diff --git a/examples/misc/multillm_memory.py b/examples/misc/multillm_memory.py
index ff8f275e5..3389a9475 100644
--- a/examples/misc/multillm_memory.py
+++ b/examples/misc/multillm_memory.py
@@ -10,21 +10,20 @@ Example: GPT-4 analyzes a tech stack → Claude writes documentation →
 Data analyst analyzes user data → All models can reference previous research.
 """
 
-from dotenv import load_dotenv
 import logging
-from mem0 import MemoryClient
+
+from dotenv import load_dotenv
 from litellm import completion
 
+from mem0 import MemoryClient
+
 load_dotenv()
 
 # Configure logging
 logging.basicConfig(
     level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.StreamHandler(),
-        logging.FileHandler('research_team.log')
-    ]
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler(), logging.FileHandler("research_team.log")],
 )
 logger = logging.getLogger(__name__)
 
@@ -36,16 +35,16 @@ memory = MemoryClient()
 RESEARCH_TEAM = {
     "tech_analyst": {
         "model": "gpt-4o",
-        "role": "Technical Analyst - Code review, architecture, and technical decisions"
+        "role": "Technical Analyst - Code review, architecture, and technical decisions",
     },
     "writer": {
         "model": "claude-3-5-sonnet-20241022",
-        "role": "Documentation Writer - Clear explanations and user guides"
+        "role": "Documentation Writer - Clear explanations and user guides",
     },
     "data_analyst": {
         "model": "gpt-4o-mini",
-        "role": "Data Analyst - Insights, trends, and data-driven recommendations"
-    }
+        "role": "Data Analyst - Insights, trends, and data-driven recommendations",
+    },
 }
 
 
@@ -65,11 +64,7 @@ def get_team_knowledge(topic: str, project_id: str) -> str:
     return "Team Knowledge Base: Empty - starting fresh research"
 
 
-def research_with_specialist(
-        task: str,
-        specialist: str,
-        project_id: str
-) -> str:
+def research_with_specialist(task: str, specialist: str, project_id: str) -> str:
     """Assign research task to specialist with access to team knowledge"""
 
     if specialist not in RESEARCH_TEAM:
@@ -91,30 +86,20 @@ Provide actionable insights in your area of expertise."""
     # Call the specialist's model
     response = completion(
         model=spec_info["model"],
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": task}
-        ]
+        messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": task}],
     )
 
     result = response.choices[0].message.content
 
     # Store research in shared knowledge base using both user_id and agent_id
-    research_entry = [
-        {"role": "user", "content": f"Task: {task}"},
-        {"role": "assistant", "content": result}
-    ]
+    research_entry = [{"role": "user", "content": f"Task: {task}"}, {"role": "assistant", "content": result}]
 
     memory.add(
         research_entry,
         user_id=project_id,  # Project-level memory
         agent_id=specialist,  # Agent-specific memory
-        metadata={
-            "contributor": specialist,
-            "task_type": "research",
-            "model_used": spec_info["model"]
-        },
-        output_format="v1.1"
+        metadata={"contributor": specialist, "task_type": "research", "model_used": spec_info["model"]},
+        output_format="v1.1",
     )
 
     return result
@@ -155,23 +140,23 @@ def demo_research_team():
         {
             "stage": "Technical Architecture",
             "specialist": "tech_analyst",
-            "task": "Analyze the best tech stack for a multi-tenant SaaS platform handling 10k+ users. Consider scalability, cost, and development speed."
+            "task": "Analyze the best tech stack for a multi-tenant SaaS platform handling 10k+ users. Consider scalability, cost, and development speed.",
         },
         {
             "stage": "Product Documentation",
             "specialist": "writer",
-            "task": "Based on the technical analysis, write a clear product overview and user onboarding guide for our SaaS platform."
+            "task": "Based on the technical analysis, write a clear product overview and user onboarding guide for our SaaS platform.",
         },
         {
             "stage": "Market Analysis",
             "specialist": "data_analyst",
-            "task": "Analyze market trends and pricing strategies for our SaaS platform. What metrics should we track?"
+            "task": "Analyze market trends and pricing strategies for our SaaS platform. What metrics should we track?",
         },
         {
             "stage": "Strategic Decision",
             "specialist": "tech_analyst",
-            "task": "Given our technical architecture, documentation, and market analysis - what should be our MVP feature priority?"
-        }
+            "task": "Given our technical architecture, documentation, and market analysis - what should be our MVP feature priority?",
+        },
     ]
 
     logger.info("AI Research Team: Building a SaaS Product")
@@ -181,7 +166,7 @@ def demo_research_team():
         logger.info(f"\nStage {i}: {step['stage']}")
         logger.info(f"Specialist: {step['specialist']}")
 
-        result = research_with_specialist(step['task'], step['specialist'], project)
+        result = research_with_specialist(step["task"], step["specialist"], project)
         logger.info(f"Task: {step['task']}")
         logger.info(f"Result: {result[:200]}...\n")
 
diff --git a/mem0/configs/llms/anthropic.py b/mem0/configs/llms/anthropic.py
new file mode 100644
index 000000000..5fd921a98
--- /dev/null
+++ b/mem0/configs/llms/anthropic.py
@@ -0,0 +1,56 @@
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class AnthropicConfig(BaseLlmConfig):
+    """
+    Configuration class for Anthropic-specific parameters.
+    Inherits from BaseLlmConfig and adds Anthropic-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # Anthropic-specific parameters
+        anthropic_base_url: Optional[str] = None,
+    ):
+        """
+        Initialize Anthropic configuration.
+
+        Args:
+            model: Anthropic model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: Anthropic API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            anthropic_base_url: Anthropic API base URL, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # Anthropic-specific parameters
+        self.anthropic_base_url = anthropic_base_url
diff --git a/mem0/configs/llms/azure.py b/mem0/configs/llms/azure.py
new file mode 100644
index 000000000..f4eb859a2
--- /dev/null
+++ b/mem0/configs/llms/azure.py
@@ -0,0 +1,57 @@
+from typing import Any, Dict, Optional
+
+from mem0.configs.base import AzureConfig
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class AzureOpenAIConfig(BaseLlmConfig):
+    """
+    Configuration class for Azure OpenAI-specific parameters.
+    Inherits from BaseLlmConfig and adds Azure OpenAI-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # Azure OpenAI-specific parameters
+        azure_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Initialize Azure OpenAI configuration.
+
+        Args:
+            model: Azure OpenAI model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: Azure OpenAI API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            azure_kwargs: Azure-specific configuration, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # Azure OpenAI-specific parameters
+        self.azure_kwargs = AzureConfig(**(azure_kwargs or {}))
diff --git a/mem0/configs/llms/base.py b/mem0/configs/llms/base.py
index 445674bf8..55561c632 100644
--- a/mem0/configs/llms/base.py
+++ b/mem0/configs/llms/base.py
@@ -3,12 +3,14 @@ from typing import Dict, Optional, Union
 
 import httpx
 
-from mem0.configs.base import AzureConfig
-
 
 class BaseLlmConfig(ABC):
     """
-    Config for LLMs.
+    Base configuration for LLMs with only common parameters.
+    Provider-specific configurations should be handled by separate config classes.
+
+    This class contains only the parameters that are common across all LLM providers.
+    For provider-specific parameters, use the appropriate provider config class.
     """
 
     def __init__(
@@ -21,89 +23,34 @@ class BaseLlmConfig(ABC):
         top_k: int = 1,
         enable_vision: bool = False,
         vision_details: Optional[str] = "auto",
-        # Openrouter specific
-        models: Optional[list[str]] = None,
-        route: Optional[str] = "fallback",
-        openrouter_base_url: Optional[str] = None,
-        # Openai specific
-        openai_base_url: Optional[str] = None,
-        site_url: Optional[str] = None,
-        app_name: Optional[str] = None,
-        # Ollama specific
-        ollama_base_url: Optional[str] = None,
-        # AzureOpenAI specific
-        azure_kwargs: Optional[AzureConfig] = {},
-        # AzureOpenAI specific
         http_client_proxies: Optional[Union[Dict, str]] = None,
-        # DeepSeek specific
-        deepseek_base_url: Optional[str] = None,
-        # XAI specific
-        xai_base_url: Optional[str] = None,
-        # Sarvam specific
-        sarvam_base_url: Optional[str] = "https://api.sarvam.ai/v1",
-        # LM Studio specific
-        lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
-        lmstudio_response_format: dict = None,
-        # vLLM specific
-        vllm_base_url: Optional[str] = "http://localhost:8000/v1",
-        # AWS Bedrock specific
-        aws_access_key_id: Optional[str] = None,
-        aws_secret_access_key: Optional[str] = None,
-        aws_region: Optional[str] = "us-west-2",
     ):
         """
-        Initializes a configuration class instance for the LLM.
+        Initialize a base configuration class instance for the LLM.
 
-        :param model: Controls the OpenAI model used, defaults to None
-        :type model: Optional[str], optional
-        :param temperature:  Controls the randomness of the model's output.
-        Higher values (closer to 1) make output more random, lower values make it more deterministic, defaults to 0
-        :type temperature: float, optional
-        :param api_key: OpenAI API key to be use, defaults to None
-        :type api_key: Optional[str], optional
-        :param max_tokens: Controls how many tokens are generated, defaults to 2000
-        :type max_tokens: int, optional
-        :param top_p: Controls the diversity of words. Higher values (closer to 1) make word selection more diverse,
-        defaults to 1
-        :type top_p: float, optional
-        :param top_k: Controls the diversity of words. Higher values make word selection more diverse, defaults to 0
-        :type top_k: int, optional
-        :param enable_vision: Enable vision for the LLM, defaults to False
-        :type enable_vision: bool, optional
-        :param vision_details: Details of the vision to be used [low, high, auto], defaults to "auto"
-        :type vision_details: Optional[str], optional
-        :param models: Openrouter models to use, defaults to None
-        :type models: Optional[list[str]], optional
-        :param route: Openrouter route to be used, defaults to "fallback"
-        :type route: Optional[str], optional
-        :param openrouter_base_url: Openrouter base URL to be use, defaults to "https://openrouter.ai/api/v1"
-        :type openrouter_base_url: Optional[str], optional
-        :param site_url: Openrouter site URL to use, defaults to None
-        :type site_url: Optional[str], optional
-        :param app_name: Openrouter app name to use, defaults to None
-        :type app_name: Optional[str], optional
-        :param ollama_base_url: The base URL of the LLM, defaults to None
-        :type ollama_base_url: Optional[str], optional
-        :param openai_base_url: Openai base URL to be use, defaults to "https://api.openai.com/v1"
-        :type openai_base_url: Optional[str], optional
-        :param azure_kwargs: key-value arguments for the AzureOpenAI LLM model, defaults a dict inside init
-        :type azure_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
-        :param http_client_proxies: The proxy server(s) settings used to create self.http_client, defaults to None
-        :type http_client_proxies: Optional[Dict | str], optional
-        :param deepseek_base_url: DeepSeek base URL to be use, defaults to None
-        :type deepseek_base_url: Optional[str], optional
-        :param xai_base_url: XAI base URL to be use, defaults to None
-        :type xai_base_url: Optional[str], optional
-        :param sarvam_base_url: Sarvam base URL to be use, defaults to "https://api.sarvam.ai/v1"
-        :type sarvam_base_url: Optional[str], optional
-        :param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
-        :type lmstudio_base_url: Optional[str], optional
-        :param lmstudio_response_format: LM Studio response format to be use, defaults to None
-        :type lmstudio_response_format: Optional[Dict], optional
-        :param vllm_base_url: vLLM base URL to be use, defaults to "http://localhost:8000/v1"
-        :type vllm_base_url: Optional[str], optional
+        Args:
+            model: The model identifier to use (e.g., "gpt-4o-mini", "claude-3-5-sonnet-20240620")
+                Defaults to None (will be set by provider-specific configs)
+            temperature: Controls the randomness of the model's output.
+                Higher values (closer to 1) make output more random, lower values make it more deterministic.
+                Range: 0.0 to 2.0. Defaults to 0.1
+            api_key: API key for the LLM provider. If None, will try to get from environment variables.
+                Defaults to None
+            max_tokens: Maximum number of tokens to generate in the response.
+                Range: 1 to 4096 (varies by model). Defaults to 2000
+            top_p: Nucleus sampling parameter. Controls diversity via nucleus sampling.
+                Higher values (closer to 1) make word selection more diverse.
+                Range: 0.0 to 1.0. Defaults to 0.1
+            top_k: Top-k sampling parameter. Limits the number of tokens considered for each step.
+                Higher values make word selection more diverse.
+                Range: 1 to 40. Defaults to 1
+            enable_vision: Whether to enable vision capabilities for the model.
+                Only applicable to vision-enabled models. Defaults to False
+            vision_details: Level of detail for vision processing.
+                Options: "low", "high", "auto". Defaults to "auto"
+            http_client_proxies: Proxy settings for HTTP client.
+                Can be a dict or string. Defaults to None
         """
-
         self.model = model
         self.temperature = temperature
         self.api_key = api_key
@@ -112,41 +59,4 @@ class BaseLlmConfig(ABC):
         self.top_k = top_k
         self.enable_vision = enable_vision
         self.vision_details = vision_details
-
-        # AzureOpenAI specific
         self.http_client = httpx.Client(proxies=http_client_proxies) if http_client_proxies else None
-
-        # Openrouter specific
-        self.models = models
-        self.route = route
-        self.openrouter_base_url = openrouter_base_url
-        self.openai_base_url = openai_base_url
-        self.site_url = site_url
-        self.app_name = app_name
-
-        # Ollama specific
-        self.ollama_base_url = ollama_base_url
-
-        # DeepSeek specific
-        self.deepseek_base_url = deepseek_base_url
-
-        # AzureOpenAI specific
-        self.azure_kwargs = AzureConfig(**azure_kwargs) or {}
-
-        # XAI specific
-        self.xai_base_url = xai_base_url
-
-        # Sarvam specific
-        self.sarvam_base_url = sarvam_base_url
-
-        # LM Studio specific
-        self.lmstudio_base_url = lmstudio_base_url
-        self.lmstudio_response_format = lmstudio_response_format
-
-        # vLLM specific
-        self.vllm_base_url = vllm_base_url
-
-        # AWS Bedrock specific
-        self.aws_access_key_id = aws_access_key_id
-        self.aws_secret_access_key = aws_secret_access_key
-        self.aws_region = aws_region
diff --git a/mem0/configs/llms/deepseek.py b/mem0/configs/llms/deepseek.py
new file mode 100644
index 000000000..461b5bced
--- /dev/null
+++ b/mem0/configs/llms/deepseek.py
@@ -0,0 +1,56 @@
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class DeepSeekConfig(BaseLlmConfig):
+    """
+    Configuration class for DeepSeek-specific parameters.
+    Inherits from BaseLlmConfig and adds DeepSeek-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # DeepSeek-specific parameters
+        deepseek_base_url: Optional[str] = None,
+    ):
+        """
+        Initialize DeepSeek configuration.
+
+        Args:
+            model: DeepSeek model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: DeepSeek API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            deepseek_base_url: DeepSeek API base URL, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # DeepSeek-specific parameters
+        self.deepseek_base_url = deepseek_base_url
diff --git a/mem0/configs/llms/lmstudio.py b/mem0/configs/llms/lmstudio.py
new file mode 100644
index 000000000..64abdd502
--- /dev/null
+++ b/mem0/configs/llms/lmstudio.py
@@ -0,0 +1,59 @@
+from typing import Any, Dict, Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class LMStudioConfig(BaseLlmConfig):
+    """
+    Configuration class for LM Studio-specific parameters.
+    Inherits from BaseLlmConfig and adds LM Studio-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # LM Studio-specific parameters
+        lmstudio_base_url: Optional[str] = None,
+        lmstudio_response_format: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Initialize LM Studio configuration.
+
+        Args:
+            model: LM Studio model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: LM Studio API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            lmstudio_base_url: LM Studio base URL, defaults to None
+            lmstudio_response_format: LM Studio response format, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # LM Studio-specific parameters
+        self.lmstudio_base_url = lmstudio_base_url or "http://localhost:1234/v1"
+        self.lmstudio_response_format = lmstudio_response_format
diff --git a/mem0/configs/llms/ollama.py b/mem0/configs/llms/ollama.py
new file mode 100644
index 000000000..75e1cea3f
--- /dev/null
+++ b/mem0/configs/llms/ollama.py
@@ -0,0 +1,56 @@
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class OllamaConfig(BaseLlmConfig):
+    """
+    Configuration class for Ollama-specific parameters.
+    Inherits from BaseLlmConfig and adds Ollama-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # Ollama-specific parameters
+        ollama_base_url: Optional[str] = None,
+    ):
+        """
+        Initialize Ollama configuration.
+
+        Args:
+            model: Ollama model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: Ollama API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            ollama_base_url: Ollama base URL, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # Ollama-specific parameters
+        self.ollama_base_url = ollama_base_url
diff --git a/mem0/configs/llms/openai.py b/mem0/configs/llms/openai.py
new file mode 100644
index 000000000..459557e58
--- /dev/null
+++ b/mem0/configs/llms/openai.py
@@ -0,0 +1,71 @@
+from typing import List, Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class OpenAIConfig(BaseLlmConfig):
+    """
+    Configuration class for OpenAI and OpenRouter-specific parameters.
+    Inherits from BaseLlmConfig and adds OpenAI-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # OpenAI-specific parameters
+        openai_base_url: Optional[str] = None,
+        models: Optional[List[str]] = None,
+        route: Optional[str] = "fallback",
+        openrouter_base_url: Optional[str] = None,
+        site_url: Optional[str] = None,
+        app_name: Optional[str] = None,
+    ):
+        """
+        Initialize OpenAI configuration.
+
+        Args:
+            model: OpenAI model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: OpenAI API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            openai_base_url: OpenAI API base URL, defaults to None
+            models: List of models for OpenRouter, defaults to None
+            route: OpenRouter route strategy, defaults to "fallback"
+            openrouter_base_url: OpenRouter base URL, defaults to None
+            site_url: Site URL for OpenRouter, defaults to None
+            app_name: Application name for OpenRouter, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # OpenAI-specific parameters
+        self.openai_base_url = openai_base_url
+        self.models = models
+        self.route = route
+        self.openrouter_base_url = openrouter_base_url
+        self.site_url = site_url
+        self.app_name = app_name
diff --git a/mem0/configs/llms/vllm.py b/mem0/configs/llms/vllm.py
new file mode 100644
index 000000000..45c6e2651
--- /dev/null
+++ b/mem0/configs/llms/vllm.py
@@ -0,0 +1,56 @@
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class VllmConfig(BaseLlmConfig):
+    """
+    Configuration class for vLLM-specific parameters.
+    Inherits from BaseLlmConfig and adds vLLM-specific settings.
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # vLLM-specific parameters
+        vllm_base_url: Optional[str] = None,
+    ):
+        """
+        Initialize vLLM configuration.
+
+        Args:
+            model: vLLM model to use, defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: vLLM API key, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            vllm_base_url: vLLM base URL, defaults to None
+        """
+        # Initialize base parameters
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        # vLLM-specific parameters
+        self.vllm_base_url = vllm_base_url or "http://localhost:8000/v1"
diff --git a/mem0/graphs/neptune/main.py b/mem0/graphs/neptune/main.py
index 986668316..1cfd2e3dd 100644
--- a/mem0/graphs/neptune/main.py
+++ b/mem0/graphs/neptune/main.py
@@ -1,4 +1,5 @@
 import logging
+
 from .base import NeptuneBase
 
 try:
diff --git a/mem0/llms/anthropic.py b/mem0/llms/anthropic.py
index 5f004ae8b..61c180a0a 100644
--- a/mem0/llms/anthropic.py
+++ b/mem0/llms/anthropic.py
@@ -1,17 +1,37 @@
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 try:
     import anthropic
 except ImportError:
     raise ImportError("The 'anthropic' library is required. Please install it using 'pip install anthropic'.")
 
+from mem0.configs.llms.anthropic import AnthropicConfig
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
 
 
 class AnthropicLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, AnthropicConfig, Dict]] = None):
+        # Convert to AnthropicConfig if needed
+        if config is None:
+            config = AnthropicConfig()
+        elif isinstance(config, dict):
+            config = AnthropicConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, AnthropicConfig):
+            # Convert BaseLlmConfig to AnthropicConfig
+            config = AnthropicConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
@@ -26,6 +46,7 @@ class AnthropicLLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
         Generate a response based on the given messages using Anthropic.
@@ -35,6 +56,7 @@ class AnthropicLLM(LLMBase):
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional Anthropic-specific parameters.
 
         Returns:
             str: The generated response.
@@ -48,14 +70,16 @@ class AnthropicLLM(LLMBase):
             else:
                 filtered_messages.append(message)
 
-        params = {
-            "model": self.config.model,
-            "messages": filtered_messages,
-            "system": system_message,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
+        # Get common parameters
+        params = self._get_common_params(**kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": filtered_messages,
+                "system": system_message,
+            }
+        )
+
         if tools:  # TODO: Remove tools if no issues found with new memory addition logic
             params["tools"] = tools
             params["tool_choice"] = tool_choice
diff --git a/mem0/llms/azure_openai.py b/mem0/llms/azure_openai.py
index c736c43c5..e7802bc31 100644
--- a/mem0/llms/azure_openai.py
+++ b/mem0/llms/azure_openai.py
@@ -1,16 +1,36 @@
 import json
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 from openai import AzureOpenAI
 
+from mem0.configs.llms.azure import AzureOpenAIConfig
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.base import LLMBase
 from mem0.memory.utils import extract_json
 
 
 class AzureOpenAILLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, AzureOpenAIConfig, Dict]] = None):
+        # Convert to AzureOpenAIConfig if needed
+        if config is None:
+            config = AzureOpenAIConfig()
+        elif isinstance(config, dict):
+            config = AzureOpenAIConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, AzureOpenAIConfig):
+            # Convert BaseLlmConfig to AzureOpenAIConfig
+            config = AzureOpenAIConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         # Model name should match the custom deployment name chosen for it.
@@ -68,6 +88,7 @@ class AzureOpenAILLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
         Generate a response based on the given messages using Azure OpenAI.
@@ -77,6 +98,7 @@ class AzureOpenAILLM(LLMBase):
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional Azure OpenAI-specific parameters.
 
         Returns:
             str: The generated response.
@@ -88,23 +110,29 @@ class AzureOpenAILLM(LLMBase):
 
         messages[-1]["content"] = user_prompt
 
-        common_params = {
-            "model": self.config.model,
-            "messages": messages,
-        }
+        # Get common parameters
+        params = self._get_common_params(**kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": messages,
+            }
+        )
 
         if self.config.model in {"o3-mini", "o1-preview", "o1"}:
-            params = common_params
+            # Use common params for these models
+            pass
         else:
-            params = {
-                **common_params,
-                "temperature": self.config.temperature,
-                "max_tokens": self.config.max_tokens,
-                "top_p": self.config.top_p,
-            }
-        if response_format:
-            params["response_format"] = response_format
-        if tools:  # TODO: Remove tools if no issues found with new memory addition logic
+            # Add additional parameters for other models
+            params.update(
+                {
+                    "temperature": self.config.temperature,
+                    "max_tokens": self.config.max_tokens,
+                    "top_p": self.config.top_p,
+                }
+            )
+
+        if tools:
             params["tools"] = tools
             params["tool_choice"] = tool_choice
 
diff --git a/mem0/llms/base.py b/mem0/llms/base.py
index 0c0a2a6e8..1cc6433a6 100644
--- a/mem0/llms/base.py
+++ b/mem0/llms/base.py
@@ -1,23 +1,49 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 from mem0.configs.llms.base import BaseLlmConfig
 
 
 class LLMBase(ABC):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    """
+    Base class for all LLM providers.
+    Handles common functionality and delegates provider-specific logic to subclasses.
+    """
+
+    def __init__(self, config: Optional[Union[BaseLlmConfig, Dict]] = None):
         """Initialize a base LLM class
 
-        :param config: LLM configuration option class, defaults to None
-        :type config: Optional[BaseLlmConfig], optional
+        :param config: LLM configuration option class or dict, defaults to None
+        :type config: Optional[Union[BaseLlmConfig, Dict]], optional
         """
         if config is None:
             self.config = BaseLlmConfig()
+        elif isinstance(config, dict):
+            # Handle dict-based configuration (backward compatibility)
+            self.config = BaseLlmConfig(**config)
         else:
             self.config = config
 
+        # Validate configuration
+        self._validate_config()
+
+    def _validate_config(self):
+        """
+        Validate the configuration.
+        Override in subclasses to add provider-specific validation.
+        """
+        if not hasattr(self.config, "model"):
+            raise ValueError("Configuration must have a 'model' attribute")
+
+        if not hasattr(self.config, "api_key") and not hasattr(self.config, "api_key"):
+            # Check if API key is available via environment variable
+            # This will be handled by individual providers
+            pass
+
     @abstractmethod
-    def generate_response(self, messages, tools: Optional[List[Dict]] = None, tool_choice: str = "auto"):
+    def generate_response(
+        self, messages: List[Dict[str, str]], tools: Optional[List[Dict]] = None, tool_choice: str = "auto", **kwargs
+    ):
         """
         Generate a response based on the given messages.
 
@@ -25,8 +51,27 @@ class LLMBase(ABC):
             messages (list): List of message dicts containing 'role' and 'content'.
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional provider-specific parameters.
 
         Returns:
-            str: The generated response.
+            str or dict: The generated response.
         """
         pass
+
+    def _get_common_params(self, **kwargs) -> Dict:
+        """
+        Get common parameters that most providers use.
+
+        Returns:
+            Dict: Common parameters dictionary.
+        """
+        params = {
+            "temperature": self.config.temperature,
+            "max_tokens": self.config.max_tokens,
+            "top_p": self.config.top_p,
+        }
+
+        # Add provider-specific parameters from kwargs
+        params.update(kwargs)
+
+        return params
diff --git a/mem0/llms/deepseek.py b/mem0/llms/deepseek.py
index 85a0417a6..8b5692f2e 100644
--- a/mem0/llms/deepseek.py
+++ b/mem0/llms/deepseek.py
@@ -1,16 +1,36 @@
 import json
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 from openai import OpenAI
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.deepseek import DeepSeekConfig
 from mem0.llms.base import LLMBase
 from mem0.memory.utils import extract_json
 
 
 class DeepSeekLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, DeepSeekConfig, Dict]] = None):
+        # Convert to DeepSeekConfig if needed
+        if config is None:
+            config = DeepSeekConfig()
+        elif isinstance(config, dict):
+            config = DeepSeekConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, DeepSeekConfig):
+            # Convert BaseLlmConfig to DeepSeekConfig
+            config = DeepSeekConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
@@ -56,6 +76,7 @@ class DeepSeekLLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
         Generate a response based on the given messages using DeepSeek.
@@ -65,17 +86,19 @@ class DeepSeekLLM(LLMBase):
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional DeepSeek-specific parameters.
 
         Returns:
             str: The generated response.
         """
-        params = {
-            "model": self.config.model,
-            "messages": messages,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
+        # Get common parameters
+        params = self._get_common_params(**kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": messages,
+            }
+        )
 
         if tools:
             params["tools"] = tools
diff --git a/mem0/llms/lmstudio.py b/mem0/llms/lmstudio.py
index cdcafe555..2c3f0a9fb 100644
--- a/mem0/llms/lmstudio.py
+++ b/mem0/llms/lmstudio.py
@@ -1,13 +1,35 @@
-from typing import Dict, List, Optional
+import json
+from typing import Dict, List, Optional, Union
 
 from openai import OpenAI
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.lmstudio import LMStudioConfig
 from mem0.llms.base import LLMBase
+from mem0.memory.utils import extract_json
 
 
 class LMStudioLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, LMStudioConfig, Dict]] = None):
+        # Convert to LMStudioConfig if needed
+        if config is None:
+            config = LMStudioConfig()
+        elif isinstance(config, dict):
+            config = LMStudioConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, LMStudioConfig):
+            # Convert BaseLlmConfig to LMStudioConfig
+            config = LMStudioConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         self.config.model = (
@@ -18,12 +40,43 @@ class LMStudioLLM(LLMBase):
 
         self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
 
+    def _parse_response(self, response, tools):
+        """
+        Process the response based on whether tools are used or not.
+
+        Args:
+            response: The raw response from API.
+            tools: The list of tools provided in the request.
+
+        Returns:
+            str or dict: The processed response.
+        """
+        if tools:
+            processed_response = {
+                "content": response.choices[0].message.content,
+                "tool_calls": [],
+            }
+
+            if response.choices[0].message.tool_calls:
+                for tool_call in response.choices[0].message.tool_calls:
+                    processed_response["tool_calls"].append(
+                        {
+                            "name": tool_call.function.name,
+                            "arguments": json.loads(extract_json(tool_call.function.arguments)),
+                        }
+                    )
+
+            return processed_response
+        else:
+            return response.choices[0].message.content
+
     def generate_response(
         self,
         messages: List[Dict[str, str]],
-        response_format: dict = {"type": "json_object"},
+        response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
         Generate a response based on the given messages using LM Studio.
@@ -33,21 +86,32 @@ class LMStudioLLM(LLMBase):
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional LM Studio-specific parameters.
 
         Returns:
             str: The generated response.
         """
-        params = {
-            "model": self.config.model,
-            "messages": messages,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
-        if response_format:
-            params["response_format"] = response_format
-        if self.config.lmstudio_response_format is not None:
+        # Get common parameters
+        params = self._get_common_params(**kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": messages,
+            }
+        )
+
+        # Handle response format - LM Studio defaults to json_object
+        if self.config.lmstudio_response_format:
             params["response_format"] = self.config.lmstudio_response_format
+        elif response_format:
+            params["response_format"] = response_format
+        else:
+            # Default to json_object for LM Studio
+            params["response_format"] = {"type": "json_object"}
+
+        if tools:
+            params["tools"] = tools
+            params["tool_choice"] = tool_choice
 
         response = self.client.chat.completions.create(**params)
-        return response.choices[0].message.content
+        return self._parse_response(response, tools)
diff --git a/mem0/llms/ollama.py b/mem0/llms/ollama.py
index 54d8b719f..b19342143 100644
--- a/mem0/llms/ollama.py
+++ b/mem0/llms/ollama.py
@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 try:
     from ollama import Client
@@ -6,25 +6,37 @@ except ImportError:
     raise ImportError("The 'ollama' library is required. Please install it using 'pip install ollama'.")
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.ollama import OllamaConfig
 from mem0.llms.base import LLMBase
 
 
 class OllamaLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, OllamaConfig, Dict]] = None):
+        # Convert to OllamaConfig if needed
+        if config is None:
+            config = OllamaConfig()
+        elif isinstance(config, dict):
+            config = OllamaConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, OllamaConfig):
+            # Convert BaseLlmConfig to OllamaConfig
+            config = OllamaConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
             self.config.model = "llama3.1:70b"
-        self.client = Client(host=self.config.ollama_base_url)
-        self._ensure_model_exists()
 
-    def _ensure_model_exists(self):
-        """
-        Ensure the specified model exists locally. If not, pull it from Ollama.
-        """
-        local_models = self.client.list()["models"]
-        if not any(model.get("name") == self.config.model for model in local_models):
-            self.client.pull(self.config.model)
+        self.client = Client(host=self.config.ollama_base_url)
 
     def _parse_response(self, response, tools):
         """
@@ -39,22 +51,18 @@ class OllamaLLM(LLMBase):
         """
         if tools:
             processed_response = {
-                "content": response["message"]["content"],
+                "content": response["message"]["content"] if isinstance(response, dict) else response.message.content,
                 "tool_calls": [],
             }
 
-            if response["message"].get("tool_calls"):
-                for tool_call in response["message"]["tool_calls"]:
-                    processed_response["tool_calls"].append(
-                        {
-                            "name": tool_call["function"]["name"],
-                            "arguments": tool_call["function"]["arguments"],
-                        }
-                    )
-
+            # Ollama doesn't support tool calls in the same way, so we return the content
             return processed_response
         else:
-            return response["message"]["content"]
+            # Handle both dict and object responses
+            if isinstance(response, dict):
+                return response["message"]["content"]
+            else:
+                return response.message.content
 
     def generate_response(
         self,
@@ -62,33 +70,37 @@ class OllamaLLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
-        Generate a response based on the given messages using OpenAI.
+        Generate a response based on the given messages using Ollama.
 
         Args:
             messages (list): List of message dicts containing 'role' and 'content'.
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional Ollama-specific parameters.
 
         Returns:
             str: The generated response.
         """
+        # Build parameters for Ollama
         params = {
             "model": self.config.model,
             "messages": messages,
-            "options": {
-                "temperature": self.config.temperature,
-                "num_predict": self.config.max_tokens,
-                "top_p": self.config.top_p,
-            },
         }
-        if response_format:
-            params["format"] = "json"
 
-        if tools:
-            params["tools"] = tools
+        # Add options for Ollama (temperature, num_predict, top_p)
+        options = {
+            "temperature": self.config.temperature,
+            "num_predict": self.config.max_tokens,
+            "top_p": self.config.top_p,
+        }
+        params["options"] = options
+
+        # Remove OpenAI-specific parameters that Ollama doesn't support
+        params.pop("max_tokens", None)  # Ollama uses different parameter names
 
         response = self.client.chat(**params)
         return self._parse_response(response, tools)
diff --git a/mem0/llms/openai.py b/mem0/llms/openai.py
index d94a0f3af..58d066d06 100644
--- a/mem0/llms/openai.py
+++ b/mem0/llms/openai.py
@@ -1,17 +1,36 @@
 import json
 import os
-import warnings
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 from openai import OpenAI
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.openai import OpenAIConfig
 from mem0.llms.base import LLMBase
 from mem0.memory.utils import extract_json
 
 
 class OpenAILLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, OpenAIConfig, Dict]] = None):
+        # Convert to OpenAIConfig if needed
+        if config is None:
+            config = OpenAIConfig()
+        elif isinstance(config, dict):
+            config = OpenAIConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, OpenAIConfig):
+            # Convert BaseLlmConfig to OpenAIConfig
+            config = OpenAIConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
@@ -26,18 +45,7 @@ class OpenAILLM(LLMBase):
             )
         else:
             api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")
-            base_url = (
-                self.config.openai_base_url
-                or os.getenv("OPENAI_API_BASE")
-                or os.getenv("OPENAI_BASE_URL")
-                or "https://api.openai.com/v1"
-            )
-            if os.environ.get("OPENAI_API_BASE"):
-                warnings.warn(
-                    "The environment variable 'OPENAI_API_BASE' is deprecated and will be removed in the 0.1.80. "
-                    "Please use 'OPENAI_BASE_URL' instead.",
-                    DeprecationWarning,
-                )
+            base_url = self.config.openai_base_url or os.getenv("OPENAI_BASE_URL") or "https://api.openai.com/v1"
 
             self.client = OpenAI(api_key=api_key, base_url=base_url)
 
@@ -77,6 +85,7 @@ class OpenAILLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
         Generate a JSON response based on the given messages using OpenAI.
@@ -86,17 +95,19 @@ class OpenAILLM(LLMBase):
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional OpenAI-specific parameters.
 
         Returns:
             json: The generated response.
         """
-        params = {
-            "model": self.config.model,
-            "messages": messages,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
+        # Get common parameters
+        params = self._get_common_params(**kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": messages,
+            }
+        )
 
         if os.getenv("OPENROUTER_API_KEY"):
             openrouter_params = {}
diff --git a/mem0/llms/vllm.py b/mem0/llms/vllm.py
index 6aa13addf..7953c4ef7 100644
--- a/mem0/llms/vllm.py
+++ b/mem0/llms/vllm.py
@@ -1,16 +1,36 @@
 import json
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 from openai import OpenAI
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.vllm import VllmConfig
 from mem0.llms.base import LLMBase
 from mem0.memory.utils import extract_json
 
 
 class VllmLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, VllmConfig, Dict]] = None):
+        # Convert to VllmConfig if needed
+        if config is None:
+            config = VllmConfig()
+        elif isinstance(config, dict):
+            config = VllmConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, VllmConfig):
+            # Convert BaseLlmConfig to VllmConfig
+            config = VllmConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+                http_client_proxies=config.http_client,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
@@ -18,8 +38,7 @@ class VllmLLM(LLMBase):
 
         self.config.api_key = self.config.api_key or os.getenv("VLLM_API_KEY") or "vllm-api-key"
         base_url = self.config.vllm_base_url or os.getenv("VLLM_BASE_URL")
-
-        self.client = OpenAI(base_url=base_url, api_key=self.config.api_key)
+        self.client = OpenAI(api_key=self.config.api_key, base_url=base_url)
 
     def _parse_response(self, response, tools):
         """
@@ -57,6 +76,7 @@ class VllmLLM(LLMBase):
         response_format=None,
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
+        **kwargs,
     ):
         """
         Generate a response based on the given messages using vLLM.
@@ -66,20 +86,19 @@ class VllmLLM(LLMBase):
             response_format (str or object, optional): Format of the response. Defaults to "text".
             tools (list, optional): List of tools that the model can call. Defaults to None.
             tool_choice (str, optional): Tool choice method. Defaults to "auto".
+            **kwargs: Additional vLLM-specific parameters.
 
         Returns:
             str: The generated response.
         """
-        params = {
-            "model": self.config.model,
-            "messages": messages,
-            "temperature": self.config.temperature,
-            "max_tokens": self.config.max_tokens,
-            "top_p": self.config.top_p,
-        }
-
-        if response_format:
-            params["response_format"] = response_format
+        # Get common parameters
+        params = self._get_common_params(**kwargs)
+        params.update(
+            {
+                "model": self.config.model,
+                "messages": messages,
+            }
+        )
 
         if tools:
             params["tools"] = tools
diff --git a/mem0/memory/memgraph_memory.py b/mem0/memory/memgraph_memory.py
index bc049c534..63f0dc7da 100644
--- a/mem0/memory/memgraph_memory.py
+++ b/mem0/memory/memgraph_memory.py
@@ -58,7 +58,7 @@ class MemoryGraph:
         # Create vector index if not exists
         if not any(idx.get("index_name") == "memzero" for idx in index_info["vector_index_exists"]):
             self.graph.query(
-            f"CREATE VECTOR INDEX memzero ON :Entity(embedding) WITH CONFIG {{'dimension': {embedding_dims}, 'capacity': 1000, 'metric': 'cos'}};"
+                f"CREATE VECTOR INDEX memzero ON :Entity(embedding) WITH CONFIG {{'dimension': {embedding_dims}, 'capacity': 1000, 'metric': 'cos'}};"
             )
         # Create label+property index if not exists
         if not any(
@@ -68,8 +68,7 @@ class MemoryGraph:
             self.graph.query("CREATE INDEX ON :Entity(user_id);")
         # Create label index if not exists
         if not any(
-            idx.get("index type") == "label" and idx.get("label") == "Entity"
-            for idx in index_info["index_exists"]
+            idx.get("index type") == "label" and idx.get("label") == "Entity" for idx in index_info["index_exists"]
         ):
             self.graph.query("CREATE INDEX ON :Entity;")
 
@@ -613,7 +612,7 @@ class MemoryGraph:
 
         result = self.graph.query(cypher, params=params)
         return result
-    
+
     def _fetch_existing_indexes(self):
         """
         Retrieves information about existing indexes and vector indexes in the Memgraph database.
@@ -621,10 +620,7 @@ class MemoryGraph:
         Returns:
             dict: A dictionary containing lists of existing indexes and vector indexes.
         """
-        
+
         index_exists = list(self.graph.query("SHOW INDEX INFO;"))
         vector_index_exists = list(self.graph.query("SHOW VECTOR INDEX INFO;"))
-        return {
-            "index_exists": index_exists,
-            "vector_index_exists": vector_index_exists
-        }
+        return {"index_exists": index_exists, "vector_index_exists": vector_index_exists}
diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py
index 5fe04fc6d..dd798d44d 100644
--- a/mem0/utils/factory.py
+++ b/mem0/utils/factory.py
@@ -1,8 +1,15 @@
 import importlib
-from typing import Optional
+from typing import Dict, Optional, Union
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.configs.llms.anthropic import AnthropicConfig
+from mem0.configs.llms.azure import AzureOpenAIConfig
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.deepseek import DeepSeekConfig
+from mem0.configs.llms.lmstudio import LMStudioConfig
+from mem0.configs.llms.ollama import OllamaConfig
+from mem0.configs.llms.openai import OpenAIConfig
+from mem0.configs.llms.vllm import VllmConfig
 from mem0.embeddings.mock import MockEmbeddings
 
 
@@ -13,36 +20,112 @@ def load_class(class_type):
 
 
 class LlmFactory:
+    """
+    Factory for creating LLM instances with appropriate configurations.
+    Supports both old-style BaseLlmConfig and new provider-specific configs.
+    """
+
+    # Provider mappings with their config classes
     provider_to_class = {
-        "ollama": "mem0.llms.ollama.OllamaLLM",
-        "openai": "mem0.llms.openai.OpenAILLM",
-        "groq": "mem0.llms.groq.GroqLLM",
-        "together": "mem0.llms.together.TogetherLLM",
-        "aws_bedrock": "mem0.llms.aws_bedrock.AWSBedrockLLM",
-        "litellm": "mem0.llms.litellm.LiteLLM",
-        "azure_openai": "mem0.llms.azure_openai.AzureOpenAILLM",
-        "openai_structured": "mem0.llms.openai_structured.OpenAIStructuredLLM",
-        "anthropic": "mem0.llms.anthropic.AnthropicLLM",
-        "azure_openai_structured": "mem0.llms.azure_openai_structured.AzureOpenAIStructuredLLM",
-        "gemini": "mem0.llms.gemini.GeminiLLM",
-        "deepseek": "mem0.llms.deepseek.DeepSeekLLM",
-        "xai": "mem0.llms.xai.XAILLM",
-        "sarvam": "mem0.llms.sarvam.SarvamLLM",
-        "lmstudio": "mem0.llms.lmstudio.LMStudioLLM",
-        "vllm": "mem0.llms.vllm.VllmLLM",
-        "langchain": "mem0.llms.langchain.LangchainLLM",
+        "ollama": ("mem0.llms.ollama.OllamaLLM", OllamaConfig),
+        "openai": ("mem0.llms.openai.OpenAILLM", OpenAIConfig),
+        "groq": ("mem0.llms.groq.GroqLLM", BaseLlmConfig),
+        "together": ("mem0.llms.together.TogetherLLM", BaseLlmConfig),
+        "aws_bedrock": ("mem0.llms.aws_bedrock.AWSBedrockLLM", BaseLlmConfig),
+        "litellm": ("mem0.llms.litellm.LiteLLM", BaseLlmConfig),
+        "azure_openai": ("mem0.llms.azure_openai.AzureOpenAILLM", AzureOpenAIConfig),
+        "openai_structured": ("mem0.llms.openai_structured.OpenAIStructuredLLM", OpenAIConfig),
+        "anthropic": ("mem0.llms.anthropic.AnthropicLLM", AnthropicConfig),
+        "azure_openai_structured": ("mem0.llms.azure_openai_structured.AzureOpenAIStructuredLLM", AzureOpenAIConfig),
+        "gemini": ("mem0.llms.gemini.GeminiLLM", BaseLlmConfig),
+        "deepseek": ("mem0.llms.deepseek.DeepSeekLLM", DeepSeekConfig),
+        "xai": ("mem0.llms.xai.XAILLM", BaseLlmConfig),
+        "sarvam": ("mem0.llms.sarvam.SarvamLLM", BaseLlmConfig),
+        "lmstudio": ("mem0.llms.lmstudio.LMStudioLLM", LMStudioConfig),
+        "vllm": ("mem0.llms.vllm.VllmLLM", VllmConfig),
+        "langchain": ("mem0.llms.langchain.LangchainLLM", BaseLlmConfig),
     }
 
     @classmethod
-    def create(cls, provider_name, config):
-        class_type = cls.provider_to_class.get(provider_name)
-        if class_type:
-            llm_instance = load_class(class_type)
-            base_config = BaseLlmConfig(**config)
-            return llm_instance(base_config)
-        else:
+    def create(cls, provider_name: str, config: Optional[Union[BaseLlmConfig, Dict]] = None, **kwargs):
+        """
+        Create an LLM instance with the appropriate configuration.
+
+        Args:
+            provider_name (str): The provider name (e.g., 'openai', 'anthropic')
+            config: Configuration object or dict. If None, will create default config
+            **kwargs: Additional configuration parameters
+
+        Returns:
+            Configured LLM instance
+
+        Raises:
+            ValueError: If provider is not supported
+        """
+        if provider_name not in cls.provider_to_class:
             raise ValueError(f"Unsupported Llm provider: {provider_name}")
 
+        class_type, config_class = cls.provider_to_class[provider_name]
+        llm_class = load_class(class_type)
+
+        # Handle configuration
+        if config is None:
+            # Create default config with kwargs
+            config = config_class(**kwargs)
+        elif isinstance(config, dict):
+            # Merge dict config with kwargs
+            config.update(kwargs)
+            config = config_class(**config)
+        elif isinstance(config, BaseLlmConfig):
+            # Convert base config to provider-specific config if needed
+            if config_class != BaseLlmConfig:
+                # Convert to provider-specific config
+                config_dict = {
+                    "model": config.model,
+                    "temperature": config.temperature,
+                    "api_key": config.api_key,
+                    "max_tokens": config.max_tokens,
+                    "top_p": config.top_p,
+                    "top_k": config.top_k,
+                    "enable_vision": config.enable_vision,
+                    "vision_details": config.vision_details,
+                    "http_client_proxies": config.http_client,
+                }
+                config_dict.update(kwargs)
+                config = config_class(**config_dict)
+            else:
+                # Use base config as-is
+                pass
+        else:
+            # Assume it's already the correct config type
+            pass
+
+        return llm_class(config)
+
+    @classmethod
+    def register_provider(cls, name: str, class_path: str, config_class=None):
+        """
+        Register a new provider.
+
+        Args:
+            name (str): Provider name
+            class_path (str): Full path to LLM class
+            config_class: Configuration class for the provider (defaults to BaseLlmConfig)
+        """
+        if config_class is None:
+            config_class = BaseLlmConfig
+        cls.provider_to_class[name] = (class_path, config_class)
+
+    @classmethod
+    def get_supported_providers(cls) -> list:
+        """
+        Get list of supported providers.
+
+        Returns:
+            list: List of supported provider names
+        """
+        return list(cls.provider_to_class.keys())
+
 
 class EmbedderFactory:
     provider_to_class = {
diff --git a/tests/llms/test_azure_openai.py b/tests/llms/test_azure_openai.py
index 7ef86e948..dadb03c9e 100644
--- a/tests/llms/test_azure_openai.py
+++ b/tests/llms/test_azure_openai.py
@@ -2,7 +2,7 @@ from unittest.mock import Mock, patch
 
 import pytest
 
-from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.azure import AzureOpenAIConfig
 from mem0.llms.azure_openai import AzureOpenAILLM
 
 MODEL = "gpt-4o"  # or your custom deployment name
@@ -20,7 +20,7 @@ def mock_openai_client():
 
 
 def test_generate_response_without_tools(mock_openai_client):
-    config = BaseLlmConfig(model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P)
+    config = AzureOpenAIConfig(model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P)
     llm = AzureOpenAILLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
@@ -40,7 +40,7 @@ def test_generate_response_without_tools(mock_openai_client):
 
 
 def test_generate_response_with_tools(mock_openai_client):
-    config = BaseLlmConfig(model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P)
+    config = AzureOpenAIConfig(model=MODEL, temperature=TEMPERATURE, max_tokens=MAX_TOKENS, top_p=TOP_P)
     llm = AzureOpenAILLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
@@ -107,7 +107,7 @@ def test_generate_with_http_proxies(default_headers):
         patch("mem0.llms.azure_openai.AzureOpenAI") as mock_azure_openai,
         patch("httpx.Client", new=mock_http_client),
     ):
-        config = BaseLlmConfig(
+        config = AzureOpenAIConfig(
             model=MODEL,
             temperature=TEMPERATURE,
             max_tokens=MAX_TOKENS,
diff --git a/tests/llms/test_deepseek.py b/tests/llms/test_deepseek.py
index b7d3f5e98..4a84079a1 100644
--- a/tests/llms/test_deepseek.py
+++ b/tests/llms/test_deepseek.py
@@ -4,6 +4,7 @@ from unittest.mock import Mock, patch
 import pytest
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.deepseek import DeepSeekConfig
 from mem0.llms.deepseek import DeepSeekLLM
 
 
@@ -24,13 +25,13 @@ def test_deepseek_llm_base_url():
     # case2: with env variable DEEPSEEK_API_BASE
     provider_base_url = "https://api.provider.com/v1/"
     os.environ["DEEPSEEK_API_BASE"] = provider_base_url
-    config = BaseLlmConfig(model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
+    config = DeepSeekConfig(model="deepseek-chat", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
     llm = DeepSeekLLM(config)
     assert str(llm.client.base_url) == provider_base_url
 
     # case3: with config.deepseek_base_url
     config_base_url = "https://api.config.com/v1/"
-    config = BaseLlmConfig(
+    config = DeepSeekConfig(
         model="deepseek-chat",
         temperature=0.7,
         max_tokens=100,
diff --git a/tests/llms/test_lm_studio.py b/tests/llms/test_lm_studio.py
index c2eace615..a6c956e64 100644
--- a/tests/llms/test_lm_studio.py
+++ b/tests/llms/test_lm_studio.py
@@ -2,7 +2,7 @@ from unittest.mock import Mock, patch
 
 import pytest
 
-from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.lmstudio import LMStudioConfig
 from mem0.llms.lmstudio import LMStudioLLM
 
 
@@ -18,7 +18,7 @@ def mock_lm_studio_client():
 
 
 def test_generate_response_without_tools(mock_lm_studio_client):
-    config = BaseLlmConfig(
+    config = LMStudioConfig(
         model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
         temperature=0.7,
         max_tokens=100,
@@ -45,7 +45,7 @@ def test_generate_response_without_tools(mock_lm_studio_client):
 
 
 def test_generate_response_specifying_response_format(mock_lm_studio_client):
-    config = BaseLlmConfig(
+    config = LMStudioConfig(
         model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
         temperature=0.7,
         max_tokens=100,
diff --git a/tests/llms/test_ollama.py b/tests/llms/test_ollama.py
index 0b797bfae..0f1e6ac3e 100644
--- a/tests/llms/test_ollama.py
+++ b/tests/llms/test_ollama.py
@@ -2,7 +2,7 @@ from unittest.mock import Mock, patch
 
 import pytest
 
-from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.ollama import OllamaConfig
 from mem0.llms.ollama import OllamaLLM
 
 
@@ -16,7 +16,7 @@ def mock_ollama_client():
 
 
 def test_generate_response_without_tools(mock_ollama_client):
-    config = BaseLlmConfig(model="llama3.1:70b", temperature=0.7, max_tokens=100, top_p=1.0)
+    config = OllamaConfig(model="llama3.1:70b", temperature=0.7, max_tokens=100, top_p=1.0)
     llm = OllamaLLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
diff --git a/tests/llms/test_openai.py b/tests/llms/test_openai.py
index cdc988646..265213b97 100644
--- a/tests/llms/test_openai.py
+++ b/tests/llms/test_openai.py
@@ -3,7 +3,7 @@ from unittest.mock import Mock, patch
 
 import pytest
 
-from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.openai import OpenAIConfig
 from mem0.llms.openai import OpenAILLM
 
 
@@ -17,22 +17,22 @@ def mock_openai_client():
 
 def test_openai_llm_base_url():
     # case1: default config: with openai official base url
-    config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
+    config = OpenAIConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
     llm = OpenAILLM(config)
     # Note: openai client will parse the raw base_url into a URL object, which will have a trailing slash
     assert str(llm.client.base_url) == "https://api.openai.com/v1/"
 
     # case2: with env variable OPENAI_API_BASE
     provider_base_url = "https://api.provider.com/v1"
-    os.environ["OPENAI_API_BASE"] = provider_base_url
-    config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
+    os.environ["OPENAI_BASE_URL"] = provider_base_url
+    config = OpenAIConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key")
     llm = OpenAILLM(config)
     # Note: openai client will parse the raw base_url into a URL object, which will have a trailing slash
     assert str(llm.client.base_url) == provider_base_url + "/"
 
     # case3: with config.openai_base_url
     config_base_url = "https://api.config.com/v1"
-    config = BaseLlmConfig(
+    config = OpenAIConfig(
         model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0, api_key="api_key", openai_base_url=config_base_url
     )
     llm = OpenAILLM(config)
@@ -41,7 +41,7 @@ def test_openai_llm_base_url():
 
 
 def test_generate_response_without_tools(mock_openai_client):
-    config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0)
+    config = OpenAIConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0)
     llm = OpenAILLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
@@ -61,7 +61,7 @@ def test_generate_response_without_tools(mock_openai_client):
 
 
 def test_generate_response_with_tools(mock_openai_client):
-    config = BaseLlmConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0)
+    config = OpenAIConfig(model="gpt-4o", temperature=0.7, max_tokens=100, top_p=1.0)
     llm = OpenAILLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},