diff --git a/litellm/integrations/traceloop.py b/litellm/integrations/traceloop.py
index 019f77771b..c188ac8bda 100644
--- a/litellm/integrations/traceloop.py
+++ b/litellm/integrations/traceloop.py
@@ -11,13 +11,14 @@ class TraceloopLogger:
 
         try:
             tracer = self.tracer_wrapper.get_tracer()
-    
+
             model = kwargs.get("model")
 
             # LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK
-            if "gpt" in model:
+            if kwargs.get("litellm_params").get("custom_llm_provider") == "openai":
                 return
 
+            optional_params = kwargs.get("optional_params", {})
             with tracer.start_as_current_span(
                 "litellm.completion",
                 kind=SpanKind.CLIENT,
@@ -26,12 +27,38 @@ class TraceloopLogger:
                     span.set_attribute(
                         SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")
                     )
-                    span.set_attribute(
-                        SpanAttributes.LLM_REQUEST_MAX_TOKENS, kwargs.get("max_tokens")
-                    )
-                    span.set_attribute(
-                        SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature")
-                    )
+                    if "stop" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_CHAT_STOP_SEQUENCES, optional_params.get("stop")
+                        )
+                    if "frequency_penalty" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_FREQUENCY_PENALTY, optional_params.get("frequency_penalty")
+                        )
+                    if "presence_penalty" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_PRESENCE_PENALTY, optional_params.get("presence_penalty")
+                        )
+                    if "top_p" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
+                        )
+                    if "tools" in optional_params or "functions" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_REQUEST_FUNCTIONS, optional_params.get("tools", optional_params.get("functions"))
+                        )
+                    if "user" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_USER, optional_params.get("user")
+                        )
+                    if "max_tokens" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_REQUEST_MAX_TOKENS, kwargs.get("max_tokens")
+                        )
+                    if "temperature" in optional_params:
+                        span.set_attribute(
+                            SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature")
+                        )
 
                     for idx, prompt in enumerate(kwargs.get("messages")):
                         span.set_attribute(
diff --git a/litellm/utils.py b/litellm/utils.py
index bdd935119f..fce23ee720 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -222,7 +222,7 @@ class Delta(OpenAIObject):
 class Choices(OpenAIObject):
     def __init__(self, finish_reason=None, index=0, message=None, **params):
         super(Choices, self).__init__(**params)
-        self.finish_reason = map_finish_reason(finish_reason) # set finish_reason for all responses
+        self.finish_reason = map_finish_reason(finish_reason) or "stop" # set finish_reason for all responses
         self.index = index
         if message is None:
             self.message = Message(content=None)