diff --git a/agentops/llm_tracker.py b/agentops/llm_tracker.py
index 539bd56a..507b8504 100644
--- a/agentops/llm_tracker.py
+++ b/agentops/llm_tracker.py
@@ -7,6 +7,7 @@
 from .event import LLMEvent, ErrorEvent
 from .helpers import get_ISO_time, check_call_stack_for_agent_id
 import inspect
+import pprint
 
 
 class LlmTracker:
@@ -26,44 +27,51 @@ class LlmTracker:
 
     def __init__(self, client):
         self.client = client
-        self.completion = ""
-        self.llm_event: LLMEvent = None
 
     def _handle_response_v0_openai(self, response, kwargs, init_timestamp):
         """Handle responses for OpenAI versions <v1.0.0"""
 
-        self.completion = ""
-        self.llm_event = None
+        self.llm_event = LLMEvent(
+            init_timestamp=init_timestamp,
+            params=kwargs
+        )
 
         def handle_stream_chunk(chunk):
-            self.llm_event = LLMEvent(
-                init_timestamp=init_timestamp,
-                params=kwargs
-            )
+            # NOTE: prompt/completion usage not returned in response when streaming
+            # We take the first ChatCompletionChunk and accumulate the deltas from all subsequent chunks to build one full chat completion
+            if self.llm_event.returns == None:
+                self.llm_event.returns = chunk
 
             try:
-                # NOTE: prompt/completion usage not returned in response when streaming
-                model = chunk['model']
-                choices = chunk['choices']
-                token = choices[0]['delta'].get('content', '')
-                finish_reason = choices[0]['finish_reason']
-                if token:
-                    self.completion += token
-
-                if finish_reason:
-                    self.llm_event.agent_id = check_call_stack_for_agent_id()
-                    self.llm_event.prompt = kwargs["messages"]
-                    self.llm_event.completion = {"role": "assistant", "content": self.completion}
-                    self.llm_event.returns = {"finish_reason": finish_reason, "content": self.completion}
-                    self.llm_event.model = model
+                accumulated_delta = self.llm_event.returns['choices'][0]['delta']
+                self.llm_event.agent_id = check_call_stack_for_agent_id()
+                self.llm_event.model = chunk['model']
+                self.llm_event.prompt = kwargs["messages"]
+                choice = chunk['choices'][0]  # NOTE: We assume for completion only choices[0] is relevant
+
+                if choice['delta'].get('content'):
+                    accumulated_delta['content'] += choice['delta'].content
+
+                if choice['delta'].get('role'):
+                    accumulated_delta['role'] = choice['delta'].get('role')
+
+                if choice['finish_reason']:
+                    # Streaming is done. Record LLMEvent
+                    self.llm_event.returns.choices[0]['finish_reason'] = choice['finish_reason']
+                    self.llm_event.completion = {
+                        "role": accumulated_delta['role'], "content": accumulated_delta['content']}
                     self.llm_event.end_timestamp = get_ISO_time()
 
                     self.client.record(self.llm_event)
             except Exception as e:
                 self.client.record(ErrorEvent(trigger_event=self.llm_event, exception=e))
-                # TODO: This error is specific to only one path of failure. Should be more generic or have different logger for different paths
+                kwargs_str = pprint.pformat(kwargs)
+                chunk = pprint.pformat(chunk)
                 logger.warning(
-                    f"🖇 AgentOps: Unable to parse a chunk for LLM call {kwargs} - skipping upload to AgentOps")
+                    f"🖇 AgentOps: Unable to parse a chunk for LLM call. Skipping upload to AgentOps\n"
+                    f"chunk:\n {chunk}\n"
+                    f"kwargs:\n {kwargs_str}\n"
+                )
 
         # if the response is a generator, decorate the generator
         if inspect.isasyncgen(response):
@@ -84,7 +92,8 @@ def generator():
 
         self.llm_event = LLMEvent(
             init_timestamp=init_timestamp,
-            params=kwargs
+            params=kwargs,
+            returns=response
         )
         # v0.0.0 responses are dicts
         try:
@@ -93,16 +102,19 @@ def generator():
             self.llm_event.prompt_tokens = response['usage']['prompt_tokens']
             self.llm_event.completion = {"role": "assistant", "content": response['choices'][0]['message']['content']}
             self.llm_event.completion_tokens = response['usage']['completion_tokens']
-            self.llm_event.returns = {"content": response['choices'][0]['message']['content']}
             self.llm_event.model = response["model"]
             self.llm_event.end_timestamp = get_ISO_time()
 
             self.client.record(self.llm_event)
         except Exception as e:
             self.client.record(ErrorEvent(trigger_event=self.llm_event, exception=e))
-            # TODO: This error is specific to only one path of failure. Should be more generic or have different logger for different paths
+            kwargs_str = pprint.pformat(kwargs)
+            response = pprint.pformat(response)
             logger.warning(
-                f"🖇 AgentOps: Unable to parse a chunk for LLM call {kwargs} - skipping upload to AgentOps")
+                f"🖇 AgentOps: Unable to parse response for LLM call. Skipping upload to AgentOps\n"
+                f"response:\n {response}\n"
+                f"kwargs:\n {kwargs_str}\n"
+            )
 
         return response
 
@@ -112,43 +124,53 @@ def _handle_response_v1_openai(self, response, kwargs, init_timestamp):
         from openai.types.chat import ChatCompletionChunk
         from openai.resources import AsyncCompletions
 
-        self.completion = ""
-        self.llm_event = None
+        self.llm_event = LLMEvent(
+            init_timestamp=init_timestamp,
+            params=kwargs
+        )
 
         def handle_stream_chunk(chunk: ChatCompletionChunk):
-
-            self.llm_event = LLMEvent(
-                init_timestamp=init_timestamp,
-                params=kwargs
-            )
+            # NOTE: prompt/completion usage not returned in response when streaming
+            # We take the first ChatCompletionChunk and accumulate the deltas from all subsequent chunks to build one full chat completion
+            if self.llm_event.returns == None:
+                self.llm_event.returns = chunk
 
             try:
-                # NOTE: prompt/completion usage not returned in response when streaming
-                model = chunk.model
-                choices = chunk.choices
-                token = choices[0].delta.content
-                finish_reason = choices[0].finish_reason
-                function_call = choices[0].delta.function_call
-                tool_calls = choices[0].delta.tool_calls
-                role = choices[0].delta.role
-                if token:
-                    self.completion += token
-
-                if finish_reason:
-                    self.llm_event.agent_id = check_call_stack_for_agent_id()
-                    self.llm_event.prompt = kwargs["messages"]
-                    self.llm_event.completion = {"role": "assistant", "content": self.completion}
-                    self.llm_event.returns = {"finish_reason": finish_reason, "content": self.completion,
-                                              "function_call": function_call, "tool_calls": tool_calls, "role": role}
-                    self.llm_event.model = model
+                accumulated_delta = self.llm_event.returns.choices[0].delta
+                self.llm_event.agent_id = check_call_stack_for_agent_id()
+                self.llm_event.model = chunk.model
+                self.llm_event.prompt = kwargs["messages"]
+                choice = chunk.choices[0]  # NOTE: We assume for completion only choices[0] is relevant
+
+                if choice.delta.content:
+                    accumulated_delta.content += choice.delta.content
+
+                if choice.delta.role:
+                    accumulated_delta.role = choice.delta.role
+
+                if choice.delta.tool_calls:
+                    accumulated_delta.tool_calls = choice.delta.tool_calls
+
+                if choice.delta.function_call:
+                    accumulated_delta.function_call = choice.delta.function_call
+
+                if choice.finish_reason:
+                    # Streaming is done. Record LLMEvent
+                    self.llm_event.returns.choices[0].finish_reason = choice.finish_reason
+                    self.llm_event.completion = {"role": accumulated_delta.role, "content": accumulated_delta.content,
+                                                 "function_call": accumulated_delta.function_call, "tool_calls": accumulated_delta.tool_calls}
                     self.llm_event.end_timestamp = get_ISO_time()
 
                     self.client.record(self.llm_event)
             except Exception as e:
                 self.client.record(ErrorEvent(trigger_event=self.llm_event, exception=e))
-                # TODO: This error is specific to only one path of failure. Should be more generic or have different logger for different paths
+                kwargs_str = pprint.pformat(kwargs)
+                chunk = pprint.pformat(chunk)
                 logger.warning(
-                    f"🖇 AgentOps: Unable to parse a chunk for LLM call {kwargs} - skipping upload to AgentOps")
+                    f"🖇 AgentOps: Unable to parse a chunk for LLM call. Skipping upload to AgentOps\n"
+                    f"chunk:\n {chunk}\n"
+                    f"kwargs:\n {kwargs_str}\n"
+                )
 
         # if the response is a generator, decorate the generator
         if isinstance(response, Stream):
@@ -180,20 +202,24 @@ async def async_generator():
         )
         # v1.0.0+ responses are objects
         try:
+            self.llm_event.returns = response.model_dump()
             self.llm_event.agent_id = check_call_stack_for_agent_id()
             self.llm_event.prompt = kwargs["messages"]
             self.llm_event.prompt_tokens = response.usage.prompt_tokens
             self.llm_event.completion = response.choices[0].message.model_dump()
             self.llm_event.completion_tokens = response.usage.completion_tokens
-            self.llm_event.returns = response.model_dump()
             self.llm_event.model = response.model
 
             self.client.record(self.llm_event)
         except Exception as e:
             self.client.record(ErrorEvent(trigger_event=self.llm_event, exception=e))
-            # TODO: This error is specific to only one path of failure. Should be more generic or have different logger for different paths
+            kwargs_str = pprint.pformat(kwargs)
+            response = pprint.pformat(response)
             logger.warning(
-                f"🖇 AgentOps: Unable to parse a chunk for LLM call {kwargs} - skipping upload to AgentOps")
+                f"🖇 AgentOps: Unable to parse response for LLM call. Skipping upload to AgentOps\n"
+                f"response:\n {response}\n"
+                f"kwargs:\n {kwargs_str}\n"
+            )
 
         return response