diff --git a/src/pipecat/processors/frameworks/strands_agents.py b/src/pipecat/processors/frameworks/strands_agents.py
index 67005d74b..d129c2063 100644
--- a/src/pipecat/processors/frameworks/strands_agents.py
+++ b/src/pipecat/processors/frameworks/strands_agents.py
@@ -84,11 +84,11 @@ class StrandsAgentsProcessor(FrameProcessor):
             text: The user input text to process through the agent or graph.
         """
         logger.debug(f"Invoking Strands agent with: {text}")
+        ttfb_tracking = True
         try:
             await self.push_frame(LLMFullResponseStartFrame())
             await self.start_processing_metrics()
             await self.start_ttfb_metrics()
-            ttfb_tracking = True
 
             if self.graph:
                 # Graph does not stream; await full result then emit assistant text
@@ -108,9 +108,9 @@ class StrandsAgentsProcessor(FrameProcessor):
                                     await self.push_frame(LLMTextFrame(str(block["text"])))
                         # Update usage metrics
                         await self._report_usage_metrics(
-                            agent_result.metrics.accumulated_usage.get('inputTokens', 0), 
-                            agent_result.metrics.accumulated_usage.get('outputTokens', 0), 
-                            agent_result.metrics.accumulated_usage.get('totalTokens', 0)
+                            agent_result.metrics.accumulated_usage.get("inputTokens", 0),
+                            agent_result.metrics.accumulated_usage.get("outputTokens", 0),
+                            agent_result.metrics.accumulated_usage.get("totalTokens", 0),
                         )
                 except Exception as parse_err:
                     logger.warning(f"Failed to extract messages from GraphResult: {parse_err}")
@@ -123,12 +123,20 @@ class StrandsAgentsProcessor(FrameProcessor):
                         if ttfb_tracking:
                             await self.stop_ttfb_metrics()
                             ttfb_tracking = False
-                    
+
                     # Update usage metrics
-                    if isinstance(event, dict) and "event" in event and "metadata" in event['event']:
-                        if 'usage' in event['event']['metadata']:
-                            usage = event['event']['metadata']['usage']
-                            await self._report_usage_metrics(usage.get('inputTokens', 0), usage.get('outputTokens', 0), usage.get('totalTokens', 0))
+                    if (
+                        isinstance(event, dict)
+                        and "event" in event
+                        and "metadata" in event["event"]
+                    ):
+                        if "usage" in event["event"]["metadata"]:
+                            usage = event["event"]["metadata"]["usage"]
+                            await self._report_usage_metrics(
+                                usage.get("inputTokens", 0),
+                                usage.get("outputTokens", 0),
+                                usage.get("totalTokens", 0),
+                            )
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:
@@ -139,7 +147,7 @@ class StrandsAgentsProcessor(FrameProcessor):
                 ttfb_tracking = False
             await self.stop_processing_metrics()
             await self.push_frame(LLMFullResponseEndFrame())
-    
+
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate performance metrics.
 
@@ -149,14 +157,11 @@ class StrandsAgentsProcessor(FrameProcessor):
         return True
 
     async def _report_usage_metrics(
-        self,
-        prompt_tokens: int,
-        completion_tokens: int,
-        total_tokens: int
+        self, prompt_tokens: int, completion_tokens: int, total_tokens: int
     ):
         tokens = LLMTokenUsage(
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
-            total_tokens=total_tokens
+            total_tokens=total_tokens,
         )
-        await self.start_llm_usage_metrics(tokens)
\ No newline at end of file
+        await self.start_llm_usage_metrics(tokens)