Simplify LLMContextFrame handling in process_frame methods

Now that LLMContextFrame is the only frame that provides a context,
remove the intermediate `context = None` / `if context:` pattern
and handle context processing directly in the isinstance branch.

src/pipecat/services/anthropic/llm.py

@@ -552,18 +552,14 @@ class AnthropicLLMService(LLMService):
         """
         await super().process_frame(frame, direction)
 
-        context = None
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
+            await self._process_context(frame.context)
         elif isinstance(frame, LLMEnablePromptCachingFrame):
             logger.debug(f"Setting enable prompt caching to: [{frame.enable}]")
             self._settings.enable_prompt_caching = frame.enable
         else:
             await self.push_frame(frame, direction)
 
-        if context:
-            await self._process_context(context)
-
     def _estimate_tokens(self, text: str) -> int:
         return int(len(re.split(r"[^\w]+", text)) * 1.3)
 

src/pipecat/services/aws/llm.py

@@ -564,15 +564,11 @@ class AWSBedrockLLMService(LLMService):
         """
         await super().process_frame(frame, direction)
 
-        context = None
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
+            await self._process_context(frame.context)
         else:
             await self.push_frame(frame, direction)
 
-        if context:
-            await self._process_context(context)
-
     def _estimate_tokens(self, text: str) -> int:
         return int(len(re.split(r"[^\w]+", text)) * 1.3)
 

src/pipecat/services/aws/nova_sonic/llm.py

@@ -524,8 +524,7 @@ class AWSNovaSonicLLMService(LLMService):
         await super().process_frame(frame, direction)
 
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
+            await self._handle_context(frame.context)
-            await self._handle_context(context)
         elif isinstance(frame, InputAudioRawFrame):
             await self._handle_input_audio_frame(frame)
         elif isinstance(frame, InterruptionFrame):

src/pipecat/services/google/llm.py

@@ -637,16 +637,11 @@ class GoogleLLMService(LLMService):
         """
         await super().process_frame(frame, direction)
 
-        context = None
-
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
+            await self._process_context(frame.context)
         else:
             await self.push_frame(frame, direction)
 
-        if context:
-            await self._process_context(context)
-
     async def stop(self, frame):
         """Override stop to gracefully close the client."""
         await super().stop(frame)

src/pipecat/services/mem0/memory.py

@@ -265,12 +265,8 @@ class Mem0MemoryService(FrameProcessor):
         """
         await super().process_frame(frame, direction)
 
-        context = None
-
         if isinstance(frame, LLMContextFrame):
             context = frame.context
-
-        if context:
             try:
                 # Get the latest user message to use as a query for memory retrieval
                 context_messages = context.get_messages()
@@ -300,5 +296,4 @@ class Mem0MemoryService(FrameProcessor):
                 )
                 await self.push_frame(frame)  # Still pass the original frame through
         else:
-            # For non-context frames, just pass them through
             await self.push_frame(frame, direction)

src/pipecat/services/openai/base_llm.py

@@ -532,17 +532,11 @@ class BaseOpenAILLMService(LLMService):
         """
         await super().process_frame(frame, direction)
 
-        context = None
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
-        else:
-            await self.push_frame(frame, direction)
-
-        if context:
             try:
                 await self.push_frame(LLMFullResponseStartFrame())
                 await self.start_processing_metrics()
-                await self._process_context(context)
+                await self._process_context(frame.context)
             except httpx.TimeoutException as e:
                 await self._call_event_handler("on_completion_timeout")
                 await self.push_error(error_msg="LLM completion timeout", exception=e)
@@ -551,3 +545,5 @@ class BaseOpenAILLMService(LLMService):
             finally:
                 await self.stop_processing_metrics()
                 await self.push_frame(LLMFullResponseEndFrame())
+        else:
+            await self.push_frame(frame, direction)

src/pipecat/services/openai/responses/llm.py

@@ -674,17 +674,11 @@ class OpenAIResponsesLLMService(_BaseOpenAIResponsesLLMService, WebsocketLLMServ
         """
         await super().process_frame(frame, direction)
 
-        context = None
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
-        else:
-            await self.push_frame(frame, direction)
-
-        if context:
             try:
                 await self.push_frame(LLMFullResponseStartFrame())
                 await self.start_processing_metrics()
-                await self._process_context(context)
+                await self._process_context(frame.context)
             except asyncio.CancelledError:
                 # The pipeline cancelled us (e.g. due to an interruption).
                 # Ask the server to stop generating and flag that we need
@@ -717,6 +711,8 @@ class OpenAIResponsesLLMService(_BaseOpenAIResponsesLLMService, WebsocketLLMServ
             finally:
                 await self.stop_processing_metrics()
                 await self.push_frame(LLMFullResponseEndFrame())
+        else:
+            await self.push_frame(frame, direction)
 
     # -- core inference -------------------------------------------------------
 
@@ -960,17 +956,11 @@ class OpenAIResponsesHttpLLMService(_BaseOpenAIResponsesLLMService):
         """
         await super().process_frame(frame, direction)
 
-        context = None
         if isinstance(frame, LLMContextFrame):
-            context = frame.context
-        else:
-            await self.push_frame(frame, direction)
-
-        if context:
             try:
                 await self.push_frame(LLMFullResponseStartFrame())
                 await self.start_processing_metrics()
-                await self._process_context(context)
+                await self._process_context(frame.context)
             except httpx.TimeoutException as e:
                 await self._call_event_handler("on_completion_timeout")
                 await self.push_error(error_msg="LLM completion timeout", exception=e)
@@ -979,6 +969,8 @@ class OpenAIResponsesHttpLLMService(_BaseOpenAIResponsesLLMService):
             finally:
                 await self.stop_processing_metrics()
                 await self.push_frame(LLMFullResponseEndFrame())
+        else:
+            await self.push_frame(frame, direction)
 
     @traced_llm
     async def _process_context(self, context: LLMContext):