conductor-checkpoint-msg_01AVxUgejEA9piS6narw4omz

graphiti_core/llm_client/anthropic_client.py

@@ -214,22 +214,15 @@ class AnthropicClient(LLMClient):
         try:
             # Create the appropriate tool based on whether response_model is provided
             tools, tool_choice = self._create_tool(response_model)
-
-            # Build the message creation parameters
-            create_params: dict[str, typing.Any] = {
-                'system': system_message.content,
-                'max_tokens': max_creation_tokens,
-                'messages': user_messages_cast,
-                'model': self.model,
-                'tools': tools,
-                'tool_choice': tool_choice,
-            }
-
-            # Only include temperature if it's not None
-            if self.temperature is not None:
-                create_params['temperature'] = self.temperature
-
-            result = await self.client.messages.create(**create_params)
+            result = await self.client.messages.create(
+                system=system_message.content,
+                max_tokens=max_creation_tokens,
+                temperature=self.temperature,
+                messages=user_messages_cast,
+                model=self.model,
+                tools=tools,
+                tool_choice=tool_choice,
+            )
 
             # Extract the tool output from the response
             for content_item in result.content:

graphiti_core/llm_client/config.py

@@ -39,7 +39,7 @@ class LLMConfig:
         api_key: str | None = None,
         model: str | None = None,
         base_url: str | None = None,
-        temperature: float | None = None,
+        temperature: float = DEFAULT_TEMPERATURE,
         max_tokens: int = DEFAULT_MAX_TOKENS,
         small_model: str | None = None,
     ):