diff --git a/memgpt/local_llm/chat_completion_proxy.py b/memgpt/local_llm/chat_completion_proxy.py
index ea5b904f..ae983339 100644
--- a/memgpt/local_llm/chat_completion_proxy.py
+++ b/memgpt/local_llm/chat_completion_proxy.py
@@ -1,30 +1,16 @@
-"""MemGPT sends a ChatCompletion request
-
-Under the hood, we use the functions argument to turn
-"""
-
-
 """Key idea: create drop-in replacement for agent's ChatCompletion call that runs on an OpenLLM backend"""
 
 import os
-import json
 import requests
+import json
 
-from .webui_settings import DETERMINISTIC, SIMPLE
+from .webui.api import get_webui_completion
 from .llm_chat_completion_wrappers import airoboros
+from .utils import DotDict
 
 HOST = os.getenv("OPENAI_API_BASE")
 HOST_TYPE = os.getenv("BACKEND_TYPE")  # default None == ChatCompletion
-
-
-class DotDict(dict):
-    """Allow dot access on properties similar to OpenAI response object"""
-
-    def __getattr__(self, attr):
-        return self.get(attr)
-
-    def __setattr__(self, key, value):
-        self[key] = value
+DEBUG = True
 
 
 async def get_chat_completion(
@@ -36,60 +22,52 @@ async def get_chat_completion(
     if function_call != "auto":
         raise ValueError(f"function_call == {function_call} not supported (auto only)")
 
-    if True or model == "airoboros_v2.1":
+    if model == "airoboros_v2.1":
+        llm_wrapper = airoboros.Airoboros21Wrapper()
+    else:
+        # Warn the user that we're using the fallback
+        print(
+            f"Warning: could not find an LLM wrapper for {model}, using the airoboros wrapper"
+        )
         llm_wrapper = airoboros.Airoboros21Wrapper()
 
     # First step: turn the message sequence into a prompt that the model expects
     prompt = llm_wrapper.chat_completion_to_prompt(messages, functions)
-    # print(prompt)
-
-    if HOST_TYPE != "webui":
-        raise ValueError(HOST_TYPE)
-
-    request = SIMPLE
-    request["prompt"] = prompt
+    if DEBUG:
+        print(prompt)
 
     try:
-        URI = f"{HOST}/v1/generate"
-        response = requests.post(URI, json=request)
-        if response.status_code == 200:
-            # result = response.json()['results'][0]['history']
-            result = response.json()
-            # print(f"raw API response: {result}")
-            result = result["results"][0]["text"]
-            print(f"json API response.text: {result}")
+        if HOST_TYPE == "webui":
+            result = get_webui_completion(prompt)
         else:
-            raise Exception(f"API call got non-200 response code")
+            raise ValueError(HOST_TYPE)
+    except requests.exceptions.ConnectionError as e:
+        raise ValueError(f"Was unable to connect to host {HOST}")
 
-        # cleaned_result, chatcompletion_result = parse_st_json_output(result)
-        chat_completion_result = llm_wrapper.output_to_chat_completion_response(result)
+    chat_completion_result = llm_wrapper.output_to_chat_completion_response(result)
+    if DEBUG:
         print(json.dumps(chat_completion_result, indent=2))
-        # print(cleaned_result)
 
-        # unpack with response.choices[0].message.content
-        response = DotDict(
-            {
-                "model": None,
-                "choices": [
-                    DotDict(
-                        {
-                            "message": DotDict(chat_completion_result),
-                            "finish_reason": "stop",  # TODO vary based on webui response
-                        }
-                    )
-                ],
-                "usage": DotDict(
+    # unpack with response.choices[0].message.content
+    response = DotDict(
+        {
+            "model": None,
+            "choices": [
+                DotDict(
                     {
-                        # TODO fix
-                        "prompt_tokens": 0,
-                        "completion_tokens": 0,
-                        "total_tokens": 0,
+                        "message": DotDict(chat_completion_result),
+                        "finish_reason": "stop",  # TODO vary based on backend response
                     }
-                ),
-            }
-        )
-        return response
-
-    except Exception as e:
-        # TODO
-        raise e
+                )
+            ],
+            "usage": DotDict(
+                {
+                    # TODO fix, actually use real info
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": 0,
+                }
+            ),
+        }
+    )
+    return response
diff --git a/memgpt/local_llm/llm_chat_completion_wrappers/airoboros.py b/memgpt/local_llm/llm_chat_completion_wrappers/airoboros.py
index 6b3a117f..98d3625e 100644
--- a/memgpt/local_llm/llm_chat_completion_wrappers/airoboros.py
+++ b/memgpt/local_llm/llm_chat_completion_wrappers/airoboros.py
@@ -12,12 +12,16 @@ class Airoboros21Wrapper(LLMChatCompletionWrapper):
     def __init__(
         self,
         simplify_json_content=True,
-        include_assistant_prefix=True,
         clean_function_args=True,
+        include_assistant_prefix=True,
+        include_opening_brace_in_prefix=True,
+        include_section_separators=True,
     ):
         self.simplify_json_content = simplify_json_content
-        self.include_assistant_prefix = include_assistant_prefix
         self.clean_func_args = clean_function_args
+        self.include_assistant_prefix = include_assistant_prefix
+        self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
+        self.include_section_separators = include_section_separators
 
     def chat_completion_to_prompt(self, messages, functions):
         """Example for airoboros: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#prompt-format
@@ -77,11 +81,41 @@ class Airoboros21Wrapper(LLMChatCompletionWrapper):
             # TODO we're ignoring schema['parameters']['required']
             return func_str
 
-        prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
+        # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
+        prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
         prompt += f"\nAvailable functions:"
         for function_dict in functions:
             prompt += f"\n{create_function_description(function_dict)}"
 
+        def create_function_call(function_call):
+            """Go from ChatCompletion to Airoboros style function trace (in prompt)
+
+            ChatCompletion data (inside message['function_call']):
+                "function_call": {
+                    "name": ...
+                    "arguments": {
+                        "arg1": val1,
+                        ...
+                    }
+
+            Airoboros output:
+                {
+                  "function": "send_message",
+                  "params": {
+                    "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
+                  }
+                }
+            """
+            airo_func_call = {
+                "function": function_call["name"],
+                "params": json.loads(function_call["arguments"]),
+            }
+            return json.dumps(airo_func_call, indent=2)
+
+        # Add a sep for the conversation
+        if self.include_section_separators:
+            prompt += "\n### INPUT"
+
         # Last are the user/assistant messages
         for message in messages[1:]:
             assert message["role"] in ["user", "assistant", "function"], message
@@ -96,16 +130,25 @@ class Airoboros21Wrapper(LLMChatCompletionWrapper):
                         prompt += f"\nUSER: {message['content']}"
             elif message["role"] == "assistant":
                 prompt += f"\nASSISTANT: {message['content']}"
+                # need to add the function call if there was one
+                if message["function_call"]:
+                    prompt += f"\n{create_function_call(message['function_call'])}"
             elif message["role"] == "function":
-                # TODO
-                continue
+                # TODO find a good way to add this
                 # prompt += f"\nASSISTANT: (function return) {message['content']}"
+                prompt += f"\nFUNCTION RETURN: {message['content']}"
+                continue
             else:
                 raise ValueError(message)
 
+        # Add a sep for the response
+        if self.include_section_separators:
+            prompt += "\n### RESPONSE"
+
         if self.include_assistant_prefix:
-            # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
             prompt += f"\nASSISTANT:"
+            if self.include_opening_brance_in_prefix:
+                prompt += "\n{"
 
         return prompt
 
@@ -135,7 +178,13 @@ class Airoboros21Wrapper(LLMChatCompletionWrapper):
             }
         }
         """
-        function_json_output = json.loads(raw_llm_output)
+        if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
+            raw_llm_output = "{" + raw_llm_output
+
+        try:
+            function_json_output = json.loads(raw_llm_output)
+        except Exception as e:
+            raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output}")
         function_name = function_json_output["function"]
         function_parameters = function_json_output["params"]
 
diff --git a/memgpt/local_llm/utils.py b/memgpt/local_llm/utils.py
new file mode 100644
index 00000000..42a0ce27
--- /dev/null
+++ b/memgpt/local_llm/utils.py
@@ -0,0 +1,8 @@
+class DotDict(dict):
+    """Allow dot access on properties similar to OpenAI response object"""
+
+    def __getattr__(self, attr):
+        return self.get(attr)
+
+    def __setattr__(self, key, value):
+        self[key] = value
diff --git a/memgpt/local_llm/webui/api.py b/memgpt/local_llm/webui/api.py
new file mode 100644
index 00000000..3cff08e0
--- /dev/null
+++ b/memgpt/local_llm/webui/api.py
@@ -0,0 +1,33 @@
+import os
+import requests
+
+from .settings import SIMPLE
+
+HOST = os.getenv("OPENAI_API_BASE")
+HOST_TYPE = os.getenv("BACKEND_TYPE")  # default None == ChatCompletion
+WEBUI_API_SUFFIX = "/v1/generate"
+DEBUG = True
+
+
+def get_webui_completion(prompt, settings=SIMPLE):
+    """See https://github.com/oobabooga/text-generation-webui for instructions on how to run the LLM web server"""
+
+    # Settings for the generation, includes the prompt + stop tokens, max length, etc
+    request = settings
+    request["prompt"] = prompt
+
+    try:
+        URI = f"{HOST}{WEBUI_API_SUFFIX}"
+        response = requests.post(URI, json=request)
+        if response.status_code == 200:
+            result = response.json()
+            result = result["results"][0]["text"]
+            if DEBUG:
+                print(f"json API response.text: {result}")
+        else:
+            raise Exception(f"API call got non-200 response code")
+    except:
+        # TODO handle gracefully
+        raise
+
+    return result
diff --git a/memgpt/local_llm/webui/settings.py b/memgpt/local_llm/webui/settings.py
new file mode 100644
index 00000000..2e9ecbce
--- /dev/null
+++ b/memgpt/local_llm/webui/settings.py
@@ -0,0 +1,12 @@
+SIMPLE = {
+    "stopping_strings": [
+        "\nUSER:",
+        "\nASSISTANT:",
+        # '\n' +
+        # '</s>',
+        # '<|',
+        # '\n#',
+        # '\n\n\n',
+    ],
+    "truncation_length": 4096,  # assuming llama2 models
+}
diff --git a/memgpt/local_llm/webui_settings.py b/memgpt/local_llm/webui_settings.py
deleted file mode 100644
index 2601f642..00000000
--- a/memgpt/local_llm/webui_settings.py
+++ /dev/null
@@ -1,54 +0,0 @@
-DETERMINISTIC = {
-    "max_new_tokens": 250,
-    "do_sample": False,
-    "temperature": 0,
-    "top_p": 0,
-    "typical_p": 1,
-    "repetition_penalty": 1.18,
-    "repetition_penalty_range": 0,
-    "encoder_repetition_penalty": 1,
-    "top_k": 1,
-    "min_length": 0,
-    "no_repeat_ngram_size": 0,
-    "num_beams": 1,
-    "penalty_alpha": 0,
-    "length_penalty": 1,
-    "early_stopping": False,
-    "guidance_scale": 1,
-    "negative_prompt": "",
-    "seed": -1,
-    "add_bos_token": True,
-    "stopping_strings": [
-        "\nUSER:",
-        "\nASSISTANT:",
-        # '\n' +
-        # '</s>',
-        # '<|',
-        # '\n#',
-        # '\n\n\n',
-    ],
-    "truncation_length": 4096,
-    "ban_eos_token": False,
-    "skip_special_tokens": True,
-    "top_a": 0,
-    "tfs": 1,
-    "epsilon_cutoff": 0,
-    "eta_cutoff": 0,
-    "mirostat_mode": 2,
-    "mirostat_tau": 4,
-    "mirostat_eta": 0.1,
-    "use_mancer": False,
-}
-
-SIMPLE = {
-    "stopping_strings": [
-        "\nUSER:",
-        "\nASSISTANT:",
-        # '\n' +
-        # '</s>',
-        # '<|',
-        # '\n#',
-        # '\n\n\n',
-    ],
-    "truncation_length": 4096,
-}