Simplify, at the expense of a bit of not-yet-needed flexibility: rather than associating a loose thought_metadata with each thought, use a signature. Thought signatures are the only "thought metadata" we use today.

src/pipecat/adapters/services/anthropic_adapter.py

@@ -185,8 +185,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
             isinstance(message.message, dict)
             and message.message.get("type") == "thought"
             and (text := message.message.get("text"))
-            and isinstance(metadata := message.message.get("metadata"), dict)
-            and (signature := metadata.get("signature"))
+            and (signature := message.message.get("signature"))
         ):
             return {
                 "role": "assistant",

src/pipecat/frames/frames.py

@@ -642,15 +642,16 @@ class LLMThoughtEndFrame(ControlFrame):
     """Frame indicating the end of an LLM thought.
 
     Parameters:
-        thought_metadata: Optional metadata associated with the thought,
-            e.g. an Anthropic thought signature.
+        signature: Optional signature associated with the thought.
+            This is used by Anthropic, which includes a signature at the end of
+            each thought.
     """
 
-    thought_metadata: Optional[Dict[str, Any]] = None
+    signature: Any = None
 
     def __str__(self):
         pts = format_pts(self.pts)
-        return f"{self.name}(pts: {pts}, metadata: {self.thought_metadata})"
+        return f"{self.name}(pts: {pts}, signature: {self.signature})"
 
 
 @dataclass

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -905,7 +905,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
                 message={
                     "type": "thought",
                     "text": thought,
-                    "metadata": frame.thought_metadata,
+                    "signature": frame.signature,
                 },
             )
         )

src/pipecat/services/anthropic/llm.py

@@ -421,11 +421,7 @@ class AnthropicLLMService(LLMService):
                     elif hasattr(event.delta, "thinking"):
                         await self.push_frame(LLMThoughtTextFrame(text=event.delta.thinking))
                     elif hasattr(event.delta, "signature"):
-                        await self.push_frame(
-                            LLMThoughtEndFrame(
-                                thought_metadata={"signature": event.delta.signature}
-                            )
-                        )
+                        await self.push_frame(LLMThoughtEndFrame(signature=event.delta.signature))
                 elif event.type == "content_block_start":
                     if event.content_block.type == "tool_use":
                         tool_use_block = event.content_block