Fix/supported llm params (#3290)

docs/components/llms/models/openai.mdx

@@ -6,6 +6,8 @@ title: OpenAI
 
 To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
 
+> **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
+
 ## Usage
 
 <CodeGroup>

mem0/llms/anthropic.py

@@ -70,8 +70,7 @@ class AnthropicLLM(LLMBase):
             else:
                 filtered_messages.append(message)
 
-        # Get common parameters
-        params = self._get_common_params(**kwargs)
+        params = self._get_supported_params(messages=messages, **kwargs)
         params.update(
             {
                 "model": self.config.model,

mem0/llms/azure_openai.py

@@ -110,27 +110,13 @@ class AzureOpenAILLM(LLMBase):
 
         messages[-1]["content"] = user_prompt
 
-        # 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"}:
-            # Use common params for these models
-            pass
-        else:
-            # Add additional parameters for other models
-            params.update(
-                {
-                    "temperature": self.config.temperature,
-                    "max_tokens": self.config.max_tokens,
-                    "top_p": self.config.top_p,
-                }
-            )
+        params = self._get_supported_params(messages=messages, **kwargs)
+        
+        # Add model and messages
+        params.update({
+            "model": self.config.model,
+            "messages": messages,
+        })
 
         if tools:
             params["tools"] = tools

mem0/llms/base.py

@@ -40,6 +40,60 @@ class LLMBase(ABC):
             # This will be handled by individual providers
             pass
 
+    def _is_reasoning_model(self, model: str) -> bool:
+        """
+        Check if the model is a reasoning model or GPT-5 series that doesn't support certain parameters.
+        
+        Args:
+            model: The model name to check
+            
+        Returns:
+            bool: True if the model is a reasoning model or GPT-5 series
+        """
+        reasoning_models = {
+            "o1", "o1-preview", "o3-mini", "o3",
+            "gpt-5", "gpt-5o", "gpt-5o-mini", "gpt-5o-micro",
+        }
+        
+        if model.lower() in reasoning_models:
+            return True
+        
+        model_lower = model.lower()
+        if any(reasoning_model in model_lower for reasoning_model in ["gpt-5", "o1", "o3"]):
+            return True
+            
+        return False
+
+    def _get_supported_params(self, **kwargs) -> Dict:
+        """
+        Get parameters that are supported by the current model.
+        Filters out unsupported parameters for reasoning models and GPT-5 series.
+        
+        Args:
+            **kwargs: Additional parameters to include
+            
+        Returns:
+            Dict: Filtered parameters dictionary
+        """
+        model = getattr(self.config, 'model', '')
+        
+        if self._is_reasoning_model(model):
+            supported_params = {}
+            
+            if "messages" in kwargs:
+                supported_params["messages"] = kwargs["messages"]
+            if "response_format" in kwargs:
+                supported_params["response_format"] = kwargs["response_format"]
+            if "tools" in kwargs:
+                supported_params["tools"] = kwargs["tools"]
+            if "tool_choice" in kwargs:
+                supported_params["tool_choice"] = kwargs["tool_choice"]
+                
+            return supported_params
+        else:
+            # For regular models, include all common parameters
+            return self._get_common_params(**kwargs)
+
     @abstractmethod
     def generate_response(
         self, messages: List[Dict[str, str]], tools: Optional[List[Dict]] = None, tool_choice: str = "auto", **kwargs

mem0/llms/deepseek.py

@@ -91,8 +91,7 @@ class DeepSeekLLM(LLMBase):
         Returns:
             str: The generated response.
         """
-        # Get common parameters
-        params = self._get_common_params(**kwargs)
+        params = self._get_supported_params(messages=messages, **kwargs)
         params.update(
             {
                 "model": self.config.model,

mem0/llms/lmstudio.py

@@ -91,8 +91,7 @@ class LMStudioLLM(LLMBase):
         Returns:
             str: The generated response.
         """
-        # Get common parameters
-        params = self._get_common_params(**kwargs)
+        params = self._get_supported_params(messages=messages, **kwargs)
         params.update(
             {
                 "model": self.config.model,
@@ -100,13 +99,11 @@ class LMStudioLLM(LLMBase):
             }
         )
 
-        # 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:

mem0/llms/openai.py

@@ -101,14 +101,12 @@ class OpenAILLM(LLMBase):
         Returns:
             json: The generated response.
         """
-        # Get common parameters
-        params = self._get_common_params(**kwargs)
-        params.update(
-            {
-                "model": self.config.model,
-                "messages": messages,
-            }
-        )
+        params = self._get_supported_params(messages=messages, **kwargs)
+        
+        params.update({
+            "model": self.config.model,
+            "messages": messages,
+        })
 
         if os.getenv("OPENROUTER_API_KEY"):
             openrouter_params = {}

mem0/llms/vllm.py

@@ -91,8 +91,7 @@ class VllmLLM(LLMBase):
         Returns:
             str: The generated response.
         """
-        # Get common parameters
-        params = self._get_common_params(**kwargs)
+        params = self._get_supported_params(messages=messages, **kwargs)
         params.update(
             {
                 "model": self.config.model,