diff --git a/examples/foundational/13g-sambanova-transcription.py b/examples/foundational/13g-sambanova-transcription.py
new file mode 100644
index 000000000..01feec7a1
--- /dev/null
+++ b/examples/foundational/13g-sambanova-transcription.py
@@ -0,0 +1,109 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import time
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.sambanova.stt import SambaNovaSTTService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+
+STOP_SECS = 2.0
+
+
+class TranscriptionLogger(FrameProcessor):
+    """Measures transcription latency.
+
+    Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
+    then outputs the timing between when the VAD first classified audio input as not-speech and
+    the delivery of the last transcription frame.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._last_transcription_time = time.time()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserStoppedSpeakingFrame):
+            logger.debug(
+                f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
+            )
+
+        if isinstance(frame, TranscriptionFrame):
+            self._last_transcription_time = time.time()
+
+
+# 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,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
+    ),
+}
+
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
+
+    
+    stt = SambaNovaSTTService(
+        model='Whisper-Large-v3',
+        api_key=os.getenv('SAMBANOVA_API_KEY'),
+    )
+
+    tl = TranscriptionLogger()
+
+    pipeline = Pipeline([transport.input(), stt, tl])
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+    )
+
+    @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=handle_sigint)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from pipecat.examples.run import main
+
+    main(run_example, transport_params=transport_params)
diff --git a/examples/foundational/14s-function-calling-sambanova.py b/examples/foundational/14s-function-calling-sambanova.py
new file mode 100644
index 000000000..7c29e7110
--- /dev/null
+++ b/examples/foundational/14s-function-calling-sambanova.py
@@ -0,0 +1,152 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+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.vad.silero import SileroVADAnalyzer
+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.llm_response import LLMUserAggregatorParams
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.sambanova.llm import SambaNovaLLMService
+from pipecat.services.sambanova.stt import SambaNovaSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+# 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(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
+
+    stt = SambaNovaSTTService(
+        model='Whisper-Large-v3',
+        api_key=os.getenv('SAMBANOVA_API_KEY'),
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = SambaNovaLLMService(
+        api_key=os.getenv('SAMBANOVA_API_KEY'),
+        model='Llama-4-Maverick-17B-128E-Instruct',
+    )
+    # 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.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"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_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 = OpenAILLMContext(messages, tools)
+    context_aggregator = llm.create_context_aggregator(
+        context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
+    )
+
+    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,
+        ),
+    )
+
+    @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([context_aggregator.user().get_context_frame()])
+
+    @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=handle_sigint)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from pipecat.examples.run import main
+
+    main(run_example, transport_params=transport_params)