diff --git a/memgpt/cli/cli_config.py b/memgpt/cli/cli_config.py
index 855f8fad..3f5c440a 100644
--- a/memgpt/cli/cli_config.py
+++ b/memgpt/cli/cli_config.py
@@ -65,10 +65,12 @@ def configure_llm_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials)
     model_endpoint_type, model_endpoint = None, None
 
     # get default
-    default_model_endpoint_type = config.default_llm_config.model_endpoint_type
-    if config.default_llm_config.model_endpoint_type is not None and config.default_llm_config.model_endpoint_type not in [
-        provider for provider in LLM_API_PROVIDER_OPTIONS if provider != "local"
-    ]:  # local model
+    default_model_endpoint_type = config.default_llm_config.model_endpoint_type if config.default_embedding_config else None
+    if (
+        config.default_llm_config
+        and config.default_llm_config.model_endpoint_type is not None
+        and config.default_llm_config.model_endpoint_type not in [provider for provider in LLM_API_PROVIDER_OPTIONS if provider != "local"]
+    ):  # local model
         default_model_endpoint_type = "local"
 
     provider = questionary.select(
@@ -270,7 +272,7 @@ def configure_llm_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials)
         backend_options = builtins.list(DEFAULT_ENDPOINTS.keys())
         # assert backend_options_old == backend_options, (backend_options_old, backend_options)
         default_model_endpoint_type = None
-        if config.default_llm_config.model_endpoint_type in backend_options:
+        if config.default_llm_config and config.default_llm_config.model_endpoint_type in backend_options:
             # set from previous config
             default_model_endpoint_type = config.default_llm_config.model_endpoint_type
         model_endpoint_type = questionary.select(
@@ -296,7 +298,7 @@ def configure_llm_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials)
                     model_endpoint = questionary.text("Enter default endpoint:", default=default_model_endpoint).ask()
                     if model_endpoint is None:
                         raise KeyboardInterrupt
-            elif config.default_llm_config.model_endpoint:
+            elif config.default_llm_config and config.default_llm_config.model_endpoint:
                 model_endpoint = questionary.text("Enter default endpoint:", default=config.default_llm_config.model_endpoint).ask()
                 if model_endpoint is None:
                     raise KeyboardInterrupt
@@ -425,7 +427,7 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
         other_option_str = "[enter model name manually]"
 
         # Check if the model we have set already is even in the list (informs our default)
-        valid_model = config.default_llm_config.model in hardcoded_model_options
+        valid_model = config.default_llm_config and config.default_llm_config.model in hardcoded_model_options
         model = questionary.select(
             "Select default model (recommended: gpt-4):",
             choices=hardcoded_model_options + [see_all_option_str, other_option_str],
@@ -440,7 +442,7 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
             model = questionary.select(
                 "Select default model (recommended: gpt-4):",
                 choices=fetched_model_options + [other_option_str],
-                default=config.default_llm_config.model if valid_model else fetched_model_options[0],
+                default=config.default_llm_config.model if (valid_model and config.default_llm_config) else fetched_model_options[0],
             ).ask()
             if model is None:
                 raise KeyboardInterrupt
@@ -590,7 +592,7 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
         if model_endpoint_type == "ollama":
             default_model = (
                 config.default_llm_config.model
-                if config.default_llm_config.model and config.default_llm_config.model_endpoint_type == "ollama"
+                if config.default_llm_config and config.default_llm_config.model_endpoint_type == "ollama"
                 else DEFAULT_OLLAMA_MODEL
             )
             model = questionary.text(
@@ -602,9 +604,7 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
             model = None if len(model) == 0 else model
 
         default_model = (
-            config.default_llm_config.model
-            if config.default_llm_config.model and config.default_llm_config.model_endpoint_type == "vllm"
-            else ""
+            config.default_llm_config.model if config.default_llm_config and config.default_llm_config.model_endpoint_type == "vllm" else ""
         )
 
         # vllm needs huggingface model tag
@@ -773,7 +773,7 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
 def configure_embedding_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials):
     # configure embedding endpoint
 
-    default_embedding_endpoint_type = config.default_embedding_config.embedding_endpoint_type
+    default_embedding_endpoint_type = config.default_embedding_config.embedding_endpoint_type if config.default_embedding_config else None
 
     embedding_endpoint_type, embedding_endpoint, embedding_dim, embedding_model = None, None, None, None
     embedding_provider = questionary.select(
@@ -839,7 +839,7 @@ def configure_embedding_endpoint(config: MemGPTConfig, credentials: MemGPTCreden
 
         # get model type
         default_embedding_model = (
-            config.default_embedding_config.embedding_model if config.default_embedding_config.embedding_model else "BAAI/bge-large-en-v1.5"
+            config.default_embedding_config.embedding_model if config.default_embedding_config else "BAAI/bge-large-en-v1.5"
         )
         embedding_model = questionary.text(
             "Enter HuggingFace model tag (e.g. BAAI/bge-large-en-v1.5):",
@@ -849,7 +849,7 @@ def configure_embedding_endpoint(config: MemGPTConfig, credentials: MemGPTCreden
             raise KeyboardInterrupt
 
         # get model dimentions
-        default_embedding_dim = config.default_embedding_config.embedding_dim if config.default_embedding_config.embedding_dim else "1024"
+        default_embedding_dim = config.default_embedding_config.embedding_dim if config.default_embedding_config else "1024"
         embedding_dim = questionary.text("Enter embedding model dimentions (e.g. 1024):", default=str(default_embedding_dim)).ask()
         if embedding_dim is None:
             raise KeyboardInterrupt