Merge pull request #3863 from pipecat-ai/filipi/manual_summarization

Manual context summarization

changelog/3863.added.2.md

@@ -0,0 +1 @@
+- Added `LLMContextSummaryConfig` (summary generation params: `target_context_tokens`, `min_messages_after_summary`, `summarization_prompt`) and `LLMAutoContextSummarizationConfig` (auto-trigger thresholds: `max_context_tokens`, `max_unsummarized_messages`, plus a nested `summary_config`). These replace the monolithic `LLMContextSummarizationConfig`.

changelog/3863.added.md

@@ -0,0 +1 @@
+- Added `LLMSummarizeContextFrame` to trigger on-demand context summarization from anywhere in the pipeline (e.g. a function call tool). Accepts an optional `config: LLMContextSummaryConfig` to override summary generation settings per request.

changelog/3863.changed.md

@@ -0,0 +1 @@
+- ⚠️ Renamed `LLMAssistantAggregatorParams` fields: `enable_context_summarization` → `enable_auto_context_summarization` and `context_summarization_config` → `auto_context_summarization_config` (now accepts `LLMAutoContextSummarizationConfig`). The old names still work with a `DeprecationWarning` for one release cycle.

changelog/3863.deprecated.md

@@ -0,0 +1 @@
+- Deprecated `LLMContextSummarizationConfig`. Use `LLMAutoContextSummarizationConfig` with a nested `LLMContextSummaryConfig` instead. The old class emits a `DeprecationWarning`.

examples/foundational/54-context-summarization-openai.py

@@ -41,7 +41,10 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig
+from pipecat.utils.context.llm_context_summarization import (
+    LLMAutoContextSummarizationConfig,
+    LLMContextSummaryConfig,
+)
 
 load_dotenv(override=True)
 
@@ -120,14 +123,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             vad_analyzer=SileroVADAnalyzer(),
         ),
         assistant_params=LLMAssistantAggregatorParams(
-            enable_context_summarization=True,
+            enable_auto_context_summarization=True,
             # Optional: customize context summarization behavior
             # Using low limits to demonstrate the feature quickly
-            context_summarization_config=LLMContextSummarizationConfig(
+            auto_context_summarization_config=LLMAutoContextSummarizationConfig(
                 max_context_tokens=1000,  # Trigger summarization at 1000 tokens
-                target_context_tokens=800,  # Target context size for the summarization
                 max_unsummarized_messages=10,  # Or when 10 new messages accumulate
-                min_messages_after_summary=2,  # Keep last 2 messages uncompressed
+                summary_config=LLMContextSummaryConfig(
+                    target_context_tokens=800,  # Target context size for the summarization
+                    min_messages_after_summary=2,  # Keep last 2 messages uncompressed
+                ),
             ),
         ),
     )

examples/foundational/54a-context-summarization-google.py

@@ -41,7 +41,10 @@ from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig
+from pipecat.utils.context.llm_context_summarization import (
+    LLMAutoContextSummarizationConfig,
+    LLMContextSummaryConfig,
+)
 
 load_dotenv(override=True)
 
@@ -120,14 +123,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             vad_analyzer=SileroVADAnalyzer(),
         ),
         assistant_params=LLMAssistantAggregatorParams(
-            enable_context_summarization=True,
+            enable_auto_context_summarization=True,
             # Optional: customize context summarization behavior
             # Using low limits to demonstrate the feature quickly
-            context_summarization_config=LLMContextSummarizationConfig(
+            auto_context_summarization_config=LLMAutoContextSummarizationConfig(
                 max_context_tokens=1000,  # Trigger summarization at 1000 tokens
-                target_context_tokens=800,  # Target context size for the summarization
                 max_unsummarized_messages=10,  # Or when 10 new messages accumulate
-                min_messages_after_summary=2,  # Keep last 2 messages uncompressed
+                summary_config=LLMContextSummaryConfig(
+                    target_context_tokens=800,  # Target context size for the summarization
+                    min_messages_after_summary=2,  # Keep last 2 messages uncompressed
+                ),
             ),
         ),
     )

examples/foundational/54b-context-summarization-manual-openai.py

@@ -0,0 +1,179 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Example demonstrating manual context summarization via a function call.
+
+This example shows how to trigger context summarization on demand rather than
+automatically. The user can ask the bot to "summarize the conversation" and the
+bot will call a function that pushes an LLMSummarizeContextFrame into the
+pipeline, causing the LLM service to compress the conversation history.
+
+Unlike example 54, automatic summarization is NOT enabled here. Summarization
+only happens when the user explicitly requests it through the function call.
+"""
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import LLMRunFrame, LLMSummarizeContextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
+from pipecat.turns.user_turn_strategies import UserTurnStrategies
+
+load_dotenv(override=True)
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def summarize_conversation(params: FunctionCallParams):
+    """Trigger manual context summarization via a pipeline frame."""
+    logger.info("Tool called: summarize_conversation")
+    await params.result_callback({"status": "summarization_requested"})
+    await params.llm.queue_frame(LLMSummarizeContextFrame())
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    llm.register_function("summarize_conversation", summarize_conversation)
+
+    summarize_function = FunctionSchema(
+        name="summarize_conversation",
+        description=(
+            "Summarize and compress the conversation history. "
+            "Call this when the user asks you to summarize the conversation "
+            "or when you want to free up context space."
+        ),
+        properties={},
+        required=[],
+    )
+    tools = ToolsSchema(standard_tools=[summarize_function])
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your "
+                "capabilities in a succinct way. Your output will be spoken aloud, so avoid "
+                "special characters that can't easily be spoken, such as emojis or bullet points. "
+                "Respond to what the user said in a creative and helpful way. "
+                "If the user asks you to summarize the conversation, call the "
+                "summarize_conversation function. After summarization, briefly acknowledge "
+                "that the conversation history has been compressed."
+            ),
+        },
+    ]
+
+    context = LLMContext(messages, tools=tools)
+
+    # Automatic summarization is NOT enabled here (enable_auto_context_summarization
+    # defaults to False). The summarizer is still created internally so that
+    # LLMSummarizeContextFrame frames pushed via the function call are handled.
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(
+            user_turn_strategies=UserTurnStrategies(
+                stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
+            ),
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        ),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            user_aggregator,  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            assistant_aggregator,  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/foundational/54c-context-summarization-dedicated-llm.py

@@ -44,7 +44,10 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig
+from pipecat.utils.context.llm_context_summarization import (
+    LLMAutoContextSummarizationConfig,
+    LLMContextSummaryConfig,
+)
 
 load_dotenv(override=True)
 
@@ -147,23 +150,25 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             vad_analyzer=SileroVADAnalyzer(),
         ),
         assistant_params=LLMAssistantAggregatorParams(
-            enable_context_summarization=True,
-            context_summarization_config=LLMContextSummarizationConfig(
+            enable_auto_context_summarization=True,
+            auto_context_summarization_config=LLMAutoContextSummarizationConfig(
                 # Trigger thresholds (low values to demonstrate quickly)
                 max_context_tokens=1000,
                 max_unsummarized_messages=10,
-                # Summary generation
-                target_context_tokens=800,
-                min_messages_after_summary=2,
-                summarization_prompt=CUSTOM_SUMMARIZATION_PROMPT,
-                # Custom summary format - wrap in XML tags so the system
-                # prompt can identify summaries vs. live conversation
-                summary_message_template="<context_summary>\n{summary}\n</context_summary>",
-                # Use a dedicated cheap LLM for summarization instead of
-                # the primary conversation model
-                llm=summarization_llm,
-                # Cancel summarization if it takes longer than 60 seconds
-                summarization_timeout=60.0,
+                summary_config=LLMContextSummaryConfig(
+                    # Summary generation
+                    target_context_tokens=800,
+                    min_messages_after_summary=2,
+                    summarization_prompt=CUSTOM_SUMMARIZATION_PROMPT,
+                    # Custom summary format - wrap in XML tags so the system
+                    # prompt can identify summaries vs. live conversation
+                    summary_message_template="<context_summary>\n{summary}\n</context_summary>",
+                    # Use a dedicated cheap LLM for summarization instead of
+                    # the primary conversation model
+                    llm=summarization_llm,
+                    # Cancel summarization if it takes longer than 60 seconds
+                    summarization_timeout=60.0,
+                ),
             ),
         ),
     )

src/pipecat/frames/frames.py

@@ -43,6 +43,7 @@ if TYPE_CHECKING:
     from pipecat.processors.aggregators.llm_context import LLMContext, NotGiven
     from pipecat.processors.frame_processor import FrameProcessor
     from pipecat.services.settings import ServiceSettings
+    from pipecat.utils.context.llm_context_summarization import LLMContextSummaryConfig
     from pipecat.utils.tracing.tracing_context import TracingContext
 
 
@@ -2000,6 +2001,22 @@ class LLMAssistantPushAggregationFrame(ControlFrame):
     """
 
 
+@dataclass
+class LLMSummarizeContextFrame(ControlFrame):
+    """Frame requesting on-demand context summarization.
+
+    Push this frame into the pipeline to trigger a manual context summarization.
+
+    Parameters:
+        config: Optional per-request override for summary generation settings
+            (prompt, token budget, messages to keep). If ``None``, the
+            summarizer's default :class:`~pipecat.utils.context.llm_context_summarization.LLMContextSummaryConfig`
+            is used.
+    """
+
+    config: Optional["LLMContextSummaryConfig"] = None
+
+
 @dataclass
 class LLMContextSummaryRequestFrame(ControlFrame):
     """Frame requesting context summarization from an LLM service.

src/pipecat/processors/aggregators/llm_context_summarizer.py

@@ -19,14 +19,16 @@ from pipecat.frames.frames import (
     LLMContextSummaryRequestFrame,
     LLMContextSummaryResultFrame,
     LLMFullResponseStartFrame,
+    LLMSummarizeContextFrame,
 )
 from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
 from pipecat.utils.asyncio.task_manager import BaseTaskManager
 from pipecat.utils.base_object import BaseObject
 from pipecat.utils.context.llm_context_summarization import (
     DEFAULT_SUMMARIZATION_TIMEOUT,
-    LLMContextSummarizationConfig,
+    LLMAutoContextSummarizationConfig,
     LLMContextSummarizationUtil,
+    LLMContextSummaryConfig,
 )
 
 if TYPE_CHECKING:
@@ -55,9 +57,20 @@ class SummaryAppliedEvent:
 class LLMContextSummarizer(BaseObject):
     """Summarizer for managing LLM context summarization.
 
-    This class manages automatic context summarization when token or message
-    limits are reached. It monitors the LLM context size, triggers
-    summarization requests, and applies the results to compress conversation history.
+    This class manages context summarization, either automatically when token or
+    message limits are reached, or on-demand when an ``LLMSummarizeContextFrame``
+    is received. It monitors the LLM context size, triggers summarization requests,
+    and applies the results to compress conversation history.
+
+    When ``auto_trigger=True`` (the default), summarization is triggered
+    automatically based on the configured thresholds in
+    ``LLMAutoContextSummarizationConfig``. When ``auto_trigger=False``,
+    threshold checks are skipped and summarization only happens when an
+    ``LLMSummarizeContextFrame`` is explicitly pushed into the pipeline.
+
+    Both modes can coexist: set ``auto_trigger=True`` and also push
+    ``LLMSummarizeContextFrame`` at any time to force an immediate summarization
+    (subject to the ``_summarization_in_progress`` guard).
 
     Event handlers available:
 
@@ -88,18 +101,26 @@ class LLMContextSummarizer(BaseObject):
         self,
         *,
         context: LLMContext,
-        config: Optional[LLMContextSummarizationConfig] = None,
+        config: Optional[LLMAutoContextSummarizationConfig] = None,
+        auto_trigger: bool = True,
     ):
         """Initialize the context summarizer.
 
         Args:
             context: The LLM context to monitor and summarize.
-            config: Configuration for summarization behavior. If None, uses default config.
+            config: Auto-summarization configuration controlling both trigger
+                thresholds and default summary generation parameters. If None,
+                uses default ``LLMAutoContextSummarizationConfig`` values.
+            auto_trigger: Whether to automatically trigger summarization when
+                thresholds are reached. When False, summarization only happens
+                when an ``LLMSummarizeContextFrame`` is pushed into the pipeline.
+                Defaults to True.
         """
         super().__init__()
 
         self._context = context
-        self._config = config or LLMContextSummarizationConfig()
+        self._auto_config = config or LLMAutoContextSummarizationConfig()
+        self._auto_trigger = auto_trigger
 
         self._task_manager: Optional[BaseTaskManager] = None
 
@@ -137,6 +158,8 @@ class LLMContextSummarizer(BaseObject):
         """
         if isinstance(frame, LLMFullResponseStartFrame):
             await self._handle_llm_response_start(frame)
+        elif isinstance(frame, LLMSummarizeContextFrame):
+            await self._handle_manual_summarization_request(frame)
         elif isinstance(frame, LLMContextSummaryResultFrame):
             await self._handle_summary_result(frame)
         elif isinstance(frame, InterruptionFrame):
@@ -151,12 +174,24 @@ class LLMContextSummarizer(BaseObject):
         if self._should_summarize():
             await self._request_summarization()
 
-    async def _handle_interruption(self):
-        """Handle interruption by canceling summarization in progress.
+    async def _handle_manual_summarization_request(self, frame: LLMSummarizeContextFrame):
+        """Handle an explicit on-demand summarization request.
+
+        Reuses the same ``_request_summarization()`` code path as auto mode,
+        so bookkeeping (``_summarization_in_progress``,
+        ``_pending_summary_request_id``) is always updated correctly.
 
         Args:
-            frame: The interruption frame.
+            frame: The manual summarization request frame, optionally carrying
+                a per-request :class:`~pipecat.utils.context.llm_context_summarization.LLMContextSummaryConfig`.
         """
+        if self._summarization_in_progress:
+            logger.debug(f"{self}: Summarization already in progress, ignoring manual request")
+            return
+        await self._request_summarization(config_override=frame.config)
+
+    async def _handle_interruption(self):
+        """Handle interruption by canceling summarization in progress."""
         # Reset summarization state to allow new requests. This is necessary because
         # the request frame (LLMContextSummaryRequestFrame) may have been cancelled
         # during interruption. We preserve _pending_summary_request_id to handle the
@@ -179,13 +214,17 @@ class LLMContextSummarizer(BaseObject):
 
         Returns:
             True if all conditions are met:
+            - ``auto_trigger`` is enabled
             - No summarization currently in progress
             - AND either:
-              - Token count exceeds max_context_tokens
-              - OR message count exceeds max_unsummarized_messages since last summary
+              - Token count exceeds ``max_context_tokens``
+              - OR message count exceeds ``max_unsummarized_messages`` since last summary
         """
         logger.trace(f"{self}: Checking if context summarization is needed")
 
+        if not self._auto_trigger:
+            return False
+
         if self._summarization_in_progress:
             logger.debug(f"{self}: Summarization already in progress")
             return False
@@ -195,20 +234,20 @@ class LLMContextSummarizer(BaseObject):
         num_messages = len(self._context.messages)
 
         # Check if we've reached the token limit
-        token_limit = self._config.max_context_tokens
+        token_limit = self._auto_config.max_context_tokens
         token_limit_exceeded = total_tokens >= token_limit
 
         # Check if we've exceeded max unsummarized messages
         messages_since_summary = len(self._context.messages) - 1
         message_threshold_exceeded = (
-            messages_since_summary >= self._config.max_unsummarized_messages
+            messages_since_summary >= self._auto_config.max_unsummarized_messages
         )
 
         logger.trace(
             f"{self}: Context has {num_messages} messages, "
             f"~{total_tokens} tokens (limit: {token_limit}), "
             f"{messages_since_summary} messages since last summary "
-            f"(message threshold: {self._config.max_unsummarized_messages})"
+            f"(message threshold: {self._auto_config.max_unsummarized_messages})"
         )
 
         # Trigger if either limit is exceeded
@@ -223,23 +262,30 @@ class LLMContextSummarizer(BaseObject):
             reason.append(f"~{total_tokens} tokens (>={token_limit} limit)")
         if message_threshold_exceeded:
             reason.append(
-                f"{messages_since_summary} messages (>={self._config.max_unsummarized_messages} threshold)"
+                f"{messages_since_summary} messages (>={self._auto_config.max_unsummarized_messages} threshold)"
             )
 
         logger.debug(f"{self}: ✓ Summarization needed - {', '.join(reason)}")
         return True
 
-    async def _request_summarization(self):
+    async def _request_summarization(
+        self, config_override: Optional[LLMContextSummaryConfig] = None
+    ):
         """Request context summarization from LLM service.
 
         Creates a summarization request frame and either handles it directly
         using a dedicated LLM (if configured) or emits it via event handler
-        for the pipeline's primary LLM. Tracks the request ID to match async
-        responses and prevent race conditions.
+        for the pipeline's primary LLM.
+        Tracks the request ID to match async responses and prevent race conditions.
+
+        Args:
+            config_override: Optional per-request summary configuration. If provided,
+                overrides the default summary generation settings from
+                ``self._auto_config.summary_config``.
         """
         # Generate unique request ID
         request_id = str(uuid.uuid4())
-        min_keep = self._config.min_messages_after_summary
+        summary_config = config_override or self._auto_config.summary_config
 
         # Mark summarization in progress
         self._summarization_in_progress = True
@@ -251,16 +297,16 @@ class LLMContextSummarizer(BaseObject):
         request_frame = LLMContextSummaryRequestFrame(
             request_id=request_id,
             context=self._context,
-            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,
+            min_messages_to_keep=summary_config.min_messages_after_summary,
+            target_context_tokens=summary_config.target_context_tokens,
+            summarization_prompt=summary_config.summary_prompt,
+            summarization_timeout=summary_config.summarization_timeout,
         )
 
-        if self._config.llm:
+        if summary_config.llm:
             # Use dedicated LLM directly — no need to involve the pipeline
             self.task_manager.create_task(
-                self._generate_summary_with_dedicated_llm(self._config.llm, request_frame),
+                self._generate_summary_with_dedicated_llm(summary_config.llm, request_frame),
                 f"{self}-dedicated-llm-summary",
             )
         else:
@@ -323,7 +369,9 @@ class LLMContextSummarizer(BaseObject):
         """
         logger.debug(f"{self}: Received summary result (request_id={frame.request_id})")
 
-        # Check if this is the result we're waiting for
+        # Check if this is the result we're waiting for. Both auto and manual
+        # summarization set _pending_summary_request_id via _request_summarization(),
+        # so this check always applies.
         if frame.request_id != self._pending_summary_request_id:
             logger.debug(f"{self}: Ignoring stale summary result (request_id={frame.request_id})")
             return
@@ -360,7 +408,7 @@ class LLMContextSummarizer(BaseObject):
         if last_summarized_index >= len(self._context.messages):
             return False
 
-        min_keep = self._config.min_messages_after_summary
+        min_keep = self._auto_config.summary_config.min_messages_after_summary
         remaining = len(self._context.messages) - 1 - last_summarized_index
         if remaining < min_keep:
             return False
@@ -377,6 +425,7 @@ class LLMContextSummarizer(BaseObject):
             summary: The generated summary text.
             last_summarized_index: Index of the last message that was summarized.
         """
+        config = self._auto_config.summary_config
         messages = self._context.messages
 
         # Find the first system message to preserve. LLMSpecificMessage instances are excluded
@@ -397,7 +446,7 @@ class LLMContextSummarizer(BaseObject):
 
         # Create summary message as a user message (the summary is context
         # provided *to* the assistant, not something the assistant said)
-        summary_content = self._config.summary_message_template.format(summary=summary)
+        summary_content = config.summary_message_template.format(summary=summary)
         summary_message = {"role": "user", "content": summary_content}
 
         # Reconstruct context

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -79,7 +79,10 @@ from pipecat.turns.user_stop import BaseUserTurnStopStrategy, UserTurnStoppedPar
 from pipecat.turns.user_turn_completion_mixin import UserTurnCompletionConfig
 from pipecat.turns.user_turn_controller import UserTurnController
 from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies, UserTurnStrategies
-from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig
+from pipecat.utils.context.llm_context_summarization import (
+    LLMAutoContextSummarizationConfig,
+    LLMContextSummarizationConfig,
+)
 from pipecat.utils.string import TextPartForConcatenation, concatenate_aggregated_text
 from pipecat.utils.time import time_now_iso8601
 
@@ -125,18 +128,54 @@ class LLMAssistantAggregatorParams:
             in text frames by adding spaces between tokens. This parameter is
             ignored when used with the newer LLMAssistantAggregator, which
             handles word spacing automatically.
-        enable_context_summarization: Enable automatic context summarization when token
-            limits are reached (disabled by default). When enabled, older conversation
-            messages are automatically compressed into summaries to manage context size.
-        context_summarization_config: Configuration for context summarization behavior.
-            Controls thresholds, message preservation, and summarization prompts. If None
-            and summarization is enabled, uses default configuration values.
+        enable_auto_context_summarization: Enable automatic context summarization when token
+            or message-count limits are reached (disabled by default). When enabled,
+            older conversation messages are automatically compressed into summaries to
+            manage context size.
+        auto_context_summarization_config: Configuration for automatic context
+            summarization. Controls trigger thresholds, message preservation, and
+            summarization prompts. If None, uses default
+            ``LLMAutoContextSummarizationConfig`` values.
     """
 
     expect_stripped_words: bool = True
-    enable_context_summarization: bool = False
+    enable_auto_context_summarization: bool = False
+    auto_context_summarization_config: Optional[LLMAutoContextSummarizationConfig] = None
+
+    # ---------------------------------------------------------------------------
+    # Deprecated field names — kept for backward compatibility.
+    # Use enable_auto_context_summarization and auto_context_summarization_config instead.
+    # ---------------------------------------------------------------------------
+    enable_context_summarization: Optional[bool] = None
     context_summarization_config: Optional[LLMContextSummarizationConfig] = None
 
+    def __post_init__(self):
+        if self.enable_context_summarization is not None:
+            warnings.warn(
+                "LLMAssistantAggregatorParams.enable_context_summarization is deprecated. "
+                "Use enable_auto_context_summarization instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            self.enable_auto_context_summarization = self.enable_context_summarization
+            self.enable_context_summarization = None
+
+        if self.context_summarization_config is not None:
+            warnings.warn(
+                "LLMAssistantAggregatorParams.context_summarization_config is deprecated. "
+                "Use auto_context_summarization_config (LLMAutoContextSummarizationConfig) instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            if isinstance(self.context_summarization_config, LLMContextSummarizationConfig):
+                self.auto_context_summarization_config = (
+                    self.context_summarization_config.to_auto_config()
+                )
+            else:
+                # Accept LLMAutoContextSummarizationConfig passed to the deprecated field
+                self.auto_context_summarization_config = self.context_summarization_config  # type: ignore[assignment]
+            self.context_summarization_config = None
+
 
 @dataclass
 class UserTurnStoppedMessage:
@@ -825,16 +864,18 @@ class LLMAssistantAggregator(LLMContextAggregator):
         self._thought_aggregation: List[TextPartForConcatenation] = []
         self._thought_start_time: str = ""
 
-        # Context summarization
-        self._summarizer: Optional[LLMContextSummarizer] = None
-        if self._params.enable_context_summarization:
-            self._summarizer = LLMContextSummarizer(
-                context=self._context,
-                config=self._params.context_summarization_config,
-            )
-            self._summarizer.add_event_handler(
-                "on_request_summarization", self._on_request_summarization
-            )
+        # Context summarization — always create the summarizer so that manually
+        # pushed LLMSummarizeContextFrame frames are always handled.
+        # Auto-triggering based on thresholds is only enabled when
+        # enable_auto_context_summarization is True.
+        self._summarizer: Optional[LLMContextSummarizer] = LLMContextSummarizer(
+            context=self._context,
+            config=self._params.auto_context_summarization_config,
+            auto_trigger=self._params.enable_auto_context_summarization,
+        )
+        self._summarizer.add_event_handler(
+            "on_request_summarization", self._on_request_summarization
+        )
 
         self._register_event_handler("on_assistant_turn_started")
         self._register_event_handler("on_assistant_turn_stopped")

src/pipecat/utils/context/llm_context_summarization.py

@@ -10,7 +10,8 @@ This module provides reusable functionality for automatically compressing conver
 context when token limits are reached, enabling efficient long-running conversations.
 """
 
-from dataclasses import dataclass
+import warnings
+from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, List, Optional
 
 if TYPE_CHECKING:
@@ -54,26 +55,18 @@ The conversation transcript follows. Generate only the summary, no other text.""
 
 
 @dataclass
-class LLMContextSummarizationConfig:
-    """Configuration for context summarization behavior.
+class LLMContextSummaryConfig:
+    """Configuration for summary generation parameters.
 
-    Controls when and how conversation context is automatically compressed
-    to manage token limits in long-running conversations.
+    Contains settings that control how a summary is generated. Used by both
+    automatic and manual summarization modes.
 
     Parameters:
-        max_context_tokens: Maximum allowed context size in tokens. When this
-            limit is reached, summarization is triggered to compress the context.
-            The tokens are calculated using the industry-standard approximation
-            of 1 token ≈ 4 characters.
         target_context_tokens: Maximum token size for the generated summary.
             This value is passed directly to the LLM as the max_tokens parameter
             when generating the summary. Should be sized appropriately to allow
             the summary plus recent preserved messages to fit within reasonable
             context limits.
-        max_unsummarized_messages: Maximum number of new messages that can
-            accumulate since the last summary before triggering a new
-            summarization. This ensures regular compression even if token
-            limits are not reached.
         min_messages_after_summary: Number of recent messages to preserve
             uncompressed after each summarization. These messages maintain
             immediate conversational context.
@@ -94,6 +87,94 @@ class LLMContextSummarizationConfig:
             is aborted with an error and future summarizations are unblocked.
     """
 
+    target_context_tokens: int = 6000
+    min_messages_after_summary: int = 4
+    summarization_prompt: Optional[str] = None
+    summary_message_template: str = "Conversation summary: {summary}"
+    llm: Optional["LLMService"] = None
+    summarization_timeout: float = DEFAULT_SUMMARIZATION_TIMEOUT
+
+    def __post_init__(self):
+        """Validate configuration parameters."""
+        if self.target_context_tokens <= 0:
+            raise ValueError("target_context_tokens must be positive")
+        if self.min_messages_after_summary < 0:
+            raise ValueError("min_messages_after_summary must be non-negative")
+
+    @property
+    def summary_prompt(self) -> str:
+        """Get the summarization prompt to use.
+
+        Returns:
+            The custom prompt if set, otherwise the default summarization prompt.
+        """
+        return self.summarization_prompt or DEFAULT_SUMMARIZATION_PROMPT
+
+
+@dataclass
+class LLMAutoContextSummarizationConfig:
+    """Configuration for automatic context summarization.
+
+    Controls when conversation context is automatically compressed and how
+    that summary is generated. Summarization is triggered when either the
+    token limit or the unsummarized message count threshold is exceeded.
+
+    Parameters:
+        max_context_tokens: Maximum allowed context size in tokens. When this
+            limit is reached, summarization is triggered to compress the context.
+            The tokens are calculated using the industry-standard approximation
+            of 1 token ≈ 4 characters.
+        max_unsummarized_messages: Maximum number of new messages that can
+            accumulate since the last summary before triggering a new
+            summarization. This ensures regular compression even if token
+            limits are not reached.
+        summary_config: Configuration for summary generation parameters
+            (prompt, token budget, messages to keep). If not provided, uses
+            default ``LLMContextSummaryConfig`` values.
+    """
+
+    max_context_tokens: int = 8000
+    max_unsummarized_messages: int = 20
+    summary_config: LLMContextSummaryConfig = field(default_factory=LLMContextSummaryConfig)
+
+    def __post_init__(self):
+        """Validate configuration parameters."""
+        if self.max_context_tokens <= 0:
+            raise ValueError("max_context_tokens must be positive")
+        if self.max_unsummarized_messages < 1:
+            raise ValueError("max_unsummarized_messages must be at least 1")
+
+        # Auto-adjust target_context_tokens if it exceeds max_context_tokens
+        if self.summary_config.target_context_tokens > self.max_context_tokens:
+            # Use 80% of max_context_tokens as a reasonable default
+            self.summary_config.target_context_tokens = int(self.max_context_tokens * 0.8)
+
+
+@dataclass
+class LLMContextSummarizationConfig:
+    """Configuration for context summarization behavior.
+
+    .. deprecated::
+        Use :class:`LLMAutoContextSummarizationConfig` with a nested
+        :class:`LLMContextSummaryConfig` instead::
+
+            LLMAutoContextSummarizationConfig(
+                max_context_tokens=8000,
+                max_unsummarized_messages=20,
+                summary_config=LLMContextSummaryConfig(
+                    target_context_tokens=6000,
+                    min_messages_after_summary=4,
+                ),
+            )
+
+    Parameters:
+        max_context_tokens: Maximum allowed context size in tokens.
+        target_context_tokens: Maximum token size for the generated summary.
+        max_unsummarized_messages: Maximum new messages before triggering summarization.
+        min_messages_after_summary: Number of recent messages to preserve.
+        summarization_prompt: Custom prompt for summary generation.
+    """
+
     max_context_tokens: int = 8000
     target_context_tokens: int = 6000
     max_unsummarized_messages: int = 20
@@ -105,6 +186,12 @@ class LLMContextSummarizationConfig:
 
     def __post_init__(self):
         """Validate configuration parameters."""
+        warnings.warn(
+            "LLMContextSummarizationConfig is deprecated. "
+            "Use LLMAutoContextSummarizationConfig with a nested LLMContextSummaryConfig instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
         if self.max_context_tokens <= 0:
             raise ValueError("max_context_tokens must be positive")
         if self.target_context_tokens <= 0:
@@ -129,6 +216,25 @@ class LLMContextSummarizationConfig:
         """
         return self.summarization_prompt or DEFAULT_SUMMARIZATION_PROMPT
 
+    def to_auto_config(self) -> LLMAutoContextSummarizationConfig:
+        """Convert to the new :class:`LLMAutoContextSummarizationConfig`.
+
+        Returns:
+            An equivalent ``LLMAutoContextSummarizationConfig`` instance.
+        """
+        return LLMAutoContextSummarizationConfig(
+            max_context_tokens=self.max_context_tokens,
+            max_unsummarized_messages=self.max_unsummarized_messages,
+            summary_config=LLMContextSummaryConfig(
+                target_context_tokens=self.target_context_tokens,
+                min_messages_after_summary=self.min_messages_after_summary,
+                summarization_prompt=self.summarization_prompt,
+                summary_message_template=self.summary_message_template,
+                llm=self.llm,
+                summarization_timeout=self.summarization_timeout,
+            ),
+        )
+
 
 @dataclass
 class LLMMessagesToSummarize:

tests/test_context_summarization.py

@@ -14,8 +14,10 @@ from pipecat.frames.frames import LLMContextSummaryRequestFrame, LLMContextSumma
 from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
 from pipecat.services.llm_service import LLMService
 from pipecat.utils.context.llm_context_summarization import (
+    LLMAutoContextSummarizationConfig,
     LLMContextSummarizationConfig,
     LLMContextSummarizationUtil,
+    LLMContextSummaryConfig,
 )
 
 
@@ -167,43 +169,109 @@ class TestContextSummarizationMixin(unittest.TestCase):
         self.assertIn("USER: First part Second part", transcript)
 
 
-class TestLLMContextSummarizationConfig(unittest.TestCase):
-    """Tests for LLMContextSummarizationConfig."""
+class TestLLMContextSummaryConfig(unittest.TestCase):
+    """Tests for LLMContextSummaryConfig."""
 
     def test_default_config(self):
         """Test default configuration values."""
-        config = LLMContextSummarizationConfig()
+        config = LLMContextSummaryConfig()
 
-        self.assertEqual(config.max_context_tokens, 8000)
-        self.assertEqual(config.max_unsummarized_messages, 20)
+        self.assertEqual(config.target_context_tokens, 6000)
         self.assertEqual(config.min_messages_after_summary, 4)
         self.assertIsNone(config.summarization_prompt)
 
     def test_custom_config(self):
         """Test custom configuration."""
-        config = LLMContextSummarizationConfig(
-            max_context_tokens=2500,
+        config = LLMContextSummaryConfig(
             target_context_tokens=2000,
-            max_unsummarized_messages=15,
             min_messages_after_summary=4,
             summarization_prompt="Custom prompt",
         )
 
-        self.assertEqual(config.max_context_tokens, 2500)
         self.assertEqual(config.target_context_tokens, 2000)
-        self.assertEqual(config.max_unsummarized_messages, 15)
         self.assertEqual(config.min_messages_after_summary, 4)
         self.assertEqual(config.summary_prompt, "Custom prompt")
 
     def test_summary_prompt_property(self):
         """Test summary_prompt property uses default when None."""
-        config = LLMContextSummarizationConfig()
+        config = LLMContextSummaryConfig()
         self.assertIn("summarizing a conversation", config.summary_prompt.lower())
 
-        config_with_custom = LLMContextSummarizationConfig(summarization_prompt="Custom")
+        config_with_custom = LLMContextSummaryConfig(summarization_prompt="Custom")
         self.assertEqual(config_with_custom.summary_prompt, "Custom")
 
 
+class TestLLMAutoContextSummarizationConfig(unittest.TestCase):
+    """Tests for LLMAutoContextSummarizationConfig."""
+
+    def test_default_config(self):
+        """Test default configuration values."""
+        config = LLMAutoContextSummarizationConfig()
+
+        self.assertEqual(config.max_context_tokens, 8000)
+        self.assertEqual(config.max_unsummarized_messages, 20)
+        self.assertEqual(config.summary_config.target_context_tokens, 6000)
+        self.assertEqual(config.summary_config.min_messages_after_summary, 4)
+
+    def test_custom_config(self):
+        """Test custom configuration."""
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=2500,
+            max_unsummarized_messages=15,
+            summary_config=LLMContextSummaryConfig(
+                target_context_tokens=2000,
+                min_messages_after_summary=4,
+                summarization_prompt="Custom prompt",
+            ),
+        )
+
+        self.assertEqual(config.max_context_tokens, 2500)
+        self.assertEqual(config.max_unsummarized_messages, 15)
+        self.assertEqual(config.summary_config.target_context_tokens, 2000)
+        self.assertEqual(config.summary_config.min_messages_after_summary, 4)
+        self.assertEqual(config.summary_config.summary_prompt, "Custom prompt")
+
+    def test_target_tokens_auto_adjusted(self):
+        """Test that target_context_tokens is auto-adjusted when it exceeds max."""
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=1000,
+            summary_config=LLMContextSummaryConfig(target_context_tokens=9000),
+        )
+        self.assertLessEqual(config.summary_config.target_context_tokens, config.max_context_tokens)
+
+
+class TestLLMContextSummarizationConfigDeprecated(unittest.TestCase):
+    """Tests for deprecated LLMContextSummarizationConfig."""
+
+    def test_emits_deprecation_warning(self):
+        """Test that instantiating the deprecated config emits a DeprecationWarning."""
+        with self.assertWarns(DeprecationWarning):
+            LLMContextSummarizationConfig()
+
+    def test_to_auto_config(self):
+        """Test conversion to the new LLMAutoContextSummarizationConfig."""
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", DeprecationWarning)
+            old_config = LLMContextSummarizationConfig(
+                max_context_tokens=2500,
+                target_context_tokens=2000,
+                max_unsummarized_messages=15,
+                min_messages_after_summary=4,
+                summarization_prompt="Custom",
+            )
+
+        new_config = old_config.to_auto_config()
+
+        self.assertIsInstance(new_config, LLMAutoContextSummarizationConfig)
+        self.assertEqual(new_config.max_context_tokens, 2500)
+        self.assertEqual(new_config.max_unsummarized_messages, 15)
+        self.assertEqual(new_config.summary_config.target_context_tokens, 2000)
+        self.assertEqual(new_config.summary_config.min_messages_after_summary, 4)
+        self.assertEqual(new_config.summary_config.summarization_prompt, "Custom")
+
+
 class TestFunctionCallHandling(unittest.TestCase):
     """Tests for function call handling in summarization."""
 
@@ -670,10 +738,12 @@ class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
                 {"role": "user", "content": f"Test message {i} that adds tokens to context."}
             )
 
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=50,  # Very low to trigger easily
-            llm=dedicated_llm,
-            summarization_timeout=5.0,
+            summary_config=LLMContextSummaryConfig(
+                llm=dedicated_llm,
+                summarization_timeout=5.0,
+            ),
         )
         return context, config
 
@@ -736,7 +806,7 @@ class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
         dedicated_llm._generate_summary = slow_summary
 
         context, config = self._create_context_and_config(dedicated_llm)
-        config.summarization_timeout = 0.1  # Very short timeout
+        config.summary_config.summarization_timeout = 0.1  # Very short timeout
         summarizer = LLMContextSummarizer(context=context, config=config)
         await summarizer.setup(self.task_manager)
 
@@ -826,7 +896,7 @@ class TestDedicatedLLMSummarization(unittest.IsolatedAsyncioTestCase):
                 {"role": "user", "content": f"Test message {i} that adds tokens to context."}
             )
 
-        config = LLMContextSummarizationConfig(max_context_tokens=50)
+        config = LLMAutoContextSummarizationConfig(max_context_tokens=50)
         summarizer = LLMContextSummarizer(context=context, config=config)
         await summarizer.setup(self.task_manager)
 

tests/test_llm_context_summarizer.py

@@ -12,6 +12,7 @@ from pipecat.frames.frames import (
     LLMContextSummaryRequestFrame,
     LLMContextSummaryResultFrame,
     LLMFullResponseStartFrame,
+    LLMSummarizeContextFrame,
 )
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_context_summarizer import (
@@ -19,7 +20,10 @@ from pipecat.processors.aggregators.llm_context_summarizer import (
     SummaryAppliedEvent,
 )
 from pipecat.utils.asyncio.task_manager import TaskManager, TaskManagerParams
-from pipecat.utils.context.llm_context_summarization import LLMContextSummarizationConfig
+from pipecat.utils.context.llm_context_summarization import (
+    LLMAutoContextSummarizationConfig,
+    LLMContextSummaryConfig,
+)
 
 
 class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
@@ -35,7 +39,7 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summarization_triggered_by_token_limit(self):
         """Test that summarization is triggered when token limit is reached."""
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=100,  # Very low to trigger easily
             max_unsummarized_messages=100,  # High so it doesn't trigger by message count
         )
@@ -71,7 +75,7 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summarization_triggered_by_message_count(self):
         """Test that summarization is triggered when message count threshold is reached."""
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=100000,  # Very high so it doesn't trigger by tokens
             max_unsummarized_messages=5,  # Low to trigger easily
         )
@@ -101,7 +105,7 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summarization_not_triggered_below_thresholds(self):
         """Test that summarization is not triggered when below thresholds."""
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=10000,
             max_unsummarized_messages=20,
         )
@@ -130,7 +134,7 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summarization_in_progress_prevents_duplicate(self):
         """Test that a summarization in progress prevents triggering another."""
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=50,  # Very low
             max_unsummarized_messages=100,
         )
@@ -161,7 +165,10 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summary_result_handling(self):
         """Test that summary results are processed and applied correctly."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=50,
+            summary_config=LLMContextSummaryConfig(min_messages_after_summary=2),
+        )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -208,7 +215,7 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_interruption_cancels_summarization(self):
         """Test that an interruption cancels pending summarization."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50)
+        config = LLMAutoContextSummarizationConfig(max_context_tokens=50)
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -238,7 +245,10 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_stale_summary_result_ignored(self):
         """Test that stale summary results are ignored."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=50,
+            summary_config=LLMContextSummaryConfig(min_messages_after_summary=2),
+        )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -294,9 +304,116 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
         await summarizer.cleanup()
 
+    async def test_manual_summarization_via_frame(self):
+        """Test that LLMSummarizeContextFrame triggers summarization on demand."""
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=100000,  # High — auto trigger would never fire
+            max_unsummarized_messages=100,
+        )
+
+        summarizer = LLMContextSummarizer(
+            context=self.context,
+            config=config,
+            auto_trigger=False,  # Disable auto; only manual requests should work
+        )
+        await summarizer.setup(self.task_manager)
+
+        request_frame = None
+
+        @summarizer.event_handler("on_request_summarization")
+        async def on_request_summarization(summarizer, frame):
+            nonlocal request_frame
+            request_frame = frame
+
+        # Add messages
+        for i in range(5):
+            self.context.add_message({"role": "user", "content": f"Message {i}"})
+
+        # Auto-trigger should NOT fire even on LLMFullResponseStartFrame
+        await summarizer.process_frame(LLMFullResponseStartFrame())
+        self.assertIsNone(request_frame)
+
+        # Manual trigger via LLMSummarizeContextFrame should fire
+        await summarizer.process_frame(LLMSummarizeContextFrame())
+        self.assertIsNotNone(request_frame)
+        self.assertIsInstance(request_frame, LLMContextSummaryRequestFrame)
+
+        # The request must have a valid request_id and carry the current context
+        self.assertTrue(request_frame.request_id)
+        self.assertEqual(request_frame.context, self.context)
+
+        await summarizer.cleanup()
+
+    async def test_manual_summarization_with_config_override(self):
+        """Test that LLMSummarizeContextFrame can override default summary config."""
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=100000,
+            summary_config=LLMContextSummaryConfig(
+                target_context_tokens=6000,
+                min_messages_after_summary=4,
+            ),
+        )
+
+        summarizer = LLMContextSummarizer(context=self.context, config=config)
+        await summarizer.setup(self.task_manager)
+
+        request_frame = None
+
+        @summarizer.event_handler("on_request_summarization")
+        async def on_request_summarization(summarizer, frame):
+            nonlocal request_frame
+            request_frame = frame
+
+        for i in range(5):
+            self.context.add_message({"role": "user", "content": f"Message {i}"})
+
+        # Push a manual frame with custom config overrides
+        custom_config = LLMContextSummaryConfig(
+            target_context_tokens=500,
+            min_messages_after_summary=1,
+        )
+        await summarizer.process_frame(LLMSummarizeContextFrame(config=custom_config))
+
+        self.assertIsNotNone(request_frame)
+        # The request should use the overridden values
+        self.assertEqual(request_frame.target_context_tokens, 500)
+        self.assertEqual(request_frame.min_messages_to_keep, 1)
+
+        await summarizer.cleanup()
+
+    async def test_manual_summarization_blocked_when_in_progress(self):
+        """Test that a second LLMSummarizeContextFrame is ignored while one is in progress."""
+        config = LLMAutoContextSummarizationConfig(max_context_tokens=100000)
+
+        summarizer = LLMContextSummarizer(context=self.context, config=config)
+        await summarizer.setup(self.task_manager)
+
+        request_count = 0
+
+        @summarizer.event_handler("on_request_summarization")
+        async def on_request_summarization(summarizer, frame):
+            nonlocal request_count
+            request_count += 1
+
+        for i in range(5):
+            self.context.add_message({"role": "user", "content": f"Message {i}"})
+
+        # First manual request
+        await summarizer.process_frame(LLMSummarizeContextFrame())
+        self.assertEqual(request_count, 1)
+
+        # Second manual request while first is in progress — should be ignored
+        await summarizer.process_frame(LLMSummarizeContextFrame())
+        self.assertEqual(request_count, 1)
+
+        await summarizer.cleanup()
+
     async def test_summary_message_role_is_user(self):
         """Test that the summary message uses the user role."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=50,
+            summary_config=LLMContextSummaryConfig(min_messages_after_summary=2),
+        )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -335,7 +452,10 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summary_message_default_template(self):
         """Test that the default summary_message_template is used."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=50,
+            summary_config=LLMContextSummaryConfig(min_messages_after_summary=2),
+        )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -377,10 +497,12 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_summary_message_custom_template(self):
         """Test that a custom summary_message_template is applied."""
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=50,
-            min_messages_after_summary=2,
-            summary_message_template="<context_summary>\n{summary}\n</context_summary>",
+            summary_config=LLMContextSummaryConfig(
+                min_messages_after_summary=2,
+                summary_message_template="<context_summary>\n{summary}\n</context_summary>",
+            ),
         )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
@@ -420,7 +542,10 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_on_summary_applied_event(self):
         """Test that on_summary_applied event fires with correct data."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=50,
+            summary_config=LLMContextSummaryConfig(min_messages_after_summary=2),
+        )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -474,7 +599,10 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_on_summary_applied_not_fired_on_error(self):
         """Test that on_summary_applied event is NOT fired when summarization fails."""
-        config = LLMContextSummarizationConfig(max_context_tokens=50, min_messages_after_summary=2)
+        config = LLMAutoContextSummarizationConfig(
+            max_context_tokens=50,
+            summary_config=LLMContextSummaryConfig(min_messages_after_summary=2),
+        )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)
         await summarizer.setup(self.task_manager)
@@ -515,9 +643,9 @@ class TestLLMContextSummarizer(unittest.IsolatedAsyncioTestCase):
 
     async def test_request_frame_includes_timeout(self):
         """Test that the request frame includes the configured summarization_timeout."""
-        config = LLMContextSummarizationConfig(
+        config = LLMAutoContextSummarizationConfig(
             max_context_tokens=50,
-            summarization_timeout=60.0,
+            summary_config=LLMContextSummaryConfig(summarization_timeout=60.0),
         )
 
         summarizer = LLMContextSummarizer(context=self.context, config=config)