Remove redundant duplicate type checks in direct_function.py

After the typing modernization, `dict or dict` and `list or list`
were left behind where `Dict`/`List` had been replaced by `dict`/`list`.

.claude/skills/cleanup/SKILL.md

@@ -144,7 +144,7 @@ class InputParams(BaseModel):
 
 #### Examples
 
-Validated against `examples/foundational/07-interruptible.py`:
+Validated against `examples/07-interruptible.py`:
 
 - Proper `create_transport()` usage
 - Correct pipeline structure

.dockerignore

@@ -1,30 +0,0 @@
-# flyctl launch added from .gitignore
-**/.vscode
-**/env
-**/__pycache__
-**/*~
-**/venv
-#*#
-
-# Distribution / packaging
-**/.Python
-**/build
-**/develop-eggs
-**/dist
-**/downloads
-**/eggs
-**/.eggs
-**/lib
-**/lib64
-**/parts
-**/sdist
-**/var
-**/wheels
-**/share/python-wheels
-**/*.egg-info
-**/.installed.cfg
-**/*.egg
-**/MANIFEST
-**/.DS_Store
-**/.env
-fly.toml

.github/workflows/python-compatibility.yaml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.10.19', '3.11.14', '3.12.12', '3.13.12']
+        python-version: ['3.11.15', '3.12.13', '3.13.12', '3.14.3']
 
     name: Python ${{ matrix.python-version }}
     steps:
@@ -42,7 +42,7 @@ jobs:
 
       - name: Test uv sync with all extras
         run: |
-          uv sync --group dev --all-extras --no-extra krisp
+          uv sync --group dev --all-extras
 
       - name: Verify installation
         run: |

.github/workflows/sync-quickstart.yaml

@@ -1,51 +0,0 @@
-name: Sync Quickstart to pipecat-quickstart repo
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - 'examples/quickstart/**'
-  workflow_dispatch: # Manual trigger
-
-jobs:
-  sync-quickstart:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout main repo
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Checkout quickstart repo
-        uses: actions/checkout@v4
-        with:
-          repository: pipecat-ai/pipecat-quickstart
-          token: ${{ secrets.QUICKSTART_SYNC_TOKEN }}
-          path: quickstart-repo
-
-      - name: Sync files (excluding uv.lock and README.md)
-        run: |
-          # Copy all files except uv.lock and README.md
-          find examples/quickstart -type f \
-            -not -name "README.md" \
-            -not -name "uv.lock" \
-            -exec cp {} quickstart-repo/ \;
-
-      - name: Commit and push changes
-        run: |
-          cd quickstart-repo
-          git config user.name "GitHub Action"
-          git config user.email "action@github.com"
-          git add .
-
-          # Only commit if there are changes
-          if ! git diff --staged --quiet; then
-            git commit -m "Sync from pipecat main repo
-            
-            Updated files from examples/quickstart/
-            Commit: ${{ github.sha }}
-            "
-            git push
-          else
-            echo "No changes to sync"
-          fi

.pre-commit-config.yaml

@@ -1,8 +1,13 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.1
+  - repo: local
     hooks:
       - id: ruff
-        language_version: python3
-        args: [--fix]
+        name: ruff
+        entry: uv run ruff check --fix
+        language: system
+        types: [python]
       - id: ruff-format
+        name: ruff-format
+        entry: uv run ruff format
+        language: system
+        types: [python]

.readthedocs.yaml

@@ -11,7 +11,7 @@ build:
   jobs:
     post_install:
       - pip install uv
-      - UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
+      - UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra mlx-whisper
 
 sphinx:
   configuration: docs/api/conf.py

CHANGELOG.md.template

@@ -1,62 +0,0 @@
-# Changelog
-
-All notable changes to the **<project name>** SDK 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).
-
-Please make sure to add your changes to the appropriate categories:
-
-## [Unreleased]
-
-### Added
-
-<!-- for new functionality -->
-
-- n/a
-
-### Changed
-
-<!-- for changed functionality -->
-
-- n/a
-
-### Deprecated
-
-<!-- for soon-to-be removed functionality -->
-
-- n/a
-
-### Removed
-
-<!-- for removed functionality -->
-
-- n/a
-
-### Fixed
-
-<!-- for fixed bugs -->
-
-- n/a
-
-### Performance
-
-<!-- for performance-relevant changes -->
-
-- n/a
-
-### Security
-
-<!-- for security-relevant changes -->
-
-- n/a
-
-### Other
-
-<!-- for everything else -->
-
-- n/a
-
-## [0.1.0] - YYYY-MM-DD
-
-Initial release.

CLAUDE.md

@@ -10,7 +10,7 @@ Pipecat is an open-source Python framework for building real-time voice and mult
 
 ```bash
 # Setup development environment
-uv sync --group dev --all-extras --no-extra gstreamer --no-extra krisp
+uv sync --group dev --all-extras --no-extra gstreamer
 
 # Install pre-commit hooks
 uv run pre-commit install

COMMUNITY_INTEGRATIONS.md

@@ -23,7 +23,7 @@ Create your integration following the patterns and examples shown in the "Integr
 Your repository must contain these components:
 
 - **Source code** - Complete implementation following Pipecat patterns
-- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
+- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples))
 - **README.md** - Must include:
   - Introduction and explanation of your integration
   - Installation instructions
@@ -65,12 +65,25 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
 
 #### Websocket-based Services
 
+**Base class:** `WebsocketSTTService`
+
+**Use for:** Services where you manage the websocket connection directly. Combines `STTService` with `WebsocketService` for automatic reconnection and keepalive support.
+
+**Examples:**
+
+- [CartesiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/stt.py)
+- [ElevenLabsRealtimeSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/stt.py)
+
+#### SDK-based Streaming Services
+
 **Base class:** `STTService`
 
+**Use for:** Streaming services where the provider's Python SDK manages the connection internally.
+
 **Examples:**
 
 - [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
-- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
+- [GoogleSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/stt.py)
 
 #### File-based Services
 
@@ -108,55 +121,59 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
 
 #### Key requirements:
 
-- **Frame sequence:** Output must follow this frame sequence pattern:
-  - `LLMFullResponseStartFrame` - Signals the start of an LLM response
-  - `LLMTextFrame` - Contains LLM content, typically streamed as tokens
-  - `LLMFullResponseEndFrame` - Signals the end of an LLM response
+- **`_process_context(self, context: LLMContext)`** — The main method that processes an LLM context and generates a response. Each LLM service overrides `process_frame` to extract context from `LLMContextFrame` and calls `_process_context`.
 
-- **Context aggregation:** Implement context aggregation to collect user and assistant content:
-  - Aggregators come in pairs with a `user()` instance and `assistant()` instance
-  - Context must adhere to the `LLMContext` universal format
-  - Aggregators should handle adding messages, function calls, and images to the context
+- **`adapter_class`** — Class attribute pointing to a `BaseLLMAdapter` subclass. Defaults to `OpenAILLMAdapter`. Non-OpenAI services must implement their own adapter (see `src/pipecat/adapters/base_llm_adapter.py`) with methods:
+  - `get_llm_invocation_params(context)` — Extract provider-specific params from universal context
+  - `to_provider_tools_format(tools_schema)` — Convert standard tools to provider format
+  - `get_messages_for_logging(context)` — Format messages for logging
+  - Reference adapters: `src/pipecat/adapters/services/` (anthropic, gemini, bedrock, etc.)
+
+- **Frame sequence:** Output must follow this frame sequence pattern:
+  - `LLMFullResponseStartFrame` — Signals the start of an LLM response
+  - `LLMTextFrame` — Contains LLM content, typically streamed as tokens
+  - `LLMFullResponseEndFrame` — Signals the end of an LLM response
+
+- **Thought frames (reasoning models):** If the model supports extended thinking / chain-of-thought, emit thought frames alongside the response:
+  - `LLMThoughtStartFrame` — Signals the start of a thought
+  - `LLMThoughtTextFrame` — Contains thought content, streamed as tokens
+  - `LLMThoughtEndFrame` — Signals the end of a thought
+
+- **Context aggregation** is handled by the framework via `LLMContext` + `LLMContextAggregatorPair`. The LLM service just processes context it receives — no need to implement aggregators.
 
 ### TTS (Text-to-Speech) Services
 
-#### AudioContextWordTTSService
+#### WebsocketTTSService
 
-**Use for:** Websocket-based services supporting word/timestamp alignment
+**Use for:** Websocket-based streaming services (with or without word timestamps)
 
-**Example:**
+**Examples:**
 
 - [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
+- [ElevenLabsTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
 
 #### InterruptibleTTSService
 
-**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
+**Use for:** Websocket-based services without word timestamps that reconnect on interruption (e.g. don't support a context ID or interruption message)
 
 **Example:**
 
 - [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
 
-#### WordTTSService
-
-**Use for:** HTTP-based services supporting word/timestamp alignment
-
-**Example:**
-
-- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
-
 #### TTSService
 
-**Use for:** HTTP-based services without word/timestamp alignment
+**Use for:** HTTP-based services (word timestamps are supported in the base class)
 
-**Example:**
+**Examples:**
 
 - [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
+- [OpenAITTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/openai/tts.py)
 
 #### Key requirements:
 
-- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
+- For websocket services, use asyncio WebSocket implementation
 - Handle idle service timeouts with keepalives
-- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
+- TTS services push both audio (`TTSAudioRawFrame`) and text (`TTSTextFrame`) frames
 
 ### Telephony Serializers
 
@@ -200,14 +217,25 @@ Vision services process images and provide analysis such as descriptions, object
 
 #### Key requirements:
 
-- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
-- The method processes the latest image in the context and yields frames with analysis results
-- Typically yields `TextFrame` objects containing descriptions or answers
+- Must implement `run_vision` method that takes a `UserImageRawFrame` and returns an `AsyncGenerator[Frame, None]`
+- The method processes the image frame and yields frames with analysis results
+- Must yield the frame sequence: `VisionFullResponseStartFrame`, `VisionTextFrame`, `VisionFullResponseEndFrame`
 
 ## Implementation Guidelines
 
 ### Naming Conventions
 
+#### Package and Repository Naming
+
+Use the `pipecat-{vendor}` naming convention for your PyPI package and repository:
+
+- `pipecat-{vendor}` — for single-service integrations (e.g., `pipecat-deepdub`)
+- `pipecat-{vendor}-{type}` — when a vendor offers multiple service types (e.g., `pipecat-upliftai-stt`, `pipecat-upliftai-tts`)
+
+This convention makes community packages easily discoverable via PyPI search and clearly identifies them as part of the Pipecat ecosystem.
+
+#### Class Naming
+
 - **STT:** `VendorSTTService`
 - **LLM:** `VendorLLMService`
 - **TTS:**
@@ -381,7 +409,7 @@ Note that `self.sample_rate` is a `@property` set in the TTSService base class,
 
 Use Pipecat's tracing decorators:
 
-- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
+- **STT:** `@traced_stt` - decorate `_handle_transcription(self, transcript, is_final, language)` (the standard method name convention)
 - **LLM:** `@traced_llm` - decorate the `_process_context()` method
 - **TTS:** `@traced_tts` - decorate the `run_tts()` method
 
@@ -389,8 +417,9 @@ Use Pipecat's tracing decorators:
 
 ### Packaging and Distribution
 
+- Name your package `pipecat-{vendor}` (see [Naming Conventions](#naming-conventions))
 - Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
-- Consider releasing to PyPI for easier installation
+- Publish to PyPI for easier installation
 - Follow semantic versioning principles
 - Maintain a changelog
 
@@ -403,17 +432,15 @@ For REST-based communication, use aiohttp. Pipecat includes this as a required d
 - Wrap API calls in appropriate try/catch blocks
 - Handle rate limits and network failures gracefully
 - Provide meaningful error messages
-- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
+- When errors occur, raise exceptions AND push errors to notify the pipeline:
 
 ```python
-from pipecat.frames.frames import ErrorFrame
-
 try:
     # Your API call
     result = await self._make_api_call()
 except Exception as e:
-    # Push error frame to pipeline
-    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
+    # Push error upstream to notify the pipeline
+    await self.push_error(f"{self} error: {e}", exception=e)
     # Raise or handle as appropriate
     raise
 ```

README.md

@@ -8,7 +8,7 @@
 
 **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? Try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
+> Want to dive right in? Run `pipecat init quickstart` or follow the [quickstart guide](https://docs.pipecat.ai/getting-started/quickstart).
 
 ## 🚀 What You Can Build
 
@@ -65,6 +65,10 @@ claude plugin marketplace add pipecat-ai/skills
 
 and install any of the available plugins.
 
+### 🧩 Community Integrations
+
+Build and share your own Pipecat service integrations! Browse existing [community integrations](https://docs.pipecat.ai/server/services/community-integrations) or check out our [guide](COMMUNITY_INTEGRATIONS.md) to create your own.
+
 ### 📺️ Pipecat TV Channel
 
 Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
@@ -75,25 +79,26 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/simple-chatbot/image.png" width="400" /></a>&nbsp;
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
     <br/>
-    <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>&nbsp;
-    <a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
+    <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/daily-multi-translation"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/daily-multi-translation/image.png" width="400" /></a>&nbsp;
+    <a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/vision/vision-moondream.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/assets/moondream.png" width="400" /></a>
 </p>
 
 ## 🧩 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), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [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), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [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), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [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), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                                                                                                                                                               |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [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), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [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), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [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), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox),                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Serializers         | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/video/lemonslice), [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/google-imagen), [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), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [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), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [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), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [Mistral](https://docs.pipecat.ai/server/services/stt/mistral), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [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), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [Nebius](https://docs.pipecat.ai/server/services/llm/nebius), [Novita](https://docs.pipecat.ai/server/services/llm/novita), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nvidia), [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), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/llm/sarvam), [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                                                                                                                                                                                                                         |
+| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [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), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [Kokoro](https://docs.pipecat.ai/server/services/tts/kokoro), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Mistral](https://docs.pipecat.ai/server/services/tts/mistral), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Smallest](https://docs.pipecat.ai/server/services/tts/smallest), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [xAI](https://docs.pipecat.ai/server/services/tts/xai), [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), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox),                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [LiveKit (WebRTC)](https://docs.pipecat.ai/server/services/transport/livekit), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), [WhatsApp](https://docs.pipecat.ai/server/services/transport/whatsapp), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| Serializers         | [Exotel](https://docs.pipecat.ai/server/services/serializers/exotel), [Genesys](https://docs.pipecat.ai/server/services/serializers/genesys), [Plivo](https://docs.pipecat.ai/server/services/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/services/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/services/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/services/serializers/vonage)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/transport/lemonslice), [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/google-imagen), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp Viva](https://docs.pipecat.ai/guides/features/krisp-viva), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter), [RNNoise](https://docs.pipecat.ai/server/utilities/audio/rnnoise-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Community           | [Browse community integrations →](https://docs.pipecat.ai/server/services/community-integrations)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 
@@ -137,15 +142,15 @@ You can get started with Pipecat running on your local machine, then move your a
 
 ## 🧪 Code examples
 
-- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
+- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples) — small snippets that build on each other, introducing one or two concepts at a time
 - [Example apps](https://github.com/pipecat-ai/pipecat-examples) — complete applications that you can use as starting points for development
 
 ## 🛠️ Contributing to the framework
 
 ### Prerequisites
 
-**Minimum Python Version:** 3.10
-**Recommended Python Version:** 3.12
+**Minimum Python Version:** 3.11
+**Recommended Python Version:** >= 3.12
 
 ### Setup Steps
 
@@ -161,7 +166,6 @@ You can get started with Pipecat running on your local machine, then move your a
    ```bash
    uv sync --group dev --all-extras \
      --no-extra gstreamer \
-     --no-extra krisp \
      --no-extra local \
    ```
 

changelog/4253.added.md

@@ -0,0 +1 @@
+- Added `MistralSTTService` for real-time speech-to-text using Mistral's Voxtral Realtime API (`voxtral-mini-transcribe-realtime-2602`). Supports streaming transcription with interim results, automatic language detection, and VAD-driven utterance lifecycle.

changelog/4304.fixed.md

@@ -0,0 +1 @@
+- Fixed `pipecat-ai[tavus]` not installing the required `daily-python` dependency. Installing the `tavus` extra now correctly pulls in `pipecat-ai[daily]`.

changelog/4311.changed.md

@@ -0,0 +1 @@
+- STT services now reconnect safely when settings change: reconnection is deferred until the current user turn ends (i.e., until `UserStoppedSpeakingFrame` is received) rather than interrupting an active speech session. Audio frames received while the reconnect is in progress are buffered and replayed once the new connection is ready. `CartesiaSTTService` and `DeepgramSTTService` both use this new behavior.

changelog/4311.fixed.md

@@ -0,0 +1 @@
+- Fixed audio loss and potential errors when STT settings were updated mid-speech. Previously, `CartesiaSTTService` and `DeepgramSTTService` would immediately disconnect and reconnect when settings changed, dropping any in-flight audio. Reconnection is now deferred until the user stops speaking, and audio arriving during the reconnect window is buffered and replayed.

changelog/4313.added.2.md

@@ -0,0 +1 @@
+- Added `buttons` field to `OutputDTMFFrame` and `OutputDTMFUrgentFrame` for sending multi-key DTMF sequences as a `list[KeypadEntry]`. Use `OutputDTMFFrame.from_string("123#")` (or the equivalent on `OutputDTMFUrgentFrame`) to build one from a dial string, and `to_string()` to convert back.

changelog/4313.added.3.md

@@ -0,0 +1 @@
+- Added `DailyOutputDTMFFrame` and `DailyOutputDTMFUrgentFrame` frames. In addition to the inherited `buttons`, they accept `session_id`, `digit_duration_ms` and `method`, which are forwarded to Daily's `send_dtmf` as `sessionId`, `digitDurationMs` and `method`.

changelog/4313.added.md

@@ -0,0 +1 @@
+- Added `DailyTransport.send_dtmf()` to expose the Daily call client's DTMF sending capability, enabling applications to send tones during a call (e.g. IVR navigation).

docs/api/README.md

@@ -1,108 +1,60 @@
-# Pipecat Documentation
+# Pipecat API Documentation
 
-This directory contains the source files for auto-generating Pipecat's server API reference documentation.
-
-## Setup
-
-1. Install documentation dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-2. Make the build scripts executable:
-
-```bash
-chmod +x build-docs.sh rtd-test.py
-```
+This directory contains the source files for auto-generating Pipecat's API reference documentation.
 
 ## Building Documentation
 
-From this directory, you can build the documentation in several ways:
-
-### Local Build
+From this directory:
 
 ```bash
-# Using the build script (automatically opens docs when done)
-./build-docs.sh
+# Build docs (warnings shown but don't fail the build)
+cd docs/api && uv run ./build-docs.sh
 
-# Or directly with sphinx-build
-sphinx-build -b html . _build/html -W --keep-going
+# Build with strict mode (warnings treated as errors)
+cd docs/api && uv run ./build-docs.sh --strict
 ```
 
-### ReadTheDocs Test Build
+The build script will:
 
-To test the documentation build process exactly as it would run on ReadTheDocs:
-
-```bash
-./rtd-test.py
-```
-
-This script:
-
-- Creates a fresh virtual environment
-- Installs all dependencies as specified in requirements files
-- Handles conflicting dependencies (like grpcio versions for Riva)
-- Builds the documentation in an isolated environment
-- Provides detailed logging of the build process
-
-Use this script to verify your documentation will build correctly on ReadTheDocs before pushing changes.
-
-## Viewing Documentation
-
-The built documentation will be available at `_build/html/index.html`. To open:
-
-```bash
-# On MacOS
-open _build/html/index.html
-
-# On Linux
-xdg-open _build/html/index.html
-
-# On Windows
-start _build/html/index.html
-```
+1. Install documentation dependencies via `uv sync --group docs`
+2. Clean previous build output
+3. Run `sphinx-build` to generate HTML documentation
+4. Open the result in your browser (macOS)
 
 ## Directory Structure
 
 ```
 .
-├── api/            # Auto-generated API documentation
-├── _build/         # Built documentation
-├── _static/        # Static files (images, css, etc.)
-├── conf.py         # Sphinx configuration
+├── api/            # Auto-generated API documentation (created during build)
+├── _build/         # Built documentation output
+├── conf.py         # Sphinx configuration (mock imports, extensions, etc.)
 ├── index.rst       # Main documentation entry point
-├── requirements-base.txt    # Base documentation dependencies
-├── requirements-riva.txt    # Riva-specific dependencies
 ├── build-docs.sh   # Local build script
-└── rtd-test.py     # ReadTheDocs test build script
+└── rtd-test.sh     # ReadTheDocs test build script (uses pip, not uv)
 ```
 
-## Notes
+## How It Works
 
-- Documentation is auto-generated from Python docstrings
-- Service modules are automatically detected and included
-- The build process matches our ReadTheDocs configuration
-- Warnings are treated as errors (-W flag) to maintain consistency
-- The --keep-going flag ensures all errors are reported
-- Dependencies are split into multiple requirements files to handle version conflicts
+- `conf.py` runs `sphinx-apidoc` during Sphinx's `setup()` phase to generate `.rst` files from Python source
+- Sphinx autodoc imports each module to extract docstrings
+- Modules with unavailable dependencies are listed in `autodoc_mock_imports` in `conf.py`
+- Napoleon extension converts Google-style docstrings to reStructuredText
 
 ## Troubleshooting
 
-If you encounter missing service modules:
+**Module not appearing in docs:**
 
-1. Verify the service is installed with its extras: `pip install pipecat-ai[service-name]`
-2. Check the build logs for import errors
-3. Ensure the service module is properly initialized in the package
-4. Run `./rtd-test.py` to test in an isolated environment matching ReadTheDocs
+1. Check the build output for `autodoc: failed to import` warnings
+2. If the module has an unresolvable import dependency, add it to `autodoc_mock_imports` in `conf.py`
+3. Verify the module is importable: `uv run python -c "import pipecat.module.name"`
 
-For dependency conflicts:
+**Duplicate object warnings:**
 
-1. Check the requirements files for version specifications
-2. Use `rtd-test.py` to verify dependency resolution
-3. Consider adding service-specific requirements files if needed
+These come from re-export modules or Sphinx discovering the same class through multiple import paths. Usually cosmetic.
 
-For more information:
+**Docstring formatting warnings:**
 
-- [ReadTheDocs Configuration](.readthedocs.yaml)
-- [Sphinx Documentation](https://www.sphinx-doc.org/)
+Docstrings use reStructuredText, not Markdown. Common issues:
+- Use `Example::` with indented code blocks, not `` ```python ``
+- Ensure blank lines between directive content and subsequent sections
+- Use `Parameters:` (not `Attributes:`) for dataclass field documentation to avoid duplicate entries

docs/api/build-docs.sh

@@ -1,8 +1,16 @@
 #!/bin/bash
 
+# Usage: ./build-docs.sh [--strict]
+#   --strict: Treat warnings as errors (default: warnings only)
+
+SPHINX_OPTS=""
+if [ "$1" = "--strict" ]; then
+    SPHINX_OPTS="-W --keep-going"
+fi
+
 # Build docs using uv
 echo "Installing dependencies with uv..."
-uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
+uv sync --group docs --all-extras --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra mlx-whisper
 
 # Check if sphinx-build is available
 if ! uv run sphinx-build --version &> /dev/null; then
@@ -14,8 +22,7 @@ fi
 rm -rf _build
 
 echo "Building documentation..."
-# Build docs matching ReadTheDocs configuration
-uv run sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
+uv run sphinx-build -b html -d _build/doctrees . _build/html $SPHINX_OPTS
 
 if [ $? -eq 0 ]; then
     echo "Documentation built successfully!"

docs/api/conf.py

@@ -4,6 +4,19 @@ import sys
 from datetime import datetime
 from pathlib import Path
 
+# Fix Pydantic v2 + Sphinx autodoc incompatibility: ConfigDict(extra="allow") fails
+# during Sphinx's import because __pydantic_extra__ annotation on BaseModel resolves to
+# `Dict[str, Any] | None` whose get_origin() is Union, not dict. Patch the check to
+# accept Union-wrapped dict types (i.e., Optional[Dict[str, Any]]).
+import pydantic._internal._generate_schema as _pydantic_gs
+
+_ORIG_DICT_TYPES = _pydantic_gs.DICT_TYPES
+# Expand the accepted types to include Union (Optional[Dict[str, Any]])
+import types
+import typing
+
+_pydantic_gs.DICT_TYPES = [*_ORIG_DICT_TYPES, typing.Union, types.UnionType]
+
 # Configure logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger("sphinx-build")
@@ -48,8 +61,6 @@ autodoc_default_options = {
 # Mock imports for optional dependencies
 autodoc_mock_imports = [
     # Krisp - has build issues on some platforms
-    "pipecat_ai_krisp",
-    "krisp",
     "krisp_audio",
     # System-specific GUI libraries
     "_tkinter",
@@ -78,16 +89,6 @@ autodoc_mock_imports = [
     "einops",
     "intel_extension_for_pytorch",
     "huggingface_hub",
-    # riva dependencies
-    "riva",
-    "riva.client",
-    "riva.client.Auth",
-    "riva.client.ASRService",
-    "riva.client.StreamingRecognitionConfig",
-    "riva.client.RecognitionConfig",
-    "riva.client.AudioEncoding",
-    "riva.client.proto.riva_tts_pb2",
-    "riva.client.SpeechSynthesisService",
     # MLX dependencies (Apple Silicon specific)
     "mlx",
     "mlx_whisper",  # Note: might need underscore format too
@@ -98,7 +99,6 @@ autodoc_mock_imports = [
     "cartesia",
     "camb",
     "sarvamai",
-    "openpipe",
     "openai.types.beta.realtime",
     "langchain_core",
     "langchain_core.messages",
@@ -110,6 +110,8 @@ autodoc_mock_imports = [
     "fastapi.middleware",
     "fastapi.responses",
     "uvicorn",
+    # Deepgram dependencies
+    "deepgram",
 ]
 
 # HTML output settings
@@ -136,6 +138,8 @@ def import_core_modules():
         "pipecat.runner",
         "pipecat.serializers",
         "pipecat.transcriptions",
+        "pipecat.turns",
+        "pipecat.extensions",
         "pipecat.utils",
     ]
 
@@ -180,7 +184,6 @@ def setup(app):
     logger.info(f"Source directory: {source_dir}")
 
     excludes = [
-        str(project_root / "src/pipecat/pipeline/to_be_updated"),
         str(project_root / "src/pipecat/examples"),
         str(project_root / "src/pipecat/tests"),
         "**/test_*.py",

docs/api/index.rst

@@ -32,4 +32,5 @@ Quick Links
    Services <api/pipecat.services>
    Transcriptions <api/pipecat.transcriptions>
    Transports <api/pipecat.transports>
+   Turns <api/pipecat.turns>
    Utils <api/pipecat.utils>

env.example

@@ -80,9 +80,6 @@ GOOGLE_TEST_CREDENTIALS=...
 # Gradium
 GRAPDIUM_API_KEY=...
 
-# Grok
-GROK_API_KEY=...
-
 # Groq
 GROQ_API_KEY=...
 
@@ -124,18 +121,21 @@ MINIMAX_GROUP_ID=...
 # Mistral
 MISTRAL_API_KEY=...
 
+# Nebius
+NEBIUS_API_KEY=...
+
 # Neuphonic
 NEUPHONIC_API_KEY=...
 
+# Novita
+NOVITA_API_KEY=...
+
 # NVIDIA
 NVIDIA_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
 
-# OpenPipe
-OPENPIPE_API_KEY=...
-
 # OpenRouter
 OPENROUTER_API_KEY=...
 
@@ -176,6 +176,9 @@ SENTRY_DSN=...
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
+# Smallest
+SMALLEST_API_KEY=...
+
 # Smart turn
 LOCAL_SMART_TURN_MODEL_PATH=...
 FAL_SMART_TURN_API_KEY=...
@@ -209,3 +212,6 @@ WHATSAPP_TOKEN=...
 WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
 WHATSAPP_PHONE_NUMBER_ID=...
 WHATSAPP_APP_SECRET=...
+
+# xAI / Grok
+XAI_API_KEY=...

examples/README.md

@@ -1,31 +1,150 @@
 # Pipecat Examples
 
-This directory contains examples to help you learn how to build with Pipecat.
+This directory contains examples showing how to build voice and multimodal agents with Pipecat.
 
-## Getting Started
+## Setup
 
-New to Pipecat? Start here:
+1. Follow the [README](https://github.com/pipecat-ai/pipecat/blob/main/README.md#%EF%B8%8F-contributing-to-the-framework) steps to get your local environment configured.
 
-- **[Quickstart](quickstart/)** - Get your first voice AI bot running in 5 minutes _(coming soon)_
-- **[Client/Server Web](client-server-web/)** - Learn to build web applications with Pipecat's client SDKs _(coming soon)_
-- **[Phone Bot with Twilio](phone-bot-twilio/)** - Connect your bot to a phone number _(coming soon)_
+   > **Run from root directory**: Make sure you are running the steps from the root directory.
 
-## Foundational Examples
+   > **Using local audio?**: The `LocalAudioTransport` requires a system dependency for `portaudio`. Install the dependency to use the transport.
 
-Single-file examples that introduce core Pipecat concepts one at a time. These examples:
+2. Copy the [`env.example`](../env.example) file and add API keys for services you plan to use:
 
-- Build on each other progressively
-- Focus on specific features or integrations
-- Are used for testing with every Pipecat release
+   ```bash
+   cp env.example .env
+   # Edit .env with your API keys
+   ```
 
-See the **[Foundational Examples README](foundational/)** for the complete list.
+3. Run any example:
 
-## More Advanced Examples
+   ```bash
+   uv run python getting-started/01-say-one-thing.py
+   ```
 
-Ready to explore complex use cases? Visit **[pipecat-examples](https://github.com/pipecat-ai/pipecat-examples)** for:
+4. Open the web interface at http://localhost:7860/client/ and click "Connect"
 
-- Production-ready applications
-- Multi-platform client implementations
-- Telephony integrations
-- Multimodal and creative applications
-- Deployment and monitoring examples
+## Running examples with other transports
+
+Most examples support running with other transports, like Twilio or Daily.
+
+### Daily
+
+You need to create a Daily account at https://dashboard.daily.co/u/signup. Once signed up, you can create your own room from the dashboard and set the environment variables `DAILY_ROOM_URL` and `DAILY_API_KEY`. Alternatively, you can let the example create a room for you (still needs `DAILY_API_KEY` environment variable). Then, start any example with `-t daily`:
+
+```bash
+uv run getting-started/06-voice-agent.py -t daily
+```
+
+### Twilio
+
+It is also possible to run the example through a Twilio phone number. You will need to setup a few things:
+
+1. Install and run [ngrok](https://ngrok.com/download).
+
+```bash
+ngrok http 7860
+```
+
+2. Configure your Twilio phone number. One way is to setup a TwiML app and set the request URL to the ngrok URL from step (1). Then, set your phone number to use the new TwiML app.
+
+Then, run the example with:
+
+```bash
+uv run getting-started/06-voice-agent.py -t twilio -x NGROK_HOST_NAME
+```
+
+## Directory Structure
+
+### [`getting-started/`](./getting-started/)
+
+Progressive introduction to Pipecat, from minimal TTS to a full voice agent with function calling.
+
+### [`voice/`](./voice/)
+
+Full STT + LLM + TTS voice agent pipelines showcasing different speech service providers (Deepgram, ElevenLabs, Cartesia, etc.)
+
+### [`function-calling/`](./function-calling/)
+
+Function calling with different LLM providers (OpenAI, Anthropic, Google, etc.)
+
+### [`transcription/`](./transcription/)
+
+Speech-to-text examples with various STT providers.
+
+### [`vision/`](./vision/)
+
+Image description and vision capabilities with different multimodal LLMs.
+
+### [`realtime/`](./realtime/)
+
+Realtime and multimodal live APIs (OpenAI Realtime, Gemini Live, AWS Nova Sonic, Ultravox, Grok).
+
+### [`persistent-context/`](./persistent-context/)
+
+Maintaining conversation context across sessions with different providers.
+
+### [`context-summarization/`](./context-summarization/)
+
+Summarizing conversation context to manage token limits.
+
+### [`update-settings/`](./update-settings/)
+
+Changing service settings at runtime, organized by service type:
+
+- **[`stt/`](./update-settings/stt/)** — Speech-to-text settings
+- **[`tts/`](./update-settings/tts/)** — Text-to-speech settings
+- **[`llm/`](./update-settings/llm/)** — LLM settings
+
+### [`turn-management/`](./turn-management/)
+
+Turn detection, interruption handling, and user input management.
+
+### [`thinking-and-mcp/`](./thinking-and-mcp/)
+
+LLM thinking/reasoning modes and MCP (Model Context Protocol) tool server integration.
+
+### [`transports/`](./transports/)
+
+Transport layer examples (WebRTC, Daily, LiveKit).
+
+### [`video-avatar/`](./video-avatar/)
+
+Video avatar integrations (Tavus, HeyGen, Simli, LemonSlice).
+
+### [`video-processing/`](./video-processing/)
+
+Video processing, mirroring, GStreamer, and custom video tracks.
+
+### [`audio/`](./audio/)
+
+Audio recording, background sounds, and sound effects.
+
+### [`observability/`](./observability/)
+
+Pipeline monitoring: observers, heartbeats, and Sentry metrics.
+
+### [`rag/`](./rag/)
+
+Retrieval-augmented generation, grounding, and long-term memory (Mem0, Gemini).
+
+### [`features/`](./features/)
+
+Miscellaneous features: wake phrases, live translation, service switching, voice switching, and more.
+
+## Advanced Usage
+
+### Customizing Network Settings
+
+```bash
+uv run python <example-name> --host 0.0.0.0 --port 8080
+```
+
+### Troubleshooting
+
+- **No audio/video**: Check browser permissions for microphone and camera
+- **Connection errors**: Verify API keys in `.env` file
+- **Port conflicts**: Use `--port` to change the port
+
+For more examples, visit the [pipecat-examples repository](https://github.com/pipecat-ai/pipecat-examples).

examples/audio/audio-bot-background-sound.py

@@ -34,7 +34,7 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 load_dotenv(override=True)
 
 OFFICE_SOUND_FILE = os.path.join(
-    os.path.dirname(__file__), "assets", "office-ambience-24000-mono.mp3"
+    os.path.dirname(__file__), "../assets", "office-ambience-24000-mono.mp3"
 )
 
 # We use lambdas to defer transport parameter creation until the transport
@@ -128,7 +128,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Re-enabling background sound and starting bot...")
         await task.queue_frame(MixerEnableFrame(True))
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/context-summarization/context-summarization-dedicated-llm.py

@@ -211,7 +211,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info("Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/context-summarization/context-summarization-google.py

@@ -36,7 +36,7 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -172,7 +172,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info("Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/context-summarization/context-summarization-manual-openai.py

@@ -146,7 +146,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info("Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

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

@@ -172,7 +172,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info("Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/features/features-before-and-after-events.py

@@ -120,7 +120,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         # Custom frames are pushed in order so they can be used for synchronization purposes.
         await task.queue_frames([CustomBeforeProcessFrame(), LLMRunFrame(), CustomAfterPushFrame()])
 

examples/features/features-concurrent-llm-evaluation.py

@@ -77,7 +77,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     groq_llm = GroqLLMService(
         api_key=os.getenv("GROQ_API_KEY"),
         settings=GroqLLMService.Settings(
-            model="meta-llama/llama-4-maverick-17b-128e-instruct",
             system_instruction="You are a very helpful assistant. Your goal is to demonstrate your capabilities in detail in a creative and helpful way.",
         ),
     )
@@ -145,10 +144,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         openai_context.add_message(
-            {"role": "user", "content": "Please introduce yourself to the user."}
+            {"role": "developer", "content": "Please introduce yourself to the user."}
         )
         groq_context.add_message(
-            {"role": "user", "content": "Please introduce yourself to the user."}
+            {"role": "developer", "content": "Please introduce yourself to the user."}
         )
         await task.queue_frames([LLMRunFrame()])
 

examples/features/features-concurrent-llm-rtvi-ignored-sources.py

@@ -155,10 +155,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info("Client connected")
         main_context.add_message(
-            {"role": "user", "content": "Please introduce yourself to the user."}
+            {"role": "developer", "content": "Please introduce yourself to the user."}
         )
         evaluator_context.add_message(
-            {"role": "user", "content": "Ready to evaluate user messages."}
+            {"role": "developer", "content": "Ready to evaluate user messages."}
         )
         await task.queue_frames([LLMRunFrame()])
 

examples/features/features-custom-frame-processor.py

@@ -141,7 +141,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected: {client}")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/features/features-gpu-container-local-bot.py

@@ -109,7 +109,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     # Handle "latency-ping" messages. The client will send app messages that look like

examples/features/features-pattern-pair-voice-switching.py

@@ -45,7 +45,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.frames.frames import LLMRunFrame, TTSUpdateSettingsFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -54,6 +54,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
+from pipecat.processors.aggregators.llm_text_processor import LLMTextProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -100,39 +101,43 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     # Create pattern pair aggregator for voice switching
-    pattern_aggregator = PatternPairAggregator()
+    llm_text_aggregator = PatternPairAggregator()
 
     # Add pattern for voice switching
-    pattern_aggregator.add_pattern(
+    llm_text_aggregator.add_pattern(
         type="voice",
         start_pattern="<voice>",
         end_pattern="</voice>",
-        action=MatchAction.REMOVE,  # Remove tags from final text
+        action=MatchAction.AGGREGATE,
     )
 
     # Register handler for voice switching
     async def on_voice_tag(match: PatternMatch):
         voice_name = match.text.strip().lower()
         if voice_name in VOICE_IDS:
-            # First flush any existing audio to finish the current context
-            await tts.flush_audio()
-            # Then set the new voice
-            await tts.set_voice(VOICE_IDS[voice_name])
+            await llm_text_processor.push_frame(
+                TTSUpdateSettingsFrame(
+                    delta=CartesiaTTSService.Settings(voice=VOICE_IDS[voice_name])
+                )
+            )
             logger.info(f"Switched to {voice_name} voice")
         else:
             logger.warning(f"Unknown voice: {voice_name}")
 
-    pattern_aggregator.on_pattern_match("voice", on_voice_tag)
+    llm_text_aggregator.on_pattern_match("voice", on_voice_tag)
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
+    # Process LLM text through the pattern aggregator before TTS
+    llm_text_processor = LLMTextProcessor(text_aggregator=llm_text_aggregator)
+
     # Initialize TTS with narrator voice as default
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         settings=CartesiaTTSService.Settings(
             voice=VOICE_IDS["narrator"],
         ),
-        text_aggregator=pattern_aggregator,
+        skip_aggregator_types=["voice"],  # Skip voice tags in TTS speech
     )
 
     # System prompt for storytelling with voice switching
@@ -204,7 +209,8 @@ Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue.""
             stt,
             user_aggregator,
             llm,
-            tts,  # TTS with pattern aggregator
+            llm_text_processor,
+            tts,
             transport.output(),
             assistant_aggregator,
         ]

examples/features/features-service-switcher.py

@@ -164,7 +164,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
         await asyncio.sleep(15)
         print(f"Switching to {stt_deepgram}")

examples/features/features-switch-languages.py

@@ -162,7 +162,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Kick off the conversation.
         context.add_message(
             {
-                "role": "user",
+                "role": "developer",
                 "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {tts.current_language}.",
             }
         )

examples/features/features-switch-voices.py

@@ -172,7 +172,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Kick off the conversation.
         context.add_message(
             {
-                "role": "user",
+                "role": "developer",
                 "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {tts.current_voice}.",
             }
         )

examples/features/features-wake-phrase.py

@@ -120,7 +120,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/foundational/01-say-one-thing-piper.py

@@ -1,71 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.piper.tts import PiperHttpTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        tts = PiperHttpTTSService(
-            base_url=os.getenv("PIPER_BASE_URL"),
-            aiohttp_session=session,
-            sample_rate=24000,
-        )
-
-        task = PipelineTask(
-            Pipeline([tts, transport.output()]),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        # Register an event handler so we can play the audio when the client joins
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/01-say-one-thing-rime.py

@@ -1,72 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.rime.tts import RimeHttpTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        tts = RimeHttpTTSService(
-            api_key=os.getenv("RIME_API_KEY", ""),
-            aiohttp_session=session,
-            settings=RimeHttpTTSService.Settings(
-                voice="rex",
-            ),
-        )
-
-        task = PipelineTask(
-            Pipeline([tts, transport.output()]),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        # Register an event handler so we can play the audio when the client joins
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/01b-livekit-audio.py

@@ -1,64 +0,0 @@
-#
-# Copyright (c) 2024-2026, 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.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.runner.livekit import configure
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    (url, token, room_name) = await configure()
-
-    transport = LiveKitTransport(
-        url=url,
-        token=token,
-        room_name=room_name,
-        params=LiveKitParams(audio_out_enabled=True),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    runner = PipelineRunner()
-
-    task = PipelineTask(Pipeline([tts, transport.output()]))
-
-    # Register an event handler so we can play the audio when the
-    # participant joins.
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant_id):
-        await asyncio.sleep(1)
-        await task.queue_frame(
-            TTSSpeakFrame(
-                "Hello there! How are you doing today? Would you like to talk about the weather?"
-            )
-        )
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/01c-nvidia-riva-tts.py

@@ -1,64 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.nvidia.tts import NvidiaTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_out_enabled=True),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
-
-    task = PipelineTask(
-        Pipeline([tts, transport.output()]),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    # Register an event handler so we can play the audio when the client joins
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/03-still-frame.py

@@ -1,84 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import TextFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.fal.image import FalImageGenService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-    "webrtc": lambda: TransportParams(
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        imagegen = FalImageGenService(
-            settings=FalImageGenService.Settings(
-                image_size="square_hd",
-            ),
-            aiohttp_session=session,
-            key=os.getenv("FAL_KEY"),
-        )
-
-        task = PipelineTask(
-            Pipeline([imagegen, transport.output()]),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        # Register an event handler so we can play the audio when the client joins
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            await task.queue_frame(TextFrame("a cat in the style of picasso"))
-
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,202 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-import tkinter as tk
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import (
-    Frame,
-    LLMContextFrame,
-    OutputAudioRawFrame,
-    TextFrame,
-    TTSAudioRawFrame,
-    URLImageRawFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
-from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.sentence import SentenceAggregator
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaHttpTTSService
-from pipecat.services.fal.image import FalImageGenService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        tk_root = tk.Tk()
-        tk_root.title("Calendar")
-
-        runner = PipelineRunner()
-
-        async def get_month_data(month):
-            messages = [
-                {
-                    "role": "user",
-                    "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
-                }
-            ]
-
-            class ImageDescription(FrameProcessor):
-                def __init__(self):
-                    super().__init__()
-                    self.text = ""
-
-                async def process_frame(self, frame: Frame, direction: FrameDirection):
-                    await super().process_frame(frame, direction)
-
-                    if isinstance(frame, TextFrame):
-                        self.text = frame.text
-                    await self.push_frame(frame, direction)
-
-            class AudioGrabber(FrameProcessor):
-                def __init__(self):
-                    super().__init__()
-                    self.audio = bytearray()
-                    self.frame = None
-
-                async def process_frame(self, frame: Frame, direction: FrameDirection):
-                    await super().process_frame(frame, direction)
-
-                    if isinstance(frame, TTSAudioRawFrame):
-                        self.audio.extend(frame.audio)
-                        self.frame = OutputAudioRawFrame(
-                            bytes(self.audio), frame.sample_rate, frame.num_channels
-                        )
-                    await self.push_frame(frame, direction)
-
-            class ImageGrabber(FrameProcessor):
-                def __init__(self):
-                    super().__init__()
-                    self.frame = None
-
-                async def process_frame(self, frame: Frame, direction: FrameDirection):
-                    await super().process_frame(frame, direction)
-
-                    if isinstance(frame, URLImageRawFrame):
-                        self.frame = frame
-                    await self.push_frame(frame, direction)
-
-            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-            tts = CartesiaHttpTTSService(
-                api_key=os.getenv("CARTESIA_API_KEY"),
-                settings=CartesiaHttpTTSService.Settings(
-                    voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-                ),
-            )
-
-            imagegen = FalImageGenService(
-                settings=FalImageGenService.Settings(
-                    image_size="square_hd",
-                ),
-                aiohttp_session=session,
-                key=os.getenv("FAL_KEY"),
-            )
-
-            sentence_aggregator = SentenceAggregator()
-
-            description = ImageDescription()
-
-            audio_grabber = AudioGrabber()
-
-            image_grabber = ImageGrabber()
-
-            # With `SyncParallelPipeline` we synchronize audio and images by
-            # pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
-            # I3 A3). To do that, each pipeline runs concurrently and
-            # `SyncParallelPipeline` will wait for the input frame to be
-            # processed.
-            #
-            # Note that `SyncParallelPipeline` requires the last processor in
-            # each of the pipelines to be synchronous. In this case, we use
-            # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
-            # requests and wait for the response.
-            pipeline = Pipeline(
-                [
-                    llm,  # LLM
-                    sentence_aggregator,  # Aggregates LLM output into full sentences
-                    description,  # Store sentence
-                    SyncParallelPipeline(
-                        [tts, audio_grabber],  # Generate and store audio for the given sentence
-                        [imagegen, image_grabber],  # Generate and storeimage for the given sentence
-                    ),
-                ]
-            )
-
-            task = PipelineTask(pipeline)
-            await task.queue_frame(LLMContextFrame(LLMContext(messages)))
-            await task.stop_when_done()
-
-            await runner.run(task)
-
-            return {
-                "month": month,
-                "text": description.text,
-                "image": image_grabber.frame,
-                "audio": audio_grabber.frame,
-            }
-
-        transport = TkLocalTransport(
-            tk_root,
-            TkTransportParams(
-                audio_out_enabled=True,
-                video_out_enabled=True,
-                video_out_width=1024,
-                video_out_height=1024,
-            ),
-        )
-
-        pipeline = Pipeline([transport.output()])
-
-        task = PipelineTask(pipeline)
-
-        # We only specify a few months as we create tasks all at once and we
-        # might get rate limited otherwise.
-        months: list[str] = [
-            "January",
-            "February",
-        ]
-
-        # We create one task per month. This will be executed concurrently.
-        month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
-
-        # Now we wait for each month task in the order they're completed. The
-        # benefit is we'll have as little delay as possible before the first
-        # month, and likely no delay between months, but the months won't
-        # display in order.
-        async def show_images(month_tasks):
-            for month_data_task in asyncio.as_completed(month_tasks):
-                data = await month_data_task
-                await task.queue_frames([data["image"], data["audio"]])
-
-            await runner.stop_when_done()
-
-        async def run_tk():
-            while not task.has_finished():
-                tk_root.update()
-                tk_root.update_idletasks()
-                await asyncio.sleep(0.1)
-
-        await asyncio.gather(runner.run(task), show_images(month_tasks), run_tk())
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -1,153 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, LLMRunFrame, MetricsFrame
-from pipecat.metrics.metrics import (
-    LLMUsageMetricsData,
-    ProcessingMetricsData,
-    TTFBMetricsData,
-    TTSUsageMetricsData,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-class MetricsLogger(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, MetricsFrame):
-            for d in frame.data:
-                if isinstance(d, TTFBMetricsData):
-                    print(f"!!! MetricsFrame: {frame}, ttfb: {d.value}")
-                elif isinstance(d, ProcessingMetricsData):
-                    print(f"!!! MetricsFrame: {frame}, processing: {d.value}")
-                elif isinstance(d, LLMUsageMetricsData):
-                    tokens = d.value
-                    print(
-                        f"!!! MetricsFrame: {frame}, tokens: {tokens.prompt_tokens}, characters: {tokens.completion_tokens}"
-                    )
-                elif isinstance(d, TTSUsageMetricsData):
-                    print(f"!!! MetricsFrame: {frame}, characters: {d.value}")
-        await self.push_frame(frame, direction)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-
-    ml = MetricsLogger()
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            ml,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,128 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.filters.krisp_filter import KrispFilter
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        audio_in_filter=KrispFilter(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        audio_in_filter=KrispFilter(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        audio_in_filter=KrispFilter(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = DeepgramTTSService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-helios-en",
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,151 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.grok.llm import GrokLLMService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = GrokLLMService(
-        api_key=os.getenv("GROK_API_KEY"),
-        settings=GrokLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-    # 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])
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/14o-function-calling-gemini-openai-format.py

@@ -1,162 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.google.openai.llm import GoogleLLMOpenAIBetaService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-        settings=ElevenLabsTTSService.Settings(
-            voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        ),
-    )
-
-    llm = GoogleLLMOpenAIBetaService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMOpenAIBetaService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-    # You can aslo register a function_name of None to get all functions
-    # sent to the same callback with an additional function_name parameter.
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
-
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function])
-    messages = [
-        {
-            "role": "user",
-            "content": "Start a conversation with 'Hey there' to get the current weather.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages, tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/19-openai-realtime-beta.py

@@ -1,219 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-from datetime import datetime
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
-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.transcript_processor import TranscriptProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai_realtime_beta import (
-    InputAudioNoiseReduction,
-    InputAudioTranscription,
-    OpenAIRealtimeBetaLLMService,
-    SemanticTurnDetection,
-    SessionProperties,
-)
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-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 users location.",
-        },
-    },
-    required=["location", "format"],
-)
-
-restaurant_function = FunctionSchema(
-    name="get_restaurant_recommendation",
-    description="Get a restaurant recommendation",
-    properties={
-        "location": {
-            "type": "string",
-            "description": "The city and state, e.g. San Francisco, CA",
-        },
-    },
-    required=["location"],
-)
-
-# Create tools schema
-tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    session_properties = SessionProperties(
-        input_audio_transcription=InputAudioTranscription(),
-        # Set openai TurnDetection parameters. Not setting this at all will turn it
-        # on by default
-        turn_detection=SemanticTurnDetection(),
-        # Or set to False to disable openai turn detection and use transport VAD
-        # turn_detection=False,
-        input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
-        # tools=tools,
-        instructions="""You are a helpful and friendly AI.
-
-Act like a human, but remember that you aren't a human and that you can't do human
-things in the real world. Your voice and personality should be warm and engaging, with a lively and
-playful tone.
-
-If interacting in a non-English language, start by using the standard accent or dialect familiar to
-the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
-even if you're asked about them.
-
-You are participating in a voice conversation. Keep your responses concise, short, and to the point
-unless specifically asked to elaborate on a topic.
-
-You have access to the following tools:
-- get_current_weather: Get the current weather for a given location.
-- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
-
-Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
-    )
-
-    llm = OpenAIRealtimeBetaLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        session_properties=session_properties,
-    )
-
-    # you can either register a single function for all function calls, or specific functions
-    # llm.register_function(None, fetch_weather_from_api)
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    transcript = TranscriptProcessor()
-
-    # Create a standard OpenAI LLM context object using the normal messages format. The
-    # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
-    # openai WebSocket API can understand.
-    context = OpenAILLMContext(
-        [{"role": "user", "content": "Say hello!"}],
-        tools,
-    )
-
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            context_aggregator.user(),
-            llm,  # LLM
-            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
-            transport.output(),  # Transport bot output
-            transcript.assistant(),  # After the transcript output, to time with the audio output
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    # Register event handler for transcript updates
-    @transcript.event_handler("on_transcript_update")
-    async def on_transcript_update(processor, frame):
-        for msg in frame.messages:
-            if isinstance(msg, TranscriptionMessage):
-                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-                line = f"{timestamp}{msg.role}: {msg.content}"
-                logger.info(f"Transcript: {line}")
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/19a-azure-realtime-beta.py

@@ -1,214 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-from datetime import datetime
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai_realtime_beta import (
-    AzureRealtimeBetaLLMService,
-    InputAudioTranscription,
-    SessionProperties,
-)
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-# Define weather function using standardized schema
-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 users location.",
-        },
-    },
-    required=["location", "format"],
-)
-
-restaurant_function = FunctionSchema(
-    name="get_restaurant_recommendation",
-    description="Get a restaurant recommendation",
-    properties={
-        "location": {
-            "type": "string",
-            "description": "The city and state, e.g. San Francisco, CA",
-        },
-    },
-    required=["location"],
-)
-
-# Create tools schema
-tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    session_properties = SessionProperties(
-        input_audio_transcription=InputAudioTranscription(model="whisper-1"),
-        # Set openai TurnDetection parameters. Not setting this at all will turn it
-        # on by default
-        # turn_detection=TurnDetection(silence_duration_ms=1000),
-        # Or set to False to disable openai turn detection and use transport VAD
-        # turn_detection=False,
-        # tools=tools,
-        instructions="""You are a helpful and friendly AI.
-
-Act like a human, but remember that you aren't a human and that you can't do human
-things in the real world. Your voice and personality should be warm and engaging, with a lively and
-playful tone.
-
-If interacting in a non-English language, start by using the standard accent or dialect familiar to
-the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
-even if you're asked about them.
--
-You are participating in a voice conversation. Keep your responses concise, short, and to the point
-unless specifically asked to elaborate on a topic.
-
-You have access to the following tools:
-- get_current_weather: Get the current weather for a given location.
-- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
-
-Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
-    )
-
-    llm = AzureRealtimeBetaLLMService(
-        api_key=os.getenv("AZURE_REALTIME_API_KEY"),
-        base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
-        session_properties=session_properties,
-    )
-
-    # you can either register a single function for all function calls, or specific functions
-    # llm.register_function(None, fetch_weather_from_api)
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    # Create a standard OpenAI LLM context object using the normal messages format. The
-    # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
-    # openai WebSocket API can understand.
-    context = OpenAILLMContext(
-        [{"role": "user", "content": "Say hello!"}],
-        # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
-        #     [
-        #         {
-        #             "role": "user",
-        #             "content": [
-        #                 {"type": "text", "text": "Say"},
-        #                 {"type": "text", "text": "yo what's up!"},
-        #             ],
-        #         }
-        #     ],
-        tools,
-    )
-
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            context_aggregator.user(),
-            llm,  # LLM
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/19b-openai-realtime-beta-text.py

@@ -1,215 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-from datetime import datetime
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai_realtime_beta import (
-    InputAudioNoiseReduction,
-    InputAudioTranscription,
-    OpenAIRealtimeBetaLLMService,
-    SemanticTurnDetection,
-    SessionProperties,
-)
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-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 users location.",
-        },
-    },
-    required=["location", "format"],
-)
-
-restaurant_function = FunctionSchema(
-    name="get_restaurant_recommendation",
-    description="Get a restaurant recommendation",
-    properties={
-        "location": {
-            "type": "string",
-            "description": "The city and state, e.g. San Francisco, CA",
-        },
-    },
-    required=["location"],
-)
-
-# Create tools schema
-tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    session_properties = SessionProperties(
-        input_audio_transcription=InputAudioTranscription(),
-        modalities=["text"],
-        # Set openai TurnDetection parameters. Not setting this at all will turn it
-        # on by default
-        turn_detection=SemanticTurnDetection(),
-        # Or set to False to disable openai turn detection and use transport VAD
-        # turn_detection=False,
-        input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
-        # tools=tools,
-        instructions="""You are a helpful and friendly AI.
-
-Act like a human, but remember that you aren't a human and that you can't do human
-things in the real world. Your voice and personality should be warm and engaging, with a lively and
-playful tone.
-
-If interacting in a non-English language, start by using the standard accent or dialect familiar to
-the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
-even if you're asked about them.
-
-You are participating in a voice conversation. Keep your responses concise, short, and to the point
-unless specifically asked to elaborate on a topic.
-
-You have access to the following tools:
-- get_current_weather: Get the current weather for a given location.
-- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
-
-Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
-    )
-
-    llm = OpenAIRealtimeBetaLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        session_properties=session_properties,
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    # you can either register a single function for all function calls, or specific functions
-    # llm.register_function(None, fetch_weather_from_api)
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    # Create a standard OpenAI LLM context object using the normal messages format. The
-    # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
-    # openai WebSocket API can understand.
-    context = OpenAILLMContext(
-        [{"role": "user", "content": "Say hello!"}],
-        tools,
-    )
-
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            context_aggregator.user(),
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/20b-persistent-context-openai-realtime-beta.py

@@ -1,267 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import glob
-import json
-import os
-from datetime import datetime
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContext,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai_realtime_beta import (
-    InputAudioTranscription,
-    OpenAIRealtimeBetaLLMService,
-    SessionProperties,
-    TurnDetection,
-)
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-BASE_FILENAME = "/tmp/pipecat_conversation_"
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-async def get_saved_conversation_filenames(params: FunctionCallParams):
-    # Construct the full pattern including the BASE_FILENAME
-    full_pattern = f"{BASE_FILENAME}*.json"
-
-    # Use glob to find all matching files
-    matching_files = glob.glob(full_pattern)
-    logger.debug(f"matching files: {matching_files}")
-
-    await params.result_callback({"filenames": matching_files})
-
-
-async def save_conversation(params: FunctionCallParams):
-    timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
-    filename = f"{BASE_FILENAME}{timestamp}.json"
-    logger.debug(
-        f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
-    )
-    try:
-        with open(filename, "w") as file:
-            messages = params.context.get_messages_for_persistent_storage()
-            # remove the last message, which is the instruction we just gave to save the conversation
-            messages.pop()
-            json.dump(messages, file, indent=2)
-        await params.result_callback({"success": True})
-    except Exception as e:
-        await params.result_callback({"success": False, "error": str(e)})
-
-
-async def load_conversation(params: FunctionCallParams):
-    async def _reset():
-        filename = params.arguments["filename"]
-        logger.debug(f"loading conversation from {filename}")
-        try:
-            with open(filename, "r") as file:
-                params.context.set_messages(json.load(file))
-                await params.llm.reset_conversation()
-                await params.llm._create_response()
-        except Exception as e:
-            await params.result_callback({"success": False, "error": str(e)})
-
-    asyncio.create_task(_reset())
-
-
-tools = [
-    {
-        "type": "function",
-        "name": "get_current_weather",
-        "description": "Get the current weather",
-        "parameters": {
-            "type": "object",
-            "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 users location.",
-                },
-            },
-            "required": ["location", "format"],
-        },
-    },
-    {
-        "type": "function",
-        "name": "save_conversation",
-        "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
-        "parameters": {
-            "type": "object",
-            "properties": {},
-            "required": [],
-        },
-    },
-    {
-        "type": "function",
-        "name": "get_saved_conversation_filenames",
-        "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
-        "parameters": {
-            "type": "object",
-            "properties": {},
-            "required": [],
-        },
-    },
-    {
-        "type": "function",
-        "name": "load_conversation",
-        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "filename": {
-                    "type": "string",
-                    "description": "The filename of the conversation history to load.",
-                }
-            },
-            "required": ["filename"],
-        },
-    },
-]
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    session_properties = SessionProperties(
-        input_audio_transcription=InputAudioTranscription(),
-        # Set openai TurnDetection parameters. Not setting this at all will turn
-        # it on by default
-        turn_detection=TurnDetection(silence_duration_ms=1000),
-        # Or set to False to disable openai turn detection and use transport VAD
-        # turn_detection=False,
-        # tools=tools,
-        instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
-
-Act like a human, but remember that you aren't a human and that you can't do human
-things in the real world. Your voice and personality should be warm and engaging, with a lively and
-playful tone.
-
-If interacting in a non-English language, start by using the standard accent or dialect familiar to
-the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
-even if you're asked about them.
--
-You are participating in a voice conversation. Keep your responses concise, short, and to the point
-unless specifically asked to elaborate on a topic.
-
-Remember, your responses should be short. Just one or two sentences, usually.""",
-    )
-
-    llm = OpenAIRealtimeBetaLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        session_properties=session_properties,
-    )
-
-    # you can either register a single function for all function calls, or specific functions
-    # llm.register_function(None, fetch_weather_from_api)
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("save_conversation", save_conversation)
-    llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
-    llm.register_function("load_conversation", load_conversation)
-
-    context = OpenAILLMContext([], tools)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),
-            llm,  # LLM
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/26-gemini-live.py

@@ -1,133 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMMessagesAppendFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.audio.vad_processor import VADProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-# Load environment variables
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        # set stop_secs to something roughly similar to the internal setting
-        # of the Multimodal Live api, just to align events.
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        # set stop_secs to something roughly similar to the internal setting
-        # of the Multimodal Live api, just to align events.
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        # set stop_secs to something roughly similar to the internal setting
-        # of the Multimodal Live api, just to align events.
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Create the Gemini Multimodal Live LLM service
-    system_instruction = f"""
-    You are a helpful AI assistant.
-    Your goal is to demonstrate your capabilities in a helpful and engaging way.
-    Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
-    Respond to what the user said in a creative and helpful way.
-    """
-
-    llm = GeminiLiveLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GeminiLiveLLMService.Settings(
-            system_instruction=system_instruction,
-            voice="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
-        ),
-    )
-
-    vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)))
-
-    # Build the pipeline
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            vad_processor,
-            llm,
-            transport.output(),
-        ]
-    )
-
-    # Configure the pipeline task
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    # Handle client connection event
-    @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(
-            [
-                LLMMessagesAppendFrame(
-                    messages=[
-                        {
-                            "role": "user",
-                            "content": f"Greet the user and introduce yourself.",
-                        }
-                    ]
-                )
-            ]
-        )
-
-    # Handle client disconnection events
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    # Run the pipeline
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,155 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService, GeminiModalities
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-SYSTEM_INSTRUCTION = f"""
-"You are Gemini Chatbot, a friendly, helpful robot.
-
-Your goal is to demonstrate your capabilities in a succinct way.
-
-Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
-
-Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most.
-"""
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # KNOWN ISSUE: If using GeminiLiveVertexLLMService, you cannot specify a
-    # modality other than AUDIO (at least not if using the service's default
-    # model, which is a native audio model:
-    # https://cloud.google.com/vertex-ai/generative-ai/docs/live-api/tools#native-audio).
-    llm = GeminiLiveLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GeminiLiveLLMService.Settings(
-            system_instruction=SYSTEM_INSTRUCTION,
-            modalities=GeminiModalities.TEXT,
-        ),
-        tools=[{"google_search": {}}, {"code_execution": {}}],
-    )
-
-    # Optionally, you can set the response modalities via a function
-    # llm.set_model_modalities(
-    #     GeminiMultimodalModalities.TEXT
-    # )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
-    )
-
-    messages = [
-        {
-            "role": "user",
-            "content": 'Start by saying "Hello, I\'m Gemini".',
-        },
-    ]
-
-    # Set up conversation context and management
-    # The context_aggregator will automatically collect conversation context
-    context = LLMContext(messages)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            # Set stop_secs to something roughly similar to the internal setting
-            # of the Multimodal Live api, just to align events. This doesn't
-            # really matter because we can only use the Multimodal Live API's
-            # phrase endpointing, for now.
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5))
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,141 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.fal_smart_turn import FalSmartTurnAnalyzer
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
-from pipecat.turns.user_turn_strategies import UserTurnStrategies
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            user_turn_strategies=UserTurnStrategies(
-                stop=[
-                    TurnAnalyzerUserTurnStopStrategy(
-                        turn_analyzer=FalSmartTurnAnalyzer(
-                            api_key=os.getenv("FAL_SMART_TURN_API_KEY"),
-                            aiohttp_session=aiohttp.ClientSession(),
-                        )
-                    )
-                ]
-            ),
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/39-mcp-stdio.py

@@ -1,250 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import io
-import json
-import os
-import re
-import shutil
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from mcp import StdioServerParameters
-from PIL import Image
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    Frame,
-    FunctionCallResultFrame,
-    LLMRunFrame,
-    URLImageRawFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.mcp_service import MCPClient
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-class UrlToImageProcessor(FrameProcessor):
-    def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
-        super().__init__(**kwargs)
-        self._aiohttp_session = aiohttp_session
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, FunctionCallResultFrame):
-            await self.push_frame(frame, direction)
-            image_url = self.extract_url(frame.result)
-            if image_url:
-                await self.run_image_process(image_url)
-                # sometimes we get multiple image urls- process 1 at a time
-                await asyncio.sleep(1)
-        else:
-            await self.push_frame(frame, direction)
-
-    def extract_url(self, text: str):
-        try:
-            data = json.loads(text)
-            if "artObject" in data:
-                return data["artObject"]["webImage"]["url"]
-            if "artworks" in data and len(data["artworks"]):
-                return data["artworks"][0]["webImage"]["url"]
-        except (json.JSONDecodeError, KeyError, TypeError):
-            pass
-
-        return None
-
-    async def run_image_process(self, image_url: str):
-        try:
-            logger.debug(f"handling image from url: '{image_url}'")
-            async with self._aiohttp_session.get(image_url) as response:
-                image_stream = io.BytesIO(await response.content.read())
-                image = Image.open(image_stream)
-                image = image.convert("RGB")
-                frame = URLImageRawFrame(
-                    url=image_url, image=image.tobytes(), size=image.size, format="RGB"
-                )
-                await self.push_frame(frame)
-        except Exception as e:
-            error_msg = f"Error handling image url {image_url}: {str(e)}"
-            logger.error(error_msg)
-
-
-# full list of tools available from rijksmuseum MCP:
-# - get_artwork_details
-# - get_artwork_image
-# - get_user_sets
-# - get_user_set_details
-# - open_image_in_browser
-# - get_artist_timeline
-
-mcp_tools_filter = ["get_artwork_details", "get_artwork_image", "open_image_in_browser"]
-
-
-def open_image_output_filter(output: str):
-    pattern = r"Successfully opened image in browser: "
-    text_to_print = re.sub(pattern, "", output)
-    print(f"🖼️ link to high resolution artwork: {text_to_print}")
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Create an HTTP session for API calls
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-        )
-
-        system_prompt = f"""
-        You are a helpful LLM in a voice call.
-        Your goal is to demonstrate your capabilities in a succinct way.
-        You have access to tools to search the Rijksmuseum collection.
-        Offer, for example, to show a floral still life, use the `search_artwork` tool.
-        The tool may respond with a JSON object with an `artworks` array. Choose the art from that array.
-        Once the tool has responded, tell the user the title and use the `open_image_in_browser` tool.
-        Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
-        Respond to what the user said in a creative and helpful way.
-        Don't overexplain what you are doing.
-        Just respond with short sentences when you are carrying out tool calls.
-        """
-
-        llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            settings=AnthropicLLMService.Settings(
-                system_instruction=system_prompt,
-            ),
-        )
-
-        try:
-            mcp = MCPClient(
-                server_params=StdioServerParameters(
-                    command=shutil.which("npx"),
-                    # https://github.com/r-huijts/rijksmuseum-mcp
-                    args=["-y", "mcp-server-rijksmuseum"],
-                    env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
-                ),
-                # Optional
-                tools_filter=mcp_tools_filter,  # Optional
-                tools_output_filters={"open_image_in_browser": open_image_output_filter},
-            )
-        except Exception as e:
-            logger.error(f"error setting up mcp")
-            logger.exception("error trace:")
-
-        mcp_image = UrlToImageProcessor(aiohttp_session=session)
-
-        tools = {}
-        try:
-            tools = await mcp.register_tools(llm)
-        except Exception as e:
-            logger.error(f"error registering tools")
-            logger.exception("error trace:")
-
-        context = LLMContext(tools=tools)
-        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-        )
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                user_aggregator,  # User spoken responses
-                llm,  # LLM
-                tts,  # TTS
-                mcp_image,  # URL image -> output
-                transport.output(),  # Transport bot output
-                assistant_aggregator,  # Assistant spoken responses and tool context
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected: {client}")
-            # Kick off the conversation.
-            await task.queue_frames([LLMRunFrame()])
-
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    if not os.getenv("RIJKSMUSEUM_API_KEY"):
-        logger.error(
-            f"Please set RIJKSMUSEUM_API_KEY environment variable for this example. See https://github.com/r-huijts/rijksmuseum-mcp and https://www.rijksmuseum.nl/en/register?redirectUrl=https://www.https://www.rijksmuseum.nl/en/rijksstudio/my/profile"
-        )
-        import sys
-
-        sys.exit(1)
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/39a-mcp-streamable-http.py

@@ -1,162 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from mcp.client.session_group import StreamableHttpParameters
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.mcp_service import MCPClient
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    system_prompt = f"""
-    You are a helpful LLM in a voice call.
-    Your goal is to answer questions about the user's GitHub repositories and account.
-    You have access to a number of tools provided by Github. Use any and all tools to help users.
-    Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
-    Don't overexplain what you are doing.
-    Just respond with short sentences when you are carrying out tool calls.
-    """
-
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        system_instruction=system_prompt,
-    )
-
-    try:
-        # Github MCP docs: https://github.com/github/github-mcp-server
-        # Enable Github Copilot on your GitHub account. Free tier is ok. (https://github.com/settings/copilot)
-        # Generate a personal access token. It must be a Fine-grained token, classic tokens are not supported. (https://github.com/settings/personal-access-tokens)
-        # Set permissions you want to use (eg. "all repositories", "profile: read/write", etc)
-        mcp = MCPClient(
-            server_params=StreamableHttpParameters(
-                url="https://api.githubcopilot.com/mcp/",
-                headers={"Authorization": f"Bearer {os.getenv('GITHUB_PERSONAL_ACCESS_TOKEN')}"},
-            )
-        )
-    except Exception as e:
-        logger.error(f"error setting up mcp")
-        logger.exception("error trace:")
-
-    tools = {}
-    try:
-        tools = await mcp.register_tools(llm)
-    except Exception as e:
-        logger.error(f"error registering tools")
-        logger.exception("error trace:")
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User spoken responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses and tool context
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    if not os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN"):
-        logger.error(
-            f"Please set GITHUB_PERSONAL_ACCESS_TOKEN environment variable for this example."
-        )
-        import sys
-
-        sys.exit(1)
-
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/39b-mcp-streamable-http-gemini-live.py

@@ -1,163 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from mcp.client.session_group import StreamableHttpParameters
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
-from pipecat.services.mcp_service import MCPClient
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    try:
-        # Github MCP docs: https://github.com/github/github-mcp-server
-        # Enable Github Copilot on your GitHub account. Free tier is ok. (https://github.com/settings/copilot)
-        # Generate a personal access token. It must be a Fine-grained token, classic tokens are not supported. (https://github.com/settings/personal-access-tokens)
-        # Set permissions you want to use (eg. "all repositories", "profile: read/write", etc)
-        mcp = MCPClient(
-            server_params=StreamableHttpParameters(
-                url="https://api.githubcopilot.com/mcp/",
-                headers={"Authorization": f"Bearer {os.getenv('GITHUB_PERSONAL_ACCESS_TOKEN')}"},
-            )
-        )
-    except Exception as e:
-        logger.error(f"error setting up mcp")
-        logger.exception("error trace:")
-
-    tools = {}
-    try:
-        tools = await mcp.get_tools_schema()
-    except Exception as e:
-        logger.error(f"error registering tools")
-        logger.exception("error trace:")
-
-    system = f"""
-    You are a helpful LLM in a voice call.
-    Your goal is to answer questions about the user's GitHub repositories and account.
-    You have access to a number of tools provided by Github. Use any and all tools to help users.
-    Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
-    Don't overexplain what you are doing.
-    Just respond with short sentences when you are carrying out tool calls.
-    """
-
-    llm = GeminiLiveLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        system_instruction=system,
-        tools=tools,
-    )
-
-    await mcp.register_tools_schema(tools, llm)
-
-    context = LLMContext([{"role": "user", "content": "Please introduce yourself."}])
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            user_aggregator,  # User spoken responses
-            llm,  # LLM
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses and tool context
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-        # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    if not os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN"):
-        logger.error(
-            f"Please set GITHUB_PERSONAL_ACCESS_TOKEN environment variable for this example."
-        )
-        import sys
-
-        sys.exit(1)
-
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,252 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import asyncio
-import io
-import json
-import os
-import shutil
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from mcp import StdioServerParameters
-from mcp.client.session_group import StreamableHttpParameters
-from PIL import Image
-
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    Frame,
-    FunctionCallResultFrame,
-    LLMRunFrame,
-    URLImageRawFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.mcp_service import MCPClient
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-class UrlToImageProcessor(FrameProcessor):
-    def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
-        super().__init__(**kwargs)
-        self._aiohttp_session = aiohttp_session
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, FunctionCallResultFrame):
-            await self.push_frame(frame, direction)
-            image_url = self.extract_url(frame.result)
-            if image_url:
-                await self.run_image_process(image_url)
-                # sometimes we get multiple image urls- process 1 at a time
-                await asyncio.sleep(1)
-        else:
-            await self.push_frame(frame, direction)
-
-    def extract_url(self, text: str):
-        try:
-            data = json.loads(text)
-            if "artObject" in data:
-                return data["artObject"]["webImage"]["url"]
-            if "artworks" in data and len(data["artworks"]):
-                return data["artworks"][0]["webImage"]["url"]
-        except (json.JSONDecodeError, KeyError, TypeError):
-            pass
-
-    async def run_image_process(self, image_url: str):
-        try:
-            logger.debug(f"handling image from url: '{image_url}'")
-            async with self._aiohttp_session.get(image_url) as response:
-                image_stream = io.BytesIO(await response.content.read())
-                image = Image.open(image_stream)
-                image = image.convert("RGB")
-                frame = URLImageRawFrame(
-                    url=image_url, image=image.tobytes(), size=image.size, format="RGB"
-                )
-                await self.push_frame(frame)
-        except Exception as e:
-            error_msg = f"Error handling image url {image_url}: {str(e)}"
-            logger.error(error_msg)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Create an HTTP session for API calls
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-        )
-
-        system_prompt = f"""
-        You are a helpful LLM in a voice call.
-        Your goal is to demonstrate your capabilities in a succinct way.
-        You have access to tools to search the Rijksmuseum collection and the user's GitHub repositories and account.
-        Offer, for example, to show a floral still life, use the `search_artwork` tool.
-        The tool may respond with a JSON object with an `artworks` array. Choose the art from that array.
-        Once the tool has responded, tell the user the title and use the `open_image_in_browser` tool.
-        You can also offer to answer users questions about their GitHub repositories and account.
-        Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
-        Respond to what the user said in a creative and helpful way.
-        Don't overexplain what you are doing.
-        Just respond with short sentences when you are carrying out tool calls.
-        """
-
-        llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            settings=AnthropicLLMService.Settings(
-                system_instruction=system_prompt,
-            ),
-        )
-
-        try:
-            rijksmuseum_mcp = MCPClient(
-                server_params=StdioServerParameters(
-                    command=shutil.which("npx"),
-                    # https://github.com/r-huijts/rijksmuseum-mcp
-                    args=["-y", "mcp-server-rijksmuseum"],
-                    env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
-                )
-            )
-        except Exception as e:
-            logger.error(f"error setting up rijksmuseum mcp")
-            logger.exception("error trace:")
-        try:
-            # Github MCP docs: https://github.com/github/github-mcp-server
-            # Enable Github Copilot on your GitHub account. Free tier is ok. (https://github.com/settings/copilot)
-            # Generate a personal access token. It must be a Fine-grained token, classic tokens are not supported. (https://github.com/settings/personal-access-tokens)
-            # Set permissions you want to use (eg. "all repositories", "profile: read/write", etc)
-            github_mcp = MCPClient(
-                server_params=StreamableHttpParameters(
-                    url="https://api.githubcopilot.com/mcp/",
-                    headers={
-                        "Authorization": f"Bearer {os.getenv('GITHUB_PERSONAL_ACCESS_TOKEN')}"
-                    },
-                )
-            )
-        except Exception as e:
-            logger.error(f"error setting up mcp.run")
-            logger.exception("error trace:")
-
-        rijksmuseum_tools = {}
-        github_tools = {}
-        try:
-            rijksmuseum_tools = await rijksmuseum_mcp.register_tools(llm)
-            github_tools = await github_mcp.register_tools(llm)
-        except Exception as e:
-            logger.error(f"error registering tools")
-            logger.exception("error trace:")
-
-        all_standard_tools = rijksmuseum_tools.standard_tools + github_tools.standard_tools
-        all_tools = ToolsSchema(standard_tools=all_standard_tools)
-
-        context = LLMContext(tools=all_tools)
-        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-        )
-        mcp_image_processor = UrlToImageProcessor(aiohttp_session=session)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                user_aggregator,  # User spoken responses
-                llm,  # LLM
-                tts,  # TTS
-                mcp_image_processor,  # URL image -> output
-                transport.output(),  # Transport bot output
-                assistant_aggregator,  # Assistant spoken responses and tool context
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected: {client}")
-            # Kick off the conversation.
-            await task.queue_frames([LLMRunFrame()])
-
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    if not os.getenv("RIJKSMUSEUM_API_KEY") or not os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN"):
-        logger.error(
-            f"Please set `RIJKSMUSEUM_API_KEY` and `GITHUB_PERSONAL_ACCESS_TOKEN` environment variables. See https://github.com/r-huijts/rijksmuseum-mcp."
-        )
-        import sys
-
-        sys.exit(1)
-
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/README.md

@@ -1,144 +0,0 @@
-# Pipecat Foundational Examples
-
-This directory contains examples showing how to build voice and multimodal agents with Pipecat. Each example demonstrates specific features, progressing from basic to advanced concepts.
-
-## Setup
-
-1. Follow the [README](https://github.com/pipecat-ai/pipecat/blob/main/README.md#%EF%B8%8F-contributing-to-the-framework) steps to get your local environment configured.
-
-   > **Run from root directory**: Make sure you are running the steps from the root directory.
-
-   > **Using local audio?**: The `LocalAudioTransport` requires a system dependency for `portaudio`. Install the dependency to use the transport.
-
-2. Copy the [`env.example`](../../env.example) file and add API keys for services you plan to use:
-
-   ```bash
-   cp env.example .env
-   # Edit .env with your API keys
-   ```
-
-3. Navigate to the examples directory if you aren't already there:
-
-   ```bash
-   cd examples/foundational
-   ```
-
-4. Run any example:
-
-   ```bash
-   uv run python 01-say-one-thing.py
-   ```
-
-5. Open the web interface at http://localhost:7860/client/ and click "Connect"
-
-## Running examples with other transports
-
-Most examples support running with other transports, like Twilio or Daily.
-
-### Daily
-
-You need to create a Daily account at https://dashboard.daily.co/u/signup. Once signed up, you can create your own room from the dashboard and set the environment variables `DAILY_ROOM_URL` and `DAILY_API_KEY`. Alternatively, you can let the example create a room for you (still needs `DAILY_API_KEY` environment variable). Then, start any example with `-t daily`:
-
-```bash
-uv run 07-interruptible.py -t daily
-```
-
-### Twilio
-
-It is also possible to run the example through a Twilio phone number. You will need to setup a few things:
-
-1. Install and run [ngrok](https://ngrok.com/download).
-
-```bash
-ngrok http 7860
-```
-
-2. Configure your Twilio phone number. One way is to setup a TwiML app and set the request URL to the ngrok URL from step (1). Then, set your phone number to use the new TwiML app.
-
-Then, run the example with:
-
-```bash
-uv run 07-interruptible.py -t twilio -x NGROK_HOST_NAME
-```
-
-## Examples by Feature
-
-### Basics
-
-- **[01-say-one-thing.py](./01-say-one-thing.py)**: Most basic bot that says one phrase and exits (Transport, TTS, Event handlers)
-- **[02-llm-say-one-thing.py](./02-llm-say-one-thing.py)**: Bot generates a response with an LLM (LLM initialization)
-- **[03-still-frame.py](./03-still-frame.py)**: Displays a static image (Video transport, Image service)
-- **[04-transport.py](./04-transport.py)**: Different transport options (WebRTC, Daily, Livekit)
-
-### Conversational AI
-
-- **[07-interruptible.py](./07-interruptible.py)**: Basic voice assistant bot (STT, TTS, LLM, Interruptible speech)
-- **[10-wake-phrase.py](./10-wake-phrase.py)**: Bot activated by wake phrase (WakeCheckFilter)
-- **[22-natural-conversation.py](./22-natural-conversation.py)**: Smart turn detection (Multiple LLMs, Turn management)
-- **[38-smart-turn-fal.py](./38-smart-turn-fal.py)**: ML-based turn detection (Fal service, Local models)
-
-### Common Utilities
-
-- **[17-detect-user-idle.py](./17-detect-user-idle.py)**: Handle inactive users (UserIdleProcessor)
-- **[24-user-mute-strategy.py](./24-user-mute-strategy.py)**: Selectively mute user input (LLMUserAggregator user mute strategies)
-- **[28-transcription-processor.py](./28-transcription-processor.py)**: Record conversation text (TranscriptProcessor)
-- **[30-observer.py](./30-observer.py)**: Access frame data (Custom observers)
-- **[31-heartbeats.py](./31-heartbeats.py)**: Detect idle pipelines (Pipeline monitoring)
-- **[34-audio-recording.py](./34-audio-recording.py)**: Record conversation audio (Composite and track-level recording)
-
-### Advanced LLM Features
-
-- **[14-function-calling.py](./14-function-calling.py)**: Bot with tool usage (Function schemas, Tool registration)
-- **[20a-persistent-context-openai.py](./20a-persistent-context-openai.py)**: Persistent conversation context (Memory management)
-- **[32-gemini-grounding-metadata.py](./32-gemini-grounding-metadata.py)**: Web search capabilities (Google search integration)
-- **[33-gemini-rag.py](./33-gemini-rag.py)**: Retrieval-augmented generation (Data sources, Grounding)
-- **[37-mem0.py](./37-mem0.py)**: Long-term agent memory (Mem0 service integration)
-
-### Media Handling
-
-- **[05-sync-speech-and-images.py](./05-sync-speech-and-images.py)**: Synchronized narration with images (Custom processors, SyncParallelPipeline)
-- **[06a-image-sync.py](./06a-image-sync.py)**: Dynamic image updates while speaking (Synchronized A/V pipelines)
-- **[09-mirror.py](./09-mirror.py)**: Mirror user's audio and video (Custom frame processors)
-- **[11-sound-effects.py](./11-sound-effects.py)**: Add sounds when bot speaks (Sound playback, Event synchronization)
-- **[23-bot-background-sound.py](./23-bot-background-sound.py)**: Play background audio (SoundfileMixer)
-
-### Vision & Multimodal
-
-- **[12a-describe-video-gemini-flash.py](./12a-describe-video-gemini-flash.py)**: Bot describes user's video (Video input, Multimodal LLMs)
-- **[26c-gemini-live-video.py](./26c-gemini-live-video.py)**: Gemini with video input (Streaming video, Function calls)
-
-### Voice & Language
-
-- **[13-transcription.py](./13-transcription.py)**: Speech transcription demo (STT providers, Real-time transcription)
-- **[15-switch-voices.py](./15-switch-voices.py)**: Dynamic voice/language changing (ParallelPipelines, FunctionFilters)
-- **[25-google-audio-in.py](./25-google-audio-in.py)**: Gemini for speech recognition (Alternative transcription)
-- **[35-pattern-pair-voice-switching.py](./35-pattern-pair-voice-switching.py)**: Dynamic TTS voice switching (XML parsing, PatternPairAggregator)
-- **[36-user-email-gathering.py](./36-user-email-gathering.py)**: Spelling mode for TTS (Confirmation patterns, XML tags)
-
-### Integration Examples
-
-- **[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-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)
-- **[56-lemonslice-transport.py](./56-lemonslice-transport.py)**: LemonSlice avatar integration (A/V Synced Avatar integration)
-
-### Performance & Optimization
-
-- **[16-gpu-container-local-bot.py](./16-gpu-container-local-bot.py)**: GPU-accelerated local bot (Performance measurement)
-
-## Advanced Usage
-
-### Customizing Network Settings
-
-```bash
-uv run python <example-name> --host 0.0.0.0 --port 8080
-```
-
-### Troubleshooting
-
-- **No audio/video**: Check browser permissions for microphone and camera
-- **Connection errors**: Verify API keys in `.env` file
-- **Port conflicts**: Use `--port` to change the port
-
-For more examples, visit our the [pipecat-examples repository](https://github.com/pipecat-ai/pipecat-examples).

examples/function-calling/function-calling-anthropic-async-stream.py

@@ -0,0 +1,210 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Example: async function call with intermediate updates.
+
+The ``track_current_location`` tool simulates a GPS tracker reporting the
+device's position during a road trip from San Francisco to San Diego.  It
+sends two intermediate updates (via ``params.result_callback`` with
+``is_final=False``) as the vehicle passes through cities along the way, then
+delivers the final destination (via ``params.result_callback``).  Each update
+returns the same structure with a different city:
+
+  Update 1 – {gps, city: "San Francisco"}   ← trip start
+  Update 2 – {gps, city: "Los Angeles"}     ← passing through
+  Final     – {gps, city: "San Diego"}      ← destination reached
+
+Because the function is registered with ``cancel_on_interruption=False``, the
+LLM can keep talking while the trip is in progress; each position update
+arrives as a developer message so the LLM can narrate the journey to the user.
+"""
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    FunctionCallResultProperties,
+    LLMRunFrame,
+    TTSSpeakFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def track_current_location(params: FunctionCallParams):
+    """Simulate a GPS tracker reporting position during a road trip.
+
+    Step 1 – San Francisco (trip start)     (update)
+    Step 2 – Los Angeles   (passing through) (update)
+    Step 3 – San Diego     (destination)     (final result)
+    """
+
+    # First update: initial city estimate.
+    gps = {"lat": 37.7310, "lng": -122.4527}
+    await params.result_callback(
+        {"gps": gps, "city": "San Francisco"},
+        properties=FunctionCallResultProperties(is_final=False),
+    )
+
+    # Second update: revised city estimate.
+    await asyncio.sleep(10)
+    gps = {"lat": 33.96003, "lng": -118.40639}
+    await params.result_callback(
+        {"gps": gps, "city": "Los Angeles"},
+        properties=FunctionCallResultProperties(is_final=False),
+    )
+
+    # Final result: confirmed city.
+    await asyncio.sleep(10)
+    gps = {"lat": 32.743569, "lng": -117.20466}
+    await params.result_callback({"gps": gps, "city": "San Diego"})
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        settings=CartesiaTTSService.Settings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
+    )
+
+    llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        enable_async_tool_cancellation=True,
+        settings=AnthropicLLMService.Settings(
+            system_instruction=(
+                "You are a helpful assistant in a voice conversation. "
+                "Your responses will be spoken aloud, so avoid emojis, bullet points, or other "
+                "formatting that can't be spoken. "
+                "You have access to a function that starts tracking the user's location and "
+                "provides regular updates on it. When you receive the final location, tell the user "
+                "the destination has been reached."
+            ),
+        ),
+    )
+
+    # cancel_on_interruption=False makes this an async function call: the LLM
+    # continues the conversation immediately and receives updates/result later.
+    llm.register_function(
+        "track_current_location",
+        track_current_location,
+        cancel_on_interruption=False,
+        timeout_secs=30,
+    )
+
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
+    location_function = FunctionSchema(
+        name="track_current_location",
+        description="Start tracking the user's current GPS location, reporting position updates until the user reaches their destination.",
+        properties={},
+        required=[],
+    )
+    tools = ToolsSchema(standard_tools=[location_function])
+
+    context = LLMContext(tools=tools)
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_aggregator,
+            llm,
+            tts,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/function-calling/function-calling-anthropic-async.py

@@ -0,0 +1,180 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    # Simulate a long-running API call, so we can test async function calls (cancel_on_interruption=False).
+    await asyncio.sleep(20)
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        settings=CartesiaTTSService.Settings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
+    )
+
+    llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        enable_async_tool_cancellation=True,
+        settings=AnthropicLLMService.Settings(
+            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
+        ),
+    )
+
+    # 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,
+        cancel_on_interruption=False,
+        timeout_secs=30,
+    )
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
+    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",
+            },
+        },
+        required=["location"],
+    )
+    restaurant_function = FunctionSchema(
+        name="get_restaurant_recommendation",
+        description="Get a restaurant recommendation",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+        },
+        required=["location"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
+
+    context = LLMContext(tools=tools)
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            user_aggregator,  # User spoken responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            assistant_aggregator,  # Assistant spoken responses and tool context
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/function-calling/function-calling-anthropic-video.py

@@ -164,7 +164,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Kick off the conversation.
         context.add_message(
             {
-                "role": "user",
+                "role": "developer",
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )

examples/function-calling/function-calling-anthropic.py

@@ -138,7 +138,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/function-calling/function-calling-aws-video.py

@@ -169,7 +169,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Kick off the conversation.
         context.add_message(
             {
-                "role": "user",
+                "role": "developer",
                 "content": f"Please introduce yourself to the user briefly; don't mention the camera. Use '{client_id}' as the user ID during function calls.",
             }
         )

examples/function-calling/function-calling-aws.py

@@ -147,7 +147,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/function-calling/function-calling-fireworks.py

@@ -136,7 +136,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/function-calling/function-calling-google-async-stream.py

@@ -0,0 +1,214 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Example: async function call with intermediate updates.
+
+The ``track_current_location`` tool simulates a GPS tracker reporting the
+device's position during a road trip from San Francisco to San Diego.  It
+sends two intermediate updates (via ``params.result_callback`` with
+``is_final=False``) as the vehicle passes through cities along the way, then
+delivers the final destination (via ``params.result_callback``).  Each update
+returns the same structure with a different city:
+
+  Update 1 – {gps, city: "San Francisco"}   ← trip start
+  Update 2 – {gps, city: "Los Angeles"}     ← passing through
+  Final     – {gps, city: "San Diego"}      ← destination reached
+
+Because the function is registered with ``cancel_on_interruption=False``, the
+LLM can keep talking while the trip is in progress; each position update
+arrives as a developer message so the LLM can narrate the journey to the user.
+"""
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    FunctionCallResultProperties,
+    LLMRunFrame,
+    TTSSpeakFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def track_current_location(params: FunctionCallParams):
+    """Simulate a GPS tracker reporting position during a road trip.
+
+    Step 1 – San Francisco (trip start)     (update)
+    Step 2 – Los Angeles   (passing through) (update)
+    Step 3 – San Diego     (destination)     (final result)
+    """
+
+    # First update: initial city estimate.
+    gps = {"lat": 37.7310, "lng": -122.4527}
+    await params.result_callback(
+        {"gps": gps, "city": "San Francisco"},
+        properties=FunctionCallResultProperties(is_final=False),
+    )
+
+    # Second update: revised city estimate.
+    await asyncio.sleep(10)
+    gps = {"lat": 33.96003, "lng": -118.40639}
+    await params.result_callback(
+        {"gps": gps, "city": "Los Angeles"},
+        properties=FunctionCallResultProperties(is_final=False),
+    )
+
+    # Final result: confirmed city.
+    await asyncio.sleep(10)
+    gps = {"lat": 32.743569, "lng": -117.20466}
+    await params.result_callback({"gps": gps, "city": "San Diego"})
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        settings=CartesiaTTSService.Settings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
+    )
+
+    llm = GoogleLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        enable_async_tool_cancellation=True,
+        settings=GoogleLLMService.Settings(
+            system_instruction=(
+                "You are a helpful assistant in a voice conversation. "
+                "Your responses will be spoken aloud, so avoid emojis, bullet points, or other "
+                "formatting that can't be spoken. "
+                "You have access to a function that starts tracking the user's location and "
+                "provides regular updates on it. When you receive the final location, tell the user "
+                "the destination has been reached."
+            ),
+        ),
+    )
+
+    # cancel_on_interruption=False makes this an async function call: the LLM
+    # continues the conversation immediately and receives updates/result later.
+    llm.register_function(
+        "track_current_location",
+        track_current_location,
+        cancel_on_interruption=False,
+        timeout_secs=30,
+    )
+
+    @llm.event_handler("on_function_calls_started")
+    async def on_function_calls_started(service, function_calls):
+        await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now."))
+
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
+    location_function = FunctionSchema(
+        name="track_current_location",
+        description="Start tracking the user's current GPS location, reporting position updates until the user reaches their destination.",
+        properties={},
+        required=[],
+    )
+    tools = ToolsSchema(standard_tools=[location_function])
+
+    context = LLMContext(tools=tools)
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_aggregator,
+            llm,
+            tts,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        context.add_message(
+            {"role": "developer", "content": "Please introduce yourself to the user."}
+        )
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/function-calling/function-calling-google-async.py

@@ -0,0 +1,256 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+
+load_dotenv(override=True)
+
+
+async def get_weather(params: FunctionCallParams):
+    # Simulate a long-running API call, so we can test async function calls (cancel_on_interruption=False).
+    await asyncio.sleep(20)
+    location = params.arguments["location"]
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+async def get_image(params: FunctionCallParams):
+    """Fetch the user image and push it to the LLM.
+
+    When called, this function pushes a UserImageRequestFrame upstream to the
+    transport. As a result, the transport will request the user image and push a
+    UserImageRawFrame downstream which will be added to the context by the LLM
+    assistant aggregator. The result_callback will be invoked once the image is
+    retrieved and processed.
+    """
+    user_id = params.arguments["user_id"]
+    question = params.arguments["question"]
+    logger.debug(f"Requesting image with user_id={user_id}, question={question}")
+
+    # Request a user image frame and indicate that it should be added to the
+    # context. Also associate it to the function call. Pass the result_callback
+    # so it can be invoked when the image is actually retrieved.
+    await params.llm.push_frame(
+        UserImageRequestFrame(
+            user_id=user_id,
+            text=question,
+            append_to_context=True,
+            function_name=params.function_name,
+            tool_call_id=params.tool_call_id,
+            result_callback=params.result_callback,
+        ),
+        FrameDirection.UPSTREAM,
+    )
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        settings=CartesiaTTSService.Settings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
+    )
+
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to three tools: get_weather, get_restaurant_recommendation, and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+"""
+
+    llm = GoogleLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        enable_async_tool_cancellation=True,
+        settings=GoogleLLMService.Settings(
+            system_instruction=system_prompt,
+        ),
+    )
+    llm.register_function("get_weather", get_weather, cancel_on_interruption=False, timeout_secs=30)
+    llm.register_function("get_image", get_image)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    @llm.event_handler("on_function_calls_started")
+    async def on_function_calls_started(service, function_calls):
+        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
+
+    @llm.event_handler("on_function_calls_cancelled")
+    async def on_function_calls_cancelled(service, function_calls):
+        for item in function_calls:
+            logger.info(f"Function call cancelled: {item.function_name} [{item.tool_call_id}]")
+
+    weather_function = FunctionSchema(
+        name="get_weather",
+        description="Get the current weather",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+            "format": {
+                "type": "string",
+                "enum": ["celsius", "fahrenheit"],
+                "description": "The temperature unit to use. Infer this from the user's location.",
+            },
+        },
+        required=["location", "format"],
+    )
+    restaurant_function = FunctionSchema(
+        name="get_restaurant_recommendation",
+        description="Get a restaurant recommendation",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+        },
+        required=["location"],
+    )
+    get_image_function = FunctionSchema(
+        name="get_image",
+        description="Called when the user requests a description of their camera feed",
+        properties={
+            "user_id": {
+                "type": "string",
+                "description": "The ID of the user to grab the image from",
+            },
+            "question": {
+                "type": "string",
+                "description": "The question that the user is asking about the image",
+            },
+        },
+        required=["user_id", "question"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, get_image_function, restaurant_function])
+
+    context = LLMContext(tools=tools)
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_aggregator,
+            llm,
+            tts,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected: {client}")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        client_id = get_transport_client_id(transport, client)
+
+        # Kick off the conversation.
+        context.add_message(
+            {
+                "role": "developer",
+                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
+            }
+        )
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/function-calling/function-calling-google-vertex.py

@@ -105,7 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     messages = [
         {
-            "role": "user",
+            "role": "developer",
             "content": "Start a conversation with 'Hey there' to get the current weather.",
         },
     ]

examples/function-calling/function-calling-google-video.py

@@ -164,7 +164,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Kick off the conversation.
         context.add_message(
             {
-                "role": "user",
+                "role": "developer",
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )

examples/function-calling/function-calling-google.py

@@ -220,7 +220,7 @@ indicate you should use the get_image tool are:
         # Kick off the conversation.
         context.add_message(
             {
-                "role": "user",
+                "role": "developer",
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )

examples/function-calling/function-calling-grok.py

@@ -0,0 +1,157 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.xai.llm import GrokLLMService
+from pipecat.services.xai.tts import XAIHttpTTSService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    async with aiohttp.ClientSession() as session:
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = XAIHttpTTSService(
+            api_key=os.getenv("XAI_API_KEY"),
+            aiohttp_session=session,
+            settings=XAIHttpTTSService.Settings(
+                voice="eve",
+            ),
+        )
+
+        llm = GrokLLMService(
+            api_key=os.getenv("XAI_API_KEY"),
+            settings=GrokLLMService.Settings(
+                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
+            ),
+        )
+        # 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])
+        context = LLMContext(tools=tools)
+        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+        )
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                stt,
+                user_aggregator,
+                llm,
+                tts,
+                transport.output(),
+                assistant_aggregator,
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        )
+
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # Kick off the conversation.
+            context.add_message(
+                {"role": "developer", "content": "Please introduce yourself to the user."}
+            )
+            await task.queue_frames([LLMRunFrame()])
+
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
+            await task.cancel()
+
+        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+        await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()