feat: consolidate reasoning model checks (#3862)

letta/llm_api/anthropic_client.py

@@ -182,7 +182,7 @@ class AnthropicClient(LLMClientBase):
         }
 
         # Extended Thinking
-        if llm_config.enable_reasoner:
+        if self.is_reasoning_model(llm_config) and llm_config.enable_reasoner:
             data["thinking"] = {
                 "type": "enabled",
                 "budget_tokens": llm_config.max_reasoning_tokens,
@@ -200,7 +200,7 @@ class AnthropicClient(LLMClientBase):
             # Special case for summarization path
             tools_for_request = None
             tool_choice = None
-        elif llm_config.enable_reasoner:
+        elif self.is_reasoning_model(llm_config) and llm_config.enable_reasoner:
             # NOTE: reasoning models currently do not allow for `any`
             tool_choice = {"type": "auto", "disable_parallel_tool_use": True}
             tools_for_request = [OpenAITool(function=f) for f in tools]
@@ -296,6 +296,13 @@ class AnthropicClient(LLMClientBase):
             token_count -= 8
         return token_count
 
+    def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
+        return (
+            llm_config.model.startswith("claude-3-7-sonnet")
+            or llm_config.model.startswith("claude-sonnet-4")
+            or llm_config.model.startswith("claude-opus-4")
+        )
+
     @trace_method
     def handle_llm_error(self, e: Exception) -> Exception:
         if isinstance(e, anthropic.APITimeoutError):

letta/llm_api/google_vertex_client.py

@@ -504,6 +504,9 @@ class GoogleVertexClient(LLMClientBase):
                 return 1
         return 0
 
+    def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
+        return llm_config.model.startswith("gemini-2.5-flash") or llm_config.model.startswith("gemini-2.5-pro")
+
     @trace_method
     def handle_llm_error(self, e: Exception) -> Exception:
         # Fallback to base implementation

letta/llm_api/llm_client_base.py

@@ -174,6 +174,10 @@ class LLMClientBase:
         """
         raise NotImplementedError(f"Streaming is not supported for {llm_config.model_endpoint_type}")
 
+    @abstractmethod
+    def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
+        raise NotImplementedError
+
     @abstractmethod
     def handle_llm_error(self, e: Exception) -> Exception:
         """

letta/llm_api/openai_client.py

@@ -276,6 +276,9 @@ class OpenAIClient(LLMClientBase):
         response: ChatCompletion = await client.chat.completions.create(**request_data)
         return response.model_dump()
 
+    def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
+        return is_openai_reasoning_model(llm_config.model)
+
     @trace_method
     def convert_response_to_chat_completion(
         self,
@@ -298,7 +301,7 @@ class OpenAIClient(LLMClientBase):
             )
 
         # If we used a reasoning model, create a content part for the ommitted reasoning
-        if is_openai_reasoning_model(llm_config.model):
+        if self.is_reasoning_model(llm_config):
             chat_completion_response.choices[0].message.omitted_reasoning_content = True
 
         return chat_completion_response