Make the mechanism of adding spaces when concatenating TTS (or speech-to-speech LLM) output text explicit and deterministic, rather than heuristic-based.

This fixes a bug where spaces were sometimes missing from assistant messages in context.

CHANGELOG.md

@@ -11,6 +11,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - `LivekitFrameSerializer` has been removed. Use `LiveKitTransport` instead.
 
+### Fixed
+
+- Fixed a bug related to `LLMAssistantAggregator` where spaces were sometimes
+  missing from assistant messages in context.
+
 ## [0.0.93] - 2025-11-07
 
 ### Added

src/pipecat/frames/frames.py

@@ -330,10 +330,18 @@ class TextFrame(DataFrame):
 
     text: str
     skip_tts: bool = field(init=False)
+    # Whether any necessary inter-frame (leading/trailing) spaces are already
+    # included in the text.
+    # NOTE: Ideally this would be available at init time with a default value,
+    # but that would impact how subclasses can be initialized (it would require
+    # mandatory fields of theirs to have defaults to preserve
+    # non-default-before-default argument order)
+    includes_inter_frame_spaces: bool = field(init=False)
 
     def __post_init__(self):
         super().__post_init__()
         self.skip_tts = False
+        self.includes_inter_frame_spaces = False
 
     def __str__(self):
         pts = format_pts(self.pts)

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -92,6 +92,14 @@ class LLMContextAggregator(FrameProcessor):
 
         self._aggregation: List[str] = []
 
+        # Whether to add spaces between text parts.
+        # (Currently only used by LLMAssistantAggregator, but could be expanded
+        # to LLMUserAggregator in the future if needed; that would require
+        # additional work since LLMUserAggregator currently trims spaces from
+        # incoming frames before determining whether it "really" received any
+        # text).
+        self._add_spaces = True
+
     @property
     def messages(self) -> List[LLMContextMessage]:
         """Get messages from the LLM context.
@@ -183,7 +191,7 @@ class LLMContextAggregator(FrameProcessor):
         Returns:
             The concatenated aggregation string.
         """
-        return concatenate_aggregated_text(self._aggregation)
+        return concatenate_aggregated_text(self._aggregation, self._add_spaces)
 
 
 class LLMUserAggregator(LLMContextAggregator):
@@ -813,6 +821,10 @@ class LLMAssistantAggregator(LLMContextAggregator):
         if len(frame.text) == 0:
             return
 
+        # Track whether we need to add spaces between text parts
+        # Assumption: we can just keep track of the latest frame's value
+        self._add_spaces = not frame.includes_inter_frame_spaces
+
         self._aggregation.append(frame.text)
 
     def _context_updated_task_finished(self, task: asyncio.Task):

src/pipecat/processors/frameworks/langchain.py

@@ -107,7 +107,9 @@ class LangchainProcessor(FrameProcessor):
                 {self._transcript_key: text},
                 config={"configurable": {"session_id": self._participant_id}},
             ):
-                await self.push_frame(TextFrame(self.__get_token_value(token)))
+                frame = TextFrame(self.__get_token_value(token))
+                frame.includes_inter_frame_spaces = True
+                await self.push_frame(frame)
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:

src/pipecat/processors/transcript_processor.py

@@ -101,6 +101,12 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
         self._current_text_parts: List[str] = []
         self._aggregation_start_time: Optional[str] = None
 
+        # Whether to add spaces between text parts.
+        # (The use of this could be expanded to the UserTranscriptProcessor in
+        # the future if needed; currently the UserTranscriptProcessor assumes
+        # that user transcription frames do not need aggregation).
+        self._add_spaces = True
+
     async def _emit_aggregated_text(self):
         """Aggregates and emits text fragments as a transcript message.
 
@@ -141,7 +147,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
                 Result: "Hello there how are you"
         """
         if self._current_text_parts and self._aggregation_start_time:
-            content = concatenate_aggregated_text(self._current_text_parts)
+            content = concatenate_aggregated_text(self._current_text_parts, self._add_spaces)
             if content:
                 logger.trace(f"Emitting aggregated assistant message: {content}")
                 message = TranscriptionMessage(
@@ -185,6 +191,10 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
             if not self._aggregation_start_time:
                 self._aggregation_start_time = time_now_iso8601()
 
+            # Track whether we need to add spaces between text parts
+            # Assumption: we can just keep track of the latest frame's value
+            self._add_spaces = not frame.includes_inter_frame_spaces
+
             self._current_text_parts.append(frame.text)
 
             # Push frame.

src/pipecat/services/google/gemini_live/llm.py

@@ -1453,7 +1453,10 @@ class GeminiLiveLLMService(LLMService):
 
             self._bot_text_buffer += text
             self._search_result_buffer += text  # Also accumulate for grounding
-            await self.push_frame(LLMTextFrame(text=text))
+            frame = LLMTextFrame(text=text)
+            # Gemini Live text already includes any necessary inter-chunk spaces
+            frame.includes_inter_frame_spaces = True
+            await self.push_frame(frame)
 
         # Check for grounding metadata in server content
         if msg.server_content and msg.server_content.grounding_metadata:
@@ -1645,7 +1648,11 @@ class GeminiLiveLLMService(LLMService):
             await self.push_frame(TTSStartedFrame())
             await self.push_frame(LLMFullResponseStartFrame())
 
-        await self.push_frame(TTSTextFrame(text=text))
+        frame = TTSTextFrame(text=text)
+        # Gemini Live text already includes any necessary inter-chunk spaces
+        frame.includes_inter_frame_spaces = True
+
+        await self.push_frame(frame)
 
     async def _handle_msg_grounding_metadata(self, message: LiveServerMessage):
         """Handle dedicated grounding metadata messages."""

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

@@ -679,13 +679,19 @@ class OpenAIRealtimeLLMService(LLMService):
         # We receive text deltas (as opposed to audio transcript deltas) when
         # the output modality is "text"
         if evt.delta:
-            await self.push_frame(LLMTextFrame(evt.delta))
+            frame = LLMTextFrame(evt.delta)
+            # OpenAI Realtime text already includes any necessary inter-chunk spaces
+            frame.includes_inter_frame_spaces = True
+            await self.push_frame(frame)
 
     async def _handle_evt_audio_transcript_delta(self, evt):
         # We receive audio transcript deltas (as opposed to text deltas) when
         # the output modality is "audio" (the default)
         if evt.delta:
-            await self.push_frame(TTSTextFrame(evt.delta))
+            frame = TTSTextFrame(evt.delta)
+            # OpenAI Realtime text already includes any necessary inter-chunk spaces
+            frame.includes_inter_frame_spaces = True
+            await self.push_frame(frame)
 
     async def _handle_evt_function_call_arguments_done(self, evt):
         """Handle completion of function call arguments.

src/pipecat/utils/string.py

@@ -198,7 +198,7 @@ def parse_start_end_tags(
     return (None, current_tag_index)
 
 
-def concatenate_aggregated_text(text_parts: List[str]) -> str:
+def concatenate_aggregated_text(text_parts: List[str], add_spaces: bool) -> str:
     """Concatenate a list of text parts into a single string.
 
     This function joins the provided list of text parts into a single string,
@@ -209,25 +209,14 @@ def concatenate_aggregated_text(text_parts: List[str]) -> str:
 
     Args:
         text_parts: A list of strings representing parts of text to concatenate.
+        add_spaces: Whether to add spaces between text parts during concatenation.
 
     Returns:
         A single concatenated string.
     """
-    # Check specifically for space characters, previously isspace() was used
+    # Concatenate text parts with or without spaces based on the flag
-    # but that includes all whitespace characters (e.g. \n), not just spaces.
+    separator = " " if add_spaces else ""
-    has_leading_spaces = any(part and part[0] == " " for part in text_parts[1:])
+    result = separator.join(text_parts)
-    has_trailing_spaces = any(part and part[-1] == " " for part in text_parts[:-1])
-
-    # If there are embedded spaces in the fragments, use direct concatenation
-    contains_spacing_between_fragments = has_leading_spaces or has_trailing_spaces
-
-    # Apply corresponding joining method
-    if contains_spacing_between_fragments:
-        # Fragments already have spacing - just concatenate
-        result = "".join(text_parts)
-    else:
-        # Word-by-word fragments - join with spaces
-        result = " ".join(text_parts)
 
     # Clean up any excessive whitespace
     result = result.strip()

tests/test_context_aggregators.py

@@ -35,6 +35,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.task import PipelineParams
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response import (
     LLMAssistantAggregatorParams,
     LLMUserAggregatorParams,
@@ -651,12 +652,20 @@ class BaseTestAssistantContextAggregator:
         aggregator = self.AGGREGATOR_CLASS(
             context, params=self.create_assistant_aggregator_params(expect_stripped_words=False)
         )
+
+        # The newer LLMAssistantAggregator expects TextFrames to declare
+        # when they include inter-frame spaces.
+        def make_text_frame(text: str) -> TextFrame:
+            frame = TextFrame(text=text)
+            frame.includes_inter_frame_spaces = True
+            return frame
+
         frames_to_send = [
             LLMFullResponseStartFrame(),
-            TextFrame(text="Hello "),
+            make_text_frame("Hello "),
-            TextFrame(text="Pipecat. "),
+            make_text_frame("Pipecat. "),
-            TextFrame(text="How are "),
+            make_text_frame("How are "),
-            TextFrame(text="you?"),
+            make_text_frame("you?"),
             LLMFullResponseEndFrame(),
         ]
         expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES]
@@ -697,14 +706,22 @@ class BaseTestAssistantContextAggregator:
         aggregator = self.AGGREGATOR_CLASS(
             context, params=self.create_assistant_aggregator_params(expect_stripped_words=False)
         )
+
+        # The newer LLMAssistantAggregator expects TextFrames to declare
+        # when they include inter-frame spaces.
+        def make_text_frame(text: str) -> TextFrame:
+            frame = TextFrame(text=text)
+            frame.includes_inter_frame_spaces = True
+            return frame
+
         frames_to_send = [
             LLMFullResponseStartFrame(),
-            TextFrame(text="Hello "),
+            make_text_frame("Hello "),
-            TextFrame(text="Pipecat."),
+            make_text_frame("Pipecat."),
             LLMFullResponseEndFrame(),
             LLMFullResponseStartFrame(),
-            TextFrame(text="How are "),
+            make_text_frame(text="How are "),
-            TextFrame(text="you?"),
+            make_text_frame(text="you?"),
             LLMFullResponseEndFrame(),
         ]
         expected_down_frames = [*self.EXPECTED_CONTEXT_FRAMES, *self.EXPECTED_CONTEXT_FRAMES]
@@ -724,16 +741,24 @@ class BaseTestAssistantContextAggregator:
         aggregator = self.AGGREGATOR_CLASS(
             context, params=self.create_assistant_aggregator_params(expect_stripped_words=False)
         )
+
+        # The newer LLMAssistantAggregator expects TextFrames to declare
+        # when they include inter-frame spaces.
+        def make_text_frame(text: str) -> TextFrame:
+            frame = TextFrame(text=text)
+            frame.includes_inter_frame_spaces = True
+            return frame
+
         frames_to_send = [
             LLMFullResponseStartFrame(),
-            TextFrame(text="Hello "),
+            make_text_frame("Hello "),
-            TextFrame(text="Pipecat."),
+            make_text_frame("Pipecat."),
             LLMFullResponseEndFrame(),
             SleepFrame(AGGREGATION_SLEEP),
             InterruptionFrame(),
             LLMFullResponseStartFrame(),
-            TextFrame(text="How are "),
+            make_text_frame("How are "),
-            TextFrame(text="you?"),
+            make_text_frame("you?"),
             LLMFullResponseEndFrame(),
         ]
         expected_down_frames = [
@@ -969,7 +994,7 @@ class TestOpenAIAssistantContextAggregator(
 class TestLLMAssistantAggregator(
     BaseTestAssistantContextAggregator, unittest.IsolatedAsyncioTestCase
 ):
-    CONTEXT_CLASS = OpenAILLMContext
+    CONTEXT_CLASS = LLMContext
     AGGREGATOR_CLASS = LLMAssistantAggregator
     EXPECTED_CONTEXT_FRAMES = [LLMContextFrame, LLMContextAssistantTimestampFrame]
 

tests/test_transcript_processor.py

@@ -438,17 +438,22 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
             received_updates.append(frame)
 
         # Test the specific pattern shared
+        def make_tts_text_frame(text: str) -> TTSTextFrame:
+            frame = TTSTextFrame(text=text)
+            frame.includes_inter_frame_spaces = True
+            return frame
+
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),
-            TTSTextFrame(text="Hello"),
+            make_tts_text_frame("Hello"),
-            TTSTextFrame(text=" there"),
+            make_tts_text_frame(" there"),
-            TTSTextFrame(text="!"),
+            make_tts_text_frame("!"),
-            TTSTextFrame(text=" How"),
+            make_tts_text_frame(" How"),
-            TTSTextFrame(text="'s"),
+            make_tts_text_frame("'s"),
-            TTSTextFrame(text=" it"),
+            make_tts_text_frame(" it"),
-            TTSTextFrame(text=" going"),
+            make_tts_text_frame(" going"),
-            TTSTextFrame(text="?"),
+            make_tts_text_frame("?"),
             BotStoppedSpeakingFrame(),
         ]