diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py
index ce413be65..6cea6450b 100644
--- a/litellm/llms/anthropic.py
+++ b/litellm/llms/anthropic.py
@@ -2,14 +2,16 @@ import os, types
 import json
 from enum import Enum
 import requests
-import time
+import time, uuid
 from typing import Callable, Optional
-from litellm.utils import ModelResponse, Usage
+from litellm.utils import ModelResponse, Usage, map_finish_reason
 import litellm
 from .prompt_templates.factory import (
     prompt_factory,
     custom_prompt,
     construct_tool_use_system_prompt,
+    extract_between_tags,
+    parse_xml_params,
 )
 import httpx
 
@@ -114,6 +116,7 @@ def completion(
     headers={},
 ):
     headers = validate_environment(api_key, headers)
+    _is_function_call = False
     if model in custom_prompt_dict:
         # check if the model has a registered custom prompt
         model_prompt_details = custom_prompt_dict[model]
@@ -148,12 +151,14 @@ def completion(
 
     ## Handle Tool Calling
     if "tools" in optional_params:
+        _is_function_call = True
         tool_calling_system_prompt = construct_tool_use_system_prompt(
             tools=optional_params["tools"]
         )
         optional_params["system"] = (
-            optional_params("system", "\n") + tool_calling_system_prompt
+            optional_params.get("system", "\n") + tool_calling_system_prompt
         )  # add the anthropic tool calling prompt to the system prompt
+        optional_params.pop("tools")
 
     data = {
         "model": model,
@@ -221,8 +226,33 @@ def completion(
             )
         else:
             text_content = completion_response["content"][0].get("text", None)
-            model_response.choices[0].message.content = text_content  # type: ignore
-            model_response.choices[0].finish_reason = completion_response["stop_reason"]
+            ## TOOL CALLING - OUTPUT PARSE
+            if _is_function_call == True:
+                function_name = extract_between_tags("tool_name", text_content)[0]
+                function_arguments_str = extract_between_tags("invoke", text_content)[
+                    0
+                ].strip()
+                function_arguments_str = f"<invoke>{function_arguments_str}</invoke>"
+                function_arguments = parse_xml_params(function_arguments_str)
+                _message = litellm.Message(
+                    tool_calls=[
+                        {
+                            "id": f"call_{uuid.uuid4()}",
+                            "type": "function",
+                            "function": {
+                                "name": function_name,
+                                "arguments": json.dumps(function_arguments),
+                            },
+                        }
+                    ],
+                    content=None,
+                )
+                model_response.choices[0].message = _message
+            else:
+                model_response.choices[0].message.content = text_content  # type: ignore
+            model_response.choices[0].finish_reason = map_finish_reason(
+                completion_response["stop_reason"]
+            )
 
         ## CALCULATING USAGE
         prompt_tokens = completion_response["usage"]["input_tokens"]
diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py
index cc75237e0..2b0f4a2cf 100644
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@@ -1,6 +1,6 @@
 from enum import Enum
 import requests, traceback
-import json
+import json, re, xml.etree.ElementTree as ET
 from jinja2 import Template, exceptions, Environment, meta
 from typing import Optional, Any
 
@@ -520,6 +520,21 @@ def anthropic_messages_pt(messages: list):
     return new_messages
 
 
+def extract_between_tags(tag: str, string: str, strip: bool = False) -> list[str]:
+    ext_list = re.findall(f"<{tag}>(.+?)</{tag}>", string, re.DOTALL)
+    if strip:
+        ext_list = [e.strip() for e in ext_list]
+    return ext_list
+
+
+def parse_xml_params(xml_content):
+    root = ET.fromstring(xml_content)
+    params = {}
+    for child in root.findall(".//parameters/*"):
+        params[child.tag] = child.text
+    return params
+
+
 ###
 
 
diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index a9d41be8d..068ddc78f 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -99,6 +99,47 @@ def test_completion_claude_3():
         pytest.fail(f"Error occurred: {e}")
 
 
+def test_completion_claude_3_function_call():
+    litellm.set_verbose = True
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        },
+                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                    },
+                    "required": ["location"],
+                },
+            },
+        }
+    ]
+    messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
+    try:
+        # test without max tokens
+        response = completion(
+            model="anthropic/claude-3-opus-20240229",
+            messages=messages,
+            tools=tools,
+            tool_choice="auto",
+        )
+        # Add any assertions, here to check response args
+        print(response)
+        assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
+        assert isinstance(
+            response.choices[0].message.tool_calls[0].function.arguments, str
+        )
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
 def test_completion_claude_3_stream():
     litellm.set_verbose = False
     messages = [{"role": "user", "content": "Hello, world"}]