diff --git a/examples/demos/hackathon_feb16/sarasowti.py b/examples/demos/hackathon_feb16/sarasowti.py
index 5943d47f..668fcf86 100644
--- a/examples/demos/hackathon_feb16/sarasowti.py
+++ b/examples/demos/hackathon_feb16/sarasowti.py
@@ -1,6 +1,6 @@
 from dotenv import load_dotenv
 from swarms import Agent
-from swarms.utils.function_caller_model import OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 from pydantic import BaseModel, Field
 from swarms.structs.conversation import Conversation
 
@@ -180,8 +180,9 @@ Maintain a warm, friendly, and authentic presence while ensuring all interaction
 # Initialize Agents using swarms
 ########################################
 
-model = OpenAIFunctionCaller(
-    base_model=CallLog,
+model = LiteLLM(
+    model_name="gpt-4.1",
+    response_format=CallLog,
     system_prompt=MASTER_AGENT_SYS_PROMPT,
 )
 
@@ -192,7 +193,7 @@ counselor_agent = Agent(
     agent_description="Provides empathetic and effective college counseling and guidance.",
     system_prompt=COUNSELOR_AGENT_SYS_PROMPT,
     max_loops=1,
-    model_name="gpt-4o",
+    model_name="gpt-4.1",
     dynamic_temperature_enabled=True,
 )
 
@@ -202,7 +203,7 @@ buddy_agent = Agent(
     agent_description="Acts as a supportive, friendly companion to the student.",
     system_prompt=BUDDY_AGENT_SYS_PROMPT,
     max_loops=1,
-    model_name="gpt-4o",
+    model_name="gpt-4.1",
     dynamic_temperature_enabled=True,
 )
 
diff --git a/examples/demos/spike/agent_rearrange_test.py b/examples/demos/spike/agent_rearrange_test.py
index e6aa044d..eaf94368 100644
--- a/examples/demos/spike/agent_rearrange_test.py
+++ b/examples/demos/spike/agent_rearrange_test.py
@@ -9,7 +9,7 @@ Todo
 import os
 from dotenv import load_dotenv
 from swarms import Agent, AgentRearrange
-from swarm_models import OpenAIChat, OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 from pydantic import BaseModel
 from typing import List
 
@@ -31,10 +31,8 @@ load_dotenv()
 api_key = os.getenv("GROQ_API_KEY")
 
 # Initialize the model
-model = OpenAIChat(
-    openai_api_base="https://api.groq.com/openai/v1",
-    openai_api_key=api_key,
-    model_name="llama-3.1-70b-versatile",
+model = LiteLLM(
+    model_name="groq/llama-3.1-70b-versatile",
     temperature=0.1,
 )
 
@@ -52,11 +50,11 @@ You are a college selection final decision maker. Your role is to:
     
 """
 
-function_caller = OpenAIFunctionCaller(
+function_caller = LiteLLM(
+    model_name="gpt-4.1",
     system_prompt=FINAL_AGENT_PROMPT,
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
-    base_model=CollegesRecommendation,
-    parallel_tool_calls=True,
+    response_format=CollegesRecommendation,
+    temperature=0.1,
 )
 
 # Student Profile Analyzer Agent
diff --git a/examples/demos/spike/function_caller_example.py b/examples/demos/spike/function_caller_example.py
index 0578df7d..dbf8f032 100644
--- a/examples/demos/spike/function_caller_example.py
+++ b/examples/demos/spike/function_caller_example.py
@@ -8,7 +8,7 @@ Todo
 
 import os
 from dotenv import load_dotenv
-from swarm_models import OpenAIChat, OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 from pydantic import BaseModel
 from typing import List
 
@@ -30,21 +30,19 @@ load_dotenv()
 api_key = os.getenv("GROQ_API_KEY")
 
 # Initialize the model
-model = OpenAIChat(
-    openai_api_base="https://api.groq.com/openai/v1",
-    openai_api_key=api_key,
-    model_name="llama-3.1-70b-versatile",
+model = LiteLLM(
+    model_name="groq/llama-3.1-70b-versatile",
     temperature=0.1,
 )
 
-function_caller = OpenAIFunctionCaller(
+function_caller = LiteLLM(
+    model_name="gpt-4.1",
     system_prompt="""You are a college selection final decision maker. Your role is to:
     - Balance all relevant factors and stakeholder input.
     - Only return the output in the schema format.
     """,
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
-    base_model=CollegesRecommendation,
-    # parallel_tool_calls=True,
+    response_format=CollegesRecommendation,
+    temperature=0.1,
 )
 
 
diff --git a/examples/demos/spike/test.py b/examples/demos/spike/test.py
index 3c1f5fb5..2bda04ea 100644
--- a/examples/demos/spike/test.py
+++ b/examples/demos/spike/test.py
@@ -9,7 +9,7 @@ Todo
 import os
 from dotenv import load_dotenv
 from swarms import Agent, SequentialWorkflow
-from swarm_models import OpenAIChat, OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 from pydantic import BaseModel
 from typing import List
 
@@ -31,10 +31,8 @@ load_dotenv()
 api_key = os.getenv("GROQ_API_KEY")
 
 # Initialize the model
-model = OpenAIChat(
-    openai_api_base="https://api.groq.com/openai/v1",
-    openai_api_key=api_key,
-    model_name="llama-3.1-70b-versatile",
+model = LiteLLM(
+    model_name="groq/llama-3.1-70b-versatile",
     temperature=0.1,
 )
 
@@ -52,11 +50,11 @@ You are a college selection final decision maker. Your role is to:
     
 """
 
-function_caller = OpenAIFunctionCaller(
+function_caller = LiteLLM(
+    model_name="gpt-4.1",
     system_prompt=FINAL_AGENT_PROMPT,
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
-    base_model=CollegesRecommendation,
-    parallel_tool_calls=True,
+    response_format=CollegesRecommendation,
+    temperature=0.1,
 )
 
 # Student Profile Analyzer Agent
diff --git a/examples/multi_agent/asb/auto_agent.py b/examples/multi_agent/asb/auto_agent.py
index 7c7ee1d1..7ac9039c 100644
--- a/examples/multi_agent/asb/auto_agent.py
+++ b/examples/multi_agent/asb/auto_agent.py
@@ -5,7 +5,7 @@ from typing import Any, Callable, Dict, Optional, Type, Union
 
 from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError, create_model
-from swarm_models.openai_function_caller import OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 
 
 class DynamicParser:
@@ -216,14 +216,13 @@ Your role is to make decisions and complete tasks independently without seeking
 Always respond in a strict JSON format as described below. Ensure your responses can be parsed with Python's `json.loads`:
 """
 
-# Initialize the OpenAIFunctionCaller
-model = OpenAIFunctionCaller(
+# Initialize the LiteLLM
+model = LiteLLM(
+    model_name="gpt-4.1",
     system_prompt=SYSTEM_PROMPT,
     max_tokens=4000,
     temperature=0.9,
-    base_model=AgentResponse,  # Pass the Pydantic schema as the base model
-    parallel_tool_calls=False,
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
+    response_format=AgentResponse,  # Pass the Pydantic schema as the response format
 )
 
 # Example usage
diff --git a/examples/multi_agent/hiearchical_swarm/hierarchical_swarm_example.py b/examples/multi_agent/hiearchical_swarm/hierarchical_swarm_example.py
index 5be9008f..7050d5ed 100644
--- a/examples/multi_agent/hiearchical_swarm/hierarchical_swarm_example.py
+++ b/examples/multi_agent/hiearchical_swarm/hierarchical_swarm_example.py
@@ -7,7 +7,7 @@ from swarms.structs.hiearchical_swarm import (
     HierarchicalSwarm,
     SwarmSpec,
 )
-from swarms.utils.function_caller_model import OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 
 load_dotenv()
 
@@ -15,9 +15,9 @@ load_dotenv()
 # ------------------------------------------------------------------------------
 # Director LLM: Responsible for orchestrating tasks among the agents
 # ------------------------------------------------------------------------------
-llm = OpenAIFunctionCaller(
-    base_model=SwarmSpec,
-    api_key=os.getenv("OPENAI_API_KEY"),
+llm = LiteLLM(
+    model_name="gpt-4.1",
+    response_format=SwarmSpec,
     system_prompt=(
         "As the Director of this Hierarchical Agent Swarm, you are in charge of "
         "coordinating and overseeing all tasks, ensuring that each is executed "
@@ -57,7 +57,7 @@ def main():
     # --------------------------------------------------------------------------
     analysis_agent = Agent(
         agent_name="Stock-Analysis-Agent",
-        model_name="gpt-4o",
+        model_name="gpt-4.1",
         max_loops=1,
         interactive=False,
         streaming_on=False,
diff --git a/examples/multi_agent/hiearchical_swarm/hs_stock_team.py b/examples/multi_agent/hiearchical_swarm/hs_stock_team.py
index d4cbe763..c336469f 100644
--- a/examples/multi_agent/hiearchical_swarm/hs_stock_team.py
+++ b/examples/multi_agent/hiearchical_swarm/hs_stock_team.py
@@ -7,16 +7,16 @@ from swarms.structs.hiearchical_swarm import (
     HierarchicalSwarm,
     SwarmSpec,
 )
-from swarms.utils.function_caller_model import OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 
 load_dotenv()
 
 # ------------------------------------------------------------------------------
 # Trading Director: Responsible for orchestrating tasks among multiple stock analysts
 # ------------------------------------------------------------------------------
-director_llm = OpenAIFunctionCaller(
-    base_model=SwarmSpec,
-    api_key=os.getenv("OPENAI_API_KEY"),
+director_llm = LiteLLM(
+    model_name="gpt-4.1",
+    response_format=SwarmSpec,
     system_prompt=(
         "You are the Trading Director in charge of coordinating a team of specialized "
         "Stock Analysts. Your responsibilities include:\n\n"
@@ -51,7 +51,7 @@ def main():
     # --------------------------------------------------------------------------
     macro_agent = Agent(
         agent_name="Macro-Economic-Analysis-Agent",
-        model_name="gpt-4o",
+        model_name="gpt-4.1",
         max_loops=1,
         interactive=False,
         streaming_on=False,
@@ -81,7 +81,7 @@ def main():
     # --------------------------------------------------------------------------
     sector_agent = Agent(
         agent_name="Sector-Performance-Analysis-Agent",
-        model_name="gpt-4o",
+        model_name="gpt-4.1",
         max_loops=1,
         interactive=False,
         streaming_on=False,
@@ -113,7 +113,7 @@ def main():
     # --------------------------------------------------------------------------
     technical_agent = Agent(
         agent_name="Technical-Analysis-Agent",
-        model_name="gpt-4o",
+        model_name="gpt-4.1",
         max_loops=1,
         interactive=False,
         streaming_on=False,
@@ -145,7 +145,7 @@ def main():
     # --------------------------------------------------------------------------
     risk_agent = Agent(
         agent_name="Risk-Analysis-Agent",
-        model_name="gpt-4o",
+        model_name="gpt-4.1",
         max_loops=1,
         interactive=False,
         streaming_on=False,
diff --git a/pyproject.toml b/pyproject.toml
index d563d02e..64a354e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -75,7 +75,6 @@ numpy = "*"
 litellm = "*"
 httpx = "*"
 mcp = "*"
-openai = "*"
 aiohttp = "*"
 orjson = "*"
 schedule = "*"
diff --git a/requirements.txt b/requirements.txt
index d1103e64..6eb2936b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,7 +24,6 @@ httpx
 aiohttp
 mcp
 numpy
-openai
 orjson
 schedule
 uvloop
diff --git a/swarms/structs/meme_agent_persona_generator.py b/swarms/structs/meme_agent_persona_generator.py
index 1866ea45..aed37a49 100644
--- a/swarms/structs/meme_agent_persona_generator.py
+++ b/swarms/structs/meme_agent_persona_generator.py
@@ -9,7 +9,7 @@ from pydantic import BaseModel, Field
 
 from swarms.structs.agent import Agent
 from swarms.structs.swarm_router import SwarmRouter
-from swarms.utils.function_caller_model import OpenAIFunctionCaller
+from swarms.utils.litellm_wrapper import LiteLLM
 
 load_dotenv()
 
@@ -162,11 +162,11 @@ class MemeAgentGenerator:
             list: List of created agents
         """
         logger.info("Creating agents for task")
-        model = OpenAIFunctionCaller(
+        model = LiteLLM(
+            model_name="gpt-4.1",
             system_prompt=BOSS_SYSTEM_PROMPT,
-            api_key=os.getenv("OPENAI_API_KEY"),
             temperature=0.1,
-            base_model=MemeSwarmConfig,
+            response_format=MemeSwarmConfig,
         )
 
         agents_dictionary = model.run(task)
@@ -227,7 +227,7 @@ class MemeAgentGenerator:
             agent_name=agent_name,
             description=agent_description,
             system_prompt=agent_system_prompt,
-            model_name="gpt-4o-mini",
+            model_name="gpt-4.1",
             max_loops=max_loops,
             autosave=True,
             dashboard=False,
diff --git a/swarms/structs/model_router.py b/swarms/structs/model_router.py
index 32015e4a..e2668697 100644
--- a/swarms/structs/model_router.py
+++ b/swarms/structs/model_router.py
@@ -4,7 +4,6 @@ from concurrent.futures import ThreadPoolExecutor
 from typing import Optional
 from pydantic import BaseModel, Field
 
-from swarms.utils.function_caller_model import OpenAIFunctionCaller
 from swarms.utils.any_to_str import any_to_str
 from swarms.utils.formatter import formatter
 from swarms.utils.litellm_wrapper import LiteLLM
@@ -174,7 +173,7 @@ class ModelRouter:
         temperature (float): Temperature parameter for model randomness
         max_workers (int): Maximum concurrent workers for batch processing
         model_output (ModelOutput): Pydantic model for structured outputs
-        model_caller (OpenAIFunctionCaller): Function calling interface
+        model_caller (LiteLLM): Function calling interface
     """
 
     def __init__(
@@ -210,11 +209,11 @@ class ModelRouter:
             if self.max_workers == "auto":
                 self.max_workers = os.cpu_count()
 
-            self.model_caller = OpenAIFunctionCaller(
-                base_model=ModelOutput,
+            self.model_caller = LiteLLM(
+                model_name="gpt-4o",
+                response_format=ModelOutput,
                 temperature=self.temperature,
                 system_prompt=self.system_prompt,
-                api_key=api_key,
             )
         except Exception as e:
             raise RuntimeError(
diff --git a/swarms/utils/function_caller_model.py b/swarms/utils/function_caller_model.py
deleted file mode 100644
index fb9135fc..00000000
--- a/swarms/utils/function_caller_model.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-from concurrent.futures import ThreadPoolExecutor
-from typing import List
-
-from pydantic import BaseModel
-
-
-from openai import OpenAI
-
-SUPPORTED_MODELS = [
-    "o3-mini-2025-1-31",
-    "o1-2024-12-17",
-    "gpt-4o-mini-2024-07-18",
-    "gpt-4o-2024-08-06",
-]
-
-
-def check_api_key():
-    api_key = os.getenv("OPENAI_API_KEY")
-    if api_key is None:
-        raise ValueError(
-            "API key is not set. Please set the API key using the api_key parameter."
-        )
-    return api_key
-
-
-class OpenAIFunctionCaller:
-    """
-    A class to interact with the OpenAI API for generating text based on a system prompt and a task.
-
-    Attributes:
-    - system_prompt (str): The system prompt to guide the AI's response.
-    - api_key (str): The API key for the OpenAI service.
-    - temperature (float): The temperature parameter for the AI model, controlling randomness.
-    - base_model (BaseModel): The Pydantic model to parse the response into.
-    - max_tokens (int): The maximum number of tokens in the response.
-    - client (OpenAI): The OpenAI client instance.
-    """
-
-    def __init__(
-        self,
-        system_prompt: str,
-        base_model: BaseModel,
-        api_key: str = os.getenv("OPENAI_API_KEY"),
-        temperature: float = 0.1,
-        max_tokens: int = 5000,
-        model_name: str = "gpt-4o-2024-08-06",
-    ):
-        self.system_prompt = system_prompt
-        self.api_key = api_key
-        self.temperature = temperature
-        self.base_model = base_model
-        self.max_tokens = max_tokens
-        self.model_name = model_name
-
-        self.client = OpenAI(api_key=self.api_key)
-
-    def run(self, task: str):
-        """
-        Run the OpenAI model with the system prompt and task to generate a response.
-
-        Args:
-        - task (str): The task to be completed.
-        - *args: Additional positional arguments for the OpenAI API.
-        - **kwargs: Additional keyword arguments for the OpenAI API.
-
-        Returns:
-        - BaseModel: The parsed response based on the base_model.
-        """
-        try:
-            completion = self.client.beta.chat.completions.parse(
-                model=self.model_name,
-                messages=[
-                    {"role": "system", "content": self.system_prompt},
-                    {"role": "user", "content": task},
-                ],
-                response_format=self.base_model,
-                max_tokens=self.max_tokens,
-                temperature=self.temperature,
-            )
-
-            return completion.choices[0].message.parsed
-
-        except Exception as e:
-            print(f"There was an error: {e}")
-
-    def check_model_support(self):
-        # need to print the supported models
-        for model in SUPPORTED_MODELS:
-            print(model)
-
-        return SUPPORTED_MODELS
-
-    def batch_run(self, tasks: List[str]) -> List[BaseModel]:
-        """
-        Batch run the OpenAI model with the system prompt and task to generate a response.
-        """
-        return [self.run(task) for task in tasks]
-
-    def concurrent_run(self, tasks: List[str]) -> List[BaseModel]:
-        """
-        Concurrent run the OpenAI model with the system prompt and task to generate a response.
-        """
-        with ThreadPoolExecutor(max_workers=len(tasks)) as executor:
-            return list(executor.map(self.run, tasks))
-
-
-# class TestModel(BaseModel):
-#     name: str
-#     age: int
-
-# # Example usage
-# model = OpenAIFunctionCaller(
-#     system_prompt="You are a helpful assistant that returns structured data about people.",
-#     base_model=TestModel,
-#     api_key=os.getenv("OPENAI_API_KEY"),
-#     temperature=0.7,
-#     max_tokens=1000
-# )
-
-# # Test with a more appropriate prompt for the TestModel schema
-# response = model.run("Tell me about a person named John who is 25 years old")
-# print(response)
diff --git a/tests/test_comprehensive_test.py b/tests/test_comprehensive_test.py
index 2ef00e1e..5c1dc65d 100644
--- a/tests/test_comprehensive_test.py
+++ b/tests/test_comprehensive_test.py
@@ -159,7 +159,7 @@ def write_markdown_report(
 def create_test_agent(
     name: str,
     system_prompt: str = None,
-    model_name: str = "gpt-4o-mini",
+    model_name: str = "gpt-4.1",
     tools: List[Callable] = None,
     **kwargs,
 ) -> Agent:
@@ -463,9 +463,7 @@ def test_spreadsheet_swarm():
 def test_hierarchical_swarm():
     """Test HierarchicalSwarm structure"""
     try:
-        from swarms.utils.function_caller_model import (
-            OpenAIFunctionCaller,
-        )
+        from swarms.utils.litellm_wrapper import LiteLLM
         from swarms.structs.hiearchical_swarm import SwarmSpec
 
         # Create worker agents
@@ -481,9 +479,9 @@ def test_hierarchical_swarm():
         ]
 
         # Create director agent with explicit knowledge of available agents
-        director = OpenAIFunctionCaller(
-            base_model=SwarmSpec,
-            api_key=API_KEY,
+        director = LiteLLM(
+            model_name="gpt-4.1",
+            response_format=SwarmSpec,
             system_prompt=(
                 "As the Director of this Hierarchical Agent Swarm, you coordinate tasks among agents. "
                 "You must ONLY assign tasks to the following available agents:\n"