(memory, list):
+                    memory.append({"_meta": message_metadata})
+                else:
+                    logger.warning("Memory is not a list; can't append metadata")
+            except Exception as e:
+                logger.warning(f"Failed to append metadata to memory: {e}")
+
             save_memory(memory)
 
         sentiment_score = is_positive(message.content) # doesnt work but im scared to change the logic now please ignore
diff --git a/modules/markovmemory.py b/modules/markovmemory.py
index f903f59..3235035 100644
--- a/modules/markovmemory.py
+++ b/modules/markovmemory.py
@@ -34,13 +34,15 @@ def save_memory(memory):
     with open(MEMORY_FILE, "w") as f:
         json.dump(memory, f, indent=4)
 
-# Train a Markov model using memory and optional additional data
 def train_markov_model(memory, additional_data=None):
     if not memory:
         return None
-    text = "\n".join(memory)
+    filtered_memory = [line for line in memory if isinstance(line, str)]
     if additional_data:
-        text += "\n" + "\n".join(additional_data)
+        filtered_memory.extend(line for line in additional_data if isinstance(line, str))
+    if not filtered_memory:
+        return None
+    text = "\n".join(filtered_memory)
     model = markovify.NewlineText(text, state_size=2)
     return model