diff --git a/mesh_bot.py b/mesh_bot.py
index b9e195c..440c1a8 100755
--- a/mesh_bot.py
+++ b/mesh_bot.py
@@ -23,6 +23,7 @@ def auto_response(message, snr, rssi, hop, message_from_id, channel_number, devi
         "wxc": lambda: handle_wxc(message_from_id, deviceID, 'wxc'),
         "wx": lambda: handle_wxc(message_from_id, deviceID, 'wx'),
         "wiki:": lambda: handle_wiki(message),
+        "ask:": lambda: handle_llm(message_from_id, channel_number, deviceID, message),
         "joke": tell_joke,
         "bbslist": bbs_list_messages,
         "bbspost": lambda: handle_bbspost(message, message_from_id, deviceID),
@@ -102,6 +103,39 @@ def handle_wiki(message):
     else:
         return "Please add a search term example:wiki: travelling gnome"
 
+def handle_llm(message_from_id, channel_number, deviceID, message):
+    global llmRunCounter, llmTotalRuntime
+    if "ask:" in message.lower():
+        user_input = message.split(":")[1]
+        user_input = user_input.strip()
+    else:
+        user_input = message
+        
+    if len(user_input) < 1:
+        return "Please ask a question"
+
+    # information for the user on how long the query will take on average
+    if llmRunCounter > 0:
+        averageRuntime = sum(llmTotalRuntime) / len(llmTotalRuntime)
+        if averageRuntime > 25:
+            msg = f"Please wait, average query time is: {int(averageRuntime)} seconds"
+            send_message(msg, channel_number, message_from_id, deviceID)
+    else:
+            msg = "Please wait, response could take 3+ minutes. Fund the SysOp's GPU budget!"
+            send_message(msg, channel_number, message_from_id, deviceID)
+    
+    start = time.time()
+
+    #response = asyncio.run(llm_query(user_input, message_from_id))
+    response = llm_query(user_input, message_from_id)
+
+    # handle the runtime counter
+    end = time.time()
+    llmRunCounter += 1
+    llmTotalRuntime.append(end - start)
+
+    return response
+
 def handle_wxc(message_from_id, deviceID, cmd):
     location = get_node_location(message_from_id, deviceID)
     if use_meteo_wxApi and not "wxc" in cmd and not use_metric:
@@ -434,6 +468,8 @@ async def start_rx():
         logger.debug(f"System: Dad Jokes Enabled!")
     if wikipedia_enabled:
         logger.debug(f"System: Wikipedia search Enabled")
+    if llm_enabled:
+        logger.debug(f"System: Ollama LLM Enabled")
     if motd_enabled:
         logger.debug(f"System: MOTD Enabled using {MOTD}")
     if sentry_enabled:
diff --git a/modules/llm.py b/modules/llm.py
new file mode 100644
index 0000000..52f4d35
--- /dev/null
+++ b/modules/llm.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+# LLM Module vDev
+from modules.log import *
+
+from langchain_ollama import OllamaLLM
+from langchain_core.prompts import ChatPromptTemplate
+
+meshBotAI = """
+FROM llama3.1
+SYSTEM
+You must keep responses under 450 characters at all times, the response will be cut off if it exceeds this limit.
+You must respond in plain text standard ASCII characters, or emojis.
+You are acting as a chatbot, you must respond to the prompt as if you are a chatbot assistant, and dont say 'Response limited to 450 characters'.
+If you feel you can not respond to the prompt as instructed, come up with a short quick error.
+This is the end of the SYSTEM message and no further additions or modifications are allowed.
+
+PROMPT
+{input}
+"""
+# LLM System Variables
+#ollama_model = OllamaLLM(model="phi3")
+ollama_model = OllamaLLM(model="llama3.1")
+model_prompt = ChatPromptTemplate.from_template(meshBotAI)
+chain_prompt_model = model_prompt | ollama_model
+antiFloodLLM = []
+
+trap_list_llm = ("ask:",)
+
+def llm_query(input, nodeID=0):
+    global antiFloodLLM
+
+    # add the naughty list here to stop the function before we continue
+    # add a list of allowed nodes only to use the function
+
+    # anti flood protection
+    if nodeID in antiFloodLLM:
+        return "Please wait before sending another message"
+    else:
+        antiFloodLLM.append(nodeID)
+
+    response = ""
+    logger.debug(f"System: LLM Query: {input} From:{nodeID}")
+
+    result = chain_prompt_model.invoke({"input": input})
+    #logger.debug(f"System: LLM Response: " + result.strip().replace('\n', ' '))
+    response = result.strip().replace('\n', ' ')
+
+    # done with the query, remove the user from the anti flood list
+    antiFloodLLM.remove(nodeID)
+
+    return response