demo: 11labs tagalog

Add Google streaming TTS as a base TTS service

CHANGELOG.md

@@ -5,6 +5,176 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- Added `GoogleHttpTTSService` which uses Google's HTTP TTS API.
+
+- Added `TavusTransport`, a new transport implementation compatible with any 
+  Pipecat pipeline. When using the `TavusTransport`the Pipecat bot will 
+  connect in the same room as the Tavus Avatar and the user.
+
+- Added `UserBotLatencyLogObserver`. This is an observer that logs the latency
+  between when the user stops speaking and when the bot starts speaking. This
+  gives you an initial idea on how quickly the AI services respond.
+
+- Added `SarvamTTSService`, which implements Sarvam AI's TTS API:
+  https://docs.sarvam.ai/api-reference-docs/text-to-speech/convert.
+
+- Added `PipelineTask.add_observer()` and `PipelineTask.remove_observer()` to
+  allow mangaging observers at runtime. This is useful for cases where the task
+  is passed around to other code components that might want to observe the
+  pipeline dynamically.
+
+- Added `user_id` field to `TranscriptionMessage`. This allows identifying the
+  user in a multi-user scenario. Note that this requires that
+  `TranscriptionFrame` has the `user_id` properly set.
+
+- Added new `PipelineTask` event handlers `on_pipeline_started`,
+  `on_pipeline_stopped`, `on_pipeline_ended` and `on_pipeline_cancelled`, which
+  correspond to the `StartFrame`, `StopFrame`, `EndFrame` and `CancelFrame`
+  respectively.
+
+- Added additional languages to `LmntTTSService`. Languages include: `hi`, `id`,
+  `it`, `ja`, `nl`, `pl`, `ru`, `sv`, `th`, `tr`, `uk`, `vi`.
+
+- Added a `model` parameter to the `LmntTTSService` constructor, allowing
+  switching between LMNT models.
+
+- Added `MiniMaxHttpTTSService`, which implements MiniMax's T2A API for TTS.
+  Learn more: https://www.minimax.io/platform_overview
+
+- A new function `FrameProcessor.setup()` has been added to allow setting up
+  frame processors before receiving a `StartFrame`. This is what's happening
+  internally: `FrameProcessor.setup()` is called, `StartFrame` is pushed from
+  the beginning of the pipeline, your regular pipeline operations, `EndFrame`
+  or `CancelFrame` are pushed from the beginning of the pipeline and finally
+  `FrameProcessor.cleanup()` is called.
+
+- Added support for OpenTelemetry tracing in Pipecat. This initial
+  implementation includes:
+
+  - A `setup_tracing` method where you can specify your OpenTelemetry exporter
+  - Service decorators for STT (`@traced_stt`), LLM (`@traced_llm`), and TTS
+    (`@traced_tts`) which trace the execution and collect properties and
+    metrics (TTFB, token usage, character counts, etc.)
+  - Class decorators that provide execution tracking; these are generic and can
+    be used for service tracking as needed
+  - Spans that help track traces on a per conversations and turn basis:
+
+  ```
+  conversation-uuid
+  ├── turn-1
+  │   ├── stt_deepgramsttservice
+  │   ├── llm_openaillmservice
+  │   └── tts_cartesiattsservice
+  ...
+  └── turn-n
+      └── ...
+  ```
+
+  By default, Pipecat has implemented service decorators to trace execution of
+  STT, LLM, and TTS services. You can enable tracing by setting `enable_tracing`
+  to `True` in the PipelineTask.
+
+- Added `TurnTrackingObserver`, which tracks the start and end of a user/bot
+  turn pair and emits events `on_turn_started` and `on_turn_stopped`
+  corresponding to the start and end of a turn, respectively.
+
+- Allow passing observers to `run_test()` while running unit tests.
+
+### Changed
+
+- Updated `GoogleTTSService` to use Google's streaming TTS API. The default voice also updated to `en-US-Chirp3-HD-Charon`.
+
+- ⚠️Refactored the `TavusVideoService`, so it acts like a proxy, sending audio to 
+  Tavus and receiving both audio and video. This will make `TavusVideoService` usable
+  with any Pipecat pipeline and with any transport. This is a **breaking change**,
+  check the `examples/foundational/21a-tavus-layer-small-webrtc.py` to see how to
+  use it.
+
+- `DailyTransport` now uses custom microphone audio tracks instead of virtual
+  microphones. Now, multiple Daily transports can be used in the same process.
+
+- `DailyTransport` now captures audio from individual participants instead of
+  the whole room. This allows identifying audio frames per participant.
+
+- Updated the default model for `AnthropicLLMService` to
+  `claude-sonnet-4-20250514`.
+
+- Updated the default model for `GeminiMultimodalLiveLLMService` to
+  `models/gemini-2.5-flash-preview-native-audio-dialog`.
+
+- `BaseTextFilter` methods `filter()`, `update_settings()`,
+  `handle_interruption()` and `reset_interruption()` are now async.
+
+- `BaseTextAggregator` methods `aggregate()`, `handle_interruption()` and
+  `reset()` are now async.
+
+- The API version for `CartesiaTTSService` and `CartesiaHttpTTSService` has
+  been updated. Also, the `cartesia` dependency has been updated to 2.x.
+
+- `CartesiaTTSService` and `CartesiaHttpTTSService` now support Cartesia's new
+  `speed` parameter which accepts values of `slow`, `normal`, and `fast`.
+
+- `GeminiMultimodalLiveLLMService` now uses the user transcription and usage
+  metrics provided by Gemini Live.
+
+- `GoogleLLMService` has been updated to use `google-genai` instead of the
+  deprecated `google-generativeai`.
+
+### Deprecated
+
+- In `CartesiaTTSService` and `CartesiaHttpTTSService`, `emotion` has been
+  deprecated by Cartesia. Pipecat is following suit and deprecating `emotion`
+  as well.
+
+### Removed
+
+- Since `GeminiMultimodalLiveLLMService` now transcribes it's own audio, the
+  `transcribe_user_audio` arg has been removed. Audio is now transcribed
+  automatically.
+
+- Removed `SileroVAD` frame processor, just use `SileroVADAnalyzer`
+  instead. Also removed, `07a-interruptible-vad.py` example.
+
+### Fixed
+
+- Fixed a `DailyTransport` issue that would cause images needing resize to block
+  the event loop.
+
+- Fixed an issue with `ElevenLabsTTSService` where changing the model or voice
+  while the service is running wasn't working.
+
+- Fixed an issue that would cause multiple instances of the same class to behave
+  incorrectly if any of the given constructor arguments defaulted to a mutable
+  value (e.g. lists, dictionaries, objects).
+
+- Fixed an issue with `CartesiaTTSService` where `TTSTextFrame` messages weren't
+  being emitted when the model was set to `sonic`. This resulted in the
+  assistant context not being updated with assistant messages.
+
+### Performance
+
+- `DailyTransport`: process audio, video and events in separate tasks.
+
+- Don't create event handler tasks if no user event handlers have been
+  registered.
+
+### Other
+
+- Added foundation examples `07y-interruptible-minimax.py` and
+  `07z-interruptible-sarvam.py`to show how to use the `MiniMaxHttpTTSService`
+  and `SarvamTTSService`, respectively.
+
+- Added an `open-telemetry-tracing` example, showing how to setup tracing. The
+  example also includes Jaeger as an open source OpenTelemetry client to review
+  traces from the example runs.
+
+- Added foundational example `29-turn-tracking-observer.py` to show how to use
+  the `TurnTrackingObserver`.
+
 ## [0.0.67] - 2025-05-07
 
 ### Added

README.md

@@ -8,6 +8,8 @@
 
 **Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
 
+> Want to dive right in? [Install Pipecat](https://docs.pipecat.ai/getting-started/installation) then try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
+
 ## 🚀 What You Can Build
 
 - **Voice Assistants** – natural, streaming conversations with AI
@@ -49,18 +51,18 @@ You can connect to Pipecat from any platform using our official SDKs:
 
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                            |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
-| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                                                                               |
-| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Analytics & Metrics | [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                            |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                 |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
+| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Analytics & Metrics | [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

dot-env.template

@@ -95,9 +95,16 @@ OPENROUTER_API_KEY=...
 PIPER_BASE_URL=...
 
 # Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=
+LOCAL_SMART_TURN_MODEL_PATH=...
 FAL_SMART_TURN_API_KEY=...
 
 # Twilio
-TWILIO_ACCOUNT_SID=
-TWILIO_AUTH_TOKEN=
+TWILIO_ACCOUNT_SID=...
+TWILIO_AUTH_TOKEN=...
+
+# MiniMax
+MINIMAX_API_KEY=...
+MINIMAX_GROUP_ID=...
+
+# Sarvam AI
+SARVAM_API_KEY=...

examples/bot-ready-signalling/client/javascript/package-lock.json

@@ -12,7 +12,7 @@
         "@daily-co/daily-js": "0.74.0"
       },
       "devDependencies": {
-        "vite": "^6.0.9"
+        "vite": "^6.3.5"
       }
     },
     "node_modules/@babel/runtime": {
@@ -999,9 +999,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
-      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
+      "version": "6.3.5",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
+      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/bot-ready-signalling/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.9"
+    "vite": "^6.3.5"
   },
   "dependencies": {
     "@daily-co/daily-js": "0.74.0"

examples/chatbot-audio-recording/bot.py

@@ -128,7 +128,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                audio_in_sample_rate=16000,
+                audio_out_sample_rate=16000,
+                allow_interruptions=True,
+            ),
+        )
 
         @audiobuffer.event_handler("on_audio_data")
         async def on_audio_data(buffer, audio, sample_rate, num_channels):

examples/deployment/modal-example/.gitignore

@@ -1,3 +1,6 @@
+# Modal clone
+modal-examples
+
 # Python
 __pycache__/
 *.py[cod]

examples/deployment/modal-example/README.md

@@ -1,37 +1,91 @@
 # Deploying Pipecat to Modal.com
 
-Barebones deployment example for [modal.com](https://www.modal.com)
+Deployment example for [modal.com](https://www.modal.com). This example demonstrates how to deploy a FastAPI webapp to Modal with an RTVI compatible `/connect` endpoint that launches a Pipecat pipeline in a separate Modal container and returns a room/token for the client to join. This example also supports providing a parameter to the `/connect` endpoint for specifying which Pipecat pipeline to launch; openai, gemini, or vllm. The vllm pipeline points to a self-hosted OpenAI compatible LLM, using a llama model (neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16), deployed to Modal.
 
-1. Install dependencies
+![](diagram.jpg)
 
-```bash
-python -m venv venv
-source venv/bin/active # or OS equivalent
-pip install -r requirements.txt
-```
+# Running this Example
 
-2. Setup .env
+## Install the Modal CLI
 
-```bash
-cp env.example .env
-```
+Setup a Modal account and install it on your machine if you have not already, following their easy 3 steps in their [Getting Started Guide](https://modal.com/docs/guide#getting-started)
 
-Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+## Deploy a self-serve LLM
 
-3. Test the app locally
+1. Deploy Modal's OpenAI-compatible LLM service:
 
-```bash
-modal serve app.py
-```
+   ```bash
+   git clone https://github.com/modal-labs/modal-examples
+   cd modal-examples
+   modal deploy 06_gpu_and_ml/llm-serving/vllm_inference.py
+   ```
+
+   Refer to Modal's guide and example for [Deploying an OpenAI-compatible LLM service with vLLM](https://modal.com/docs/examples/vllm_inference) for more details.
+
+2. Take note of the endpoint URL from the previous step, which will look like:
+   ```
+   https://{your-workspace}--example-vllm-openai-compatible-serve.modal.run
+   ```
+   You'll need this for the `bot_vllm.py` file in the next section.
+
+    **Note:**  The default Modal LLM example uses Llama-3.1 and will shut down after 15 minutes of inactivity. Cold starts take 5-10 minutes. To prepare the service, we recommend visiting the `/docs` endpoint (`https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run/docs`) for your deployed LLM and wait for it to fully load before connecting your client.
+
+## Deploy FastAPI App and Pipecat pipeline to Modal 
+
+1. Setup environment variables
+
+   ```bash
+   cd server
+   cp env.example .env
+   # Modify .env to provide your service API Keys
+   ```
+
+   Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+
+2. Update the `modal_url` in `server/src/bot_vllm.py` to point to the url produced from the self-serve llm deploy, mentioned above.
+
+3. From within the `server` directory, test the app locally:
+
+   ```bash
+   modal serve app.py
+   ```
 
 4. Deploy to production
 
-```bash
-modal deploy app.py
-```
+   ```bash
+   modal deploy app.py
+   ```
 
-## Configuration options
+5. Note the endpoint URL produced from this deployment. It will look like:
 
-This app sets some sensible defaults for reducing cold starts, such as `minkeep_warm=1`, which will keep at least 1 warm instance ready for your bot function.
+   ```bash
+   https://{your-workspace}--pipecat-modal-fastapi-app.modal.run
+   ```
 
-It has been configured to only allow a concurrency of 1 (`max_inputs=1`) as each user will require their own running function.
+   You'll need this URL for the client's `app.js` configuration mentioned in its README.
+
+## Launch your bots on Modal
+
+### Option 1: Direct Link
+
+Simply click on the url displayed after running the server or deploy step to launch an agent and be redirected to a Daily room to talk with the launched bot. This will use the OpenAI pipeline.
+
+### Option 2: Connect via an RTVI Client
+
+Follow the instructions provided in the [client folder's README](client/javascript/README.md) for building and running a custom client that connects to your Modal endpoint. The provided client provides a dropdown for choosing which bot pipeline to run.
+
+# Navigating your llm, server, and Pipecat logs
+
+In your [Modal dashboard](https://modal.com/apps), you should have two Apps listed under Live Apps:
+
+1. `example-vllm-openai-compatible`: This App contains the containers and logs used to run your self-hosted LLM. There will be just one App Function listed: `serve`. Click on this function to view logs for your LLM.
+2. `pipecat-modal`: This App contains the containers and logs used to run your `connect` endpoints and Pipecat pipelines. It will list two App Functions:
+    1. `fastapi_app`: This function is running the endpoints that your client will interact with and initiate starting a new pipeline (`/`, `/connect`, `/status`). Click on this function to see logs for each endpoint hit.
+    2. `bot_runner`: This function handles launching and running a bot pipeline. Click on this function to get a list of all pipeline runs and access each run's logs.
+
+# Modal + Pipecat Tips
+
+- In most other Pipecat examples, we use `Popen` to launch the pipeline process from the `/connect` endpoint. In this example, we use a Modal function instead. This allows us to run the pipelines using a separately defined Modal image as well as run each pipeline in an isolated container.
+- For the FastAPI and most common Pipecat Pipeline containers, a default `debian_slim` CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
+- To minimize cold starts of the pipeline and reduce latency for users, set `min_containers=1` on the Modal Function that launches the pipeline to ensure at least one warm instance of your function is always available.
+- For next steps on running a self-hosted llm and reducing latency, check out all of [Modal's LLM examples](https://modal.com/docs/examples/vllm_inference).

examples/deployment/modal-example/app.py

@@ -1,80 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-import modal
-from bot import _voice_bot_process
-from fastapi import HTTPException
-from fastapi.responses import JSONResponse
-from loguru import logger
-
-MAX_SESSION_TIME = 15 * 60  # 15 minutes
-
-app = modal.App("pipecat-modal")
-
-
-image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
-    "requirements.txt"
-)
-
-
-@app.function(
-    image=image,
-    cpu=1.0,
-    secrets=[modal.Secret.from_dotenv()],
-    keep_warm=1,
-    enable_memory_snapshot=True,
-    max_inputs=1,  # Do not reuse instances across requests
-    retries=0,
-)
-def launch_bot_process(room_url: str, token: str):
-    _voice_bot_process(room_url, token)
-
-
-@app.function(
-    image=image,
-    secrets=[modal.Secret.from_dotenv()],
-)
-@modal.web_endpoint(method="POST")
-async def start():
-    from pipecat.transports.services.helpers.daily_rest import (
-        DailyRESTHelper,
-        DailyRoomParams,
-    )
-
-    logger.info("Request received")
-
-    async with aiohttp.ClientSession() as session:
-        daily_rest_helper = DailyRESTHelper(
-            daily_api_key=os.getenv("DAILY_API_KEY", ""),
-            daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-            aiohttp_session=session,
-        )
-
-        # Create new Daily room
-        room = await daily_rest_helper.create_room(DailyRoomParams())
-        if not room.url:
-            raise HTTPException(
-                status_code=500,
-                detail="Unable to create room",
-            )
-        logger.info(f"Created room: {room.url}")
-
-        # Create bot token for room
-        token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
-        if not token:
-            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
-
-        logger.info(f"Bot token created: {token}")
-
-        # Spawn a new bot process
-        launch_bot_process.spawn(room_url=room.url, token=token)
-
-        # Return room URL to the user to join
-        # Note: in production, you would want to return a token to the user
-        return JSONResponse(content={"room_url": room.url, token: token})

examples/deployment/modal-example/bot.py

@@ -1,95 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-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.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token: str):
-    transport = DailyTransport(
-        room_url,
-        token,
-        "bot",
-        DailyParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            transcription_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    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)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-def _voice_bot_process(room_url: str, token: str):
-    asyncio.run(main(room_url, token))

examples/deployment/modal-example/client/javascript/.gitignore

@@ -0,0 +1 @@
+node_modules

examples/deployment/modal-example/client/javascript/README.md

@@ -0,0 +1,29 @@
+# JavaScript Implementation
+
+Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
+
+## Setup
+
+1. Deploy the Modal server. See the main [README](../../README).
+
+2. Navigate to the `client/javascript` directory:
+
+```bash
+cd client/javascript
+```
+
+3. Modify the baseUrl in src/app.js to point to your deployed Modal endpoint
+
+4. Install dependencies:
+
+```bash
+npm install
+```
+
+5. Run the client app:
+
+```
+npm run dev
+```
+
+6. Visit http://localhost:5173 in your browser.

examples/deployment/modal-example/client/javascript/index.html

@@ -0,0 +1,49 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>AI Chatbot</title>
+  </head>
+
+  <body>
+    <div class="container">
+      <div class="status-bar">
+        <div class="status">
+          Status: <span id="connection-status">Disconnected</span>
+        </div>
+        <div class="controls">
+          <select id="bot-selector">
+            <option value="openai">OpenAI</option>
+            <option value="gemini">Gemini</option>
+            <option value="vllm">Llama</option>
+          </select>
+          <button id="connect-btn">Connect</button>
+          <button id="disconnect-btn" disabled>Disconnect</button>
+        </div>
+      </div>
+
+      <div class="main-content">
+        <div class="bot-container">
+          <div id="bot-video-container"></div>
+          <audio id="bot-audio" autoplay></audio>
+        </div>
+      </div>
+
+      <div class="device-bar">
+        <div class="device-controls">
+          <select id="device-selector"></select>
+          <button id="mic-toggle-btn">Mute Mic</button>
+        </div>
+      </div>
+
+      <div class="debug-panel">
+        <h3>Debug Info</h3>
+        <div id="debug-log"></div>
+      </div>
+    </div>
+
+    <script type="module" src="/src/app.js"></script>
+    <link rel="stylesheet" href="/src/style.css" />
+  </body>
+</html>

examples/deployment/modal-example/client/javascript/package.json

@@ -0,0 +1,21 @@
+{
+  "name": "client",
+  "version": "1.0.0",
+  "main": "index.js",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "description": "",
+  "devDependencies": {
+    "vite": "^6.3.5"
+  },
+  "dependencies": {
+    "@pipecat-ai/client-js": "^0.3.5",
+    "@pipecat-ai/daily-transport": "^0.3.10"
+  }
+}

examples/deployment/modal-example/client/javascript/src/app.js

@@ -0,0 +1,381 @@
+/**
+ * Copyright (c) 2024–2025, Daily
+ *
+ * SPDX-License-Identifier: BSD 2-Clause License
+ */
+
+/**
+ * RTVI Client Implementation
+ *
+ * This client connects to an RTVI-compatible bot server using WebRTC (via Daily).
+ * It handles audio/video streaming and manages the connection lifecycle.
+ *
+ * Requirements:
+ * - A running RTVI bot server (defaults to http://localhost:7860)
+ * - The server must implement the /connect endpoint that returns Daily.co room credentials
+ * - Browser with WebRTC support
+ */
+
+import { RTVIClient, RTVIEvent } from '@pipecat-ai/client-js';
+import { DailyTransport } from '@pipecat-ai/daily-transport';
+
+/**
+ * ChatbotClient handles the connection and media management for a real-time
+ * voice and video interaction with an AI bot.
+ */
+class ChatbotClient {
+  constructor() {
+    // Initialize client state
+    this.rtviClient = null;
+    this.setupDOMElements();
+    this.initializeClientAndTransport();
+    this.setupEventListeners();
+  }
+
+  /**
+   * Set up references to DOM elements and create necessary media elements
+   */
+  setupDOMElements() {
+    // Get references to UI control elements
+    this.connectBtn = document.getElementById('connect-btn');
+    this.disconnectBtn = document.getElementById('disconnect-btn');
+    this.statusSpan = document.getElementById('connection-status');
+    this.debugLog = document.getElementById('debug-log');
+    this.botVideoContainer = document.getElementById('bot-video-container');
+    this.deviceSelector = document.getElementById('device-selector');
+
+    // Create an audio element for bot's voice output
+    this.botAudio = document.createElement('audio');
+    this.botAudio.autoplay = true;
+    this.botAudio.playsInline = true;
+    document.body.appendChild(this.botAudio);
+  }
+
+  /**
+   * Set up event listeners for connect/disconnect buttons
+   */
+  setupEventListeners() {
+    this.connectBtn.addEventListener('click', () => this.connect());
+    this.disconnectBtn.addEventListener('click', () => this.disconnect());
+
+    // Populate device selector
+    this.rtviClient.getAllMics().then((mics) => {
+      console.log('Available mics:', mics);
+      mics.forEach((device) => {
+        const option = document.createElement('option');
+        option.value = device.deviceId;
+        option.textContent = device.label || `Microphone ${device.deviceId}`;
+        this.deviceSelector.appendChild(option);
+      });
+    });
+    this.deviceSelector.addEventListener('change', (event) => {
+      const selectedDeviceId = event.target.value;
+      console.log('Selected device ID:', selectedDeviceId);
+      this.rtviClient.updateMic(selectedDeviceId);
+    });
+
+    // Handle mic mute/unmute toggle
+    const micToggleBtn = document.getElementById('mic-toggle-btn');
+
+    micToggleBtn.addEventListener('click', () => {
+      let micEnabled = this.rtviClient.isMicEnabled;
+      micToggleBtn.textContent = micEnabled ? 'Unmute Mic' : 'Mute Mic';
+      this.rtviClient.enableMic(!micEnabled);
+      // Add logic to mute/unmute the mic
+      if (micEnabled) {
+        console.log('Mic muted');
+        // Add code to mute the mic
+      } else {
+        console.log('Mic unmuted');
+        // Add code to unmute the mic
+      }
+    });
+  }
+
+  /**
+   * Set up the RTVI client and Daily transport
+   */
+  async initializeClientAndTransport() {
+    // Initialize the RTVI client with a DailyTransport and our configuration
+    this.rtviClient = new RTVIClient({
+      transport: new DailyTransport(),
+      params: {
+        // REPLACE WITH YOUR MODAL URL ENDPOINT
+        baseUrl:
+          'https://<Modal workspace>--pipecat-modal-bot-launcher.modal.run',
+        endpoints: {
+          connect: '/connect',
+        },
+        requestData: {
+          bot_name: 'openai',
+        },
+      },
+      enableMic: true, // Enable microphone for user input
+      enableCam: false,
+      callbacks: {
+        // Handle connection state changes
+        onConnected: () => {
+          this.updateStatus('Connected');
+          this.connectBtn.disabled = true;
+          this.disconnectBtn.disabled = false;
+          this.log('Client connected');
+        },
+        onDisconnected: () => {
+          this.updateStatus('Disconnected');
+          this.connectBtn.disabled = false;
+          this.disconnectBtn.disabled = true;
+          this.log('Client disconnected');
+        },
+        // Handle transport state changes
+        onTransportStateChanged: (state) => {
+          this.updateStatus(`Transport: ${state}`);
+          this.log(`Transport state changed: ${state}`);
+          if (state === 'connecting') {
+            window.startTime = Date.now();
+          }
+          if (state === 'ready') {
+            this.setupMediaTracks();
+            console.warn('TIME TO BOT READY:', Date.now() - window.startTime);
+          }
+        },
+        // Handle bot connection events
+        onBotConnected: (participant) => {
+          this.log(`Bot connected: ${JSON.stringify(participant)}`);
+        },
+        onBotDisconnected: (participant) => {
+          this.log(`Bot disconnected: ${JSON.stringify(participant)}`);
+        },
+        onBotReady: (data) => {
+          this.log(`Bot ready: ${JSON.stringify(data)}`);
+          this.setupMediaTracks();
+        },
+        // Transcript events
+        onUserTranscript: (data) => {
+          // Only log final transcripts
+          if (data.final) {
+            this.log(`User: ${data.text}`);
+          }
+        },
+        onBotTranscript: (data) => {
+          this.log(`Bot: ${data.text}`);
+        },
+        // Error handling
+        onMessageError: (error) => {
+          console.log('Message error:', error);
+        },
+        onMicUpdated: (data) => {
+          console.log('Mic updated:', data);
+          this.deviceSelector.value = data.deviceId;
+        },
+        onError: (error) => {
+          console.log('Error:', JSON.stringify(error));
+        },
+      },
+    });
+
+    // Set up listeners for media track events
+    this.setupTrackListeners();
+
+    await this.rtviClient.initDevices();
+    window.client = this.rtviClient;
+  }
+
+  /**
+   * Add a timestamped message to the debug log
+   */
+  log(message) {
+    const entry = document.createElement('div');
+    entry.textContent = `${new Date().toISOString()} - ${message}`;
+
+    // Add styling based on message type
+    if (message.startsWith('User: ')) {
+      entry.style.color = '#2196F3'; // blue for user
+    } else if (message.startsWith('Bot: ')) {
+      entry.style.color = '#4CAF50'; // green for bot
+    }
+
+    this.debugLog.appendChild(entry);
+    this.debugLog.scrollTop = this.debugLog.scrollHeight;
+    console.log(message);
+  }
+
+  /**
+   * Update the connection status display
+   */
+  updateStatus(status) {
+    this.statusSpan.textContent = status;
+    this.log(`Status: ${status}`);
+  }
+
+  /**
+   * Check for available media tracks and set them up if present
+   * This is called when the bot is ready or when the transport state changes to ready
+   */
+  setupMediaTracks() {
+    if (!this.rtviClient) return;
+
+    // Get current tracks from the client
+    const tracks = this.rtviClient.tracks();
+
+    // Set up any available bot tracks
+    if (tracks.bot?.audio) {
+      this.setupAudioTrack(tracks.bot.audio);
+    }
+    if (tracks.bot?.video) {
+      this.setupVideoTrack(tracks.bot.video);
+    }
+  }
+
+  /**
+   * Set up listeners for track events (start/stop)
+   * This handles new tracks being added during the session
+   */
+  setupTrackListeners() {
+    if (!this.rtviClient) return;
+
+    // Listen for new tracks starting
+    this.rtviClient.on(RTVIEvent.TrackStarted, (track, participant) => {
+      // Only handle non-local (bot) tracks
+      if (!participant?.local) {
+        if (track.kind === 'audio') {
+          this.setupAudioTrack(track);
+        } else if (track.kind === 'video') {
+          this.setupVideoTrack(track);
+        }
+        this.log(
+          `Track started event: ${track.kind} from ${
+            participant?.name || 'unknown'
+          }`
+        );
+      } else {
+        this.log('Local mic unmuted');
+      }
+    });
+
+    // Listen for tracks stopping
+    this.rtviClient.on(RTVIEvent.TrackStopped, (track, participant) => {
+      if (participant.local) {
+        this.log('Local mic muted');
+        return;
+      }
+      this.log(
+        `Track stopped event: ${track.kind} from ${
+          participant?.name || 'unknown'
+        }`
+      );
+    });
+  }
+
+  /**
+   * Set up an audio track for playback
+   * Handles both initial setup and track updates
+   */
+  setupAudioTrack(track) {
+    this.log('Setting up audio track');
+    // Check if we're already playing this track
+    if (this.botAudio.srcObject) {
+      const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
+      if (oldTrack?.id === track.id) return;
+    }
+    // Create a new MediaStream with the track and set it as the audio source
+    this.botAudio.srcObject = new MediaStream([track]);
+  }
+
+  /**
+   * Set up a video track for display
+   * Handles both initial setup and track updates
+   */
+  setupVideoTrack(track) {
+    this.log('Setting up video track');
+    const videoEl = document.createElement('video');
+    videoEl.autoplay = true;
+    videoEl.playsInline = true;
+    videoEl.muted = true;
+    videoEl.style.width = '100%';
+    videoEl.style.height = '100%';
+    videoEl.style.objectFit = 'cover';
+
+    // Check if we're already displaying this track
+    if (this.botVideoContainer.querySelector('video')?.srcObject) {
+      const oldTrack = this.botVideoContainer
+        .querySelector('video')
+        .srcObject.getVideoTracks()[0];
+      if (oldTrack?.id === track.id) return;
+    }
+
+    // Create a new MediaStream with the track and set it as the video source
+    videoEl.srcObject = new MediaStream([track]);
+    this.botVideoContainer.innerHTML = '';
+    this.botVideoContainer.appendChild(videoEl);
+  }
+
+  /**
+   * Initialize and connect to the bot
+   * This sets up the RTVI client, initializes devices, and establishes the connection
+   */
+  async connect() {
+    try {
+      const botSelector = document.getElementById('bot-selector');
+      const selectedBot = botSelector.value;
+      this.rtviClient.params.requestData.bot_name = selectedBot;
+
+      // Initialize audio/video devices
+      this.log('Initializing devices...');
+      await this.rtviClient.initDevices();
+
+      // Connect to the bot
+      this.log(`Connecting to bot: ${selectedBot}`);
+      await this.rtviClient.connect();
+
+      this.log('Connection complete');
+    } catch (error) {
+      // Handle any errors during connection
+      console.error('Connection error:', error);
+      this.log(`Error connecting: ${JSON.stringify(error.message)}`);
+      this.log(`Error stack: ${error.stack}`);
+      this.updateStatus('Error');
+
+      // Clean up if there's an error
+      if (this.rtviClient) {
+        try {
+          await this.rtviClient.disconnect();
+        } catch (disconnectError) {
+          this.log(`Error during disconnect: ${disconnectError.message}`);
+        }
+      }
+    }
+  }
+
+  /**
+   * Disconnect from the bot and clean up media resources
+   */
+  async disconnect() {
+    if (this.rtviClient) {
+      try {
+        // Disconnect the RTVI client
+        await this.rtviClient.disconnect();
+
+        // Clean up audio
+        if (this.botAudio.srcObject) {
+          this.botAudio.srcObject.getTracks().forEach((track) => track.stop());
+          this.botAudio.srcObject = null;
+        }
+
+        // Clean up video
+        if (this.botVideoContainer.querySelector('video')?.srcObject) {
+          const video = this.botVideoContainer.querySelector('video');
+          video.srcObject.getTracks().forEach((track) => track.stop());
+          video.srcObject = null;
+        }
+        this.botVideoContainer.innerHTML = '';
+      } catch (error) {
+        this.log(`Error disconnecting: ${error.message}`);
+      }
+    }
+  }
+}
+
+// Initialize the client when the page loads
+window.addEventListener('DOMContentLoaded', () => {
+  new ChatbotClient();
+});

examples/deployment/modal-example/client/javascript/src/style.css

@@ -0,0 +1,135 @@
+body {
+  margin: 0;
+  padding: 20px;
+  font-family: Arial, sans-serif;
+  background-color: #f0f0f0;
+}
+
+.container {
+  max-width: 1200px;
+  margin: 0 auto;
+}
+
+.status-bar,
+.device-bar {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 10px;
+  background-color: #fff;
+  border-radius: 8px;
+  margin-bottom: 20px;
+}
+
+.controls,
+.device-controls {
+  display: flex;
+  align-items: center;
+  gap: 10px; /* Adds spacing between elements */
+}
+
+.device-controls {
+  margin-left: auto;
+}
+
+.controls button,
+.device-controls button {
+  padding: 8px 16px;
+  margin-left: 10px;
+  border: none;
+  border-radius: 4px;
+  cursor: pointer;
+}
+
+#bot-selector,
+#device-selector {
+  padding: 8px 16px;
+  padding-right: 40px;
+  border: none;
+  border-radius: 4px;
+  background-color: #6c757d; /* Gray background */
+  color: white; /* White text */
+  cursor: pointer;
+  appearance: none; /* Removes default browser styling for dropdowns */
+  background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='white'%3E%3Cpath d='M7 10l5 5 5-5z'/%3E%3C/svg%3E"); /* Custom arrow */
+  background-repeat: no-repeat;
+  background-position: right 8px center; /* Position the arrow */
+}
+
+#bot-selector:focus,
+#device-selector:focus {
+  outline: none;
+  box-shadow: 0 0 4px rgba(0, 0, 0, 0.3); /* Add a subtle focus effect */
+}
+
+#connect-btn {
+  background-color: #4caf50;
+  color: white;
+}
+
+#disconnect-btn {
+  background-color: #f44336;
+  color: white;
+}
+
+#mic-toggle-btn {
+}
+
+button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.main-content {
+  background-color: #fff;
+  border-radius: 8px;
+  padding: 20px;
+  margin-bottom: 20px;
+}
+
+.bot-container {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+#bot-video-container {
+  width: 640px;
+  height: 360px;
+  background-color: #e0e0e0;
+  border-radius: 8px;
+  margin: 20px auto;
+  overflow: hidden;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+#bot-video-container video {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+}
+
+.debug-panel {
+  background-color: #fff;
+  border-radius: 8px;
+  padding: 20px;
+}
+
+.debug-panel h3 {
+  margin: 0 0 10px 0;
+  font-size: 16px;
+  font-weight: bold;
+}
+
+#debug-log {
+  height: 200px;
+  overflow-y: auto;
+  background-color: #f8f8f8;
+  padding: 10px;
+  border-radius: 4px;
+  font-family: monospace;
+  font-size: 12px;
+  line-height: 1.4;
+}

examples/deployment/modal-example/env.example

@@ -1,3 +0,0 @@
-DAILY_API_KEY=
-OPENAI_API_KEY=
-CARTESIA_API_KEY=

examples/deployment/modal-example/requirements.txt

@@ -1,4 +0,0 @@
-python-dotenv==1.0.1
-modal==0.71.3
-pipecat-ai[daily,silero,cartesia,openai]
-fastapi==0.115.6

examples/deployment/modal-example/server/app.py

@@ -0,0 +1,307 @@
+"""modal_example.
+
+This module shows a simple example of how to deploy a bot using Modal and FastAPI.
+
+It includes:
+- FastAPI endpoints for starting agents and checking bot statuses.
+- Dynamic loading of bot implementations.
+- Use of a Daily transport for bot communication.
+"""
+
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import importlib
+import os
+from contextlib import asynccontextmanager
+from typing import Any, Dict, Literal
+
+import aiohttp
+import modal
+from fastapi import APIRouter, FastAPI, HTTPException
+from fastapi.responses import JSONResponse, RedirectResponse
+from pydantic import BaseModel
+
+# container specifications for the FastAPI web server
+web_image = (
+    modal.Image.debian_slim(python_version="3.13")
+    .pip_install_from_requirements("requirements.txt")
+    .pip_install("pipecat-ai[daily]")
+    .add_local_dir("src", remote_path="/root/src")
+)
+
+# container specifications for the Pipecat pipeline
+bot_image = (
+    modal.Image.debian_slim(python_version="3.13")
+    .apt_install("ffmpeg")
+    .pip_install_from_requirements("requirements.txt")
+    .pip_install("pipecat-ai[daily,elevenlabs,openai,silero,google]")
+    .add_local_dir("src", remote_path="/root/src")
+)
+
+app = modal.App("pipecat-modal", secrets=[modal.Secret.from_dotenv()])
+
+router = APIRouter()
+
+bot_jobs = {}
+daily_helpers = {}
+
+# Names of all supported bot implementations
+# These correspond to the bot files in the src directory
+BotName = Literal["openai", "gemini", "vllm"]
+
+
+def cleanup():
+    """Cleanup function to terminate all bot processes.
+
+    Called during server shutdown.
+    """
+    for entry in bot_jobs.values():
+        func = modal.FunctionCall.from_id(entry[0])
+        if func:
+            func.cancel()
+
+
+def get_bot_file(bot_name: BotName) -> str:
+    """Retrieve the bot file name corresponding to the provided bot_name.
+
+    Args:
+        bot_name (BotName): The name of the bot (e.g., 'openai', 'gemini', 'vllm').
+
+    Returns:
+        str: The file name corresponding to the bot implementation.
+
+    Raises:
+        ValueError: If the bot name is invalid or not supported.
+    """
+    # bot_implementation = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    bot_implementation = bot_name.lower().strip()
+    if not bot_implementation:
+        bot_implementation = "openai"
+    if bot_implementation not in ["openai", "gemini", "vllm"]:
+        raise ValueError(
+            f"Invalid BOT_IMPLEMENTATION: {bot_implementation}. Must be 'openai' or 'gemini' or 'vllm'"
+        )
+
+    return f"bot_{bot_implementation}"
+
+
+def get_runner(path: str, bot_file: str) -> callable:
+    """Dynamically import the run_bot function based on the bot name.
+
+    Args:
+        path (str): The path to the bot files (e.g., 'src').
+        bot_file (str): The file name of the bot implementation (e.g., 'openai', 'gemini', 'vllm').
+
+    Returns:
+        function: The run_bot function from the specified bot module.
+
+    Raises:
+        ImportError: If the specified bot module or run_bot function is not found.
+    """
+    try:
+        # Dynamically construct the module name
+        module_name = f"{path}.{bot_file}"
+        # Import the module
+        module = importlib.import_module(module_name)
+        # Get the run_bot function from the module
+        return getattr(module, "run_bot")
+    except (ImportError, AttributeError) as e:
+        raise ImportError(f"Failed to import run_bot from {module_name}: {e}")
+
+
+async def create_room_and_token() -> tuple[str, str]:
+    """Create a Daily room and generate an authentication token.
+
+    This function checks for existing room URL and token in the environment variables.
+    If not found, it creates a new room using the Daily API and generates a token for it.
+
+    Returns:
+        tuple[str, str]: A tuple containing the room URL and the authentication token.
+
+    Raises:
+        HTTPException: If room creation or token generation fails.
+    """
+    from pipecat.transports.services.helpers.daily_rest import DailyRoomParams
+
+    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", None)
+    token = os.getenv("DAILY_SAMPLE_ROOM_TOKEN", None)
+    if not room_url:
+        room = await daily_helpers["rest"].create_room(DailyRoomParams())
+        if not room.url:
+            raise HTTPException(status_code=500, detail="Failed to create room")
+        room_url = room.url
+
+        token = await daily_helpers["rest"].get_token(room_url)
+        if not token:
+            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room_url}")
+
+    return room_url, token
+
+
+@app.function(image=bot_image, min_containers=1)
+async def bot_runner(room_url, token, bot_name: BotName = "openai"):
+    """Launch the provided bot process, providing the given room URL and token for the bot to join.
+
+    Args:
+        room_url (str): The URL of the Daily room where the bot and client will communicate.
+        token (str): The authentication token for the room.
+        bot_name (BotName): The name of the bot implementation to use. Defaults to "openai".
+
+    Raises:
+        HTTPException: If the bot pipeline fails to start.
+    """
+    try:
+        path = "src"
+        bot_file = get_bot_file(bot_name)
+        run_bot = get_runner(path, bot_file)
+
+        print(f"Starting bot process: {bot_file} -u {room_url} -t {token}")
+        await run_bot(room_url, token)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start bot pipeline: {e}")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """FastAPI lifespan manager that handles startup and shutdown tasks.
+
+    - Creates aiohttp session
+    - Initializes Daily API helper
+    - Cleans up resources on shutdown
+    """
+    from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+class ConnectData(BaseModel):
+    """Data provided by client to specify the bot pipeline.
+
+    Attributes:
+        bot_name (BotName): The name of the bot to connect to. Defaults to "openai".
+    """
+
+    bot_name: BotName = "openai"
+
+
+async def start(data: ConnectData):
+    """Internal method to start a bot agent and return the room URL and token.
+
+    Args:
+        data (ConnectData): The data containing the bot name to use.
+
+    Returns:
+        tuple[str, str]: A tuple containing the room URL and token.
+    """
+    room_url, token = await create_room_and_token()
+    launch_bot_func = modal.Function.from_name("pipecat-modal", "bot_runner")
+    function_id = launch_bot_func.spawn(room_url, token, data.bot_name)
+    bot_jobs[function_id] = (function_id, room_url)
+
+    return room_url, token
+
+
+@router.get("/")
+async def start_agent():
+    """A user endpoint for launching a bot agent and redirecting to the created room URL.
+
+    This function retrieves the bot implementation from the environment,
+    starts the bot agent, and redirects the user to the room URL to
+    interact with the bot through a Daily Prebuilt Interface.
+
+    Returns:
+        RedirectResponse: A response that redirects to the room URL.
+    """
+    bot_name = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    print(f"Starting bot: {bot_name}")
+    room_url, token = await start(ConnectData(bot_name=bot_name))
+
+    return RedirectResponse(room_url)
+
+
+@router.post("/connect")
+async def rtvi_connect(data: ConnectData) -> Dict[Any, Any]:
+    """A user endpoint for launching a bot agent and retrieving the room/token credentials.
+
+    This function retrieves the bot implementation from the request, if provided,
+    starts the bot agent, and returns the room URL and token for the bot. This allows the
+    client to then connect to the bot using their own RTVI interface.
+
+    Args:
+        data (ConnectData): Optional. The data containing the bot name to use.
+
+    Returns:
+        Dict[Any, Any]: A dictionary containing the room URL and token.
+    """
+    print(f"Starting bot: {data.bot_name}")
+    if data is None or not data.bot_name:
+        data.bot_name = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    room_url, token = await start(data)
+
+    return {"room_url": room_url, "token": token}
+
+
+@router.get("/status/{fid}")
+def get_status(fid: str):
+    """Retrieve the status of a bot process by its function ID.
+
+    Args:
+        fid (str): The function ID of the bot process.
+
+    Returns:
+        JSONResponse: A JSON response containing the bot's status and result code.
+
+    Raises:
+        HTTPException: If the bot process with the given ID is not found.
+    """
+    func = modal.FunctionCall.from_id(fid)
+    if not func:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {fid} not found")
+
+    try:
+        result = func.get(timeout=0)
+        return JSONResponse({"bot_id": fid, "status": "finished", "code": result})
+    except modal.exception.OutputExpiredError:
+        return JSONResponse({"bot_id": fid, "status": "finished", "code": 404})
+    except TimeoutError:
+        return JSONResponse({"bot_id": fid, "status": "running", "code": 202})
+
+
+@app.function(image=web_image, min_containers=1)
+@modal.concurrent(max_inputs=1)
+@modal.asgi_app()
+def fastapi_app():
+    """Create and configure the FastAPI application.
+
+    This function initializes the FastAPI app with middleware, routes, and lifespan management.
+    It is decorated to be used as a Modal ASGI app.
+    """
+    from fastapi.middleware.cors import CORSMiddleware
+
+    # Initialize FastAPI app
+    web_app = FastAPI(lifespan=lifespan)
+
+    web_app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    # Include the endpoints from endpoints.py
+    web_app.include_router(router)
+
+    return web_app

examples/deployment/modal-example/server/env.example

@@ -0,0 +1,14 @@
+DAILY_API_KEY=
+
+# determines which bot file to default to: 'openai', 'gemini', or 'vllm'
+BOT_IMPLEMENTATION=openai
+
+# needed for the openai bot pipeline
+OPENAI_API_KEY=
+ELEVENLABS_API_KEY=
+
+# needed for the gemini live bot pipeline
+GOOGLE_API_KEY=
+
+# needed if you modified the API Key for your self-hosted LLM
+VLLM_API_KEY=

examples/deployment/modal-example/server/requirements.txt

@@ -0,0 +1,2 @@
+python-dotenv==1.0.1
+modal==0.71.3

examples/deployment/modal-example/server/src/bot_gemini.py

@@ -0,0 +1,198 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Gemini Bot Implementation.
+
+This module implements a chatbot using Google's Gemini Multimodal Live model.
+It includes:
+- Real-time audio/video interaction through Daily
+- Animated robot avatar
+- Speech-to-speech model
+
+The bot runs as part of a pipeline that processes audio/video frames and manages
+the conversation flow using Gemini's streaming capabilities.
+"""
+
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    SpriteFrame,
+)
+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.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+try:
+    logger.remove(0)
+    logger.add(sys.stderr, level="DEBUG")
+except ValueError:
+    # Handle the case where logger is already initialized
+    pass
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+for i in range(1, 26):
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+    # Get the filename without the extension to use as the dictionary key
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0]  # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+    """Manages the bot's visual animation states.
+
+    Switches between static (listening) and animated (talking) states based on
+    the bot's current speaking status.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._is_talking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and update animation state.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        # Switch to talking animation when bot starts speaking
+        if isinstance(frame, BotStartedSpeakingFrame):
+            if not self._is_talking:
+                await self.push_frame(talking_frame)
+                self._is_talking = True
+        # Return to static frame when bot stops speaking
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(quiet_frame)
+            self._is_talking = False
+
+        await self.push_frame(frame, direction)
+
+
+async def run_bot(room_url: str, token: str):
+    """Main bot execution function.
+
+    Sets up and runs the bot pipeline including:
+    - Daily video transport with specific audio parameters
+    - Gemini Live multimodal model integration
+    - Voice activity detection
+    - Animation processing
+    - RTVI event handling
+    """
+    # Set up Daily transport with specific audio/video parameters for Gemini
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1024,
+            camera_out_height=576,
+            vad_enabled=True,
+            vad_audio_passthrough=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+        ),
+    )
+
+    # Initialize the Gemini Multimodal Live model
+    llm = GeminiMultimodalLiveLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
+        transcribe_user_audio=True,
+    )
+
+    messages = [
+        {
+            "role": "user",
+            "content": "You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself.",
+        },
+    ]
+
+    # Set up conversation context and management
+    # The context_aggregator will automatically collect conversation context
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    ta = TalkingAnimation()
+
+    #
+    # RTVI events for Pipecat client UI
+    #
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            rtvi,
+            context_aggregator.user(),
+            llm,
+            ta,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+    await task.queue_frame(quiet_frame)
+
+    @rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        print(f"Participant left: {participant}")
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)

examples/deployment/modal-example/server/src/bot_openai.py

@@ -0,0 +1,226 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""OpenAI Bot Implementation.
+
+This module implements a chatbot using OpenAI's GPT-4 model for natural language
+processing. It includes:
+- Real-time audio/video interaction through Daily
+- Animated robot avatar
+- Text-to-speech using ElevenLabs
+- Support for both English and Spanish
+
+The bot runs as part of a pipeline that processes audio/video frames and manages
+the conversation flow.
+"""
+
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    SpriteFrame,
+)
+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.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+try:
+    logger.remove(0)
+    logger.add(sys.stderr, level="DEBUG")
+except ValueError:
+    # Handle the case where logger is already initialized
+    pass
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+# Load sequential animation frames
+for i in range(1, 26):
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+    # Get the filename without the extension to use as the dictionary key
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0]  # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+    """Manages the bot's visual animation states.
+
+    Switches between static (listening) and animated (talking) states based on
+    the bot's current speaking status.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._is_talking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and update animation state.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        # Switch to talking animation when bot starts speaking
+        if isinstance(frame, BotStartedSpeakingFrame):
+            if not self._is_talking:
+                await self.push_frame(talking_frame)
+                self._is_talking = True
+        # Return to static frame when bot stops speaking
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(quiet_frame)
+            self._is_talking = False
+
+        await self.push_frame(frame, direction)
+
+
+async def run_bot(room_url: str, token: str):
+    """Main bot execution function.
+
+    Sets up and runs the bot pipeline including:
+    - Daily video transport
+    - Speech-to-text and text-to-speech services
+    - Language model integration
+    - Animation processing
+    - RTVI event handling
+    """
+    # Set up Daily transport with video/audio parameters
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1024,
+            camera_out_height=576,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+            #
+            # Spanish
+            #
+            # transcription_settings=DailyTranscriptionSettings(
+            #     language="es",
+            #     tier="nova",
+            #     model="2-general"
+            # )
+        ),
+    )
+
+    # Initialize text-to-speech service
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        #
+        # English
+        #
+        voice_id="SAz9YHcvj6GT2YYXdXww",
+        #
+        # Spanish
+        #
+        # model="eleven_multilingual_v2",
+        # voice_id="gD1IexrzCvsXPHUuT0s3",
+    )
+
+    # Initialize LLM service
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            #
+            # English
+            #
+            "content": "You are an incessant one-upper. Start by asking the user how their day is going.",
+            #
+            # Spanish
+            #
+            # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+        },
+    ]
+
+    # Set up conversation context and management
+    # The context_aggregator will automatically collect conversation context
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    ta = TalkingAnimation()
+
+    #
+    # RTVI events for Pipecat client UI
+    #
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            rtvi,
+            context_aggregator.user(),
+            llm,
+            tts,
+            ta,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+    await task.queue_frame(quiet_frame)
+
+    @rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        print(f"Participant left: {participant}")
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)

examples/deployment/modal-example/server/src/bot_vllm.py

@@ -0,0 +1,239 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""OpenAI Bot Implementation.
+
+This module implements a chatbot using OpenAI's GPT-4 model for natural language
+processing. It includes:
+- Real-time audio/video interaction through Daily
+- Animated robot avatar
+- Text-to-speech using ElevenLabs
+- Support for both English and Spanish
+
+The bot runs as part of a pipeline that processes audio/video frames and manages
+the conversation flow.
+"""
+
+import os
+import sys
+from typing import List
+
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionMessageParam
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    SpriteFrame,
+)
+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.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+try:
+    logger.remove(0)
+    logger.add(sys.stderr, level="DEBUG")
+except ValueError:
+    # Handle the case where logger is already initialized
+    pass
+
+# REPLACE WITH YOUR MODAL URL ENDPOINT
+modal_url = "https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run"
+api_key = os.getenv("VLLM_API_KEY", "super-secret-key")
+
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+# Load sequential animation frames
+for i in range(1, 26):
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+    # Get the filename without the extension to use as the dictionary key
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0]  # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+    """Manages the bot's visual animation states.
+
+    Switches between static (listening) and animated (talking) states based on
+    the bot's current speaking status.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._is_talking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and update animation state.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        # Switch to talking animation when bot starts speaking
+        if isinstance(frame, BotStartedSpeakingFrame):
+            if not self._is_talking:
+                await self.push_frame(talking_frame)
+                self._is_talking = True
+        # Return to static frame when bot stops speaking
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(quiet_frame)
+            self._is_talking = False
+
+        await self.push_frame(frame, direction)
+
+
+async def run_bot(room_url: str, token: str):
+    """Main bot execution function.
+
+    Sets up and runs the bot pipeline including:
+    - Daily video transport
+    - Speech-to-text and text-to-speech services
+    - Language model integration
+    - Animation processing
+    - RTVI event handling
+    """
+    # Set up Daily transport with video/audio parameters
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1024,
+            camera_out_height=576,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+            #
+            # Spanish
+            #
+            # transcription_settings=DailyTranscriptionSettings(
+            #     language="es",
+            #     tier="nova",
+            #     model="2-general"
+            # )
+        ),
+    )
+
+    # Initialize text-to-speech service
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        #
+        # English
+        #
+        voice_id="D38z5RcWu1voky8WS1ja",
+        #
+        # Spanish
+        #
+        # model="eleven_multilingual_v2",
+        # voice_id="gD1IexrzCvsXPHUuT0s3",
+    )
+
+    # Initialize LLM service
+    llm = OpenAILLMService(
+        # To use OpenAI
+        api_key=api_key,
+        # Or, to use a local vLLM (or similar) api server
+        model="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",
+        base_url=f"{modal_url}/v1",
+    )
+
+    messages = [
+        {
+            "role": "system",
+            #
+            # English
+            #
+            "content": "You are a salesman for Modal, the cloud-native serverless Python computing platform.",
+            #
+            # Spanish
+            #
+            # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+        },
+    ]
+
+    # Set up conversation context and management
+    # The context_aggregator will automatically collect conversation context
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    ta = TalkingAnimation()
+
+    #
+    # RTVI events for Pipecat client UI
+    #
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            rtvi,
+            context_aggregator.user(),
+            llm,
+            tts,
+            ta,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+    await task.queue_frame(quiet_frame)
+
+    @rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        print(f"Participant left: {participant}")
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)

examples/deployment/modal-example/server/src/runner.py

@@ -0,0 +1,84 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import importlib
+import os
+
+
+def get_bot_file(arg_bot: str | None) -> str:
+    bot_implementation = arg_bot or os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    if not bot_implementation:
+        bot_implementation = "openai"
+    if bot_implementation not in ["openai", "gemini", "vllm"]:
+        raise ValueError(
+            f"Invalid BOT_IMPLEMENTATION: {bot_implementation}. Must be 'openai' or 'gemini'"
+        )
+    return f"bot_{bot_implementation}"
+
+
+def get_runner(bot_file: str):
+    """Dynamically import the run_bot function based on the bot name.
+
+    Args:
+        bot_name (str): The name of the bot implementation (e.g., 'openai', 'gemini').
+
+    Returns:
+        function: The run_bot function from the specified bot module.
+
+    Raises:
+        ImportError: If the specified bot module or run_bot function is not found.
+    """
+    try:
+        # Dynamically construct the module name
+        module_name = f"{bot_file}"
+        # Import the module
+        module = importlib.import_module(module_name)
+        # Get the run_bot function from the module
+        return getattr(module, "run_bot")
+    except (ImportError, AttributeError) as e:
+        raise ImportError(f"Failed to import run_bot from {module_name}: {e}")
+
+
+def main():
+    """Parse the args to launch the appropriate bot using the given room/token."""
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-t",
+        "--token",
+        type=str,
+        required=False,
+        help="Daily room token",
+    )
+    parser.add_argument(
+        "-b",
+        "--bot",
+        type=str,
+        required=False,
+        help="Bot runner to use (e.g., openai, gemini)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    token = args.token or os.getenv("DAILY_SAMPLE_ROOM_TOKEN")
+    bot_file = get_bot_file(args.bot)
+
+    if not url:
+        raise Exception(
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
+
+    run_bot = get_runner(bot_file)
+    asyncio.run(run_bot(url, token))
+
+
+if __name__ == "__main__":
+    main()

examples/deployment/pipecat-cloud-daily-pstn-server/README.md

@@ -100,7 +100,28 @@ phone numbers with valid values for your use case.
 
 ### Dialin Request
 
-The server will receive a request when a call is received from Daily.
+The server will receive a request when a call is received from Daily. 
+The payload that the webhook received is as follows:
+```json
+{
+  // for dial-in from webhook
+  "To": "+14152251493",
+  "From": "+14158483432",
+  "callId": "string-contains-uuid",
+  "callDomain": "string-contains-uuid",
+  "sipHeaders": {
+    "X-My-Custom-Header": "value",
+    "x-caller": "+1234567890",
+    "x-called": "+1987654321", 
+   },
+}
+```
+The `To`, `From`, `callId`, `callDomain` fields are converted to 
+`snake_case` and mapped to `dialin_settings`. In addition, `sipHeader` 
+contains any custom SIP headers received by Daily on the SIP 
+interconnect address (`sip_uri`). These are headers sent from 
+Twilio or other external SIP platforms, for example, to send the 
+caller's phone number.
 
 ### Dialout Request
 
@@ -158,6 +179,7 @@ curl -X POST http://localhost:3000/api/dial \
     "From": "+1987654321",
     "callId": "call-uuid-123",
     "callDomain": "domain-uuid-456",
+    "sipHeader": {},
     "dialout_settings": [
       {
         "phoneNumber": "+1234567890",

examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py

@@ -39,6 +39,11 @@ class RoomRequest(BaseModel):
         None, description="A flag to perform voicemail or answeing-machine detection"
     )
     call_transfer: Optional[Dict[str, Any]] = Field(None, description="to initiate a call transfer")
+    sipHeaders: Optional[Dict[str, Any]] = Field(
+        None,
+        alias="sip_headers",
+        description="Custom SIP headers received from the external SIP provider",
+    )
 
     class Config:
         populate_by_name = True
@@ -57,6 +62,14 @@ class RoomRequest(BaseModel):
     "callDomain": "string-contains-uuid"
     These need to be remapped to dialin_settings
 
+    In addition, we may receive in the body that can be 
+    sent to the bot as a custom field, sip_headers
+    "sipHeaders": {
+        "X-My-Custom-Header": "value",
+        "x-caller": "+14158483432",
+        "x-called": "+14152251493",
+    },
+
     "dialout_settings": [
         {"phoneNumber": "+14158483432", "callerId": "+14152251493"}, 
         {"sipUri": "sip:username@sip.hostname"}
@@ -157,6 +170,7 @@ async def dial(request: RoomRequest, raw_request: Request):
             "dialout_settings": request.dialout_settings,
             "voicemail_detection": request.voicemail_detection,
             "call_transfer": request.call_transfer,
+            "sip_headers": request.sipHeaders,  # passing the SIP headers to the bot
         },
     }
 

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js

@@ -65,6 +65,7 @@ export default async function handler(req, res) {
       From,
       callId,
       callDomain,
+      sipHeaders,
       dialout_settings,
       voicemail_detection,
       call_transfer
@@ -117,6 +118,7 @@ export default async function handler(req, res) {
         dialout_settings,
         voicemail_detection,
         call_transfer,
+        sip_headers: sipHeaders,
       },
     };
 

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

@@ -37,9 +37,9 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
+                audio_in_enabled=True,
                 audio_out_enabled=True,
                 transcription_enabled=True,
-                vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
             ),
         )

examples/foundational/04c-transports-daily-audio-source.py

@@ -0,0 +1,111 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from daily_runner import configure
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService, Language, LiveOptions
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_in_passthrough=False,
+                audio_out_enabled=True,
+                audio_out_sample_rate=16000,
+                transcription_enabled=False,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        stt = DeepgramSTTService(
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            live_options=LiveOptions(language=Language.EN),
+        )
+
+        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"), model="gpt-4o")
+
+        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)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_audio(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -10,12 +10,12 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.audio.vad.silero import SileroVAD
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaHttpTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
@@ -33,14 +33,13 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         params=TransportParams(
             audio_in_enabled=True,
             audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
         ),
     )
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    vad = SileroVAD()
-
-    tts = CartesiaTTSService(
+    tts = CartesiaHttpTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
@@ -59,14 +58,13 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
 
     pipeline = Pipeline(
         [
-            transport.input(),
+            transport.input(),  # Transport user input
             stt,
-            vad,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
     )
 

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

@@ -17,7 +17,10 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
+from pipecat.services.gladia.stt import GladiaSTTService
 from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -37,11 +40,20 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         ),
     )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    # STT Service
+    stt = GladiaSTTService(
+        api_key=os.getenv("GLADIA_API_KEY"),
+        params=GladiaInputParams(
+            language_config=LanguageConfig(
+                languages=[Language.TL],
+            ),
+        ),
+    )
 
     tts = ElevenLabsTTSService(
         api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+        voice_id="NEqPvTuKWuvwUMAEPBPR",
+        model="eleven_multilingual_v2",
     )
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
@@ -49,7 +61,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     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.",
+            "content": """
+            You are a helpful assistant that speaks Tagalog working at 
+            Phillipine Airlines. Answer questions about the user's flight, such 
+            as flight status, check-in procedures, baggage policies, and other 
+            travel-related inquiries. Always respond in Tagalog. 
+            """,
         },
     ]
 

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

@@ -0,0 +1,111 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.minimax.tts import MiniMaxHttpTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transcriptions.language import Language
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    # Create an HTTP session
+    async with aiohttp.ClientSession() as session:
+        transport = SmallWebRTCTransport(
+            webrtc_connection=webrtc_connection,
+            params=TransportParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = MiniMaxHttpTTSService(
+            api_key=os.getenv("MINIMAX_API_KEY", ""),
+            group_id=os.getenv("MINIMAX_GROUP_ID", ""),
+            aiohttp_session=session,
+            params=MiniMaxHttpTTSService.InputParams(language=Language.EN),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        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)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            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")
+
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
+            await task.cancel()
+
+        runner = PipelineRunner(handle_sigint=False)
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

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

@@ -0,0 +1,109 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.sarvam.tts import SarvamTTSService
+from pipecat.transcriptions.language import Language
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+    # Create an HTTP session
+    async with aiohttp.ClientSession() as session:
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = SarvamTTSService(
+            api_key=os.getenv("SARVAM_API_KEY"),
+            aiohttp_session=session,
+            params=SarvamTTSService.InputParams(language=Language.EN),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        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)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            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")
+
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
+            await task.cancel()
+
+        runner = PipelineRunner(handle_sigint=False)
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

examples/foundational/21-tavus-layer-tavus-transport.py

@@ -0,0 +1,112 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.transports.services.tavus import TavusParams, TavusTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        transport = TavusTransport(
+            bot_name="Pipecat bot",
+            api_key=os.getenv("TAVUS_API_KEY"),
+            replica_id=os.getenv("TAVUS_REPLICA_ID"),
+            session=session,
+            params=TavusParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                microphone_out_enabled=False,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
+        )
+
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+
+        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)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                audio_in_sample_rate=16000,
+                audio_out_sample_rate=24000,
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, participant):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            messages.append(
+                {
+                    "role": "system",
+                    "content": "Start by greeting the user and ask how you can help.",
+                }
+            )
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, participant):
+            logger.info(f"Client disconnected")
+            await task.cancel()
+
+        runner = PipelineRunner(handle_sigint=False)
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/21a-tavus-layer-small-webrtc.py

@@ -0,0 +1,125 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.tavus.video import TavusVideoService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+    async with aiohttp.ClientSession() as session:
+        transport = SmallWebRTCTransport(
+            webrtc_connection=webrtc_connection,
+            params=TransportParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                video_out_enabled=True,
+                video_out_is_live=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                video_out_width=1280,
+                video_out_height=720,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
+        )
+
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+
+        tavus = TavusVideoService(
+            api_key=os.getenv("TAVUS_API_KEY"),
+            replica_id=os.getenv("TAVUS_REPLICA_ID"),
+            session=session,
+        )
+
+        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)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                tavus,  # Tavus output layer
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                audio_in_sample_rate=16000,
+                audio_out_sample_rate=24000,
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            messages.append(
+                {
+                    "role": "system",
+                    "content": "Start by greeting the user and ask how you can help.",
+                }
+            )
+            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")
+
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
+            await task.cancel()
+
+        runner = PipelineRunner(handle_sigint=False)
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

examples/foundational/21b-tavus-layer-daily-transport.py

@@ -7,9 +7,9 @@
 import asyncio
 import os
 import sys
-from typing import Any, Mapping
 
 import aiohttp
+from daily_runner import configure
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -20,7 +20,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.google.llm import GoogleLLMService
 from pipecat.services.tavus.video import TavusVideoService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -32,23 +32,20 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main():
     async with aiohttp.ClientSession() as session:
-        tavus = TavusVideoService(
-            api_key=os.getenv("TAVUS_API_KEY"),
-            replica_id=os.getenv("TAVUS_REPLICA_ID"),
-            session=session,
-        )
-
-        # get persona, look up persona_name, set this as the bot name to ignore
-        persona_name = await tavus.get_persona_name()
-        room_url = await tavus.initialize()
+        (room_url, token) = await configure(session)
 
         transport = DailyTransport(
-            room_url=room_url,
-            token=None,
-            bot_name="Pipecat bot",
-            params=DailyParams(
+            room_url,
+            token,
+            "Pipecat bot",
+            DailyParams(
                 audio_in_enabled=True,
+                audio_out_enabled=True,
+                video_out_enabled=True,
+                video_out_is_live=True,
                 vad_analyzer=SileroVADAnalyzer(),
+                video_out_width=1280,
+                video_out_height=720,
             ),
         )
 
@@ -59,7 +56,13 @@ async def main():
             voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
         )
 
-        llm = OpenAILLMService(model="gpt-4o-mini")
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+
+        tavus = TavusVideoService(
+            api_key=os.getenv("TAVUS_API_KEY"),
+            replica_id=os.getenv("TAVUS_REPLICA_ID"),
+            session=session,
+        )
 
         messages = [
             {
@@ -87,10 +90,8 @@ async def main():
         task = PipelineTask(
             pipeline,
             params=PipelineParams(
-                # We just use 16000 because that's what Tavus is expecting and
-                # we avoid resampling.
                 audio_in_sample_rate=16000,
-                audio_out_sample_rate=16000,
+                audio_out_sample_rate=24000,
                 allow_interruptions=True,
                 enable_metrics=True,
                 enable_usage_metrics=True,
@@ -98,33 +99,22 @@ async def main():
             ),
         )
 
-        @transport.event_handler("on_participant_joined")
-        async def on_participant_joined(
-            transport: DailyTransport, participant: Mapping[str, Any]
-        ) -> None:
-            # Ignore the Tavus replica's microphone
-            if participant.get("info", {}).get("userName", "") == persona_name:
-                logger.debug(f"Ignoring {participant['id']}'s microphone")
-                await transport.update_subscriptions(
-                    participant_settings={
-                        participant["id"]: {
-                            "media": {"microphone": "unsubscribed"},
-                        }
-                    }
-                )
-
-            if participant.get("info", {}).get("userName", "") != persona_name:
-                # Kick off the conversation.
-                messages.append(
-                    {"role": "system", "content": "Please introduce yourself to the user."}
-                )
-                await task.queue_frames([context_aggregator.user().get_context_frame()])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            # Kick off the conversation.
+            messages.append(
+                {
+                    "role": "system",
+                    "content": "Start by greeting the user and ask how you can help.",
+                }
+            )
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):
             await task.cancel()
 
-        runner = PipelineRunner()
+        runner = PipelineRunner(handle_sigint=False)
 
         await runner.run(task)
 

examples/foundational/26-gemini-multimodal-live.py

@@ -53,7 +53,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         api_key=os.getenv("GOOGLE_API_KEY"),
         system_instruction=system_instruction,
         voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
-        transcribe_user_audio=True,
     )
 
     # Build the pipeline

examples/foundational/26a-gemini-multimodal-live-transcription.py

@@ -47,7 +47,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         api_key=os.getenv("GOOGLE_API_KEY"),
         voice_id="Aoede",  # Puck, Charon, Kore, Fenrir, Aoede
         # system_instruction="Talk like a pirate."
-        transcribe_user_audio=True,
         # inference_on_context_initialization=False,
     )
 

examples/foundational/26b-gemini-multimodal-live-function-calling.py

@@ -89,7 +89,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     llm = GeminiMultimodalLiveLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
         system_instruction=system_instruction,
-        transcribe_user_audio=True,
         tools=tools,
     )
 

examples/foundational/26c-gemini-multimodal-live-video.py

@@ -51,7 +51,6 @@ async def main():
             api_key=os.getenv("GOOGLE_API_KEY"),
             voice_id="Aoede",  # Puck, Charon, Kore, Fenrir, Aoede
             # system_instruction="Talk like a pirate."
-            transcribe_user_audio=True,
             # inference_on_context_initialization=False,
         )
 

examples/foundational/26d-gemini-multimodal-live-text.py

@@ -59,7 +59,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
 
     llm = GeminiMultimodalLiveLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        transcribe_user_audio=True,
         system_instruction=SYSTEM_INSTRUCTION,
         tools=[{"google_search": {}}, {"code_execution": {}}],
         params=InputParams(modalities=GeminiMultimodalModalities.TEXT),

examples/foundational/26e-gemini-multimodal-google-search.py

@@ -58,7 +58,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     llm = GeminiMultimodalLiveLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
         voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
-        transcribe_user_audio=True,
         system_instruction=system_instruction,
         tools=tools,
     )

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

@@ -0,0 +1,121 @@
+#
+# 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.audio.vad.silero import SileroVADAnalyzer
+from pipecat.observers.loggers.user_bot_latency_log_observer import UserBotLatencyLogObserver
+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.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    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"))
+
+    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)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+        observers=[UserBotLatencyLogObserver()],
+    )
+
+    turn_observer = task.turn_tracking_observer
+    if turn_observer:
+
+        @turn_observer.event_handler("on_turn_started")
+        async def on_turn_started(observer, turn_number):
+            logger.info(f"🔄 Turn {turn_number} started")
+
+        @turn_observer.event_handler("on_turn_ended")
+        async def on_turn_ended(observer, turn_number, duration, was_interrupted):
+            if was_interrupted:
+                logger.info(f"🔄 Turn {turn_number} interrupted after {duration:.2f}s")
+            else:
+                logger.info(f"🏁 Turn {turn_number} completed in {duration:.2f}s")
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        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")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

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

@@ -11,18 +11,17 @@ from pathlib import Path
 
 from dotenv import load_dotenv
 from loguru import logger
-from openai import audio
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame
+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.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService, LLMSearchResponseFrame
+from pipecat.services.llm_service import LLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -33,7 +32,7 @@ load_dotenv(override=True)
 
 
 # Function handlers for the LLM
-search_tool = {"google_search_retrieval": {}}
+search_tool = {"google_search": {}}
 tools = [search_tool]
 
 system_instruction = """
@@ -50,14 +49,22 @@ Start each interaction by asking the user about which place they would like to k
 """
 
 
-class LLMSearchLoggerProcessor(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
+class LLMSearchLoggerObserver(BaseObserver):
+    async def on_push_frame(self, data: FramePushed):
+        src = data.source
+        dst = data.destination
+        frame = data.frame
+        timestamp = data.timestamp
+
+        if not isinstance(src, LLMService) and not isinstance(dst, LLMService):
+            return
+
+        time_sec = timestamp / 1_000_000_000
+
+        arrow = "→"
 
         if isinstance(frame, LLMSearchResponseFrame):
-            print(f"LLMSearchLoggerProcessor: {frame}")
-
-        await self.push_frame(frame)
+            logger.debug(f"🧠 {arrow} {dst} LLM SEARCH RESPONSE FRAME: {frame} at {time_sec:.2f}s")
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
@@ -84,7 +91,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         api_key=os.getenv("GOOGLE_API_KEY"),
         system_instruction=system_instruction,
         tools=tools,
-        model="gemini-1.5-flash-002",
     )
 
     context = OpenAILLMContext(
@@ -97,22 +103,23 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     )
     context_aggregator = llm.create_context_aggregator(context)
 
-    llm_search_logger = LLMSearchLoggerProcessor()
-
     pipeline = Pipeline(
         [
             transport.input(),
             stt,
             context_aggregator.user(),
             llm,
-            llm_search_logger,
             tts,
             transport.output(),
             context_aggregator.assistant(),
         ]
     )
 
-    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(allow_interruptions=True),
+        observers=[LLMSearchLoggerObserver()],
+    )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):

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

@@ -99,21 +99,14 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     )
 
     # Register handler for voice switching
-    def on_voice_tag(match: PatternMatch):
+    async def on_voice_tag(match: PatternMatch):
         voice_name = match.content.strip().lower()
         if voice_name in VOICE_IDS:
-            voice_id = VOICE_IDS[voice_name]
-
-            # Create task to reset the TTS context after voice change
-            async def change_voice():
-                # First flush any existing audio to finish the current context
-                await tts.flush_audio()
-                # Then set the new voice
-                tts.set_voice(voice_id)
-                logger.info(f"Switched to {voice_name} voice")
-
-            # Schedule the voice change task
-            asyncio.create_task(change_voice())
+            # First flush any existing audio to finish the current context
+            await tts.flush_audio()
+            # Then set the new voice
+            tts.set_voice(VOICE_IDS[voice_name])
+            logger.info(f"Switched to {voice_name} voice")
         else:
             logger.warning(f"Unknown voice: {voice_name}")
 

examples/foundational/39-mcp-stdio.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import asyncio
 import io
 import os
@@ -80,7 +81,7 @@ class UrlToImageProcessor(FrameProcessor):
             logger.error(error_msg)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
     transport = SmallWebRTCTransport(

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 import sys
 
@@ -28,7 +29,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
     transport = SmallWebRTCTransport(

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import asyncio
 import io
 import os
@@ -81,7 +82,7 @@ class UrlToImageProcessor(FrameProcessor):
             logger.error(error_msg)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
     logger.info(f"Starting bot")
 
     transport = SmallWebRTCTransport(

examples/foundational/README.md

@@ -95,7 +95,7 @@ Depending on what you're trying to build, these learning paths will guide you th
 
 - **[18-gstreamer-filesrc.py](./18-gstreamer-filesrc.py)**: GStreamer video streaming (Video processing)
 - **[19-openai-realtime-beta.py](./19-openai-realtime-beta.py)**: OpenAI Speech-to-Speech (Direct S2S, Function calls)
-- **[21-tavus-layer.py](./21-tavus-layer.py)**: Tavus digital twin (Avatar integration)
+- **[21-tavus-layer-tavus-transport.py](./21-tavus-layer-tavus-transport.py)**: Tavus digital twin (Avatar integration)
 - **[27-simli-layer.py](./27-simli-layer.py)**: Simli avatar integration (Video synchronization)
 
 ### Performance & Optimization

examples/instant-voice/client/javascript/package-lock.json

@@ -16,7 +16,7 @@
         "@types/node": "^22.13.1",
         "@vitejs/plugin-react-swc": "^3.7.2",
         "typescript": "^5.7.3",
-        "vite": "^6.0.2"
+        "vite": "^6.3.5"
       }
     },
     "node_modules/@babel/runtime": {
@@ -1370,9 +1370,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
-      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
+      "version": "6.3.5",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
+      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/instant-voice/client/javascript/package.json

@@ -15,7 +15,7 @@
     "@types/node": "^22.13.1",
     "@vitejs/plugin-react-swc": "^3.7.2",
     "typescript": "^5.7.3",
-    "vite": "^6.0.2"
+    "vite": "^6.3.5"
   },
   "dependencies": {
     "@pipecat-ai/client-js": "^0.3.5",

examples/news-chatbot/client/javascript/package-lock.json

@@ -13,7 +13,7 @@
         "@pipecat-ai/daily-transport": "^0.3.8"
       },
       "devDependencies": {
-        "vite": "^6.0.9"
+        "vite": "^6.3.5"
       }
     },
     "node_modules/@babel/runtime": {
@@ -1114,9 +1114,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
-      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
+      "version": "6.3.5",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
+      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/news-chatbot/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.9"
+    "vite": "^6.3.5"
   },
   "dependencies": {
     "@pipecat-ai/client-js": "^0.3.5",

examples/news-chatbot/server/news_bot.py

@@ -102,9 +102,9 @@ async def main():
 
         llm = GoogleLLMService(
             api_key=os.getenv("GOOGLE_API_KEY"),
-            model="gemini-1.5-flash-002",
             system_instruction=system_instruction,
             tools=tools,
+            model="gemini-1.5-flash",
         )
 
         context = OpenAILLMContext(
@@ -153,7 +153,6 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             logger.debug("First participant joined: {}", participant["id"])
-            await transport.capture_participant_transcription(participant["id"])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/open-telemetry/README.md

@@ -0,0 +1,69 @@
+# OpenTelemetry Tracing with Pipecat
+
+This repository demonstrates OpenTelemetry tracing integration for Pipecat services, with examples for different backends.
+
+## Tracing Features in Pipecat
+
+- **Hierarchical Tracing**: Track entire conversations, turns, and service calls
+- **Service Tracing**: Detailed spans for TTS, STT, and LLM services with rich context
+- **TTFB Metrics**: Capture Time To First Byte metrics for latency analysis
+- **Usage Statistics**: Track character counts for TTS and token usage for LLMs
+
+## Trace Structure
+
+Traces are organized hierarchically:
+
+```
+Conversation (conversation)
+├── turn
+│   ├── stt_deepgramsttservice
+│   ├── llm_openaillmservice
+│   └── tts_cartesiattsservice
+└── turn
+    ├── stt_deepgramsttservice
+    ├── llm_openaillmservice
+    └── tts_cartesiattsservice
+    turn
+    └── ...
+```
+
+This organization helps you track conversation-to-conversation and turn-to-turn interactions.
+
+## Available Demos
+
+| Demo                            | Description                                                               |
+| ------------------------------- | ------------------------------------------------------------------------- |
+| [Jaeger Tracing](./jaeger/)     | Tracing with Jaeger, an open-source end-to-end distributed tracing system |
+| [Langfuse Tracing](./langfuse/) | Tracing with Langfuse, a specialized platform for LLM observability       |
+
+## Common Requirements
+
+- Python 3.10+
+- Pipecat and its dependencies
+- API keys for the services used (Deepgram, Cartesia, OpenAI)
+- The appropriate OpenTelemetry exporters
+
+## How Tracing Works
+
+The tracing system consists of:
+
+1. **TurnTrackingObserver**: Detects conversation turns
+2. **TurnTraceObserver**: Creates spans for turns and conversations
+3. **Service Decorators**: `@traced_tts`, `@traced_stt`, `@traced_llm` for service-specific tracing
+4. **Context Providers**: Share context between different parts of the pipeline
+
+## Getting Started
+
+1. Choose one of the demos from the table above
+2. Follow the README instructions in the respective directory
+
+## Common Troubleshooting
+
+- **Debugging Traces**: Set `OTEL_CONSOLE_EXPORT=true` to print traces to the console for debugging
+- **Missing Metrics**: Check that `enable_metrics=True` in PipelineParams
+- **API Key Issues**: Verify your API keys are set correctly in the .env file
+
+## References
+
+- [OpenTelemetry Python Documentation](https://opentelemetry-python.readthedocs.io/)
+- [Pipecat Documentation](https://docs.pipecat.ai/server/utilities/opentelemetry)

examples/open-telemetry/jaeger/README.md

@@ -0,0 +1,80 @@
+# Jaeger Tracing for Pipecat
+
+This demo showcases OpenTelemetry tracing integration for Pipecat services using Jaeger, allowing you to visualize service calls, performance metrics, and dependencies.
+
+## Setup Instructions
+
+### 1. Start the Jaeger Container
+
+Run Jaeger in Docker to collect and visualize traces:
+
+```bash
+docker run -d --name jaeger \
+  -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
+  -p 16686:16686 \
+  -p 4317:4317 \
+  -p 4318:4318 \
+  jaegertracing/all-in-one:latest
+```
+
+### 2. Environment Configuration
+
+Create a `.env` file with your API keys and enable tracing:
+
+```
+ENABLE_TRACING=true
+OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317  # Point to your Jaeger backend
+# OTEL_CONSOLE_EXPORT=true  # Set to any value for debug output to console
+
+# Service API keys
+DEEPGRAM_API_KEY=your_key_here
+CARTESIA_API_KEY=your_key_here
+OPENAI_API_KEY=your_key_here
+```
+
+### 3. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Run the Demo
+
+```bash
+python bot.py
+```
+
+### 5. View Traces in Jaeger
+
+Open your browser to [http://localhost:16686](http://localhost:16686) and select the "pipecat-demo" service to view traces.
+
+## Jaeger-Specific Configuration
+
+In the `bot.py` file, note the GRPC exporter configuration:
+
+```python
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+
+# Create the exporter
+otlp_exporter = OTLPSpanExporter(
+    endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4317"),
+    insecure=True,
+)
+
+# Set up tracing with the exporter
+setup_tracing(
+    service_name="pipecat-demo",
+    exporter=otlp_exporter,
+    console_export=bool(os.getenv("OTEL_CONSOLE_EXPORT")),
+)
+```
+
+## Troubleshooting
+
+- **No Traces in Jaeger**: Ensure the Docker container is running and the OTLP endpoint is correct
+- **Connection Errors**: Verify network connectivity to the Jaeger container
+- **Exporter Issues**: Try the Console exporter (`OTEL_CONSOLE_EXPORT=true`) to verify tracing works
+
+## References
+
+- [Jaeger Documentation](https://www.jaegertracing.io/docs/latest/)

examples/open-telemetry/jaeger/bot.py

@@ -0,0 +1,161 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+
+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.openai_llm_context import OpenAILLMContext
+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 TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.utils.tracing.setup import setup_tracing
+
+load_dotenv(override=True)
+
+IS_TRACING_ENABLED = bool(os.getenv("ENABLE_TRACING"))
+
+# Initialize tracing if enabled
+if IS_TRACING_ENABLED:
+    # Create the exporter
+    otlp_exporter = OTLPSpanExporter(
+        endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4317"),
+        insecure=True,
+    )
+
+    # Set up tracing with the exporter
+    setup_tracing(
+        service_name="pipecat-demo",
+        exporter=otlp_exporter,
+        console_export=bool(os.getenv("OTEL_CONSOLE_EXPORT")),
+    )
+    logger.info("OpenTelemetry tracing initialized")
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    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"), params=OpenAILLMService.InputParams(temperature=0.5)
+    )
+
+    # 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)
+
+    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"],
+    )
+    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)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        enable_tracing=IS_TRACING_ENABLED,
+        # Optionally, add a conversation ID to track the conversation
+        # conversation_id="8df26cc1-6db0-4a7a-9930-1e037c8f1fa2",
+    )
+
+    @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")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+    from run import main
+
+    main()

examples/open-telemetry/jaeger/env.example

@@ -0,0 +1,10 @@
+DEEPGRAM_API_KEY=your_deepgram_key
+CARTESIA_API_KEY=your_cartesia_key
+OPENAI_API_KEY=your_openai_key
+
+# Set to any value to enable tracing
+ENABLE_TRACING=true
+# OTLP endpoint (defaults to localhost:4317 if not set)
+OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
+# Set to any value to enable console output for debugging
+# OTEL_CONSOLE_EXPORT=true

examples/open-telemetry/jaeger/requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+python-dotenv
+pipecat-ai[webrtc,silero,cartesia,deepgram,openai,tracing]
+pipecat-ai-small-webrtc-prebuilt
+opentelemetry-exporter-otlp-proto-grpc

examples/open-telemetry/langfuse/README.md

@@ -0,0 +1,82 @@
+# Langfuse Tracing for Pipecat
+
+This demo showcases [Langfuse](https://langfuse.com) tracing integration for Pipecat services via OpenTelemetry, allowing you to visualize service calls, performance metrics, and dependencies with a focus on LLM observability.
+
+Pipecat trace in Langfuse:
+
+https://github.com/user-attachments/assets/13dd7431-bf5e-42e3-8d6d-2ed84c51195d
+
+## Setup Instructions
+
+### 1. Create a Langfuse Project and get API keys
+
+[Self-host](https://langfuse.com/self-hosting) Langfuse or create a free [Langfuse Cloud](https://cloud.langfuse.com) account.
+Create a new project and get the API keys.
+
+### 2. Environment Configuration
+
+Base64 encode your Langfuse public and secret key:
+
+```bash
+echo -n "pk-lf-1234567890:sk-lf-1234567890" | base64
+```
+
+Create a `.env` file with your API keys to enable tracing:
+
+```
+ENABLE_TRACING=true
+# OTLP endpoint for Langfuse
+OTEL_EXPORTER_OTLP_ENDPOINT=http://cloud.langfuse.com/api/public/otel
+OTEL_EXPORTER_OTLP_HEADERS=Authorization=Basic%20<base64_encoded_api_key>
+# Set to any value to enable console output for debugging
+# OTEL_CONSOLE_EXPORT=true
+
+# Service API keys
+DEEPGRAM_API_KEY=your_key_here
+CARTESIA_API_KEY=your_key_here
+OPENAI_API_KEY=your_key_here
+```
+
+### 3. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Run the Demo
+
+```bash
+python bot.py
+```
+
+### 5. View Traces in Langfuse
+
+Open your browser to [https://cloud.langfuse.com](https://cloud.langfuse.com) to view traces.
+
+## Langfuse-Specific Configuration
+
+In the `bot.py` file, note the HTTP exporter configuration:
+
+```python
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+
+# Create the exporter - configured from environment variables
+otlp_exporter = OTLPSpanExporter()
+
+# Set up tracing with the exporter
+setup_tracing(
+    service_name="pipecat-demo",
+    exporter=otlp_exporter,
+    console_export=bool(os.getenv("OTEL_CONSOLE_EXPORT")),
+)
+```
+
+## Troubleshooting
+
+- **No Traces in Langfuse**: Ensure that your credentials are correct and follow this [troubleshooting guide](https://langfuse.com/faq/all/missing-traces)
+- **Connection Errors**: Verify network connectivity to Langfuse
+- **Authorization Issues**: Check that your base64 encoding is correct and the API keys are valid
+
+## References
+
+- [Langfuse OpenTelemetry Documentation](https://langfuse.com/docs/opentelemetry/get-started)

examples/open-telemetry/langfuse/bot.py

@@ -0,0 +1,158 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+
+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.openai_llm_context import OpenAILLMContext
+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 TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.utils.tracing.setup import setup_tracing
+
+load_dotenv(override=True)
+
+IS_TRACING_ENABLED = bool(os.getenv("ENABLE_TRACING"))
+
+# Initialize tracing if enabled
+if IS_TRACING_ENABLED:
+    # Create the exporter
+    otlp_exporter = OTLPSpanExporter()
+
+    # Set up tracing with the exporter
+    setup_tracing(
+        service_name="pipecat-demo",
+        exporter=otlp_exporter,
+        console_export=bool(os.getenv("OTEL_CONSOLE_EXPORT")),
+    )
+    logger.info("OpenTelemetry tracing initialized")
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    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"), params=OpenAILLMService.InputParams(temperature=0.5)
+    )
+
+    # 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)
+
+    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"],
+    )
+    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)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        enable_tracing=IS_TRACING_ENABLED,
+        # Optionally, add a conversation ID to track the conversation
+        # conversation_id="8df26cc1-6db0-4a7a-9930-1e037c8f1fa2",
+    )
+
+    @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")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+    from run import main
+
+    main()

examples/open-telemetry/langfuse/env.example

@@ -0,0 +1,11 @@
+DEEPGRAM_API_KEY=your_deepgram_key
+CARTESIA_API_KEY=your_cartesia_key
+OPENAI_API_KEY=your_openai_key
+
+# Set to any value to enable tracing
+ENABLE_TRACING=true
+# OTLP endpoint (change to us.cloud.langfuse.com if you use the US data region)
+OTEL_EXPORTER_OTLP_ENDPOINT=http://cloud.langfuse.com/api/public/otel
+OTEL_EXPORTER_OTLP_HEADERS=Authorization=Basic%20<base64_encoded_api_keys>
+# Set to any value to enable console output for debugging
+# OTEL_CONSOLE_EXPORT=true

examples/open-telemetry/langfuse/requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+python-dotenv
+pipecat-ai[webrtc,silero,cartesia,deepgram,openai,tracing]
+pipecat-ai-small-webrtc-prebuilt
+opentelemetry-exporter-otlp-proto-http

examples/open-telemetry/run.py

@@ -0,0 +1,205 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import importlib.util
+import os
+import sys
+from contextlib import asynccontextmanager
+from inspect import iscoroutinefunction, signature
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import uvicorn
+from dotenv import load_dotenv
+from fastapi import BackgroundTasks, FastAPI
+from fastapi.responses import RedirectResponse
+from loguru import logger
+from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
+
+from pipecat.transports.network.webrtc_connection import IceServer, SmallWebRTCConnection
+
+# Load environment variables
+load_dotenv(override=True)
+
+app = FastAPI()
+
+# Store connections by pc_id
+pcs_map: Dict[str, SmallWebRTCConnection] = {}
+
+ice_servers = [
+    IceServer(
+        urls="stun:stun.l.google.com:19302",
+    )
+]
+
+# Mount the frontend at /
+app.mount("/client", SmallWebRTCPrebuiltUI)
+
+# Store program arguments
+args: argparse.Namespace = argparse.Namespace()
+
+# Store the bot module and function info
+bot_module: Any = None
+run_bot_func: Optional[Callable] = None
+is_webrtc_bot: bool = True
+
+
+def import_bot_file(file_path: str) -> Tuple[Any, Callable, bool]:
+    """Dynamically import the bot file and determine how to run it.
+
+    Returns:
+        tuple: (module, run_function, is_webrtc_bot)
+          - module: The imported module
+          - run_function: Either run_bot or main function
+          - is_webrtc_bot: True if run_bot function exists and accepts a WebRTC connection
+    """
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"Bot file not found: {file_path}")
+
+    # Extract module name without extension
+    module_name = os.path.splitext(os.path.basename(file_path))[0]
+
+    # Load the module
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if not spec or not spec.loader:
+        raise ImportError(f"Could not load spec for {file_path}")
+
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)
+
+    # Check for run_bot function first
+    if hasattr(module, "run_bot"):
+        run_func = module.run_bot
+        # Check if the function accepts a WebRTC connection
+        sig = signature(run_func)
+        is_webrtc = len(sig.parameters) > 0
+        return module, run_func, is_webrtc
+
+    # Fall back to main function
+    if hasattr(module, "main") and iscoroutinefunction(module.main):
+        return module, module.main, False
+
+    raise AttributeError(f"No run_bot or async main function found in {file_path}")
+
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+    return RedirectResponse(url="/client/")
+
+
+@app.post("/api/offer")
+async def offer(request: dict, background_tasks: BackgroundTasks):
+    global run_bot_func, is_webrtc_bot
+
+    if not run_bot_func:
+        raise RuntimeError("No bot file has been loaded")
+
+    if not is_webrtc_bot:
+        return {
+            "error": "This bot doesn't support WebRTC connections, it's running in standalone mode"
+        }
+
+    pc_id = request.get("pc_id")
+
+    if pc_id and pc_id in pcs_map:
+        pipecat_connection = pcs_map[pc_id]
+        logger.info(f"Reusing existing connection for pc_id: {pc_id}")
+        await pipecat_connection.renegotiate(
+            sdp=request["sdp"], type=request["type"], restart_pc=request.get("restart_pc", False)
+        )
+    else:
+        pipecat_connection = SmallWebRTCConnection(ice_servers)
+        await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
+
+        @pipecat_connection.event_handler("closed")
+        async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
+            logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
+            pcs_map.pop(webrtc_connection.pc_id, None)
+
+        # We've already checked that run_bot_func exists
+        assert run_bot_func is not None
+        background_tasks.add_task(run_bot_func, pipecat_connection, args)
+
+    answer = pipecat_connection.get_answer()
+    # Updating the peer connection inside the map
+    pcs_map[answer["pc_id"]] = pipecat_connection
+
+    return answer
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    yield  # Run app
+    coros = [pc.close() for pc in pcs_map.values()]
+    await asyncio.gather(*coros)
+    pcs_map.clear()
+
+
+async def run_standalone_bot() -> None:
+    """Run a standalone bot that doesn't require WebRTC"""
+    global run_bot_func
+    if run_bot_func is not None:
+        await run_bot_func()
+    else:
+        raise RuntimeError("No bot function available to run")
+
+
+def main(parser: Optional[argparse.ArgumentParser] = None):
+    global args
+
+    if not parser:
+        parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+    parser.add_argument("bot_file", nargs="?", help="Path to the bot file", default=None)
+    parser.add_argument(
+        "--host", default="localhost", help="Host for HTTP server (default: localhost)"
+    )
+    parser.add_argument(
+        "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
+    )
+    parser.add_argument("--verbose", "-v", action="count", default=0)
+    args = parser.parse_args()
+
+    logger.remove(0)
+    if args.verbose:
+        logger.add(sys.stderr, level="TRACE")
+    else:
+        logger.add(sys.stderr, level="DEBUG")
+
+    # Infer the bot file from the caller if not provided explicitly
+    bot_file = args.bot_file
+    if bot_file is None:
+        # Get the __file__ of the script that called main()
+        import inspect
+
+        caller_frame = inspect.stack()[1]
+        caller_globals = caller_frame.frame.f_globals
+        bot_file = caller_globals.get("__file__")
+
+    if not bot_file:
+        print("❌ Could not determine the bot file. Pass it explicitly to main().")
+        sys.exit(1)
+
+    # Import the bot file
+    try:
+        global run_bot_func, bot_module, is_webrtc_bot
+        bot_module, run_bot_func, is_webrtc_bot = import_bot_file(bot_file)
+        logger.info(f"Successfully loaded bot from {bot_file}")
+
+        if is_webrtc_bot:
+            logger.info("Detected WebRTC-compatible bot, starting web server...")
+            uvicorn.run(app, host=args.host, port=args.port)
+        else:
+            logger.info("Detected standalone bot, running directly...")
+            asyncio.run(run_standalone_bot())
+    except Exception as e:
+        logger.error(f"Error loading bot file: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

examples/p2p-webrtc/video-transform/client/typescript/package-lock.json

@@ -16,7 +16,7 @@
         "@types/node": "^22.13.1",
         "@vitejs/plugin-react-swc": "^3.7.2",
         "typescript": "^5.7.3",
-        "vite": "^6.0.2"
+        "vite": "^6.3.5"
       }
     },
     "node_modules/@babel/runtime": {
@@ -1371,9 +1371,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
-      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
+      "version": "6.3.5",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
+      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/p2p-webrtc/video-transform/client/typescript/package.json

@@ -15,7 +15,7 @@
     "@types/node": "^22.13.1",
     "@vitejs/plugin-react-swc": "^3.7.2",
     "typescript": "^5.7.3",
-    "vite": "^6.0.2"
+    "vite": "^6.3.5"
   },
   "dependencies": {
     "@pipecat-ai/client-js": "^0.3.2",

examples/simple-chatbot/client/javascript/package-lock.json

@@ -13,7 +13,7 @@
         "@pipecat-ai/daily-transport": "^0.3.8"
       },
       "devDependencies": {
-        "vite": "^6.0.9"
+        "vite": "^6.3.5"
       }
     },
     "node_modules/@babel/runtime": {
@@ -1114,9 +1114,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
-      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
+      "version": "6.3.5",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
+      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/simple-chatbot/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.9"
+    "vite": "^6.3.5"
   },
   "dependencies": {
     "@pipecat-ai/client-js": "^0.3.5",

examples/simple-chatbot/client/react-native/.gitignore

@@ -0,0 +1,5 @@
+ios
+android
+node_modules
+.expo
+.env