Update examples, wherever possible, to use LLMContext and associated machinery instead of OpenAILLMContext and associated machinery.

With all these examples updated, we no longer need dedicated examples illustrating `LLMContext`, so they're removed.

Here’s where we *don’t* yet use `LLMContext` and associated machinery:
- Realtime services: OpenAI Realtime, Gemini Live, and AWS Nova Sonic (support coming soon)
- `GoogleLLMOpenAIBetaService` (it’s deprecated, so we didn’t bother adding support)
- `LLMLogObserver` (support coming soon)
- `GatedOpenAILLMContextAggregator` (support coming soon)
- `LangchainProcessor` (support coming soon)
- `Mem0MemoryService` (support coming soon)
- Examples that use LLM-specific tools definitions as opposed to `ToolsSchema` (these will be updated soon)
- Examples that rely `GoogleLLMContext.upgrade_to_google` (TBD what to do with these)

Examples that use `LLMLogObserver`:
- 30-

Examples that use `GatedOpenAILLMContextAggregator`:
- 22-

Examples that use `LangchainProcessor`:
- 07b-

Examples that use `Mem0MemoryService`:
- 37-

Examples that need updating to use `ToolsSchema`:
- 15-
- 15a-
- 20a-
- 20c-
- 20d-
- 22b-
- 22c-
- 33-
- 36-

Examples that use `GoogleLLMContext.upgrade_to_google`:
- 22d-
- 25-

examples/foundational/02-llm-say-one-thing.py

@@ -9,14 +9,11 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMContextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -63,7 +60,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        await task.queue_frames([OpenAILLMContextFrame(OpenAILLMContext(messages)), EndFrame()])
+        await task.queue_frames([LLMContextFrame(LLMContext(messages)), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 

examples/foundational/04-transports-small-webrtc.py

@@ -25,7 +25,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
@@ -80,8 +81,8 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/04a-transports-daily.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.daily import configure
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
@@ -64,8 +65,8 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/04b-transports-livekit.py

@@ -26,7 +26,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.livekit import configure
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -73,8 +74,8 @@ async def main():
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/05-sync-speech-and-image.py

@@ -14,6 +14,7 @@ from loguru import logger
 from pipecat.frames.frames import (
     DataFrame,
     Frame,
+    LLMContextFrame,
     LLMFullResponseStartFrame,
     TextFrame,
 )
@@ -21,10 +22,8 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    OpenAILLMContext,
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    OpenAILLMContextFrame,
-)
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
@@ -156,7 +155,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 }
             ]
             frames.append(MonthFrame(month=month))
-            frames.append(OpenAILLMContextFrame(OpenAILLMContext(messages)))
+            frames.append(LLMContextFrame(LLMContext(messages)))
 
         task = PipelineTask(
             pipeline,

examples/foundational/05a-local-sync-speech-and-image.py

@@ -15,6 +15,7 @@ from loguru import logger
 
 from pipecat.frames.frames import (
     Frame,
+    LLMContextFrame,
     OutputAudioRawFrame,
     TextFrame,
     TTSAudioRawFrame,
@@ -24,10 +25,8 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    OpenAILLMContext,
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    OpenAILLMContextFrame,
-)
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
@@ -140,7 +139,7 @@ async def main():
             )
 
             task = PipelineTask(pipeline)
-            await task.queue_frame(OpenAILLMContextFrame(OpenAILLMContext(messages)))
+            await task.queue_frame(LLMContextFrame(LLMContext(messages)))
             await task.stop_when_done()
 
             await runner.run(task)

examples/foundational/06-listen-and-respond.py

@@ -23,7 +23,8 @@ from pipecat.metrics.metrics import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -103,8 +104,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/06a-image-sync.py

@@ -24,7 +24,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -116,8 +117,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     image_sync_aggregator = ImageSyncAggregator(
         os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),

examples/foundational/07-interruptible-cartesia-http.py

@@ -17,7 +17,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
@@ -74,8 +75,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07-interruptible.py

@@ -17,7 +17,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -73,8 +74,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07a-interruptible-speechmatics-vad.py

@@ -13,10 +13,11 @@ from pipecat.frames.frames import LLMRunFrame
 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 import (
     LLMUserAggregatorParams,
 )
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
@@ -123,8 +124,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(
+    context_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
     )

examples/foundational/07a-interruptible-speechmatics.py

@@ -17,10 +17,11 @@ from pipecat.frames.frames import LLMRunFrame
 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 import (
     LLMUserAggregatorParams,
 )
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
@@ -112,8 +113,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(
+    context_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
     )

examples/foundational/07aa-interruptible-soniox.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -73,8 +74,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07ab-interruptible-inworld-http.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -84,8 +85,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07ac-interruptible-asyncai-http.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.asyncai.tts import AsyncAIHttpTTSService
@@ -79,8 +80,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07ac-interruptible-asyncai.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.asyncai.tts import AsyncAITTSService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07ad-interruptible-aicoustics.py

@@ -21,7 +21,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -98,8 +99,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07c-interruptible-deepgram-vad.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -71,8 +72,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07c-interruptible-deepgram.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -72,8 +73,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07d-interruptible-elevenlabs-http.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -79,8 +80,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07d-interruptible-elevenlabs.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07e-interruptible-playht-http.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07e-interruptible-playht.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -77,8 +78,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07f-interruptible-azure.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.azure.llm import AzureLLMService
@@ -81,8 +82,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07g-interruptible-openai.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.openai.llm import OpenAILLMService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07h-interruptible-openpipe.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -80,8 +81,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07i-interruptible-xtts.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -78,8 +79,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07j-interruptible-gladia.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -84,8 +85,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07k-interruptible-lmnt.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -71,8 +72,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07l-interruptible-groq.py

@@ -18,8 +18,9 @@ from pipecat.frames.frames import LLMRunFrame
 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 import LLMUserAggregatorParams
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.groq.llm import GroqLLMService
@@ -74,8 +75,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(
+    context_aggregator = LLMContextAggregatorPair(
         context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
     )
 

examples/foundational/07m-interruptible-aws.py

@@ -16,7 +16,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.aws.llm import AWSBedrockLLMService
@@ -77,8 +78,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07n-interruptible-gemini.py

@@ -36,7 +36,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.google.llm import GoogleLLMService
@@ -112,8 +113,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07n-interruptible-google.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.google.llm import GoogleLLMService
@@ -84,8 +85,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07o-interruptible-assemblyai.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.assemblyai.stt import AssemblyAISTTService
@@ -77,8 +78,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07p-interruptible-krisp.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07q-interruptible-rime-http.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -80,8 +81,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07q-interruptible-rime.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -74,8 +75,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07r-interruptible-riva-nim.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.nim.llm import NimLLMService
@@ -71,8 +72,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07s-interruptible-google-audio-in.py

@@ -31,7 +31,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frame_processor import FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -244,8 +245,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
     audio_collector = UserAudioCollector(context, context_aggregator.user())
     pull_transcript_out_of_llm_output = TranscriptExtractor(context)
     fixup_context_messages = TranscriptionContextFixup(context)

examples/foundational/07t-interruptible-fish.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07v-interruptible-neuphonic-http.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -79,8 +80,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07v-interruptible-neuphonic.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -74,8 +75,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07w-interruptible-fal.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -77,8 +78,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07x-interruptible-local.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -59,8 +60,8 @@ async def main():
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/07y-interruptible-minimax.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -81,8 +82,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07z-interruptible-sarvam-http.py

@@ -18,7 +18,8 @@ from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -79,8 +80,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/07z-interruptible-sarvam.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSUpdateSettingsFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -77,8 +78,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/08-bots-arguing.py

@@ -6,13 +6,11 @@ from typing import Tuple
 import aiohttp
 from dotenv import load_dotenv
 
-from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, TextFrame
+from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.processors.aggregators import SentenceAggregator
-from pipecat.processors.aggregators.openai_llm_context import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    OpenAILLMContext,
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    OpenAILLMContextFrame,
-)
 from pipecat.runner.daily import configure
 from pipecat.services.azure import AzureLLMService, AzureTTSService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
@@ -83,7 +81,7 @@ async def main():
             sentence_aggregator = SentenceAggregator()
             pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
 
-            await source_queue.put(OpenAILLMContextFrame(OpenAILLMContext(messages)))
+            await source_queue.put(LLMContextFrame(LLMContext(messages)))
             await source_queue.put(EndFrame())
             await pipeline.run_pipeline()
 

examples/foundational/10-wake-phrase.py

@@ -17,7 +17,8 @@ from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -76,8 +77,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/11-sound-effects.py

@@ -16,6 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
     Frame,
+    LLMContextFrame,
     LLMFullResponseEndFrame,
     OutputAudioRawFrame,
     TTSSpeakFrame,
@@ -23,10 +24,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    OpenAILLMContext,
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    OpenAILLMContextFrame,
-)
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.logger import FrameLogger
 from pipecat.runner.types import RunnerArguments
@@ -74,7 +73,7 @@ class InboundSoundEffectWrapper(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, OpenAILLMContextFrame):
+        if isinstance(frame, LLMContextFrame):
             await self.push_frame(sounds["ding2.wav"])
             # In case anything else downstream needs it
             await self.push_frame(frame, direction)
@@ -126,8 +125,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
     out_sound = OutboundSoundEffectWrapper()
     in_sound = InboundSoundEffectWrapper()
     fl = FrameLogger("LLM Out")

examples/foundational/14-function-calling.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -123,8 +124,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14a-function-calling-anthropic.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.anthropic.llm import AnthropicLLMService
@@ -118,8 +119,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14aa-function-calling-aws-universal-context.py

@@ -1,219 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import asyncio
-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.base_smart_turn import SmartTurnParams
-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
-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
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import (
-    create_transport,
-    get_transport_client_id,
-    maybe_capture_participant_camera,
-)
-from pipecat.services.aws.llm import AWSBedrockLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.services.daily import DailyParams
-
-load_dotenv(override=True)
-
-
-# Global variable to store the client ID
-client_id = ""
-
-
-async def get_weather(params: FunctionCallParams):
-    location = params.arguments["location"]
-    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
-
-
-async def get_image(params: FunctionCallParams):
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
-
-    # Request the image frame
-    await params.llm.request_image_frame(
-        user_id=client_id,
-        function_name=params.function_name,
-        tool_call_id=params.tool_call_id,
-        text_content=question,
-    )
-
-    # Wait a short time for the frame to be processed
-    await asyncio.sleep(0.5)
-
-    # Return a result to complete the function call
-    await params.result_callback(
-        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
-    )
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"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 = AWSBedrockLLMService(
-        aws_region="us-west-2",
-        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        # Note: usually, prefer providing latency="optimized" param.
-        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
-        # which we need for image input.
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
-    )
-    llm.register_function("get_weather", get_weather)
-    llm.register_function("get_image", get_image)
-
-    weather_function = FunctionSchema(
-        name="get_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    get_image_function = FunctionSchema(
-        name="get_image",
-        description="Get an image from the video stream.",
-        properties={
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image.",
-            }
-        },
-        required=["question"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
-
-    system_prompt = """\
-You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
-
-Your response will be turned into speech so use only simple words and punctuation.
-
-You have access to two tools: get_weather and get_image.
-
-You can respond to questions about the weather using the get_weather tool.
-
-You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
-indicate you should use the get_image tool are:
-- What do you see?
-- What's in the video?
-- Can you describe the video?
-- Tell me about what you see.
-- Tell me something interesting about what you see.
-- What's happening in the video?
-
-If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
-    """
-
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": "Start the conversation by introducing yourself."},
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User speech to text
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses and tool context
-        ]
-    )
-
-    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(f"Client connected: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        global client_id
-        client_id = get_transport_client_id(transport, client)
-
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"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/14b-function-calling-anthropic-video.py

@@ -21,7 +21,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -165,8 +166,8 @@ If you need to use a tool, simply use the tool. Do not tell the user the tool yo
         {"role": "user", "content": "Start the conversation by introducing yourself."},
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14c-function-calling-together.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -109,8 +110,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14d-function-calling-video.py

@@ -21,7 +21,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -154,8 +155,8 @@ indicate you should use the get_image tool are:
         {"role": "system", "content": system_prompt},
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14e-function-calling-google.py

@@ -21,7 +21,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -175,8 +176,8 @@ indicate you should use the get_image tool are:
         {"role": "user", "content": "Say hello."},
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14f-function-calling-groq.py

@@ -20,8 +20,9 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 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 import LLMUserAggregatorParams
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -107,8 +108,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(
+    context_aggregator = LLMContextAggregatorPair(
         context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
     )
 

examples/foundational/14g-function-calling-grok.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -102,8 +103,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14h-function-calling-azure.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.azure.llm import AzureLLMService
@@ -110,8 +111,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14i-function-calling-fireworks.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -113,8 +114,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14j-function-calling-nim.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -107,8 +108,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14k-function-calling-cerebras.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -116,8 +117,8 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14l-function-calling-deepseek.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -116,8 +117,8 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14m-function-calling-openrouter.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.azure.tts import AzureTTSService
@@ -110,8 +111,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14n-function-calling-perplexity.py

@@ -24,7 +24,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -80,8 +81,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14p-function-calling-gemini-vertex-ai.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -112,8 +113,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14q-function-calling-qwen.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -108,8 +109,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14r-function-calling-aws.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.aws.llm import AWSBedrockLLMService
@@ -123,8 +124,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14s-function-calling-sambanova.py

@@ -20,8 +20,9 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 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 import LLMUserAggregatorParams
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -113,8 +114,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(
+    context_aggregator = LLMContextAggregatorPair(
         context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
     )
 

examples/foundational/14t-function-calling-direct.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -109,8 +110,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14u-function-calling-ollama.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -125,8 +126,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14v-function-calling-openai.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.llm_service import FunctionCallParams
@@ -131,8 +132,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14w-function-calling-mistral.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -119,8 +120,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/14x-function-calling-universal-context.py

@@ -1,176 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-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.base_smart_turn import SmartTurnParams
-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, TTSSpeakFrame
-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
-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
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"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"))
-
-    # You can also register a function_name of None to get all functions
-    # sent to the same callback with an additional function_name parameter.
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
-
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, restaurant_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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    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(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"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/14y-function-calling-google-universal-context.py

@@ -1,234 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import asyncio
-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.base_smart_turn import SmartTurnParams
-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, TTSSpeakFrame
-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
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import (
-    create_transport,
-    get_transport_client_id,
-    maybe_capture_participant_camera,
-)
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# Global variable to store the client ID
-client_id = ""
-
-
-async def get_weather(params: FunctionCallParams):
-    location = params.arguments["location"]
-    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-async def get_image(params: FunctionCallParams):
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
-
-    # Request the image frame
-    await params.llm.request_image_frame(
-        user_id=client_id,
-        function_name=params.function_name,
-        tool_call_id=params.tool_call_id,
-        text_content=question,
-    )
-
-    # Wait a short time for the frame to be processed
-    await asyncio.sleep(0.5)
-
-    # Return a result to complete the function call
-    await params.result_callback(
-        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
-    )
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"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 = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-    llm.register_function("get_weather", get_weather)
-    llm.register_function("get_image", get_image)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
-
-    weather_function = FunctionSchema(
-        name="get_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    get_image_function = FunctionSchema(
-        name="get_image",
-        description="Get an image from the video stream.",
-        properties={
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image.",
-            }
-        },
-        required=["question"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, get_image_function, restaurant_function])
-
-    system_prompt = """\
-You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
-
-Your response will be turned into speech so use only simple words and punctuation.
-
-You have access to three tools: get_weather, get_restaurant_recommendation, and get_image.
-
-You can respond to questions about the weather using the get_weather tool.
-
-You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
-indicate you should use the get_image tool are:
-- What do you see?
-- What's in the video?
-- Can you describe the video?
-- Tell me about what you see.
-- Tell me something interesting about what you see.
-- What's happening in the video?
-"""
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": "Say hello."},
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    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(f"Client connected: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        global client_id
-        client_id = get_transport_client_id(transport, client)
-
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"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/14z-function-calling-anthropic-universal-context.py

@@ -1,216 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import asyncio
-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.base_smart_turn import SmartTurnParams
-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
-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
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import (
-    create_transport,
-    get_transport_client_id,
-    maybe_capture_participant_camera,
-)
-from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.services.daily import DailyParams
-
-load_dotenv(override=True)
-
-
-# Global variable to store the client ID
-client_id = ""
-
-
-async def get_weather(params: FunctionCallParams):
-    location = params.arguments["location"]
-    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
-
-
-async def get_image(params: FunctionCallParams):
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
-
-    # Request the image frame
-    await params.llm.request_image_frame(
-        user_id=client_id,
-        function_name=params.function_name,
-        tool_call_id=params.tool_call_id,
-        text_content=question,
-    )
-
-    # Wait a short time for the frame to be processed
-    await asyncio.sleep(0.5)
-
-    # Return a result to complete the function call
-    await params.result_callback(
-        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
-    )
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"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 = AnthropicLLMService(
-        api_key=os.getenv("ANTHROPIC_API_KEY"),
-        model="claude-3-7-sonnet-latest",
-        params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
-    )
-    llm.register_function("get_weather", get_weather)
-    llm.register_function("get_image", get_image)
-
-    weather_function = FunctionSchema(
-        name="get_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    get_image_function = FunctionSchema(
-        name="get_image",
-        description="Get an image from the video stream.",
-        properties={
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image.",
-            }
-        },
-        required=["question"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
-
-    system_prompt = """\
-You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
-
-Your response will be turned into speech so use only simple words and punctuation.
-
-You have access to two tools: get_weather and get_image.
-
-You can respond to questions about the weather using the get_weather tool.
-
-You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
-indicate you should use the get_image tool are:
-- What do you see?
-- What's in the video?
-- Can you describe the video?
-- Tell me about what you see.
-- Tell me something interesting about what you see.
-- What's happening in the video?
-
-If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
-    """
-
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": "Start the conversation by introducing yourself."},
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User speech to text
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses and tool context
-        ]
-    )
-
-    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(f"Client connected: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        global client_id
-        client_id = get_transport_client_id(transport, client)
-
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"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/15-switch-voices.py

@@ -20,7 +20,8 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.filters.function_filter import FunctionFilter
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -146,8 +147,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/15a-switch-languages.py

@@ -21,7 +21,8 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.filters.function_filter import FunctionFilter
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -137,8 +138,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/16-gpu-container-local-bot.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -81,8 +82,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/17-detect-user-idle.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import EndFrame, LLMMessagesAppendFrame, LLMRunFrame,
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.user_idle_processor import UserIdleProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     async def handle_user_idle(user_idle: UserIdleProcessor, retry_count: int) -> bool:
         if retry_count == 1:

examples/foundational/21-tavus-transport.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService
@@ -64,8 +65,8 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/21a-tavus-video-service.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -83,8 +84,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = LLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/23-bot-background-sound.py

@@ -20,7 +20,8 @@ from pipecat.frames.frames import LLMRunFrame, MixerEnableFrame, MixerUpdateSett
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -93,8 +94,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/24-stt-mute-filter.py

@@ -21,7 +21,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -114,8 +115,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/27-simli-layer.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -81,8 +82,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/28-transcription-processor.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage, Transcripti
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -137,8 +138,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     # Create transcript processor and handler
     transcript = TranscriptProcessor()

examples/foundational/29-turn-tracking-observer.py

@@ -19,7 +19,8 @@ from pipecat.observers.loggers.user_bot_latency_log_observer import UserBotLaten
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -75,8 +76,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/32-gemini-grounding-metadata.py

@@ -21,7 +21,8 @@ from pipecat.observers.base_observer import BaseObserver, FramePushed
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -115,7 +116,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         tools=tools,
     )
 
-    context = OpenAILLMContext(
+    context = LLMContext(
         [
             {
                 "role": "user",
@@ -123,7 +124,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             }
         ],
     )
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/34-audio-recording.py

@@ -58,7 +58,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -133,8 +134,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/35-pattern-pair-voice-switching.py

@@ -52,7 +52,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -194,8 +195,8 @@ Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue.""
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     # Create pipeline
     pipeline = Pipeline(

examples/foundational/38-smart-turn-fal.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -81,8 +82,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/38a-smart-turn-local-coreml.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -97,8 +98,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/38b-smart-turn-local.py

@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -74,8 +75,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/39-mcp-stdio.py

@@ -29,7 +29,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -157,8 +158,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         messages = [{"role": "system", "content": system}]
 
-        context = OpenAILLMContext(messages, tools)
+        context = LLMContext(messages, tools)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
 
         pipeline = Pipeline(
             [

examples/foundational/39a-mcp-run-sse.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.anthropic.llm import AnthropicLLMService
@@ -93,8 +94,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     messages = [{"role": "system", "content": system}]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/39b-multiple-mcp.py

@@ -32,7 +32,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -169,8 +170,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         all_standard_tools = run_tools.standard_tools + tools.standard_tools
         all_tools = ToolsSchema(standard_tools=all_standard_tools)
 
-        context = OpenAILLMContext(messages, all_tools)
+        context = LLMContext(messages, all_tools)
-        context_aggregator = llm.create_context_aggregator(context)
+        context_aggregator = LLMContextAggregatorPair(context)
         mcp_image_processor = UrlToImageProcessor(aiohttp_session=session)
 
         pipeline = Pipeline(

examples/foundational/39c-mcp-run-http.py

@@ -19,7 +19,8 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -97,8 +98,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     messages = [{"role": "system", "content": system}]
 
-    context = OpenAILLMContext(messages, tools)
+    context = LLMContext(messages, tools)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/41a-text-only-webrtc.py

@@ -16,7 +16,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frameworks.rtvi import (
     ActionResult,
     RTVIAction,
@@ -91,8 +92,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator)
     rtvi = RTVIProcessor(config=RTVIConfig(config=[]))

examples/foundational/41b-text-and-audio-webrtc.py

@@ -17,7 +17,8 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frameworks.rtvi import (
     ActionResult,
     RTVIAction,
@@ -107,8 +108,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = OpenAILLMContext(messages)
+    context = LLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     action_llm_append_to_messages = create_action_llm_append_to_messages(context_aggregator)
     rtvi = RTVIProcessor(config=RTVIConfig(config=[]))