Add summarization timeout to prevent hung LLM calls

Adds a configurable summarization_timeout (default 120s) that cancels
  summary generation if the LLM hangs. On timeout, an error result is
  returned so _summarization_in_progress resets and future
  summarizations are unblocked.

src/pipecat/frames/frames.py

@@ -2019,6 +2019,8 @@ class LLMContextSummaryRequestFrame(ControlFrame):
             the summary text.
         summarization_prompt: System prompt instructing the LLM how to generate
             the summary.
+        summarization_timeout: Maximum time in seconds for the LLM to generate a
+            summary. None means no timeout.
     """
 
     request_id: str
@@ -2026,6 +2028,7 @@ class LLMContextSummaryRequestFrame(ControlFrame):
     min_messages_to_keep: int
     target_context_tokens: int
     summarization_prompt: str
+    summarization_timeout: Optional[float] = None
 
 
 @dataclass

src/pipecat/processors/aggregators/llm_context_summarizer.py

@@ -218,6 +218,7 @@ class LLMContextSummarizer(BaseObject):
             min_messages_to_keep=min_keep,
             target_context_tokens=self._config.target_context_tokens,
             summarization_prompt=self._config.summary_prompt,
+            summarization_timeout=self._config.summarization_timeout,
         )
 
         # Emit event for aggregator to broadcast

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -1284,20 +1284,35 @@ class LLMAssistantAggregator(LLMContextAggregator):
             frame: The summarization request frame.
         """
         try:
-            summary, last_index = await llm._generate_summary(frame)
+            if frame.summarization_timeout:
+                summary, last_index = await asyncio.wait_for(
+                    llm._generate_summary(frame),
+                    timeout=frame.summarization_timeout,
+                )
+            else:
+                summary, last_index = await llm._generate_summary(frame)
             result_frame = LLMContextSummaryResultFrame(
                 request_id=frame.request_id,
                 summary=summary,
                 last_summarized_index=last_index,
             )
-        except Exception as e:
-            error = f"Error generating context summary: {e}"
-            await self.push_error(error, exception=e)
+        except asyncio.TimeoutError:
+            error = f"Context summarization timed out after {frame.summarization_timeout}s"
+            logger.error(f"{self}: {error}")
             result_frame = LLMContextSummaryResultFrame(
                 request_id=frame.request_id,
                 summary="",
                 last_summarized_index=-1,
-                error=f"Error generating context summary: {e}",
+                error=error,
+            )
+        except Exception as e:
+            error = f"Error generating context summary: {e}"
+            await self.push_error(error_msg=error, exception=e)
+            result_frame = LLMContextSummaryResultFrame(
+                request_id=frame.request_id,
+                summary="",
+                last_summarized_index=-1,
+                error=error,
             )
 
         if self._summarizer:

src/pipecat/services/llm_service.py

@@ -437,7 +437,17 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService):
         error = None
 
         try:
-            summary, last_index = await self._generate_summary(frame)
+            if frame.summarization_timeout:
+                summary, last_index = await asyncio.wait_for(
+                    self._generate_summary(frame),
+                    timeout=frame.summarization_timeout,
+                )
+            else:
+                summary, last_index = await self._generate_summary(frame)
+        except asyncio.TimeoutError:
+            await self.push_error(
+                error_msg=f"Context summarization timed out after {frame.summarization_timeout}s"
+            )
         except Exception as e:
             error = f"Error generating context summary: {e}"
             await self.push_error(error, exception=e)

src/pipecat/utils/context/llm_context_summarization.py

@@ -86,6 +86,10 @@ class LLMContextSummarizationConfig:
             pipeline's primary LLM. Useful for routing summarization to a
             cheaper/faster model (e.g., Gemini Flash) while keeping an
             expensive model for conversation. If None, uses the pipeline LLM.
+        summarization_timeout: Maximum time in seconds to wait for the LLM to
+            generate a summary. If the call exceeds this timeout, summarization
+            is aborted with an error and future summarizations are unblocked.
+            Set to None to disable the timeout.
     """
 
     max_context_tokens: int = 8000
@@ -95,6 +99,7 @@ class LLMContextSummarizationConfig:
     summarization_prompt: Optional[str] = None
     summary_message_template: str = "Conversation summary: {summary}"
     llm: Optional["LLMService"] = None
+    summarization_timeout: Optional[float] = 120.0
 
     def __post_init__(self):
         """Validate configuration parameters."""