diff --git a/litellm/__pycache__/utils.cpython-311.pyc b/litellm/__pycache__/utils.cpython-311.pyc
index 76bf2d548a..11fdde95d0 100644
Binary files a/litellm/__pycache__/utils.cpython-311.pyc and b/litellm/__pycache__/utils.cpython-311.pyc differ
diff --git a/litellm/integrations/__pycache__/helicone.cpython-311.pyc b/litellm/integrations/__pycache__/helicone.cpython-311.pyc
index 731a7e377f..27ab716961 100644
Binary files a/litellm/integrations/__pycache__/helicone.cpython-311.pyc and b/litellm/integrations/__pycache__/helicone.cpython-311.pyc differ
diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py
index 559481daa7..ec4e9b634d 100644
--- a/litellm/integrations/helicone.py
+++ b/litellm/integrations/helicone.py
@@ -2,6 +2,7 @@
 #    On success, logs events to Helicone
 import dotenv, os
 import requests
+from anthropic import HUMAN_PROMPT, AI_PROMPT
 dotenv.load_dotenv() # Loading env variables using dotenv
 import traceback
 class HeliconeLogger:
@@ -11,6 +12,23 @@ class HeliconeLogger:
         # Instance variables
         self.provider_url = "https://api.openai.com/v1"
         self.key = os.getenv('HELICONE_API_KEY')
+
+    def claude_mapping(self, model, messages, response_obj):
+        prompt = f"{HUMAN_PROMPT}" 
+        for message in messages:
+            if "role" in message:
+                if message["role"] == "user":
+                    prompt += f"{HUMAN_PROMPT}{message['content']}"
+                else:
+                    prompt += f"{AI_PROMPT}{message['content']}"
+            else:
+                prompt += f"{HUMAN_PROMPT}{message['content']}"
+        prompt += f"{AI_PROMPT}"
+        claude_provider_request = {"model": model, "prompt": prompt}
+
+        claude_response_obj = {"completion": response_obj['choices'][0]['message']['content'], "model": model, "stop_reason": "stop_sequence"}
+
+        return claude_provider_request, claude_response_obj
         
     def log_success(self, model, messages, response_obj, start_time, end_time):
         # Method definition
@@ -18,6 +36,9 @@ class HeliconeLogger:
             model = model if any(accepted_model in model for accepted_model in self.helicone_model_list) else "gpt-3.5-turbo"
             provider_request = {"model": model, "messages": messages}
 
+            if "claude" in model: 
+                provider_request, response_obj = self.claude_mapping(model=model, messages=messages, response_obj=response_obj)
+
             providerResponse = {
                 "json": response_obj, 
                 "headers": {"openai-version": "2020-10-01"}, 
diff --git a/litellm/tests/test_client.py b/litellm/tests/test_client.py
index 1b95914cb7..d791f10ebd 100644
--- a/litellm/tests/test_client.py
+++ b/litellm/tests/test_client.py
@@ -30,14 +30,22 @@ def test_completion_openai():
     except Exception as e:
         traceback.print_exc()
         pytest.fail(f"Error occurred: {e}")
-test_completion_openai()
-def test_completion_non_openai():
+
+
+def test_completion_claude():
     try:
         response = completion(model="claude-instant-1", messages=messages, logger_fn=logger_fn)
         # Add any assertions here to check the response
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
+def test_completion_non_openai():
+    try:
+        response = completion(model="command-nightly", messages=messages, logger_fn=logger_fn)
+        # Add any assertions here to check the response
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
 def test_embedding_openai():
     try:
         response = embedding(model='text-embedding-ada-002', input=[user_message], logger_fn=logger_fn)