Adding some more doscstrings, cleanup

.claude-plugin/marketplace.json

@@ -1,27 +0,0 @@
-{
-  "name": "pipecat-dev-skills",
-  "owner": {
-    "name": "Pipecat"
-  },
-  "metadata": {
-    "description": "Development workflow skills for contributing to the Pipecat project",
-    "version": "1.0.0"
-  },
-  "plugins": [
-    {
-      "name": "pipecat-dev",
-      "description": "Development workflow skills for contributing to the Pipecat project",
-      "version": "1.0.0",
-      "source": "./",
-      "skills": [
-        "./.claude/skills/changelog",
-        "./.claude/skills/cleanup",
-        "./.claude/skills/code-review",
-        "./.claude/skills/docstring",
-        "./.claude/skills/pr-description",
-        "./.claude/skills/pr-submit",
-        "./.claude/skills/update-docs"
-      ]
-    }
-  ]
-}

.claude/settings.json

@@ -1,5 +0,0 @@
-{
-  "attribution": {
-    "commit": ""
-  }
-}

.claude/skills/changelog/SKILL.md

@@ -1,61 +0,0 @@
----
-name: changelog
-description: Create changelog files for important commits in a PR
----
-
-Create changelog files for the important commits in this PR. The PR number is provided as an argument.
-
-## Instructions
-
-1. Skip changelog for: documentation-only, internal refactoring, test-only, CI changes.
-
-2. First, check what commits are on the current branch compared to main:
-   ```
-   git log main..HEAD --oneline
-   ```
-
-3. For each significant change, create a changelog file in the `changelog/` folder using the format:
-   Allowed types: `added`, `changed`, `deprecated`, `removed`, `fixed`, `security`, `performance`, `other`
-   - `{PR_NUMBER}.added.md` - for new features
-   - `{PR_NUMBER}.added.2.md`, `{PR_NUMBER}.added.3.md` - for additional entries of the same type
-   - `{PR_NUMBER}.changed.md` - for changes to existing functionality
-   - `{PR_NUMBER}.fixed.md` - for bug fixes
-   - `{PR_NUMBER}.deprecated.md` - for deprecations
-   - `{PR_NUMBER}.removed.md` - for removed features
-   - `{PR_NUMBER}.security.md` - for security fixes
-   - `{PR_NUMBER}.performance.md` - for performance improvements
-   - `{PR_NUMBER}.other.md` - for other changes
-
-4. Each changelog file should at least contain a main single line starting with `- ` followed by a clear description of the change. No line wrapping.
-
-5. If the change is complicated, changelog files can have indented lines after the main line with additional details or code samples.
-
-6. Use ⚠️ emoji prefix for breaking changes.
-
-7. **Write changes in user-facing terms first.** Lead with what users of the framework will notice: new APIs, changed behavior, new parameters, fixed bugs they might have hit, etc. Implementation details (internal refactoring, how something is wired up under the hood) can be included as secondary context after the user-facing description, but should never be the *only* content of a changelog entry when there is a user-visible effect.
-
-   **Good** (user-facing first, implementation detail as context):
-   ```
-   - Turn completion instructions now persist correctly across full context updates when using `system_instruction`. Previously they were injected as a context system message, which caused warning spam and didn't survive context updates.
-   ```
-
-   **Bad** (implementation detail only, no user-facing framing):
-   ```
-   - Fixed turn completion instructions being injected as a context system message instead of using `system_instruction`.
-   ```
-
-   Ask yourself: "If I'm a developer building on Pipecat, what would I notice changed?" Start there.
-
-## Example
-
-For PR #3519 with a new feature and a bug fix:
-
-`changelog/3519.added.md`:
-```
-- Added `SomeNewFeature` for doing something useful.
-```
-
-`changelog/3519.fixed.md`:
-```
-- Fixed an issue where something was not working correctly in some user-visible scenario. The root cause was an internal implementation detail.
-```

.claude/skills/cleanup/SKILL.md

@@ -1,307 +0,0 @@
-# Code Cleanup Skill
-
-The **Code Cleanup Skill** reviews, refactors, and documents code changes in your current branch, ensuring alignment with **Pipecat's architecture, coding standards, and example patterns**.
-It focuses on **readability, correctness, performance, and consistency**, while avoiding breaking changes.
-
----
-
-## Skill Overview
-
-This skill analyzes all changes introduced in your branch and performs the following actions:
-
-1. **Analyze Branch Changes**
-   - Review uncommitted changes and outgoing commits
-2. **Refactor for Readability**
-   - Improve clarity, naming, structure, and modern Python usage
-3. **Enhance Performance**
-   - Identify safe, conservative optimization opportunities
-4. **Add Documentation**
-   - Apply Pipecat-style, Google-format docstrings
-5. **Ensure Pattern Consistency**
-   - Match existing Pipecat services, pipelines, and examples
-6. **Validate Examples**
-   - Ensure examples follow foundational patterns (e.g. `07-interruptible.py`)
-
----
-
-## Usage
-
-Invoke the skill using any of the following commands:
-
-- "Clean up my branch code"
-- "Refactor the changes in my branch"
-- "Review and improve my branch code"
-- `/cleanup`
-
----
-
-## What This Skill Does
-
-### 1. Analyze Branch Changes
-
-The skill retrieves all uncommitted changes and outgoing commits to understand:
-
-- New files added
-- Modified files
-- Code additions and deletions
-- Overall scope and intent of changes
-
----
-
-### 2. Code Refactoring
-
-#### Readability Improvements
-
-- Replace tuples with named classes or dataclasses
-- Improve variable, method, and class naming
-- Extract complex logic into well-named helper methods
-- Add missing type hints
-- Simplify nested or complex conditionals
-- Replace deprecated methods and features
-- Normalize formatting to match Pipecat style
-
-#### Performance Enhancements
-
-- Identify inefficient loops or repeated work
-- Suggest appropriate data structures
-- Optimize async workflows and I/O
-- Remove redundant operations
-
-> Performance changes are conservative and non-breaking.
-
----
-
-### 3. Documentation
-
-Documentation follows **Google-style docstrings**, consistent with Pipecat conventions.
-
-#### Class Documentation
-
-```python
-class ExampleService:
-    """Brief one-line description.
-
-    Detailed explanation of the class purpose, responsibilities,
-    and important behaviors.
-
-    Supported features:
-
-    - Feature 1
-    - Feature 2
-    - Feature 3
-    """
-```
-
-#### Method Documentation
-
-```python
-def process_data(self, data: str, options: Optional[dict] = None) -> bool:
-    """Process incoming data with optional configuration.
-
-    Args:
-        data: The input data to process.
-        options: Optional configuration dictionary.
-
-    Returns:
-        True if processing succeeded, False otherwise.
-
-    Raises:
-        ValueError: If data is empty or invalid.
-    """
-```
-
-#### Pydantic Model Parameters
-
-```python
-class InputParams(BaseModel):
-    """Configuration parameters for the service.
-
-    Parameters:
-        timeout: Request timeout in seconds.
-        retry_count: Number of retry attempts.
-        enable_logging: Whether to enable debug logging.
-    """
-
-    timeout: Optional[float] = None
-    retry_count: int = 3
-    enable_logging: bool = False
-```
-
----
-
-### 4. Pattern Consistency Checks
-
-#### Service Classes
-
-- Correct inheritance (`TTSService`, `STTService`, `LLMService`)
-- Consistent constructor signatures
-- Frame emission patterns
-- Metrics support:
-  - `can_generate_metrics()`
-  - TTFB metrics
-  - Usage metrics
-- Alignment with similar existing services
-
-#### Examples
-
-Validated against `examples/07-interruptible.py`:
-
-- Proper `create_transport()` usage
-- Correct pipeline structure
-- Task setup and observers
-- Event handler registration
-- Runner and bot entrypoint consistency
-
----
-
-### 5. Specific Implementation Patterns
-
-#### Service Implementation
-
-```python
-class ExampleTTSService(TTSService):
-
-    def __init__(self, *, api_key: Optional[str] = None, **kwargs):
-        super().__init__(**kwargs)
-        self._api_key = api_key or os.getenv("SERVICE_API_KEY")
-
-    def can_generate_metrics(self) -> bool:
-        return True
-
-    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        try:
-            await self.start_ttfb_metrics()
-            yield TTSStartedFrame()
-            # ... processing ...
-            yield TTSAudioRawFrame(...)
-        finally:
-            await self.stop_ttfb_metrics()
-```
-
----
-
-#### Example Structure Pattern
-
-```python
-transport_params = {
-    "daily": lambda: DailyParams(...),
-    "twilio": lambda: FastAPIWebsocketParams(...),
-    "webrtc": lambda: TransportParams(...),
-}
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    stt = DeepgramSTTService(...)
-    tts = SomeTTSService(...)
-    llm = OpenAILLMService(...)
-
-    context = LLMContext(messages)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(...)
-
-    pipeline = Pipeline([...])
-    task = PipelineTask(pipeline, params=..., observers=[...])
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        await task.queue_frames([LLMRunFrame()])
-
-    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)
-```
-
----
-
-## Execution Flow
-
-1. Fetch uncommitted and outgoing changes
-2. Categorize files (services, examples, tests, utilities)
-3. Analyze each file:
-   - Readability
-   - Performance
-   - Documentation
-   - Pattern consistency
-4. Generate actionable recommendations
-5. Apply Pipecat standards
-
----
-
-## Examples
-
-### Before: Tuple Usage
-
-```python
-def get_audio_info(self) -> Tuple[int, int]:
-    return (48000, 1)
-```
-
-### After: Named Class
-
-```python
-class AudioInfo:
-    """Audio configuration information.
-
-    Parameters:
-        sample_rate: Sample rate in Hz.
-        num_channels: Number of audio channels.
-    """
-
-    sample_rate: int
-    num_channels: int
-
-def get_audio_info(self) -> AudioInfo:
-    return AudioInfo(sample_rate=48000, num_channels=1)
-```
-
----
-
-### Before: Missing Documentation
-
-```python
-class NewTTSService(TTSService):
-    def __init__(self, api_key: str, voice: str):
-        self._api_key = api_key
-        self._voice = voice
-```
-
-### After: Fully Documented
-
-```python
-class NewTTSService(TTSService):
-    """Text-to-speech service using NewProvider API.
-
-    Streams PCM audio and emits TTSAudioRawFrame frames compatible
-    with Pipecat transports.
-
-    Supported features:
-    - Text-to-speech synthesis
-    - Streaming PCM audio
-    - Voice customization
-    - TTFB metrics
-    """
-
-    def __init__(self, *, api_key: str, voice: str, **kwargs):
-        """Initialize the NewTTSService.
-
-        Args:
-            api_key: API key for authentication.
-            voice: Voice identifier to use.
-            **kwargs: Additional arguments passed to the parent service.
-        """
-        super().__init__(**kwargs)
-        self._api_key = api_key
-        self.set_voice(voice)
-```
-
----
-
-## Notes
-
-- Non-breaking improvements only
-- Backward compatibility preserved
-- Conservative performance changes
-- Google-style docstrings
-- Pattern checks follow recent Pipecat code

.claude/skills/code-review/SKILL.md

@@ -1,107 +0,0 @@
----
-name: code-review
-description: Automated code review for pull requests using multiple specialized agents
-disable-model-invocation: true
-allowed-tools: Bash(gh issue view:*), Bash(gh search:*), Bash(gh issue list:*), Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr list:*)
----
-
-Provide a code review for the given pull request.
-
-**Agent assumptions (applies to all agents and subagents):**
-
-- All tools are functional and will work without error. Do not test tools or make exploratory calls. Make sure this is clear to every subagent that is launched.
-- Only call a tool if it is required to complete the task. Every tool call should have a clear purpose.
-
-To do this, follow these steps precisely:
-
-1. Launch a haiku agent to check if any of the following are true:
-   - The pull request is closed
-   - The pull request is a draft
-   - The pull request does not need code review (e.g. automated PR, trivial change that is obviously correct)
-   - Claude has already commented on this PR (check `gh pr view <PR> --comments` for comments left by claude)
-
-   If any condition is true, stop and do not proceed.
-
-Note: Still review Claude generated PR's.
-
-2. Launch a haiku agent to return a list of file paths (not their contents) for all relevant CLAUDE.md files including:
-   - The root CLAUDE.md file, if it exists
-   - Any CLAUDE.md files in directories containing files modified by the pull request
-
-3. Launch a sonnet agent to view the pull request and return a summary of the changes
-
-4. Launch 4 agents in parallel to independently review the changes. Each agent should return the list of issues, where each issue includes a description and the reason it was flagged (e.g. "CLAUDE.md adherence", "bug"). The agents should do the following:
-
-   Agents 1 + 2: CLAUDE.md compliance sonnet agents
-   Audit changes for CLAUDE.md compliance in parallel. Note: When evaluating CLAUDE.md compliance for a file, you should only consider CLAUDE.md files that share a file path with the file or parents.
-
-   Agent 3: Opus bug agent (parallel subagent with agent 4)
-   Scan for obvious bugs. Focus only on the diff itself without reading extra context. Flag only significant bugs; ignore nitpicks and likely false positives. Do not flag issues that you cannot validate without looking at context outside of the git diff.
-
-   Agent 4: Opus bug agent (parallel subagent with agent 3)
-   Look for problems that exist in the introduced code. This could be security issues, incorrect logic, etc. Only look for issues that fall within the changed code.
-
-   **CRITICAL: We only want HIGH SIGNAL issues.** Flag issues where:
-   - The code will fail to compile or parse (syntax errors, type errors, missing imports, unresolved references)
-   - The code will definitely produce wrong results regardless of inputs (clear logic errors)
-   - Clear, unambiguous CLAUDE.md violations where you can quote the exact rule being broken
-
-   Do NOT flag:
-   - Code style or quality concerns
-   - Potential issues that depend on specific inputs or state
-   - Subjective suggestions or improvements
-
-   If you are not certain an issue is real, do not flag it. False positives erode trust and waste reviewer time.
-
-   In addition to the above, each subagent should be told the PR title and description. This will help provide context regarding the author's intent.
-
-5. For each issue found in the previous step by agents 3 and 4, launch parallel subagents to validate the issue. These subagents should get the PR title and description along with a description of the issue. The agent's job is to review the issue to validate that the stated issue is truly an issue with high confidence. For example, if an issue such as "variable is not defined" was flagged, the subagent's job would be to validate that is actually true in the code. Another example would be CLAUDE.md issues. The agent should validate that the CLAUDE.md rule that was violated is scoped for this file and is actually violated. Use Opus subagents for bugs and logic issues, and sonnet agents for CLAUDE.md violations.
-
-6. Filter out any issues that were not validated in step 5. This step will give us our list of high signal issues for our review.
-
-7. If issues were found, skip to step 8 to post comments.
-
-   If NO issues were found, post a summary comment using `gh pr comment` (if `--comment` argument is provided):
-   "No issues found. Checked for bugs and CLAUDE.md compliance."
-
-8. Create a list of all comments that you plan on leaving. This is only for you to make sure you are comfortable with the comments. Do not post this list anywhere.
-
-9. Post inline comments for each issue using `gh pr review` with inline comments. For each comment:
-   - Provide a brief description of the issue
-   - For small, self-contained fixes, include a committable suggestion block
-   - For larger fixes (6+ lines, structural changes, or changes spanning multiple locations), describe the issue and suggested fix without a suggestion block
-   - Never post a committable suggestion UNLESS committing the suggestion fixes the issue entirely. If follow up steps are required, do not leave a committable suggestion.
-
-   **IMPORTANT: Only post ONE comment per unique issue. Do not post duplicate comments.**
-
-Use this list when evaluating issues in Steps 4 and 5 (these are false positives, do NOT flag):
-
-- Pre-existing issues
-- Something that appears to be a bug but is actually correct
-- Pedantic nitpicks that a senior engineer would not flag
-- Issues that a linter will catch (do not run the linter to verify)
-- General code quality concerns (e.g., lack of test coverage, general security issues) unless explicitly required in CLAUDE.md
-- Issues mentioned in CLAUDE.md but explicitly silenced in the code (e.g., via a lint ignore comment)
-
-Notes:
-
-- Use gh CLI to interact with GitHub (e.g., fetch pull requests, create comments). Do not use web fetch.
-- Create a todo list before starting.
-- You must cite and link each issue in inline comments (e.g., if referring to a CLAUDE.md, include a link to it).
-- If no issues are found, post a comment with the following format:
-
----
-
-## Code review
-
-No issues found. Checked for bugs and CLAUDE.md compliance.
-
----
-
-- When linking to code in inline comments, follow the following format precisely, otherwise the Markdown preview won't render correctly: `https://github.com/OWNER/REPO/blob/FULL_SHA/path/to/file.py#L10-L15`
-  - Requires full git sha
-  - You must provide the full sha. Commands like `https://github.com/owner/repo/blob/$(git rev-parse HEAD)/foo/bar` will not work, since your comment will be directly rendered in Markdown.
-  - Repo name must match the repo you're code reviewing
-  - # sign after the file name
-  - Line range format is L[start]-L[end]
-  - Provide at least 1 line of context before and after, centered on the line you are commenting about (eg. if you are commenting about lines 5-6, you should link to `L4-7`)

.claude/skills/docstring/SKILL.md

@@ -1,256 +0,0 @@
----
-name: docstring
-description: Document a Python module and its classes using Google style
----
-
-Document a Python module or class using Google-style docstrings following project conventions. The argument can be a class name or a module path.
-
-## Instructions
-
-1. Determine what to document based on the argument:
-
-   **If a module path is provided** (e.g. `src/pipecat/audio/vad/vad_analyzer.py`):
-   - Use that file directly
-
-   **If a class name is provided** (e.g. `VADAnalyzer`):
-   - Search for `class ClassName` in `src/pipecat/`
-   - If multiple files contain that class name, list all matches with their file paths, ask the user which one they want to document, and wait for confirmation
-
-2. Once the file is identified, read the module to understand its structure:
-   - Identify all classes, functions, and important type aliases
-   - Understand the purpose of each component
-
-4. Apply documentation in this order:
-   - Module docstring (at top, after imports)
-   - Class docstrings
-   - `__init__` methods (always document constructor parameters)
-   - Public methods (not starting with `_`)
-   - Dataclass/config classes with field descriptions
-
-5. Skip documentation for:
-   - Private methods (starting with `_`)
-   - Simple dunder methods (`__str__`, `__repr__`, `__post_init__`)
-   - Very simple pass-through properties
-   - **Already documented code** - If a class, method, or function already has a complete docstring that follows the project style, do not modify it. A docstring is complete if it has:
-     - A one-line summary
-     - Args section (if it has parameters)
-     - Returns section (if it returns something meaningful)
-   - Only add or improve documentation where it is missing or incomplete
-
-## Module Docstring Format
-
-```python
-"""[One-line description of module purpose].
-
-[Optional: Longer explanation of functionality, key classes, or use cases.]
-"""
-```
-
-Example:
-```python
-"""Neuphonic text-to-speech service implementations.
-
-This module provides WebSocket and HTTP-based integrations with Neuphonic's
-text-to-speech API for real-time audio synthesis.
-"""
-```
-
-## Class Docstring Format
-
-```python
-class ClassName:
-    """One-line summary describing what the class does.
-
-    [Longer description explaining purpose, behavior, and key features.
-    Use action-oriented language.]
-
-    [Optional: Event handlers, usage notes, or important caveats.]
-    """
-```
-
-Example:
-```python
-class FrameProcessor(BaseObject):
-    """Base class for all frame processors in the pipeline.
-
-    Frame processors are the building blocks of Pipecat pipelines, they can be
-    linked to form complex processing pipelines. They receive frames, process
-    them, and pass them to the next or previous processor in the chain.
-
-    Event handlers available:
-
-    - on_before_process_frame: Called before a frame is processed
-    - on_after_process_frame: Called after a frame is processed
-
-    Example::
-
-        @processor.event_handler("on_before_process_frame")
-        async def on_before_process_frame(processor, frame):
-            ...
-
-        @processor.event_handler("on_after_process_frame")
-        async def on_after_process_frame(processor, frame):
-            ...
-    """
-```
-
-Note: When listing event handlers, do NOT use backticks. Include an `Example::` section (with double colon for Sphinx) showing the decorator pattern and function signature for each event.
-
-## Constructor (`__init__`) Format
-
-```python
-def __init__(self, *, param1: Type, param2: Type = default, **kwargs):
-    """Initialize the [ClassName].
-
-    Args:
-        param1: Description of param1 and its purpose.
-        param2: Description of param2. Defaults to [default].
-        **kwargs: Additional arguments passed to parent class.
-    """
-```
-
-Example:
-```python
-def __init__(
-    self,
-    *,
-    api_key: str,
-    voice_id: Optional[str] = None,
-    sample_rate: Optional[int] = 22050,
-    **kwargs,
-):
-    """Initialize the Neuphonic TTS service.
-
-    Args:
-        api_key: Neuphonic API key for authentication.
-        voice_id: ID of the voice to use for synthesis.
-        sample_rate: Audio sample rate in Hz. Defaults to 22050.
-        **kwargs: Additional arguments passed to parent InterruptibleTTSService.
-    """
-```
-
-## Method Docstring Format
-
-```python
-async def method_name(self, param1: Type) -> ReturnType:
-    """One-line summary of what method does.
-
-    [Longer description if behavior isn't obvious.]
-
-    Args:
-        param1: Description of param1.
-
-    Returns:
-        Description of return value.
-
-    Raises:
-        ExceptionType: When this exception is raised.
-    """
-```
-
-Example:
-```python
-async def put(self, item: Tuple[Frame, FrameDirection, FrameCallback]):
-    """Put an item into the priority queue.
-
-    System frames (`SystemFrame`) have higher priority than any other
-    frames. If a non-frame item is provided it will have the highest priority.
-
-    Args:
-        item: The item to enqueue.
-    """
-```
-
-## Dataclass/Config Format
-
-```python
-@dataclass
-class ConfigName:
-    """One-line description of configuration.
-
-    [Explanation of when/how to use this config.]
-
-    Parameters:
-        field1: Description of field1.
-        field2: Description of field2. Defaults to [default].
-    """
-
-    field1: Type
-    field2: Type = default_value
-```
-
-Example:
-```python
-@dataclass
-class FrameProcessorSetup:
-    """Configuration parameters for frame processor initialization.
-
-    Parameters:
-        clock: The clock instance for timing operations.
-        task_manager: The task manager for handling async operations.
-        observer: Optional observer for monitoring frame processing events.
-    """
-
-    clock: BaseClock
-    task_manager: BaseTaskManager
-    observer: Optional[BaseObserver] = None
-```
-
-## Enum Documentation Format
-
-```python
-class EnumName(Enum):
-    """One-line description of the enum purpose.
-
-    [Longer description of how the enum is used.]
-
-    Parameters:
-        VALUE1: Description of VALUE1.
-        VALUE2: Description of VALUE2.
-    """
-
-    VALUE1 = 1
-    VALUE2 = 2
-```
-
-## Writing Style Guidelines
-
-- **Concise and professional** - No casual language or filler words
-- **Action-oriented** - Start with verbs: "Processes...", "Manages...", "Converts..."
-- **Purpose before implementation** - Explain WHY before HOW
-- **Clear parameter descriptions** - Include type hints, defaults, and purpose
-- **No redundant type info** - Type hints are in the signature, don't repeat in description
-- **Use backticks for code references** - Wrap class names, method names, event names, parameter names, and code snippets in backticks
-
-Good: "Neuphonic API key for authentication."
-Bad: "str: The API key (string) that is used for authenticating with Neuphonic."
-
-Good: "Triggers `on_speech_started` when the `VADAnalyzer` detects speech."
-Bad: "Triggers on_speech_started when the VADAnalyzer detects speech."
-
-## Deprecation Notice Format
-
-When documenting deprecated code:
-
-```python
-"""[Description].
-
-.. deprecated:: X.X.X
-    `ClassName` is deprecated and will be removed in a future version.
-    Use `NewClassName` instead.
-"""
-```
-
-## Checklist
-
-Before finishing, verify:
-
-- [ ] Module has a docstring at the top (after copyright header and imports)
-- [ ] All public classes have docstrings
-- [ ] All `__init__` methods document their parameters
-- [ ] All public methods have docstrings with Args/Returns/Raises as needed
-- [ ] Dataclasses use "Parameters:" section for field descriptions
-- [ ] Enums document each value in "Parameters:" section
-- [ ] Writing is concise and action-oriented
-- [ ] No documentation added to private methods (starting with `_`)
-- [ ] Existing complete docstrings were left unchanged

.claude/skills/pr-description/SKILL.md

@@ -1,128 +0,0 @@
----
-name: pr-description
-description: Update a GitHub PR description with a summary of changes
----
-
-Update a GitHub pull request description based on the changes in the PR.
-
-## Arguments
-
-```
-/pr-description <PR_NUMBER> [--fixes <ISSUE_NUMBERS>]
-```
-
-- `PR_NUMBER` (required): The pull request number to update
-- `--fixes` (optional): Comma-separated issue numbers that this PR fixes (e.g., `--fixes 123,456`)
-
-Examples:
-- `/pr-description 3534`
-- `/pr-description 3534 --fixes 123`
-- `/pr-description 3534 --fixes 123,456,789`
-
-## Instructions
-
-1. First, gather information about the PR:
-   - Use GitHub plugin to get PR details (title, current description, base branch)
-   - Use local git to get commits: `git log main..HEAD --oneline`
-   - Use local git to get the diff: `git diff main..HEAD`
-   - Parse any `--fixes` argument for issue numbers
-
-2. Check the existing PR description:
-   - If it already has a complete, accurate description that reflects the changes, do nothing
-   - If it's missing sections, incomplete, or outdated compared to the actual changes, proceed to update
-   - If it only has the template placeholder text, generate a full description
-
-3. Analyze the changes:
-   - Understand the purpose of each commit
-   - Identify any breaking changes (API changes, removed features, behavior changes)
-   - Look for new features, bug fixes, refactoring, or documentation changes
-   - Collect issue numbers from:
-     - The `--fixes` argument (if provided)
-     - Commit messages (patterns like "Fixes #123", "Closes #456", "Resolves #789")
-
-4. Generate or update the PR description with these sections:
-
-## PR Description Format
-
-### Summary (always include)
-
-Brief bullet points describing what changed and why. Focus on the *purpose* and *impact*, not implementation details.
-
-```markdown
-## Summary
-
-- Added X to enable Y
-- Fixed bug where Z would happen
-- Refactored W for better maintainability
-```
-
-### Breaking Changes (include only if applicable)
-
-Document any changes that affect existing users or APIs.
-
-```markdown
-## Breaking Changes
-
-- `ClassName.method()` now requires a `param` argument
-- Removed deprecated `old_function()` - use `new_function()` instead
-```
-
-### Testing (include when non-obvious)
-
-How to verify the changes work. Skip for trivial changes.
-
-```markdown
-## Testing
-
-- Run `uv run pytest tests/test_feature.py` to verify the fix
-- Example usage: `uv run examples/new_feature.py`
-```
-
-### Fixes (include if issues are provided or found in commits)
-
-List issues this PR fixes. GitHub will automatically close these issues when the PR is merged.
-
-```markdown
-## Fixes
-
-- Fixes #123
-- Fixes #456
-```
-
-Note: Use "Fixes #X" format (not "Closes" or "Resolves") for consistency. Each issue should be on its own line with "Fixes" to ensure GitHub auto-closes them.
-
-## Guidelines
-
-- **Be concise** - Reviewers should understand the PR in 30 seconds
-- **Focus on why** - The diff shows *what* changed, explain *why*
-- **Skip empty sections** - Only include sections that have content
-- **Use bullet points** - Easier to scan than paragraphs
-- **Don't duplicate the diff** - Avoid listing every file or line changed
-
-## Example Output
-
-```markdown
-## Summary
-
-- Added `/docstring` skill for documenting Python modules with Google-style docstrings
-- Skill finds classes by name and handles conflicts when multiple matches exist
-- Skips already-documented code to avoid unnecessary changes
-
-## Testing
-
-/docstring ClassName
-
-## Fixes
-
-- Fixes #123
-```
-
-## Checklist
-
-Before updating the PR:
-
-- [ ] Verified existing description needs updating (not already complete)
-- [ ] Summary accurately reflects the changes
-- [ ] Breaking changes are clearly documented (if any)
-- [ ] No unnecessary sections included
-- [ ] Description is concise and scannable

.claude/skills/pr-submit/SKILL.md

@@ -1,28 +0,0 @@
----
-name: pr-submit
-description: Create and submit a GitHub PR from the current branch
----
-
-Submit the current changes as a GitHub pull request.
-
-## Instructions
-
-1. Check the current state of the repository:
-   - Run `git status` to see staged, unstaged, and untracked changes
-   - Run `git diff` to see current changes
-   - Run `git log --oneline -10` to see recent commits
-
-2. If there are uncommitted changes relevant to the PR:
-   - Ask the user if they want a specific prefix for the branch name (e.g., `alice/`, `fix/`, `feat/`)
-   - Create a new branch based on the current branch
-   - Commit the changes using multiple commits if the changes are unrelated
-
-3. Push the branch and create the PR:
-   - Push with `-u` flag to set upstream tracking
-   - Create the PR using `gh pr create`
-
-4. After the PR is created:
-   - Run `/changelog <pr_number>` to generate changelog files, then commit and push them
-   - Run `/pr-description <pr_number>` to update the PR description
-
-5. Return the PR URL to the user.

.claude/skills/update-docs/SKILL.md

@@ -1,306 +0,0 @@
----
-name: update-docs
-description: Update documentation pages to match source code changes on the current branch
----
-
-Update documentation pages to reflect source code changes on the current branch. Analyzes the diff against main, maps changed source files to their corresponding doc pages, and makes targeted edits.
-
-## Arguments
-
-```
-/update-docs [DOCS_PATH]
-```
-
-- `DOCS_PATH` (optional): Path to the docs repository root. If not provided, ask the user.
-
-Examples:
-- `/update-docs /Users/me/src/docs`
-- `/update-docs`
-
-## Instructions
-
-### Step 1: Resolve docs path
-
-If `DOCS_PATH` was provided as an argument, use it. Otherwise, ask the user for the path to their docs repository.
-
-Verify the path exists and contains `server/services/` subdirectory.
-
-### Step 2: Create docs branch
-
-Get the current pipecat branch name:
-```bash
-git rev-parse --abbrev-ref HEAD
-```
-
-In the docs repo, create a new branch off main with a matching name:
-```bash
-cd DOCS_PATH && git checkout main && git pull && git checkout -b {branch-name}-docs
-```
-
-For example, if the pipecat branch is `feat/new-service`, the docs branch becomes `feat/new-service-docs`.
-
-All doc edits in subsequent steps are made on this branch.
-
-### Step 3: Detect changed source files
-
-Run:
-```bash
-git diff main..HEAD --name-only
-```
-
-Filter to files that could affect documentation:
-- `src/pipecat/services/**/*.py` (service implementations)
-- `src/pipecat/transports/**/*.py` (transport implementations)
-- `src/pipecat/serializers/**/*.py` (serializer implementations)
-- `src/pipecat/processors/**/*.py` (processor implementations)
-- `src/pipecat/audio/**/*.py` (audio utilities)
-- `src/pipecat/turns/**/*.py` (turn management)
-- `src/pipecat/observers/**/*.py` (observers)
-- `src/pipecat/pipeline/**/*.py` (pipeline core)
-
-Ignore `__init__.py`, `__pycache__`, test files, and files that only contain type re-exports.
-
-### Step 4: Map source files to doc pages
-
-For each changed source file, find the corresponding doc page. Read the mapping file at `.claude/skills/update-docs/SOURCE_DOC_MAPPING.md` and apply its tiered lookup: tier 1 (known exceptions) → tier 2 (pattern matching) → tier 3 (search fallback). **First match wins.**
-
-### Step 5: Analyze each source-doc pair
-
-For each mapped pair:
-
-1. **Read the full source file** to understand current state
-2. **Read the diff** for that file: `git diff main..HEAD -- <source_file>`
-3. **Read the current doc page** in full
-
-Identify what changed by comparing source to docs:
-
-- **Constructor parameters**: Compare `__init__` signature to the Configuration section's `<ParamField>` entries
-- **InputParams fields**: Compare `InputParams(BaseModel)` class fields to the InputParams table
-- **Event handlers**: Compare `_register_event_handler` calls and event handler definitions to Event Handlers section
-- **Class names / imports**: Check if Usage examples reference correct names
-- **Behavioral changes**: Check if Notes section needs updating
-
-### Step 6: Make targeted edits
-
-For each doc page that needs updates, edit **only the sections that need changes**. Preserve all other content exactly as-is.
-
-#### Rules
-
-- **Never remove content** unless the corresponding source code was removed
-- **Never rewrite sections** that are already accurate
-- **Match existing formatting** — if the page uses `<ParamField>` tags, use them; if it uses tables, use tables
-- **Keep descriptions concise** — match the tone and length of surrounding content
-- **Preserve CardGroup, links, and examples** unless they reference removed functionality
-- **Don't touch frontmatter** unless the class was renamed
-
-#### Section-specific guidance
-
-**Configuration** (constructor params):
-- Use `<ParamField path="name" type="type" default="value">` format if the page already uses it
-- Add new params in logical order (required first, then optional)
-- Remove params that no longer exist in source
-- Update types/defaults that changed
-
-**InputParams** (runtime settings):
-- Use markdown table format: `| Parameter | Type | Default | Description |`
-- Match the field names and types from the `InputParams(BaseModel)` class
-- Include the default values from the source
-
-**Usage** (code examples):
-- Update import paths, class names, and parameter names
-- Only modify examples if they would break or be misleading with the new API
-- Don't rewrite working examples just to add new optional params
-
-**Notes**:
-- Add notes for new behavioral gotchas or breaking changes
-- Remove notes about limitations that were fixed
-- Keep existing notes that are still accurate
-
-**Event Handlers**:
-- Update the event table and example code
-- Add new events, remove deleted ones
-- Update handler signatures if they changed
-
-**Overview / Key Features / Prerequisites**:
-- Only update if the PR fundamentally changes what the service does (new capability, removed capability, renamed class)
-- Most PRs will NOT need changes to these sections
-
-### Step 7: Update guides
-
-Guides at `DOCS_PATH/guides/` reference specific class names, parameters, imports, and code patterns. After completing reference doc edits, check if any guides need updates too.
-
-For each changed source file, collect the class names, renamed parameters, and changed imports from the diff. Search the guides directory:
-```bash
-grep -rl "ClassName\|old_param_name" DOCS_PATH/guides/
-```
-
-For each guide that references changed code:
-1. Read the full guide
-2. Update class names, parameter names, import paths, and code examples that are now incorrect
-3. **Don't rewrite prose** — only fix the specific references that changed
-4. Leave guides alone if they reference the service generally but don't use any changed APIs
-
-Guide directories:
-- `guides/learn/` — conceptual tutorials (pipeline, LLM, STT, TTS, etc.)
-- `guides/fundamentals/` — practical how-tos (metrics, recording, transcripts, etc.)
-- `guides/features/` — feature-specific guides (Gemini Live, OpenAI audio, WhatsApp, etc.)
-- `guides/telephony/` — telephony integration guides (Twilio, Plivo, Telnyx, etc.)
-
-### Step 8: Identify doc gaps
-
-After processing all mapped pairs, check for two kinds of gaps:
-
-**Missing pages**: Source files that had no doc page mapping (neither tier 1, 2, nor 3) and are not marked as "(skip)". For each, tell the user:
-- The source file path
-- The main class(es) it defines
-- Whether a new doc page should be created
-
-**Missing sections**: Mapped doc pages that are missing standard sections compared to the source. For example, a transport page with no Configuration section, or a service page with no InputParams table when the source defines `InputParams(BaseModel)`. Flag these and offer to add the missing sections.
-
-If the user wants a new page, do all three of the following:
-
-#### 8a: Create the doc page
-
-Create the new `.mdx` file using this template structure:
-```
----
-title: "Service Name"
-description: "Brief description"
----
-
-## Overview
-
-[Description from class docstring or source analysis]
-
-<CardGroup cols={2}>
-  [Cards for API reference and examples if available]
-</CardGroup>
-
-## Installation
-
-```bash
-pip install "pipecat-ai[package-name]"
-```
-
-## Prerequisites
-
-[Environment variables and account setup]
-
-## Configuration
-
-[ParamField entries for constructor params]
-
-## InputParams
-
-[Table of InputParams fields, if the service has them]
-
-## Usage
-
-### Basic Setup
-
-```python
-[Minimal working example]
-```
-
-## Notes
-
-[Important caveats]
-
-## Event Handlers
-
-[Event table and example code]
-```
-
-#### 8b: Add to docs.json
-
-Add the new page path to `DOCS_PATH/docs.json` in the correct navigation group. The path format is `server/services/{category}/{provider}` (without the `.mdx` extension).
-
-Find the matching group in the navigation structure:
-- **STT** → `"group": "Speech-to-Text"` under Services
-- **TTS** → `"group": "Text-to-Speech"` under Services
-- **LLM** → `"group": "LLM"` under Services
-- **S2S** → `"group": "Speech-to-Speech"` under Services
-- **Transport** → `"group": "Transport"` under Services
-- **Serializer** → `"group": "Serializers"` under Services
-- **Image generation** → `"group": "Image Generation"` under Services
-- **Video** → `"group": "Video"` under Services
-- **Memory** → `"group": "Memory"` under Services
-- **Vision** → `"group": "Vision"` under Services
-- **Analytics** → `"group": "Analytics & Monitoring"` under Services
-
-Insert the new entry **alphabetically** within the group's `pages` array. For example, adding a new STT service "foo":
-```json
-{
-  "group": "Speech-to-Text",
-  "pages": [
-    "server/services/stt/assemblyai",
-    "server/services/stt/aws",
-    ...
-    "server/services/stt/foo",
-    ...
-  ]
-}
-```
-
-#### 8c: Add to supported-services.mdx
-
-Add a new row to the correct category table in `DOCS_PATH/server/services/supported-services.mdx`.
-
-Use this format:
-```
-| [DisplayName](/server/services/{category}/{provider}) | `pip install "pipecat-ai[package]"` |
-```
-
-To determine the correct values:
-- **DisplayName**: Use the service's human-readable name (e.g., "ElevenLabs", "AWS Polly", "Google Gemini")
-- **package**: Look at the service's `pyproject.toml` extras or the import pattern in the source code. For example, if the service is in `src/pipecat/services/foo/`, the package is typically `foo`.
-- If no pip dependencies are required, use `No dependencies required` instead.
-
-Insert the new row **alphabetically** within the table. Match the column alignment of the existing rows.
-
-### Step 9: Output summary
-
-After all edits are complete, print a summary:
-
-```
-## Documentation Updates
-
-### Updated reference pages
-- `server/services/stt/deepgram.mdx` — Updated Configuration (added `new_param`), InputParams (updated `language` default)
-- `server/services/tts/elevenlabs.mdx` — Updated Event Handlers (added `on_connected`)
-
-### Updated guides
-- `guides/learn/speech-to-text.mdx` — Updated code example (renamed `old_param` → `new_param`)
-
-### New service pages
-- `server/services/tts/newprovider.mdx` — Created page, added to docs.json (Text-to-Speech), added to supported-services.mdx
-
-### Unmapped source files
-- `src/pipecat/services/newprovider/tts.py` — NewProviderTTSService (no doc page exists)
-
-### Skipped files
-- `src/pipecat/services/ai_service.py` — internal base class
-```
-
-## Guidelines
-
-- **Be conservative** — only change what the diff warrants. Don't "improve" docs beyond what changed in source.
-- **Read before editing** — always read the full doc page before making changes so you understand the existing structure.
-- **Preserve voice** — match the writing style of the existing doc page, don't impose a different tone.
-- **One PR at a time** — this skill operates on the current branch's diff against main. Don't look at other branches.
-- **Parallel analysis** — when multiple source files map to different doc pages, analyze and edit them in parallel for efficiency.
-- **Shared source files** — files like `services/google/google.py` are shared bases. Check which services import from them and update all affected doc pages.
-
-## Checklist
-
-Before finishing, verify:
-
-- [ ] All changed source files were checked against the mapping table
-- [ ] Each doc page edit matches the actual source code change (not guessed)
-- [ ] No content was removed unless the corresponding source was removed
-- [ ] New parameters have accurate types and defaults from source
-- [ ] Formatting matches the existing page style
-- [ ] Guides referencing changed APIs were checked and updated
-- [ ] New service pages were added to `docs.json` in the correct group, alphabetically
-- [ ] New service pages were added to `supported-services.mdx` in the correct table, alphabetically
-- [ ] Unmapped files were reported to the user

.claude/skills/update-docs/SOURCE_DOC_MAPPING.md

@@ -1,79 +0,0 @@
-# Source-to-Doc Mapping
-
-Maps pipecat source files to their documentation pages. Source paths are relative to `src/pipecat/`. Doc paths are relative to `DOCS_PATH`.
-
-## Name mismatches
-
-These source paths don't follow the standard `services/{provider}/{type}.py` → `server/services/{type}/{provider}.mdx` pattern.
-
-| Source path | Doc page |
-|---|---|
-| `services/google/llm.py` | `server/services/llm/gemini.mdx` |
-| `services/google/llm_vertex.py` | `server/services/llm/google-vertex.mdx` |
-| `services/google/google.py` | (shared base — check which services use it) |
-| `services/google/gemini_live/**` | `server/services/s2s/gemini-live.mdx` |
-| `services/google/gemini_live/llm_vertex.py` | `server/services/s2s/gemini-live-vertex.mdx` |
-| `services/aws_nova_sonic/**` | `server/services/s2s/aws.mdx` |
-| `services/ultravox/**` | `server/services/s2s/ultravox.mdx` |
-| `services/grok/realtime/**` | `server/services/s2s/grok.mdx` |
-| `services/openai/realtime/**` | `server/services/s2s/openai.mdx` |
-| `processors/frameworks/rtvi.py` | `server/frameworks/rtvi/rtvi-processor.mdx` and `server/frameworks/rtvi/rtvi-observer.mdx` |
-| `processors/transcript_processor.py` | `server/utilities/transcript-processor.mdx` |
-| `processors/user_idle_processor.py` | `server/utilities/user-idle-processor.mdx` |
-| `processors/idle_frame_processor.py` | `server/pipeline/pipeline-idle-detection.mdx` |
-| `pipeline/task.py` | `server/pipeline/pipeline-task.mdx` |
-| `pipeline/runner.py` | `server/utilities/runner/guide.mdx` |
-| `transports/base_transport.py` | `server/services/transport/transport-params.mdx` |
-
-## Skip list
-
-These files should never trigger doc updates.
-
-| Pattern | Reason |
-|---|---|
-| `services/ai_service.py` | Internal base class |
-| `services/stt_service.py` | Internal base class |
-| `services/tts_service.py` | Internal base class |
-| `services/llm_service.py` | Internal base class |
-| `services/websocket_service.py` | Internal base class |
-| `services/openai_realtime_beta/**` | Deprecated |
-| `services/openai_realtime/**` | Deprecated |
-| `services/gemini_multimodal_live/**` | Deprecated |
-| `services/aws/agent_core.py` | Internal |
-| `services/aws/sagemaker/**` | No doc page |
-| `transports/base_input.py` | Internal base class |
-| `transports/base_output.py` | Internal base class |
-| `transports/websocket/client.py` | No doc page |
-| `serializers/base_serializer.py` | Internal base class |
-| `serializers/protobuf.py` | Internal |
-| `processors/audio/**` | Internal |
-| `pipeline/pipeline.py` | Core architecture, not a service doc |
-
-## Pattern matching
-
-For files not in the tables above, apply these patterns. Convert underscores to hyphens in provider names for doc filenames.
-
-| Source pattern | Doc pattern |
-|---|---|
-| `services/{provider}/stt*.py` | `server/services/stt/{provider}.mdx` |
-| `services/{provider}/tts*.py` | `server/services/tts/{provider}.mdx` |
-| `services/{provider}/llm*.py` | `server/services/llm/{provider}.mdx` |
-| `services/{provider}/image*.py` | `server/services/image-generation/{provider}.mdx` |
-| `services/{provider}/video*.py` | `server/services/video/{provider}.mdx` |
-| `services/{provider}/realtime/**` | `server/services/s2s/{provider}.mdx` |
-| `transports/{name}/**` | `server/services/transport/{name}.mdx` |
-| `serializers/{name}.py` | `server/services/serializers/{name}.mdx` |
-| `observers/**` | `server/utilities/observers/` (match by class name) |
-| `audio/vad/**` | `server/utilities/audio/` (match by class name) |
-| `audio/filters/**` | `server/utilities/audio/` (match by class name) |
-| `audio/mixers/**` | `server/utilities/audio/` (match by class name) |
-| `processors/filters/**` | `server/utilities/filters/` (match by class name) |
-
-If the doc file doesn't exist at the resolved path, the file is **unmapped**.
-
-## Search fallback
-
-For files that don't match any table or pattern above:
-1. Extract the main class name(s) from the source file
-2. Search the docs directory for that class name: `grep -r "ClassName" DOCS_PATH/server/`
-3. If found in a doc page, use that as the mapping

.github/ISSUE_TEMPLATE/1-bug_report.yml

@@ -1,87 +0,0 @@
-name: Bug report
-description: Report a bug or unexpected behavior
-type: Bug
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Bug Report
-
-        Thank you for taking the time to fill out this bug report.
-
-  - type: markdown
-    attributes:
-      value: |
-        ### Environment
-
-  - type: input
-    id: pipecat-version
-    attributes:
-      label: pipecat version
-      description: Which version are you using?
-      placeholder: e.g., 0.0.63
-    validations:
-      required: true
-
-  - type: input
-    id: python-version
-    attributes:
-      label: Python version
-      description: Which Python version are you using?
-      placeholder: e.g., 3.12.8
-    validations:
-      required: true
-
-  - type: input
-    id: os
-    attributes:
-      label: Operating System
-      description: Which OS are you using?
-      placeholder: e.g., Ubuntu 24.04, Windows 11, macOS 12.5
-    validations:
-      required: true
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Issue description
-      description: Provide a clear description of the issue.
-    validations:
-      required: true
-
-  - type: textarea
-    id: repro
-    attributes:
-      label: Reproduction steps
-      description: List the steps to reproduce the issue.
-      placeholder: |
-        1. Do this...
-        2. Then do that...
-        3. Observe the error...
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected
-    attributes:
-      label: Expected behavior
-      description: What did you expect to happen?
-    validations:
-      required: true
-
-  - type: textarea
-    id: actual
-    attributes:
-      label: Actual behavior
-      description: What actually happened?
-    validations:
-      required: true
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Logs
-      description: If applicable, include any relevant logs or error messages
-      render: shell
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/2-question.yml

@@ -1,67 +0,0 @@
-name: Question
-description: Ask a question or get help
-type: Question
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Question
-
-        Use this form to ask a question about pipecat.
-
-  - type: markdown
-    attributes:
-      value: |
-        ### Environment (if applicable)
-
-  - type: input
-    id: pipecat-version
-    attributes:
-      label: pipecat version
-      description: Which version are you using? (if applicable)
-      placeholder: e.g., 0.0.63
-    validations:
-      required: false
-
-  - type: input
-    id: python-version
-    attributes:
-      label: Python version
-      description: Which Python version are you using? (if applicable)
-      placeholder: e.g., 3.12.8
-    validations:
-      required: false
-
-  - type: input
-    id: os
-    attributes:
-      label: Operating System
-      description: Which OS are you using? (if applicable)
-      placeholder: e.g., Ubuntu 24.04, Windows 11, macOS 12.5
-    validations:
-      required: false
-
-  - type: textarea
-    id: question
-    attributes:
-      label: Question
-      description: Provide your question in detail here.
-    validations:
-      required: true
-
-  - type: textarea
-    id: tried
-    attributes:
-      label: What I've tried
-      description: Describe what you've already tried or research you've done.
-      placeholder: I've looked at the documentation and tried...
-    validations:
-      required: false
-
-  - type: textarea
-    id: context
-    attributes:
-      label: Context
-      description: Any additional context or information that might help others understand your question better.
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/3-feature_request.yml

@@ -1,52 +0,0 @@
-name: Feature request
-description: Suggest an enhancement or new feature
-type: Enhancement
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Feature Request
-
-        Thank you for suggesting an enhancement to pipecat.
-
-  - type: textarea
-    id: problem
-    attributes:
-      label: Problem Statement
-      description: A clear description of the problem this feature would solve.
-      placeholder: I'm always frustrated when...
-    validations:
-      required: true
-
-  - type: textarea
-    id: solution
-    attributes:
-      label: Proposed Solution
-      description: A clear and concise description of what you want to happen.
-    validations:
-      required: true
-
-  - type: textarea
-    id: alternatives
-    attributes:
-      label: Alternative Solutions
-      description: Any alternative solutions or features you've considered.
-    validations:
-      required: false
-
-  - type: textarea
-    id: context
-    attributes:
-      label: Additional Context
-      description: Add any other context, mockups, or screenshots about the feature request here.
-      placeholder: You can drag and drop images here to include them.
-    validations:
-      required: false
-
-  - type: checkboxes
-    id: contribution
-    attributes:
-      label: Would you be willing to help implement this feature?
-      options:
-        - label: Yes, I'd like to contribute
-        - label: No, I'm just suggesting

.github/ISSUE_TEMPLATE/4-service-issue.yml

@@ -1,82 +0,0 @@
-name: Service Issue
-description: An issue with a third-party service
-type: Service Issue
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Service Issue
-
-        Use this form to report an issue with a third-party service integration.
-
-  - type: input
-    id: pipecat-version
-    attributes:
-      label: pipecat version
-      description: Which version are you using?
-      placeholder: e.g., 0.0.63
-    validations:
-      required: true
-
-  - type: input
-    id: service-name
-    attributes:
-      label: Service Name
-      description: Which third-party service is having issues?
-      placeholder: e.g., OpenAI, ElevenLabs, Anthropic
-    validations:
-      required: true
-
-  - type: input
-    id: service-version
-    attributes:
-      label: Service or model version
-      description: Which version of the service API or model are you using?
-      placeholder: e.g., v1, gpt-4.1
-    validations:
-      required: false
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Issue Description
-      description: Provide a clear description of the service issue.
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduction
-    attributes:
-      label: Reproduction Steps
-      description: Provide steps to reproduce the issue.
-      placeholder: |
-        1. Configure service X
-        2. Call method Y
-        3. See error Z
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected
-    attributes:
-      label: Expected Behavior
-      description: What did you expect to happen?
-    validations:
-      required: true
-
-  - type: textarea
-    id: actual
-    attributes:
-      label: Actual Behavior
-      description: What actually happened?
-    validations:
-      required: true
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Error Logs
-      description: If available, include any error messages or logs.
-      render: shell
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/5-new-service.yml

@@ -1,56 +0,0 @@
-name: New Service
-description: Request to support a new third-party service
-type: New Service
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## New Service Request
-
-        Use this form to request support for a new third-party service in pipecat.
-
-  - type: input
-    id: service-name
-    attributes:
-      label: Service Name
-      description: What is the name of the third-party service?
-      placeholder: e.g., NewAPI, SomeService
-    validations:
-      required: true
-
-  - type: input
-    id: service-website
-    attributes:
-      label: Service Website
-      description: Link to the service's website or documentation
-      placeholder: e.g., https://newapi.com
-    validations:
-      required: true
-
-  - type: textarea
-    id: service-description
-    attributes:
-      label: Service Description
-      description: Briefly describe what this service does and how it works.
-    validations:
-      required: true
-
-  - type: textarea
-    id: api-info
-    attributes:
-      label: API Information
-      description: If available, provide details about the service's API.
-      placeholder: |
-        - API documentation link
-        - Authentication method
-        - Key endpoints you'd like supported
-    validations:
-      required: false
-
-  - type: checkboxes
-    id: contribution
-    attributes:
-      label: Would you be willing to help implement this service?
-      options:
-        - label: Yes, I'd like to contribute
-        - label: No, I'm just suggesting

.github/ISSUE_TEMPLATE/6-dependency.yml

@@ -1,74 +0,0 @@
-name: Dependency Issue
-description: An issue with a Pipecat dependency (not a third-party service)
-type: Dependency Issue
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Dependency Issue
-
-        Use this form to report an issue with a Pipecat dependency.
-
-  - type: input
-    id: pipecat-version
-    attributes:
-      label: pipecat version
-      description: Which version are you using?
-      placeholder: e.g., 0.0.63
-    validations:
-      required: true
-
-  - type: input
-    id: dependency-name
-    attributes:
-      label: Dependency Name
-      description: Which Pipecat dependency is causing the issue?
-      placeholder: e.g., openai, anthropic, fastapi
-    validations:
-      required: true
-
-  - type: input
-    id: dependency-version
-    attributes:
-      label: Dependency Version
-      description: Which version of the dependency are you using?
-      placeholder: e.g., 1.2.3
-    validations:
-      required: true
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Issue Description
-      description: Provide a clear description of the dependency issue.
-    validations:
-      required: true
-
-  - type: textarea
-    id: impact
-    attributes:
-      label: Impact
-      description: How is this dependency issue affecting your usage of pipecat?
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduction
-    attributes:
-      label: Reproduction Steps
-      description: If applicable, provide steps to reproduce the issue.
-      placeholder: |
-        1. Install dependency X
-        2. Run command Y
-        3. See error Z
-    validations:
-      required: false
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Error Logs
-      description: If applicable, include any relevant error messages or logs.
-      render: shell
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/7-troubleshooting.yml

@@ -1,70 +0,0 @@
-name: Troubleshooting
-description: Help with a specific use case
-type: Troubleshooting
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Troubleshooting Request
-
-        Use this form to get help with a specific use case or implementation.
-
-  - type: input
-    id: pipecat-version
-    attributes:
-      label: pipecat version
-      description: Which version are you using?
-      placeholder: e.g., 0.0.63
-    validations:
-      required: true
-
-  - type: input
-    id: python-version
-    attributes:
-      label: Python version
-      description: Which version of Python are you using?
-      placeholder: e.g., 3.12.8
-    validations:
-      required: true
-
-  - type: input
-    id: os
-    attributes:
-      label: Operating System
-      description: Which OS are you using?
-      placeholder: e.g., Ubuntu 24.04, Windows 11, macOS 12.5
-    validations:
-      required: true
-
-  - type: textarea
-    id: use-case
-    attributes:
-      label: Use Case Description
-      description: Describe what you're trying to accomplish with pipecat.
-    validations:
-      required: true
-
-  - type: textarea
-    id: current-approach
-    attributes:
-      label: Current Approach
-      description: What have you tried so far? Include code snippets if relevant.
-      render: python
-    validations:
-      required: true
-
-  - type: textarea
-    id: errors
-    attributes:
-      label: Errors or Unexpected Behavior
-      description: Describe any errors or unexpected behavior you're encountering.
-    validations:
-      required: true
-
-  - type: textarea
-    id: additional-context
-    attributes:
-      label: Additional Context
-      description: Any other information that might help us understand your situation.
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -1 +0,0 @@
-blank_issues_enabled: false

.github/PULL_REQUEST_TEMPLATE.md

@@ -1 +0,0 @@
-#### Please describe the changes in your PR. If it is addressing an issue, please reference that as well.

.github/workflows/build.yaml

@@ -1,40 +0,0 @@
-name: build
-
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - "**"
-    paths-ignore:
-      - "docs/**"
-
-concurrency:
-  group: build-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  build:
-    name: "Build and Install"
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-
-      - name: Set up Python
-        run: uv python install 3.12
-
-      - name: Install development dependencies
-        run: uv sync --group dev
-
-      - name: Build project
-        run: uv build
-
-      - name: Install project in editable mode
-        run: uv pip install --editable .

.github/workflows/coverage.yaml

@@ -1,58 +0,0 @@
-name: coverage
-
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - "**"
-    paths-ignore:
-      - "docs/**"
-
-jobs:
-  coverage:
-    name: "Coverage"
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-
-      - name: Set up Python
-        run: uv python install 3.12
-
-      - name: Install system packages
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y portaudio19-dev
-
-      - name: Install dependencies
-        run: |
-          uv sync --group dev \
-            --extra anthropic \
-            --extra aws \
-            --extra deepgram \
-            --extra google \
-            --extra langchain \
-            --extra livekit \
-            --extra piper \
-            --extra sagemaker \
-            --extra tracing \
-            --extra websocket
-
-      - name: Run tests with coverage
-        run: |
-          uv run coverage run
-          uv run coverage xml
-
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v5
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-          slug: pipecat-ai/pipecat

.github/workflows/format.yaml

@@ -1,43 +0,0 @@
-name: format
-
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - "**"
-    paths-ignore:
-      - "docs/**"
-
-concurrency:
-  group: build-format-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  ruff-format:
-    name: "Code quality checks"
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-
-      - name: Set up Python
-        run: uv python install 3.12
-
-      - name: Install development dependencies
-        run: uv sync --group dev
-
-      - name: Ruff formatter
-        id: ruff-format
-        run: uv run ruff format --diff
-
-      - name: Ruff linter (all rules)
-        id: ruff-check
-        run: uv run ruff check

.github/workflows/generate-changelog.yml

@@ -1,174 +0,0 @@
-name: Generate Changelog for Release
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: "Release version (e.g., 0.0.97)"
-        required: true
-        type: string
-      date:
-        description: "Release date (YYYY-MM-DD format, defaults to today)"
-        required: false
-        type: string
-        default: ""
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  generate-changelog:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          enable-cache: true
-
-      - name: Install dependencies
-        run: |
-          uv sync --group dev
-
-      - name: Set release date
-        id: set_date
-        run: |
-          if [ -z "${{ inputs.date }}" ]; then
-            RELEASE_DATE=$(date +%Y-%m-%d)
-            echo "Using today's date: $RELEASE_DATE"
-          else
-            RELEASE_DATE="${{ inputs.date }}"
-            echo "Using provided date: $RELEASE_DATE"
-          fi
-          echo "release_date=$RELEASE_DATE" >> $GITHUB_OUTPUT
-
-      - name: Validate inputs
-        run: |
-          # Validate version format (basic check)
-          if ! [[ "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+.*$ ]]; then
-            echo "Error: Version must be in format X.Y.Z (e.g., 0.0.97)"
-            exit 1
-          fi
-
-          # Validate date format if provided
-          if [ -n "${{ inputs.date }}" ]; then
-            if ! date -d "${{ inputs.date }}" >/dev/null 2>&1; then
-              # Try macOS date format
-              if ! date -j -f "%Y-%m-%d" "${{ inputs.date }}" >/dev/null 2>&1; then
-                echo "Error: Date must be in YYYY-MM-DD format (e.g., 2025-12-04)"
-                exit 1
-              fi
-            fi
-          fi
-
-      - name: Check for changelog fragments
-        id: check_fragments
-        run: |
-          FRAGMENT_COUNT=$(find changelog -name "*.md" ! -name "_template.md.j2" | wc -l | tr -d ' ')
-          echo "fragment_count=$FRAGMENT_COUNT" >> $GITHUB_OUTPUT
-
-          if [ "$FRAGMENT_COUNT" -eq "0" ]; then
-            echo "❌ Error: No changelog fragments found in changelog/"
-            echo ""
-            echo "Cannot create a release without changelog entries."
-            echo "Add changelog fragments to the changelog/ directory (e.g., 1234.added.md) and try again."
-            exit 1
-          fi
-
-          # Validate fragment types
-          VALID_TYPES="added changed deprecated removed fixed performance security other"
-          INVALID_FRAGMENTS=""
-
-          for file in changelog/*.md; do
-            # Skip template
-            if [[ "$file" == "changelog/_template.md.j2" ]]; then
-              continue
-            fi
-            
-            # Extract type from filename (e.g., 1234.added.md -> added)
-            filename=$(basename "$file")
-            # Handle both 1234.added.md and 1234.added.2.md patterns
-            type=$(echo "$filename" | sed -E 's/^[0-9]+\.([a-z]+)(\.[0-9]+)?\.md$/\1/')
-            
-            # Check if type is valid
-            if ! echo "$VALID_TYPES" | grep -wq "$type"; then
-              INVALID_FRAGMENTS="$INVALID_FRAGMENTS\n  - $filename (type: '$type')"
-            fi
-          done
-
-          if [ -n "$INVALID_FRAGMENTS" ]; then
-            echo "❌ Error: Invalid changelog fragment types found:"
-            echo -e "$INVALID_FRAGMENTS"
-            echo ""
-            echo "Valid types are: $VALID_TYPES"
-            echo "Example: 1234.added.md, 5678.fixed.md"
-            exit 1
-          fi
-
-          echo "✓ Found $FRAGMENT_COUNT changelog fragment(s)"
-          echo "has_fragments=true" >> $GITHUB_OUTPUT
-
-      - name: Preview changelog
-        run: |
-          echo "## Preview of changelog for version ${{ inputs.version }}"
-          echo ""
-          uv run towncrier build --draft --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}"
-
-      - name: Build changelog
-        run: |
-          uv run towncrier build --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}" --yes
-
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "Update changelog for version ${{ inputs.version }}"
-          title: "Release ${{ inputs.version }} - Changelog Update"
-          body: |
-            ## Changelog Update for Release ${{ inputs.version }}
-
-            This PR updates the CHANGELOG.md with all changes for version **${{ inputs.version }}**.
-
-            ### Summary
-            - **Version:** ${{ inputs.version }}
-            - **Date:** ${{ steps.set_date.outputs.release_date }}
-            - **Fragments processed:** ${{ steps.check_fragments.outputs.fragment_count }}
-
-            ### What this PR does
-            - ✅ Adds new release section to CHANGELOG.md
-            - ✅ Removes processed changelog fragments
-            - ✅ Ready to merge for release
-
-            ### Next Steps
-            1. Review the changelog entries below
-            2. Make any necessary edits to CHANGELOG.md if needed
-            3. Merge this PR
-            4. Continue with your release process
-
-            ---
-
-            <details>
-            <summary>📋 Preview of changes</summary>
-
-            The changelog has been updated with entries from the following fragments:
-
-            ```bash
-            ${{ steps.check_fragments.outputs.fragment_count }} fragments processed
-            ```
-
-            </details>
-          branch: changelog-${{ inputs.version }}
-          delete-branch: true
-          labels: |
-            changelog
-            release

.github/workflows/publish.yaml

@@ -1,78 +0,0 @@
-name: publish
-
-on:
-  workflow_dispatch:
-    inputs:
-      gitref:
-        type: string
-        description: 'what git tag to build (e.g. v0.0.74)'
-        required: true
-
-jobs:
-  build:
-    name: 'Build and upload wheels'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.gitref }}
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: 'latest'
-      - name: Set up Python
-        run: uv python install 3.12
-      - name: Install development dependencies
-        run: uv sync --group dev
-      - name: Build project
-        run: uv build
-      - name: Upload wheels
-        uses: actions/upload-artifact@v4
-        with:
-          name: wheels
-          path: ./dist
-
-  publish-to-pypi:
-    name: 'Publish to PyPI'
-    runs-on: ubuntu-latest
-    needs: [build]
-    environment:
-      name: pypi
-      url: https://pypi.org/p/pipecat-ai
-    permissions:
-      id-token: write
-    steps:
-      - name: Download wheels
-        uses: actions/download-artifact@v4
-        with:
-          name: wheels
-          path: ./dist
-      - name: Publish to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          verbose: true
-          print-hash: true
-
-  publish-to-test-pypi:
-    name: 'Publish to Test PyPI'
-    runs-on: ubuntu-latest
-    needs: [build]
-    environment:
-      name: testpypi
-      url: https://test.pypi.org/p/pipecat-ai
-    permissions:
-      id-token: write
-    steps:
-      - name: Download wheels
-        uses: actions/download-artifact@v4
-        with:
-          name: wheels
-          path: ./dist
-      - name: Publish to Test PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          verbose: true
-          print-hash: true
-          repository-url: https://test.pypi.org/legacy/

.github/workflows/publish_test.yaml

@@ -1,51 +0,0 @@
-name: publish-test
-
-on: workflow_dispatch
-
-jobs:
-  build:
-    name: 'Build and upload wheels'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-        with:
-          fetch-tags: true
-          fetch-depth: 100
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: 'latest'
-      - name: Set up Python
-        run: uv python install 3.12
-      - name: Install development dependencies
-        run: uv sync --group dev
-      - name: Build project
-        run: uv build
-      - name: Upload wheels
-        uses: actions/upload-artifact@v4
-        with:
-          name: wheels
-          path: ./dist
-
-  publish-to-test-pypi:
-    name: 'Publish to Test PyPI'
-    runs-on: ubuntu-latest
-    needs: [build]
-    environment:
-      name: testpypi
-      url: https://test.pypi.org/p/pipecat-ai
-    permissions:
-      id-token: write
-    steps:
-      - name: Download wheels
-        uses: actions/download-artifact@v4
-        with:
-          name: wheels
-          path: ./dist
-      - name: Publish to Test PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          verbose: true
-          print-hash: true
-          repository-url: https://test.pypi.org/legacy/

.github/workflows/python-compatibility.yaml

@@ -1,50 +0,0 @@
-name: Python Compatibility Test
-
-on:
-  push:
-    branches: [main, develop]
-    paths: ['pyproject.toml']
-  pull_request:
-    branches: [main, develop]
-    paths: ['pyproject.toml']
-
-jobs:
-  test-compatibility:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ['3.11.15', '3.12.13', '3.13.12', '3.14.3']
-
-    name: Python ${{ matrix.python-version }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y \
-            portaudio19-dev \
-            libcairo2-dev \
-            libgirepository1.0-dev \
-            pkg-config
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          version: 'latest'
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: |
-          uv python install ${{ matrix.python-version }}
-          uv python pin ${{ matrix.python-version }}
-
-      - name: Test uv sync with all extras
-        run: |
-          uv sync --group dev --all-extras
-
-      - name: Verify installation
-        run: |
-          uv run python --version
-          uv run python -c "import pipecat; print('✅ Pipecat imports successfully')"

.github/workflows/tests.yaml

@@ -1,55 +0,0 @@
-name: tests
-
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - "**"
-    paths-ignore:
-      - "docs/**"
-
-concurrency:
-  group: build-test-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  test:
-    name: "Unit and Integration Tests"
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-
-      - name: Set up Python
-        run: uv python install 3.12
-
-      - name: Install system packages
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y portaudio19-dev
-
-      - name: Install dependencies
-        run: |
-          uv sync --group dev \
-            --extra anthropic \
-            --extra aws \
-            --extra deepgram \
-            --extra google \
-            --extra langchain \
-            --extra livekit \
-            --extra piper \
-            --extra sagemaker \
-            --extra tracing \
-            --extra websocket
-
-      - name: Test with pytest
-        run: |
-          uv run pytest

.github/workflows/update-docs.yml

@@ -1,147 +0,0 @@
-name: Update Documentation on PR Merge
-
-on:
-  pull_request_target:
-    types: [closed]
-    branches: [main]
-    paths:
-      - "src/pipecat/services/**"
-      - "src/pipecat/transports/**"
-      - "src/pipecat/serializers/**"
-      - "src/pipecat/processors/**"
-      - "src/pipecat/audio/**"
-      - "src/pipecat/turns/**"
-      - "src/pipecat/observers/**"
-      - "src/pipecat/pipeline/**"
-  workflow_dispatch:
-    inputs:
-      pr_number:
-        description: "PR number to generate docs for"
-        required: true
-        type: string
-
-jobs:
-  update-docs:
-    if: >-
-      github.event_name == 'workflow_dispatch' ||
-      github.event.pull_request.merged == true
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    permissions:
-      contents: read
-      pull-requests: read
-      id-token: write
-    steps:
-      - name: Checkout pipecat
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Checkout docs
-        uses: actions/checkout@v4
-        with:
-          repository: pipecat-ai/docs
-          token: ${{ secrets.DOCS_SYNC_TOKEN }}
-          path: _docs
-
-      - name: Resolve PR number
-        id: pr
-        run: |
-          if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
-            echo "number=${{ inputs.pr_number }}" >> "$GITHUB_OUTPUT"
-          else
-            echo "number=${{ github.event.pull_request.number }}" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Update documentation
-        uses: anthropics/claude-code-action@v1
-        env:
-          DOCS_SYNC_TOKEN: ${{ secrets.DOCS_SYNC_TOKEN }}
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          prompt: |
-            You are updating documentation for the pipecat-ai/docs repository based on
-            changes merged in PR #${{ steps.pr.outputs.number }} of pipecat-ai/pipecat.
-
-            ## Setup
-
-            1. Read the skill instructions at `.claude/skills/update-docs/SKILL.md`
-            2. Read the source-to-doc mapping at `.claude/skills/update-docs/SOURCE_DOC_MAPPING.md`
-            3. The docs repository is checked out at `./_docs/`
-
-            ## Get the diff
-
-            Run `gh pr diff ${{ steps.pr.outputs.number }}` to see what changed in the PR.
-            Also run `gh pr diff ${{ steps.pr.outputs.number }} --name-only` to get the list of changed files.
-            Filter to source files matching the directories listed in SKILL.md Step 3.
-
-            If no relevant source files were changed, exit with "No documentation changes needed."
-
-            ## Follow the skill instructions
-
-            Apply the SKILL.md workflow (Steps 3-9) with these adaptations for automation:
-
-            ### Docs path
-            Use `./_docs/` — it's already checked out. Do not ask for a path.
-
-            ### Branch management
-            - Branch name: `docs/pr-${{ steps.pr.outputs.number }}`
-            - Work inside `./_docs/` for all doc edits and git operations
-            - Check if the branch already exists on the remote:
-              ```bash
-              cd _docs && git fetch origin docs/pr-${{ steps.pr.outputs.number }} 2>/dev/null
-              ```
-              - If it exists: check it out (supports workflow re-runs)
-              - If not: create it from main
-
-            ### Git config
-            Before committing in `_docs`, set:
-            ```bash
-            git config user.name "github-actions[bot]"
-            git config user.email "github-actions[bot]@users.noreply.github.com"
-            ```
-
-            ### No interactive questions
-            Do not ask questions. If you encounter gaps (unmapped files, missing sections,
-            ambiguous changes), note them in the PR body under "## Gaps identified".
-
-            ### Creating the docs PR
-            After committing all changes in `_docs`, push and create a PR:
-            ```bash
-            cd _docs
-            git push -u origin docs/pr-${{ steps.pr.outputs.number }}
-            GH_TOKEN=$DOCS_SYNC_TOKEN gh pr create \
-              --repo pipecat-ai/docs \
-              --label auto-docs \
-              --title "docs: update for pipecat PR #${{ steps.pr.outputs.number }}" \
-              --body "$(cat <<'BODY'
-            Automated documentation update for [pipecat PR #${{ steps.pr.outputs.number }}](https://github.com/pipecat-ai/pipecat/pull/${{ steps.pr.outputs.number }}).
-
-            ## Changes
-            <summarize each doc page updated and what changed>
-
-            ## Gaps identified
-            <any unmapped files, missing doc pages, or missing sections — or "None">
-            BODY
-            )"
-            ```
-
-            ### Re-run handling
-            If `gh pr create` fails because a PR from that branch already exists,
-            push the updated commits and use `gh pr edit` to update the body instead.
-
-            ### No-op
-            If after analyzing the diff you determine no documentation changes are needed
-            (e.g., only skip-listed files changed, or changes don't affect public API docs),
-            exit cleanly without creating a branch or PR. Output "No documentation changes needed."
-
-            ## Important rules
-            - Only modify files inside `./_docs/` — never modify pipecat source code
-            - Follow the conservative editing rules from SKILL.md Step 6
-            - Read each doc page fully before editing (SKILL.md Guidelines)
-            - Use `GH_TOKEN=$DOCS_SYNC_TOKEN` for all `gh` commands targeting pipecat-ai/docs
-          claude_args: |
-            --model claude-sonnet-4-5-20250929
-            --max-turns 30
-            --allowedTools "Read,Write,Edit,Glob,Grep,Bash"

.gitignore

@@ -3,18 +3,9 @@ env/
 __pycache__/
 *~
 venv
-.venv
-.idea
-.gradle
-.next
-next-env.d.ts
-local.properties
-*.log
-*.lock
-smart_turn_audio_log
 #*#
 
-# Distribution / Packaging
+# Distribution / packaging
 .Python
 build/
 develop-eggs/
@@ -34,31 +25,4 @@ share/python-wheels/
 *.egg
 MANIFEST
 .DS_Store
-.env*
-fly.toml
-
-# Examples
-examples/**/node_modules/
-examples/**/.expo/
-examples/**/dist/
-examples/**/npm-debug.*
-examples/**/*.jks
-examples/**/*.p8
-examples/**/*.p12
-examples/**/*.key
-examples/**/*.mobileprovision
-examples/**/*.orig.*
-examples/**/web-build/
-
-# macOS
-.DS_Store
-
-# Documentation
-docs/api/_build/
-docs/api/api
-
-# uv
-.python-version
-
-# Pipecat
-whisker_setup.py
+.env

.pre-commit-config.yaml

@@ -1,13 +0,0 @@
-repos:
-  - repo: local
-    hooks:
-      - id: ruff
-        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

@@ -1,28 +0,0 @@
-version: 2
-
-build:
-  os: ubuntu-22.04
-  tools:
-    python: '3.12'
-  apt_packages:
-    - portaudio19-dev
-    - python3-dev
-    - libasound2-dev
-  jobs:
-    post_install:
-      - pip install uv
-      - 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
-  fail_on_warning: false
-
-search:
-  ranking:
-    api/*: 5
-    getting-started/*: 4
-    guides/*: 3
-
-submodules:
-  include: all
-  recursive: true

CLAUDE.md

@@ -1,157 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Project Overview
-
-Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
-
-## Common Commands
-
-```bash
-# Setup development environment
-uv sync --group dev --all-extras --no-extra gstreamer
-
-# Install pre-commit hooks
-uv run pre-commit install
-
-# Run all tests
-uv run pytest
-
-# Run a single test file
-uv run pytest tests/test_name.py
-
-# Run a specific test
-uv run pytest tests/test_name.py::test_function_name
-
-# Preview changelog
-uv run towncrier build --draft --version Unreleased
-
-# Lint and format check
-uv run ruff check
-uv run ruff format --check
-
-# Update dependencies (after editing pyproject.toml)
-uv lock && uv sync
-```
-
-## Architecture
-
-### Frame-Based Pipeline Processing
-
-All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
-
-```
-[Processor1] → [Processor2] → ... → [ProcessorN]
-```
-
-**Key components:**
-
-- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
-
-- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
-
-- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
-
-- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
-
-- **Transports** (`src/pipecat/transports/`): Transports are frame processors used for external I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`, `BaseInputTransport` and `BaseOutputTransport`.
-
-- **Pipeline Task (`src/pipecat/pipeline/task.py`)**: Runs and manages a pipeline. Pipeline tasks send the first frame, `StartFrame`, to the pipeline in order for processors to know they can start processing and pushing frames. Pipeline tasks internally create a pipeline with two additional processors, a source processor before the user-defined pipeline and a sink processor at the end. Those are used for multiple things: error handling, pipeline task level events, heartbeat monitoring, etc.
-
-- **Pipeline Runner (`src/pipecat/pipeline/runner.py`)**: High-level entry point for executing pipeline tasks. Handles signal management (SIGINT/SIGTERM) for graceful shutdown and optional garbage collection. Run a single pipeline task with `await runner.run(task)` or multiple concurrently with `await asyncio.gather(runner.run(task1), runner.run(task2))`.
-
-- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
-
-- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
-
-- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
-
-- **Observers** (`src/pipecat/observers/`): Monitor frame flow without modifying the pipeline. Passed to `PipelineTask` via the `observers` parameter. Implement `on_process_frame()` and `on_push_frame()` callbacks.
-
-### Important Patterns
-
-- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
-
-- **Turn Management**: Turn management is done through `LLMUserAggregator` and
-  `LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
-
-- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
-
-- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
-
-- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
-
-- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to execute fast.
-
-- **Async Task Management**: Always use `self.create_task(coroutine, name)` instead of raw `asyncio.create_task()`. The `TaskManager` automatically tracks tasks and cleans them up on processor shutdown. Use `await self.cancel_task(task, timeout)` for cancellation.
-
-- **Error Handling**: Use `await self.push_error(msg, exception, fatal)` to push errors upstream. Services should use `fatal=False` (the default) so application code can handle errors and take action (e.g. switch to another service).
-
-### Key Directories
-
-| Directory                  | Purpose                                            |
-| -------------------------- | -------------------------------------------------- |
-| `src/pipecat/frames/`      | Frame definitions (100+ types)                     |
-| `src/pipecat/processors/`  | FrameProcessor base + aggregators, filters, audio  |
-| `src/pipecat/pipeline/`    | Pipeline orchestration                             |
-| `src/pipecat/services/`    | AI service integrations (60+ providers)            |
-| `src/pipecat/transports/`  | Transport layer (Daily, LiveKit, WebSocket, Local) |
-| `src/pipecat/serializers/` | Frame serialization for WebSocket protocols        |
-| `src/pipecat/observers/`   | Pipeline observers for monitoring frame flow       |
-| `src/pipecat/audio/`       | VAD, filters, mixers, turn detection, DTMF         |
-| `src/pipecat/turns/`       | User turn management                               |
-
-## Code Style
-
-- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
-- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
-- **Type hints**: Required for complex async code.
-- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
-  - `@dataclass`: Frame types, context aggregator pairs, internal data containers
-  - `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
-
-### Docstring Example
-
-```python
-class MyService(LLMService):
-    """Description of what the service does.
-
-    More detailed description.
-
-    Event handlers available:
-
-    - on_connected: Called when we are connected
-
-    Example::
-
-        @service.event_handler("on_connected")
-        async def on_connected(service, frame):
-            ...
-    """
-
-    def __init__(self, param1: str, **kwargs):
-        """Initialize the service.
-
-        Args:
-            param1: Description of param1.
-            **kwargs: Additional arguments passed to parent.
-        """
-        super().__init__(**kwargs)
-```
-
-## Service Implementation
-
-When adding a new service:
-
-1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
-2. Implement required abstract methods
-3. Handle necessary frames
-4. By default, all frames should be pushed in the direction they came
-5. Push `ErrorFrame` on failures
-6. Add metrics tracking via `MetricsData` if relevant
-7. Follow the pattern of existing services in `src/pipecat/services/`
-
-## Testing
-
-Test utilities live in `src/pipecat/tests/utils.py`. Use `run_test()` to send frames through a pipeline and assert expected output frames in each direction. Use `SleepFrame(sleep=N)` to add delays between frames.

COMMUNITY_INTEGRATIONS.md

@@ -1,474 +0,0 @@
-# Community Integrations Guide
-
-Pipecat welcomes community-maintained integrations! As our ecosystem grows, we've established a process for any developer to create and maintain their own service integrations while ensuring discoverability for the Pipecat community.
-
-## Overview
-
-**What we support:** Community-maintained integrations that live in separate repositories and are maintained by their authors.
-
-**What we don't do:** The Pipecat team does not code review, test, or maintain community integrations. We provide guidance and list approved integrations for discoverability.
-
-**Why this approach:** This allows the community to move quickly while keeping the Pipecat core team focused on maintaining the framework itself.
-
-## Submitting your Integration
-
-To be listed as an official community integration, follow these steps:
-
-### Step 1: Build Your Integration
-
-Create your integration following the patterns and examples shown in the "Integration Patterns and Examples" section below.
-
-### Step 2: Set Up Your Repository
-
-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))
-- **README.md** - Must include:
-  - Introduction and explanation of your integration
-  - Installation instructions
-  - Usage instructions with Pipecat Pipeline
-  - How to run your example
-  - Pipecat version compatibility (e.g., "Tested with Pipecat v0.0.86")
-  - Company attribution: If you work for the company providing the service, please mention this in your README. This helps build confidence that the integration will be actively maintained.
-
-- **LICENSE** - Permissive license (BSD-2 like Pipecat, or equivalent open source terms)
-- **Code documentation** - Source code with docstrings (we recommend following [Pipecat's docstring conventions](https://github.com/pipecat-ai/pipecat/blob/main/CONTRIBUTING.md#docstring-conventions))
-- **Changelog** - Maintain a changelog for version updates
-
-### Step 3: Join Discord
-
-Join our Discord: https://discord.gg/pipecat
-
-### Step 4: Submit for Listing
-
-Submit a pull request to add your integration to our [Community Integrations documentation page](https://docs.pipecat.ai/server/services/community-integrations).
-
-**To submit:**
-
-1. Fork the [Pipecat docs repository](https://github.com/pipecat-ai/docs)
-2. Edit the file `server/services/community-integrations.mdx`
-3. Add your integration to the appropriate service category table with:
-   - Service name
-   - Link to your repository
-   - Maintainer GitHub username(s)
-4. Include a link to your demo video (approx 30-60 seconds) in your PR description showing:
-   - Core functionality of your integration
-   - Handling of an interruption (if applicable to service type)
-5. Submit your pull request
-
-Once your PR is submitted, post in the `#community-integrations` Discord channel to let us know.
-
-## Integration Patterns and Examples
-
-### STT (Speech-to-Text) Services
-
-#### 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)
-- [GoogleSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/stt.py)
-
-#### File-based Services
-
-**Base class:** `SegmentedSTTService`
-
-**Examples:**
-
-- [NvidiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/nvidia/stt.py)
-- [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
-
-#### Key requirements:
-
-- STT services should push `InterimTranscriptionFrames` and `TranscriptionFrames`
-- If confidence values are available, filter for values >50% confidence
-
-### LLM (Large Language Model) Services
-
-#### OpenAI-Compatible Services
-
-**Base class:** `OpenAILLMService`
-
-**Examples:**
-
-- [AzureLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/azure/llm.py)
-- [GrokLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/grok/llm.py) - Shows overriding the base class where needed
-
-#### Non-OpenAI Compatible Services
-
-**Requires:** Full implementation
-
-**Examples:**
-
-- [AnthropicLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/anthropic/llm.py)
-- [GoogleLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/llm.py)
-
-#### Key requirements:
-
-- **`_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`.
-
-- **`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
-
-#### WebsocketTTSService
-
-**Use for:** Websocket-based streaming services (with or without word timestamps)
-
-**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 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)
-
-#### TTSService
-
-**Use for:** HTTP-based services (word timestamps are supported in the base class)
-
-**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
-- Handle idle service timeouts with keepalives
-- TTS services push both audio (`TTSAudioRawFrame`) and text (`TTSTextFrame`) frames
-
-### Telephony Serializers
-
-Pipecat supports telephony provider integration using websocket connections to exchange MediaStreams. These services use a FrameSerializer to serialize and deserialize inputs from the FastAPIWebsocketTransport.
-
-**Examples:**
-
-- [Twilio](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/twilio.py)
-- [Telnyx](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/telnyx.py)
-
-#### Key requirements:
-
-- Include hang-up functionality using the provider's native API, ideally using `aiohttp`
-- Support DTMF (dual-tone multi-frequency) events if the provider supports them:
-  - Deserialize DTMF events from the provider's protocol to `InputDTMFFrame`
-  - Use `KeypadEntry` enum for valid keypad entries (0-9, \*, #, A-D)
-  - Handle invalid DTMF digits gracefully by returning `None`
-
-### Image Generation Services
-
-**Base class:** `ImageGenService`
-
-**Examples:**
-
-- [FalImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/image.py)
-- [GoogleImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/image.py)
-
-#### Key requirements:
-
-- Must implement `run_image_gen` method returning an `AsyncGenerator`
-
-### Vision Services
-
-Vision services process images and provide analysis such as descriptions, object detection, or visual question answering.
-
-**Base class:** `VisionService`
-
-**Example:**
-
-- [MoondreamVisionService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/moondream/vision.py)
-
-#### Key requirements:
-
-- 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:**
-  - Websocket: `VendorTTSService`
-  - HTTP: `VendorHttpTTSService`
-- **Image:** `VendorImageGenService`
-- **Vision:** `VendorVisionService`
-- **Telephony:** `VendorFrameSerializer`
-
-### Metrics Support
-
-Enable metrics in your service:
-
-```python
-def can_generate_metrics(self) -> bool:
-    """Check if this service can generate processing metrics.
-
-    Returns:
-        True, as this service supports metrics.
-    """
-    return True
-```
-
-### Service Settings
-
-Every AI service (STT, LLM, TTS, image generation, etc.) exposes a **Settings dataclass** that serves two roles:
-
-1. **Store mode** — the service's `self._settings` holds the current value of every runtime-updatable field.
-2. **Delta mode** — an update frame (e.g. `TTSUpdateSettingsFrame`) specifies only the fields that should change; unspecified fields remain `NOT_GIVEN`.
-
-#### Defining your Settings class
-
-Extend `STTSettings`, `TTSSettings`, `LLMSettings`, or `ImageGenSettings` (or, if your service directly subclasses `AIService`, `ServiceSettings`). The base classes already provide common fields (e.g. `model`, `voice`, `language`). You only need to add **service-specific knobs that should be runtime-updatable**:
-
-```python
-from dataclasses import dataclass, field
-
-from pipecat.services.settings import TTSSettings, NOT_GIVEN
-
-@dataclass
-class MyTTSSettings(TTSSettings):
-    """Settings for MyTTS service.
-
-    Parameters:
-        speaking_rate: Speed multiplier (0.5–2.0).
-    """
-
-    speaking_rate: float | None = field(default_factory=lambda: NOT_GIVEN)
-```
-
-**What goes in Settings vs. `__init__` params:**
-
-| Belongs in Settings                                      | Stays as `__init__` params                |
-| -------------------------------------------------------- | ----------------------------------------- |
-| Model name, voice, language                              | API keys, auth tokens                     |
-| Service-specific tuning knobs (rate, pitch, temperature) | Base URLs, endpoint overrides             |
-| Anything users may want to change mid-session            | Audio encoding, sample format             |
-|                                                          | Connection parameters (timeouts, retries) |
-
-The rule of thumb: if a caller might send an update frame to change it at runtime, it belongs in Settings. Everything else is init-only config stored as `self._xxx`.
-
-#### Wiring settings into `__init__`
-
-Accept an **optional** `settings` parameter. Build a `default_settings` object with all fields set to real values, then merge any caller overrides with `apply_update`.
-
-Add a `Settings` **class attribute** that points to your settings dataclass. This lets callers access the settings class through the service itself (e.g. `MyTTSService.Settings(...)`) without a separate import:
-
-```python
-from typing import Optional
-
-class MyTTSService(TTSService):
-    Settings = MyTTSSettings
-    _settings: Settings
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        settings: Optional[Settings] = None,
-        **kwargs,
-    ):
-        # 1. Defaults — every field has a real value (store mode).
-        default_settings = self.Settings(
-            model="my-model-v1",
-            voice="default-voice",
-            language="en",
-            speaking_rate=1.0,
-        )
-
-        # 2. Merge caller overrides (only given fields win).
-        if settings is not None:
-            default_settings.apply_update(settings)
-
-        # 3. Pass the fully-populated settings to the base class.
-        super().__init__(settings=default_settings, **kwargs)
-
-        # 4. Init-only config stored separately.
-        self._api_key = api_key
-```
-
-This pattern lets callers override only what they care about:
-
-```python
-# Uses all defaults
-svc = MyTTSService(api_key="sk-xxx")
-
-# Overrides just the voice — access Settings through the service class
-svc = MyTTSService(
-    api_key="sk-xxx",
-    settings=MyTTSService.Settings(voice="custom-voice"),
-)
-```
-
-#### Reacting to runtime changes
-
-AI services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
-
-To react to runtime setting changes, override `_update_settings`. The base implementation applies the delta to `self._settings` and returns a `dict` mapping each changed field name to its **pre-update** value. Your override should call `super()` first, then act on the changed fields. A common implementation might look like:
-
-```python
-async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
-    """Apply a settings update, reconfiguring the connection if needed."""
-    changed = await super()._update_settings(update)
-
-    if not changed:
-        return changed
-
-    await self._disconnect()
-    await self._connect()
-
-    return changed
-```
-
-The dict keys work like a set for membership tests (`"language" in changed`) and truthiness (`if changed`). Use `changed.keys() - {"language"}` for set difference, or `changed["language"]` to inspect the previous value of a field.
-
-Note that, in this example, the service requires a reconnect to apply the new language. Consider, for each setting, whether your service requires reconnection or can apply changes in-place.
-
-If your service can't yet apply certain settings at runtime, call `self._warn_unhandled_updated_settings(changed)` with any unhandled field names so users get a clear log message:
-
-```python
-async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
-    changed = await super()._update_settings(update)
-
-    if not changed:
-        return changed
-
-    if "language" in changed:
-        await self._update_language()
-    else:
-        # TODO: this should be temporary - handle changes to other settings soon!
-        self._warn_unhandled_updated_settings(changed.keys() - {"language"})
-
-    return changed
-```
-
-### Sample Rate Handling
-
-Sample rates are set via PipelineParams and passed to each frame processor at initialization. The pattern is to _not_ set the sample rate value in the constructor of a given service. Instead, use the `start()` method to initialize sample rates from the frame:
-
-```python
-async def start(self, frame: StartFrame):
-    """Start the service."""
-    await super().start(frame)
-    self._settings.output_sample_rate = self.sample_rate
-    await self._connect()
-```
-
-Note that `self.sample_rate` is a `@property` set in the TTSService base class, which provides access to the private sample rate value obtained from the StartFrame.
-
-### Tracing Decorators
-
-Use Pipecat's tracing decorators:
-
-- **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
-
-## Best Practices
-
-### Packaging and Distribution
-
-- Name your package `pipecat-{vendor}` (see [Naming Conventions](#naming-conventions))
-- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
-- Publish to PyPI for easier installation
-- Follow semantic versioning principles
-- Maintain a changelog
-
-### HTTP Communication
-
-For REST-based communication, use aiohttp. Pipecat includes this as a required dependency, so using it prevents adding an additional dependency to your integration.
-
-### Error Handling
-
-- 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 errors to notify the pipeline:
-
-```python
-try:
-    # Your API call
-    result = await self._make_api_call()
-except Exception as e:
-    # Push error upstream to notify the pipeline
-    await self.push_error(f"{self} error: {e}", exception=e)
-    # Raise or handle as appropriate
-    raise
-```
-
-### Testing
-
-- Your foundational example serves as a valuable integration-level test
-- Unit tests are nice to have. As the Pipecat teams provides better guidance, we will encourage unit testing more
-
-## Disclaimer
-
-Community integrations are community-maintained and not officially supported by the Pipecat team. Users should evaluate these integrations independently. The Pipecat team reserves the right to remove listings that become unmaintained or problematic.
-
-## Staying Up to Date
-
-Pipecat evolves rapidly to support the latest AI technologies and patterns. While we strive to minimize breaking changes, they do occur as the framework matures.
-
-**We strongly recommend:**
-
-- Join our Discord at https://discord.gg/pipecat and monitor the `#announcements` channel for release notifications
-- Follow our changelog: https://github.com/pipecat-ai/pipecat/blob/main/CHANGELOG.md
-- Test your integration against new Pipecat releases promptly
-- Update your README with the last tested Pipecat version
-
-This helps ensure your integration remains compatible and your users have clear expectations about version support.
-
-## Questions?
-
-Join our Discord community at https://discord.gg/pipecat and post in the `#community-integrations` channel for guidance and support.
-
-For additional questions, you can also reach out to us at pipecat-ai@daily.co.

CONTRIBUTING.md

@@ -1,437 +0,0 @@
-## Contributing to Pipecat
-
-**Want to add a new service integration?**
-We encourage community-maintained integrations! Please see our [Community Integration Guide](COMMUNITY_INTEGRATIONS.md) for the process and requirements.
-
-**Want to contribute to Pipecat core?**
-We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
-
-1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.
-
-2. **Clone the repository**: Clone your forked repository to your local machine.
-   ```bash
-   git clone https://github.com/your-username/pipecat
-   ```
-3. **Create a branch**: For your contribution, create a new branch.
-   ```bash
-   git checkout -b your-branch-name
-   ```
-4. **Make your changes**: Edit or add files as necessary.
-5. **Add a changelog entry**: Create a changelog fragment file (see [Changelog Entries](#changelog-entries) below).
-6. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
-7. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
-
-```bash
-git commit -m "Description of your changes"
-```
-
-8. **Push your changes**: Push your branch to your forked repository.
-
-```bash
-git push origin your-branch-name
-```
-
-9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
-   > Important: Describe the changes you've made clearly!
-
-Our maintainers will review your PR, and once everything is good, your contributions will be merged!
-
-## Changelog Entries
-
-Every pull request that makes a user-facing change should include a changelog entry. We use a changelog fragment system to avoid merge conflicts.
-
-### Creating a Changelog Fragment
-
-1. Create a new file in the `changelog/` directory with this naming pattern:
-
-   ```
-   <PR_number>.<type>.md
-   ```
-
-2. Choose the appropriate type:
-   - `added.md` - New features
-   - `changed.md` - Changes in existing functionality
-   - `deprecated.md` - Soon-to-be removed features
-   - `removed.md` - Removed features
-   - `fixed.md` - Bug fixes
-   - `performance.md` - Performance improvements
-   - `security.md` - Security fixes
-   - `other.md` - Other changes (documentation, dependencies, etc.)
-
-3. Write your changelog entry as a Markdown bullet point. Include the `-` at the start:
-
-**Example files:**
-
-`changelog/1234.added.md`:
-
-```markdown
-- Added support for Anthropic Claude 3.5 Sonnet with improved streaming performance.
-```
-
-`changelog/5678.fixed.md`:
-
-```markdown
-- Fixed an issue where audio frames were dropped during high-load scenarios.
-```
-
-**For entries with nested bullets:**
-
-`changelog/1234.changed.md`:
-
-```markdown
-- Updated service configuration:
-  - Changed default timeout to 30 seconds
-  - Added retry logic for failed connections
-```
-
-### Multiple Changes in One PR
-
-**Different types of changes:** Create separate fragment files for each type:
-
-```
-changelog/1234.added.md
-changelog/1234.fixed.md
-```
-
-**Multiple changes of the same type:** Create numbered fragment files:
-
-```
-changelog/1234.changed.md
-changelog/1234.changed.2.md
-```
-
-**Related changes:** Use nested bullets in a single fragment:
-
-```markdown
-- Updated service configuration:
-  - Changed default timeout to 30 seconds
-  - Added retry logic for failed connections
-```
-
-**Rule of thumb:** One logical change per fragment file. If changes are unrelated, use separate files.
-
-### Preview Your Changes
-
-To see what your changelog entry will look like:
-
-```bash
-towncrier build --draft --version Unreleased
-```
-
-This won't modify any files, just show you a preview.
-
-### When to Skip Changelog Entries
-
-You can skip adding a changelog entry for:
-
-- Documentation-only changes
-- Internal refactoring with no user-facing impact
-- Test-only changes
-- CI/build configuration changes
-
-If you're unsure whether your change needs a changelog entry, ask in your PR!
-
-## Dependency Management
-
-This project uses [uv](https://docs.astral.sh/uv/) for dependency management. The `uv.lock` file is committed to ensure reproducible builds.
-
-### Adding or Updating Dependencies
-
-1. Edit `pyproject.toml` to add/update dependencies
-2. Run `uv lock` to update the lockfile with new dependency resolution
-3. Run `uv sync` to install the updated dependencies locally
-4. Always commit both files together:
-   ```bash
-   git add pyproject.toml uv.lock
-   git commit -m "feat: add new dependency for feature X"
-   ```
-
-**Important:** Never manually edit `uv.lock`. It's auto-generated by `uv lock`.
-
-## Code Style and Documentation
-
-### Python Code Style
-
-We use Ruff for code linting and formatting. Please ensure your code passes all linting checks before submitting a PR.
-
-### Docstring Conventions
-
-We follow Google-style docstrings with these specific conventions:
-
-**Regular Classes:**
-
-- Class docstring describes the class purpose and key functionality
-- `__init__` method has its own docstring with complete `Args:` section documenting all parameters
-- All public methods must have docstrings with `Args:` and `Returns:` sections as appropriate
-
-**Dataclasses:**
-
-- Class docstring describes the purpose and documents all fields in a `Parameters:` section
-- No `__init__` docstring (auto-generated)
-
-**Properties:**
-
-- Must have docstrings with `Returns:` section
-
-**Abstract Methods:**
-
-- Must have docstrings explaining what subclasses should implement
-
-**`__init__.py` Files:**
-
-- **Skip docstrings** for pure import/re-export modules
-- **Add brief docstrings** for top-level packages or those with initialization logic
-
-**Enums:**
-
-- Class docstring describes the enumeration purpose
-- Use `Parameters:` section to document each enum value and its meaning
-- No `__init__` docstring (Enums don't have custom constructors)
-
-**Code Examples in Docstrings:**
-
-- Use `Examples:` as a section header for multiple examples
-- Use descriptive text followed by double colons (`::`) for each example
-- **Always include a blank line after the `::"`**
-- Indent all code consistently within each block
-- Separate multiple examples with blank lines for readability
-
-**Lists and Bullets in Docstrings:**
-
-- Use dashes (`-`) for bullet points, not asterisks (`*`)
-- **Add a blank line before bullet lists** when they follow a colon
-- Use section headers like "Supported features:" or "Behavior:" before lists
-- For complex nested information, consider using paragraph format instead
-
-**Deprecations:**
-
-- Use `warnings.warn()` in code for runtime deprecation warnings
-- Add `.. deprecated::` directive in docstrings for documentation visibility
-- Include version information and describe current status
-- Describe parameters in present tense, use directive to indicate deprecation status
-
-#### Examples:
-
-```python
-# Regular class
-class MyService(BaseService):
-    """Description of what the service does.
-
-    Provides detailed explanation of the service's functionality,
-    key features, and usage patterns.
-
-    Supported features:
-
-    - Feature one with detailed explanation
-    - Feature two with additional context
-    - Feature three for advanced use cases
-    """
-
-    def __init__(self, param1: str, old_param: str = None, **kwargs):
-        """Initialize the service.
-
-        Args:
-            param1: Description of param1.
-            old_param: Controls legacy behavior.
-
-                .. deprecated:: 1.2.0
-                    This parameter no longer has any effect and will be removed in version 2.0.
-
-            **kwargs: Additional arguments passed to parent.
-        """
-        if old_param is not None:
-            import warnings
-            warnings.warn(
-                "Parameter 'old_param' is deprecated and will be removed in version 2.0.",
-                DeprecationWarning,
-            )
-        super().__init__(**kwargs)
-
-    @property
-    def sample_rate(self) -> int:
-        """Get the current sample rate.
-
-        Returns:
-            The sample rate in Hz.
-        """
-        return self._sample_rate
-
-    async def process_data(self, data: str) -> bool:
-        """Process the provided data.
-
-        Args:
-            data: The data to process.
-
-        Returns:
-            True if processing succeeded.
-        """
-        pass
-
-# Dataclass with code examples
-@dataclass
-class MessageFrame:
-    """Frame containing messages in OpenAI format.
-
-    Supports both simple and content list message formats.
-
-    Example::
-
-        [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"}
-        ]
-
-    Parameters:
-        messages: List of messages in OpenAI format.
-    """
-
-    messages: List[dict]
-
-# Enum class
-class Status(Enum):
-    """Status codes for processing operations.
-
-    Parameters:
-        PENDING: Operation is queued but not started.
-        RUNNING: Operation is currently in progress.
-        COMPLETED: Operation finished successfully.
-        FAILED: Operation encountered an error.
-    """
-
-    PENDING = "pending"
-    RUNNING = "running"
-    COMPLETED = "completed"
-    FAILED = "failed"
-```
-
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-- Demonstrating empathy and kindness toward other people
-- Being respectful of differing opinions, viewpoints, and experiences
-- Giving and gracefully accepting constructive feedback
-- Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-- Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-- The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-- Trolling, insulting or derogatory comments, and personal or political attacks
-- Public or private harassment
-- Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-- Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official email address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at pipecat-ai@daily.co.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations

LICENSE

@@ -1,6 +1,6 @@
 BSD 2-Clause License
 
-Copyright (c) 2024–2026, Daily
+Copyright (c) 2024, Daily
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:

MANIFEST.in

@@ -1,4 +0,0 @@
-prune docs
-prune examples
-prune scripts
-prune tests

README.md

@@ -1,222 +1,159 @@
-<h1><div align="center">
- <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
-</div></h1>
+# Daily AI SDK
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/pipecat-ai/pipecat)
+Build conversational, multi-modal AI apps with real-time voice and video, like this:
 
-# 🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
+_Demo Video to come_
 
-**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.
+With built-in support for many of the best AI platforms (or [add your own](/docs)):
 
-> Want to dive right in? Run `pipecat init quickstart` or follow the [quickstart guide](https://docs.pipecat.ai/getting-started/quickstart).
+- Azure - DALL-E, ChatGPT, and Azure AI Text-to-Speech
+- Deepgram - Speech-to-text, and Aura text-to-speech
+- Eleven Labs text-to-speech
+- Fal.ai image generation
+- OpenAI DALL-E and ChatGPT
+- Whisper local speech-to-text
 
-## 🚀 What You Can Build
+## Step 1: Get Started
 
-- **Voice Assistants** – natural, streaming conversations with AI
-- **AI Companions** – coaches, meeting assistants, characters
-- **Multimodal Interfaces** – voice, video, images, and more
-- **Interactive Storytelling** – creative tools with generative media
-- **Business Agents** – customer intake, support bots, guided flows
-- **Complex Dialog Systems** – design logic with structured conversations
+## Build/Install
 
-## 🧠 Why Pipecat?
-
-- **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
-- **Pluggable**: Supports many AI services and tools
-- **Composable Pipelines**: Build complex behavior from modular components
-- **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
-
-## 🌐 Pipecat Ecosystem
-
-### 📱 Client SDKs
-
-Building client applications? You can connect to Pipecat from any platform using our official SDKs:
-
-<a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a> | <a href="https://docs.pipecat.ai/client/react/introduction">React</a> | <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a> |
-<a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a> | <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a> | <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a> | <a href="https://github.com/pipecat-ai/pipecat-esp32">ESP32</a>
-
-### 🧭 Structured conversations
-
-Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
-
-### 🪄 Beautiful UIs
-
-Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
-
-### 🛠️ Create and deploy projects
-
-Create a new project in under a minute with the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli). Then use the CLI to monitor and deploy your agent to production.
-
-### 🔍 Debugging
-
-Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
-
-### 🖥️ Terminal
-
-Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail), a terminal dashboard for Pipecat.
-
-### 🤖 Claude Code Skills
-
-Use [Pipecat Skills](https://github.com/pipecat-ai/skills) with [Claude Code](https://claude.ai/code) to scaffold projects, deploy to Pipecat Cloud, and more. Install the marketplace with:
+_Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
 
 ```
-claude plugin marketplace add pipecat-ai/skills
+python3 -m venv env
+source env/bin/activate
 ```
 
-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.
-
-## 🎬 See it in action
-
-<p float="left">
-    <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/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), [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), [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)
-
-## ⚡ Getting started
-
-You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when you're ready.
-
-1. Install uv
-
-   ```bash
-   curl -LsSf https://astral.sh/uv/install.sh | sh
-   ```
-
-   > **Need help?** Refer to the [uv install documentation](https://docs.astral.sh/uv/getting-started/installation/).
-
-2. Install the module
-
-   ```bash
-   # For new projects
-   uv init my-pipecat-app
-   cd my-pipecat-app
-   uv add pipecat-ai
-
-   # Or for existing projects
-   uv add pipecat-ai
-   ```
-
-3. Set up your environment
-
-   ```bash
-   cp env.example .env
-   ```
-
-4. To keep things lightweight, only the core framework is included by default. If you need support for third-party AI services, you can add the necessary dependencies with:
-
-   ```bash
-   uv add "pipecat-ai[option,...]"
-   ```
-
-> **Using pip?** You can still use `pip install pipecat-ai` and `pip install "pipecat-ai[option,...]"` to get set up.
-
-## 🧪 Code examples
-
-- [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:** 3.11
-**Recommended Python Version:** >= 3.12
-
-### Setup Steps
-
-1. Clone the repository and navigate to it:
-
-   ```bash
-   git clone https://github.com/pipecat-ai/pipecat.git
-   cd pipecat
-   ```
-
-2. Install development and testing dependencies:
-
-   ```bash
-   uv sync --group dev --all-extras \
-     --no-extra gstreamer \
-     --no-extra local \
-   ```
-
-3. Install the git pre-commit hooks:
-
-   ```bash
-   uv run pre-commit install
-   ```
-
-> **Note**: Some extras (local, gstreamer) require system dependencies. See documentation if you encounter build errors.
-
-### Claude Code Skills
-
-Install development workflow skills for contributing to Pipecat with [Claude Code](https://claude.ai/code):
+From the root of this repo, run the following:
 
 ```
-claude plugin marketplace add pipecat-ai/pipecat
-claude plugin install pipecat-dev@pipecat-dev-skills
+pip install -r requirements.txt
+python -m build
 ```
 
-### Running tests
+This builds the package. To use the package locally (eg to run sample files), run
 
-To run all tests, from the root directory:
-
-```bash
-uv run pytest
+```
+pip install --editable .
 ```
 
-Run a specific test suite:
+If you want to use this package from another directory, you can run:
 
-```bash
-uv run pytest tests/test_name.py
+```
+pip install path_to_this_repo
 ```
 
-## 🤝 Contributing
+## Running the samples
 
-We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
+Tou can run the simple sample like so:
 
-- **Found a bug?** Open an [issue](https://github.com/pipecat-ai/pipecat/issues)
-- **Have a feature idea?** Start a [discussion](https://discord.gg/pipecat)
-- **Want to contribute code?** Check our [CONTRIBUTING.md](CONTRIBUTING.md) guide
-- **Documentation improvements?** [Docs](https://github.com/pipecat-ai/docs) PRs are always welcome
+```
+python src/examples/theoretical-to-real/01-say-one-thing.py -u <url of your Daily meeting> -k <your Daily API Key>
+```
+## Overview
 
-Before submitting a pull request, please check existing issues and PRs to avoid duplicates.
+The Daily AI SDK allows you to build applications that can participate in WebRTC sessions and interact with AI Services. Some examples of what you can build with this:
 
-We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.
+- conversational bots that interact 1:1 with a user, using voice recognition and text-to-speech
+- assistant bots that aggregate transcriptions from multiple participants in a meeting and provide realtime summaries or other AI-generated output.
+- image-recognition bots
+- etc
 
-## 🛟 Getting help
+## Concepts
 
-➡️ [Join our Discord](https://discord.gg/pipecat)
+### Transport Service
 
-➡️ [Read the docs](https://docs.pipecat.ai)
+The SDK provides one “transport service”, which is a wrapper around Daily’s `daily-python` client (tk add link). You can use this service to listen for events related to a WebRTC session, such as “a participant joined the meeting”.
+The transport service also exposes a send queue, and a receive queue. You can use the send queue to send audio and video to the WebRTC session, and you can listen to the receive queue to see audio, video and transcription data from the WebRTC session.
 
-➡️ [Reach us on X](https://x.com/pipecat_ai)
+### AI Services
+
+The AI Service classes provide wrappers around various AI providers, and allow you to query LLMs, convert text to speech and make images from text. The audio and images can then be placed on the transport service’s send queue, where they’ll be sent to the WebRTC session.
+
+### Queue Frames
+
+Communication between the transport service and AI services, and between various AI services, takes place in Queue Frames. These frames contain an indication of the type of data as well as the data itself.
+
+## Using Transports, AI Services and Frames
+
+AI Services all define a `.run` method. This method consumes and generates `QueueFrame` frames. The kind of frames that can be consumed and generated depend on the kind of service. For instance, an LLM AI Service consumes `LLM_MESSAGE` frames (which define a history of interaction with an LLM) and emit `TEXT` frames (the response from the LLM).
+
+The `.run` method is an `AsyncIterable`, and it takes an `iterable`, `AsyncIterable` or `asyncio.Queue` that produces QueueFrames as a parameter. This makes it easy to chain AI Services, and consume input from the Transport’s `receive_queue` .
+
+AI Services also have a `.run_to_queue` method. This method is not an AsyncIterable, but instead sends processed QueueFrames to a queue. This makes it easy to send the output of an AI Service to the Transport’s `send_queue`.
+
+AI Services also define convenience functions that let you bypass creating QueueFrames for some simple cases (eg. using the TTS service to convert a string to audio output and send that audio to the transport’s `send_queue`). See below for examples.
+
+## Examples
+
+### Say Something
+
+The base TTS AI service exposes a `.say` method. After creating a transport and TTS service, you can use this method like so:
+
+```
+transport = DailyTransportService(...)
+tts = AzureTTSService()
+await tts.say("hello world", transport.send_queue)
+```
+
+This will call the TTS service to render the text to audio frames, then put the audio frames on the transport’s send queue. The transport will then send those frames along to the WebRTC session.
+
+### Speak an LLM response
+
+Given a system prompt contained in a `messages` array, you can emit the LLM’s response as audio with a chain like this:
+
+```
+transport = DailyTransportService(...) # setup parameters omitted
+tts = AzureTTSService()
+llm = AzureLLMService()
+messages = [...] # system prompt omitted for brevity
+
+await tts.run_to_queue(
+  transport.send_queue,
+  llm.run([QueueFrame.LLM_MESSAGES, messages])
+)
+```
+
+In this code, the LLM service object sends the messages to Azure’s OpenAI implementation, which streams chunks back asynchronously. Those chunks are aggregated by the TTS Service to ensure the best audio response (TTS works best when it gets complete sentence, so it can inflect correctly), then sent to Azure’s TTS service, converted to audio frames, and sent to the WebRTC session via the Daily transport.
+
+### Pre-cache an LLM response
+
+Sometimes LLMs can be slower than we’d like for natural-feeling communication. Here’s an example where we take advantage of the time it takes to speak some pre-defined text to get a head start on the LLM response:
+
+(TK link to 04- sample)
+
+In this sample, we set up a buffer queue to receive the audio frames from the LLM response before while we are joining the call and start an asynchronous task to start filling this buffer:
+
+```
+    buffer_queue = asyncio.Queue()
+    llm_response_task = asyncio.create_task(
+        elevenlabs_tts.run_to_queue(
+            buffer_queue,
+            llm.run([QueueFrame(FrameType.LLM_MESSAGE, messages)]),
+            True,
+        )
+    )
+```
+
+Then, when we’ve joined the call, we speak the static text:
+
+```
+        await azure_tts.say("My friend...", transport.send_queue)
+```
+
+As that text is being spoken, the asynchronous LLM task continues in the background. When the text is done, we pull the frames off the buffer queue and put them in the transport’s `send_queue`:
+
+```
+        async def buffer_to_send_queue():
+            while True:
+                frame = await buffer_queue.get()
+                await transport.send_queue.put(frame)
+                buffer_queue.task_done()
+                if frame.frame_type == FrameType.END_STREAM:
+                    break
+
+        await asyncio.gather(llm_response_task, buffer_to_send_queue())
+
+```
+
+One thing to note here is the last parameter to `run_to_queue` in the first code clause above: this causes the `run_to_queue` method to send an `END_STREAM` frame when it’s done rendering. This lets us know when to stop our `buffer_to_send_queue` task above.

SECURITY.md

@@ -1,5 +0,0 @@
-# Security Policy
-
-## Reporting a Vulnerability
-
-Please email `disclosures@daily.co`.

changelog/_template.md.j2

@@ -1,16 +0,0 @@
-{% for section, _ in sections.items() %}
-{% if sections[section] %}
-{% for category, val in definitions.items() if category in sections[section]%}
-### {{ definitions[category]['name'] }}
-
-{% for text, values in sections[section][category].items() %}
-{{ text }}
-(PR {{ values|join(', ') }})
-
-{% endfor %}
-{% endfor %}
-{% else %}
-No significant changes.
-
-{% endif %}
-{% endfor %}

codecov.yml

@@ -1,11 +0,0 @@
-coverage:
-  range: 50..90 # coverage lower than 50 is red, higher than 90 green, between color code
-
-  status:
-    project:
-      default:
-        target: auto # auto % coverage target
-        threshold: 5%  # allow for 5% reduction of coverage without failing
-
-    # do not run coverage on patch nor changes
-    patch: false

docs/README.md

@@ -0,0 +1,13 @@
+# Daily AI SDK Docs
+
+## [Architecture Overview](architecture.md)
+
+Learn about the thinking behind the SDK's design.
+
+## [Example Code](examples/)
+
+The repo includes several example apps in the `src/examples` directory. The docs explain how they work.
+
+## [API Reference](api/)
+
+Complete documentation of the available classes and methods in the SDK.

docs/api/Makefile

@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/api/README.md

@@ -1,60 +0,0 @@
-# Pipecat API Documentation
-
-This directory contains the source files for auto-generating Pipecat's API reference documentation.
-
-## Building Documentation
-
-From this directory:
-
-```bash
-# Build docs (warnings shown but don't fail the build)
-cd docs/api && uv run ./build-docs.sh
-
-# Build with strict mode (warnings treated as errors)
-cd docs/api && uv run ./build-docs.sh --strict
-```
-
-The build script will:
-
-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 (created during build)
-├── _build/         # Built documentation output
-├── conf.py         # Sphinx configuration (mock imports, extensions, etc.)
-├── index.rst       # Main documentation entry point
-├── build-docs.sh   # Local build script
-└── rtd-test.sh     # ReadTheDocs test build script (uses pip, not uv)
-```
-
-## How It Works
-
-- `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
-
-**Module not appearing in docs:**
-
-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"`
-
-**Duplicate object warnings:**
-
-These come from re-export modules or Sphinx discovering the same class through multiple import paths. Usually cosmetic.
-
-**Docstring formatting warnings:**
-
-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

@@ -1,34 +0,0 @@
-#!/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 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
-    echo "Error: sphinx-build is not available" >&2
-    exit 1
-fi
-
-# Clean previous build
-rm -rf _build
-
-echo "Building documentation..."
-uv run sphinx-build -b html -d _build/doctrees . _build/html $SPHINX_OPTS
-
-if [ $? -eq 0 ]; then
-    echo "Documentation built successfully!"
-    # Open docs (MacOS)
-    open _build/html/index.html
-else
-    echo "Documentation build failed!" >&2
-    exit 1
-fi

docs/api/conf.py

@@ -1,229 +0,0 @@
-import logging
-import os
-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")
-
-# Add source directory to path
-docs_dir = Path(__file__).parent
-project_root = docs_dir.parent.parent
-sys.path.insert(0, str(project_root / "src"))
-
-# Project information
-project = "pipecat-ai"
-current_year = datetime.now().year
-copyright = f"2024-{current_year}, Daily" if current_year > 2024 else "2024, Daily"
-author = "Daily"
-
-# General configuration
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.napoleon",
-    "sphinx.ext.viewcode",
-    "sphinx.ext.intersphinx",
-]
-
-suppress_warnings = [
-    "autodoc.mocked_object",
-    "toc.not_included",
-]
-
-# Napoleon settings
-napoleon_google_docstring = True
-napoleon_include_init_with_doc = True
-
-# AutoDoc settings
-autodoc_default_options = {
-    "members": True,
-    "member-order": "bysource",
-    "undoc-members": False,
-    "exclude-members": "__weakref__,model_config",
-    "show-inheritance": True,
-}
-
-# Mock imports for optional dependencies
-autodoc_mock_imports = [
-    # Krisp - has build issues on some platforms
-    "krisp_audio",
-    # System-specific GUI libraries
-    "_tkinter",
-    "tkinter",
-    # Platform-specific audio libraries (if needed)
-    "gi",
-    "gi.require_version",
-    "gi.repository",
-    # OpenCV - sometimes has import issues during docs build
-    "cv2",
-    # Heavy ML packages excluded from ReadTheDocs
-    # local-smart-turn dependencies
-    "coremltools",
-    "coremltools.models",
-    "coremltools.models.MLModel",
-    "torch",
-    "torch.nn",
-    "torch.nn.functional",
-    "torchaudio",
-    # moondream dependencies
-    "transformers",
-    "transformers.AutoTokenizer",
-    "transformers.AutoFeatureExtractor",
-    "AutoFeatureExtractor",
-    "timm",
-    "einops",
-    "intel_extension_for_pytorch",
-    "huggingface_hub",
-    # MLX dependencies (Apple Silicon specific)
-    "mlx",
-    "mlx_whisper",  # Note: might need underscore format too
-    # Pydantic v2 compatibility issues in third-party SDKs
-    "hume",
-    "hume.tts",
-    "hume.tts.types",
-    "cartesia",
-    "camb",
-    "sarvamai",
-    "openai.types.beta.realtime",
-    "langchain_core",
-    "langchain_core.messages",
-    # FastAPI - Pydantic v2 compatibility issues during Sphinx autodoc
-    "fastapi",
-    "fastapi.applications",
-    "fastapi.routing",
-    "fastapi.params",
-    "fastapi.middleware",
-    "fastapi.responses",
-    "uvicorn",
-    # Deepgram dependencies
-    "deepgram",
-]
-
-# HTML output settings
-html_theme = "sphinx_rtd_theme"
-html_static_path = ["_static"] if os.path.exists("_static") else []
-autodoc_typehints = "signature"  # Show type hints in the signature only, not in the docstring
-html_show_sphinx = False
-
-
-def import_core_modules():
-    """Import core pipecat modules for autodoc to discover."""
-    core_modules = [
-        "pipecat",
-        "pipecat.frames",
-        "pipecat.pipeline",
-        "pipecat.processors",
-        "pipecat.services",
-        "pipecat.transports",
-        "pipecat.audio",
-        "pipecat.adapters",
-        "pipecat.clocks",
-        "pipecat.metrics",
-        "pipecat.observers",
-        "pipecat.runner",
-        "pipecat.serializers",
-        "pipecat.transcriptions",
-        "pipecat.turns",
-        "pipecat.extensions",
-        "pipecat.utils",
-    ]
-
-    for module_name in core_modules:
-        try:
-            __import__(module_name)
-            logger.info(f"Successfully imported {module_name}")
-        except ImportError as e:
-            logger.warning(f"Failed to import {module_name}: {e}")
-
-
-def clean_title(title: str) -> str:
-    """Automatically clean module titles."""
-    # Remove everything after space (like 'module', 'processor', etc.)
-    title = title.split(" ")[0]
-
-    # Get the last part of the dot-separated path
-    parts = title.split(".")
-    title = parts[-1]
-
-    return title
-
-
-def setup(app):
-    """Generate API documentation during Sphinx build."""
-    from sphinx.ext.apidoc import main
-
-    docs_dir = Path(__file__).parent
-    project_root = docs_dir.parent.parent
-    output_dir = str(docs_dir / "api")
-    source_dir = str(project_root / "src" / "pipecat")
-
-    # Clean existing files
-    if Path(output_dir).exists():
-        import shutil
-
-        shutil.rmtree(output_dir)
-        logger.info(f"Cleaned existing documentation in {output_dir}")
-
-    logger.info(f"Generating API documentation...")
-    logger.info(f"Output directory: {output_dir}")
-    logger.info(f"Source directory: {source_dir}")
-
-    excludes = [
-        str(project_root / "src/pipecat/examples"),
-        str(project_root / "src/pipecat/tests"),
-        "**/test_*.py",
-        "**/tests/*.py",
-    ]
-
-    try:
-        main(
-            [
-                "-f",  # Force overwriting
-                "-e",  # Don't generate empty files
-                "-M",  # Put module documentation before submodule documentation
-                "--no-toc",  # Don't create a table of contents file
-                "--separate",  # Put documentation for each module in its own page
-                "--module-first",  # Module documentation before submodule documentation
-                "--implicit-namespaces",  # Added: Handle implicit namespace packages
-                "-o",
-                output_dir,
-                source_dir,
-            ]
-            + excludes
-        )
-
-        logger.info("API documentation generated successfully!")
-
-        # Process generated RST files to update titles
-        for rst_file in Path(output_dir).glob("**/*.rst"):  # Changed to recursive glob
-            content = rst_file.read_text()
-            lines = content.split("\n")
-
-            # Find and clean up the title
-            if lines and "=" in lines[1]:  # Title is typically the first line
-                old_title = lines[0]
-                new_title = clean_title(old_title)
-                content = content.replace(old_title, new_title)
-                rst_file.write_text(content)
-                logger.info(f"Updated title: {old_title} -> {new_title}")
-
-    except Exception as e:
-        logger.error(f"Error generating API documentation: {e}", exc_info=True)
-
-
-import_core_modules()

docs/api/index.rst

@@ -1,36 +0,0 @@
-Pipecat API Reference
-=====================
-
-Welcome to the Pipecat API reference.
-
-Use the navigation on the left to browse modules, or search using the search box.
-
-**New to Pipecat?** Check out the `main documentation <https://docs.pipecat.ai>`_ for tutorials, guides, and client SDK information.
-
-Quick Links
------------
-
-* `GitHub Repository <https://github.com/pipecat-ai/pipecat>`_
-* `Join our Community <https://discord.gg/pipecat>`_
-
-.. toctree::
-   :maxdepth: 2
-   :caption: API Reference
-   :hidden:
-
-   Adapters <api/pipecat.adapters>
-   Audio <api/pipecat.audio>
-   Clocks <api/pipecat.clocks>
-   Extensions <api/pipecat.extensions>
-   Frames <api/pipecat.frames>
-   Metrics <api/pipecat.metrics>
-   Observers <api/pipecat.observers>
-   Pipeline <api/pipecat.pipeline>
-   Processors <api/pipecat.processors>
-   Runner <api/pipecat.runner>
-   Serializers <api/pipecat.serializers>
-   Services <api/pipecat.services>
-   Transcriptions <api/pipecat.transcriptions>
-   Transports <api/pipecat.transports>
-   Turns <api/pipecat.turns>
-   Utils <api/pipecat.utils>

docs/api/make.bat

@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd

docs/api/rtd-test.sh

@@ -1,38 +0,0 @@
-#!/bin/bash
-set -e
-
-# Configuration
-DOCS_DIR=$(pwd)
-PROJECT_ROOT=$(cd ../../ && pwd)
-TEST_DIR="/tmp/rtd-test-$(date +%Y%m%d_%H%M%S)"
-
-echo "Creating test directory: $TEST_DIR"
-mkdir -p "$TEST_DIR"
-cd "$TEST_DIR"
-
-# Create virtual environment
-python -m venv venv
-source venv/bin/activate
-
-echo "Installing build dependencies..."
-pip install --upgrade pip wheel setuptools
-
-echo "Installing documentation dependencies..."
-pip install -r "$DOCS_DIR/requirements.txt"
-
-echo "Building documentation..."
-cd "$DOCS_DIR"
-sphinx-build -b html . "_build/html"
-
-echo "Build complete. Check _build/html directory for output."
-
-# Print summary
-echo -e "\n=== Build Summary ==="
-echo "Documentation: $DOCS_DIR/_build/html"
-echo "Test environment: $TEST_DIR"
-echo -e "\nTo view the documentation:"
-echo "open $DOCS_DIR/_build/html/index.html"
-
-# Print installed packages for verification
-echo -e "\n=== Installed Packages ==="
-pip freeze | grep -E "sphinx|pipecat"

docs/architecture.md

@@ -0,0 +1,2 @@
+# Daily AI SDK Architecture Guide
+

docs/examples/01-say-one-thing.md

@@ -0,0 +1,119 @@
+# 01: Say One Thing
+
+_video here - youtube?_
+
+This example uses a text-to-speech (TTS) service to say one predefined sentence. But first, a quick overview of the general structure of these examples.
+
+## Running the demos
+
+All of the demos have something like this at the bottom of the file:
+
+```python
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))
+```
+
+### `configure()`
+
+The `configure()` function comes from `src/examples/foundational/support/runner.py`, and it allows you to configure the examples from the command line directly, or using environment variables:
+
+```bash
+python 01-say-one-thing.py -u https://YOUR_DOMAIN.daily.co/YOUR_ROOM -k YOUR_API_KEY
+# or
+DAILY_ROOM_URL=https://YOUR_DOMAIN.daily.co/YOUR_ROOM DAILY_API_KEY=YOUR_API_KEY python 01-say-one-thing.py
+# or set DAILY_ROOM_URL and DAILY_API_KEY in a .env file
+python 01-say-one-thing.py
+```
+
+You'll need a Daily account to run these demos. You can sign up for free at [daily.co](https://daily.co). Once you've signed up you can create a room from the [Dashboard](https://dashboard.daily.co/rooms), and grab [your API key](https://dashboard.daily.co/developers) while you're there.
+
+Some functionality (such as transcription) requires the bot to have owner privileges in the room. `runner.py` uses the Daily REST API to create a meeting token with owner privileges. You can learn more about meeting tokens in the [Daily docs](https://docs.daily.co/reference/rest-api/meeting-tokens).
+
+### `asyncio.run()`
+
+The AI SDK makes heavy use of Python's `asyncio` module. [This is a reasonable intro to the topic](https://builtin.com/data-science/asyncio) if you haven't worked with `asyncio` and coroutines before.
+
+You can learn a bit more about the specifics of how the Daily AI SDK uses coroutines in the [Architecture Guide](../architecture.md).
+
+## The `main()` function
+
+All of the examples have a `main()` function with a similar structure:
+
+- Configure the transport
+- Configure the AI service(s) used in the demo
+- Configure any event listeners
+- Define a processing pipeline
+- Run the example's coroutine(s)
+
+### Configuring the transport
+
+The first section of the `main()` function configures the transport object:
+
+```python
+meeting_duration_minutes = 5
+transport = DailyTransportService(
+    room_url,
+    None,
+    "Say One Thing",
+    meeting_duration_minutes,
+)
+transport.mic_enabled = True
+```
+
+The [Architecture Guide](../architecture.md) explains the transport object in more detail. In this case, we're configuring a Daily transport object and enabling the virtual microphone, so our bot can play audio.
+
+### Configuring the services
+
+As described in the [Architecture Guide](../architecture.md), 'a 'Service' is a class that processes 'Frames' as part of a 'Pipeline'. In this demo app, we'll only need one service: a text-to-speech generator. We can create an instance of the `ElevenLabsTTSService` class with this line of code:
+
+```python
+tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+```
+
+You'll need to make sure and set those environment variables somewhere. The easiest way to do that is to copy the `example.env` file in the repo and rename it to `.env`, and then add your credentials to that file. `runner.py` loads the `python-dotenv` module and initializes it, making the values in that file available in the environment.
+
+### Configuring event listeners
+
+This part isn't strictly necessary for an app like this. You could include the contents of the `on_participant_joined` function directly in the body of the `main()` function, and it would run as soon as you started the script from the command line.
+
+Instead, we can use an event handler to wait to run that code until someone else joins the meeting. We'll define a function called `greet_user()`, and use the `@transport.event_handler("on_participant_joined")` decorator to tell the SDK that we want to run that function whenever a user joins the room.
+
+```python
+@transport.event_handler("on_participant_joined")
+async def greet_user(transport, participant):
+    if participant["info"]["isLocal"]:
+        return
+
+    await tts.say(
+        "Hello there, " + participant["info"]["userName"] + "!",
+        transport.send_queue,
+    )
+
+    # wait for the output queue to be empty, then leave the meeting
+    await transport.stop_when_done()
+```
+
+### Defining a processing pipeline
+
+In this example, we don't actually have much of a processing pipeline! In fact, we're doing the whole thing inside the `greet_user()` function already.
+
+Pipelines usually look like a bunch of nested calls to the `run()` or `run_to_queue()` function from different Services. In this example, we're using the `say()` function from the TTS service. This is effectively a convenience wrapper around the `run_to_queue()` function, which we'll discuss more later. It's important to `await` this function to ensure that the speech frames are queued for playback before the next line of code, because of the `stop_when_done()` function being called immediately afterward.
+
+The output of the `say()` function goes to the transport's `send_queue`. This queue is the all-important connection between the world of the Services pipeline that's generating frames asynchronously and the ordered playback of audio and visual media in the WebRTC call.
+
+### Running the coroutines
+
+In this example, we don't actually have any separate processing pipelines—everything happens as a result of an event from the transport. So we only need to run the transport's coroutine, and await its completion:
+
+```python
+await transport.run()
+```
+
+In future examples, we'll run more processes in parallel. For now, this script can run until the transport exits—which will happen based on calling `stop_when_done()` in the `greet_user()` function.
+
+## Next Steps
+
+Next, we'll start connecting multiple AI services together by building a service pipeline.
+
+## [02 - LLM Say One Thing »](02-llm-say-one-thing.md)

docs/examples/README.md

@@ -0,0 +1,5 @@
+# Daily AI SDK Examples
+
+The docs in this folder pair with the example apps located in `src/examples/foundational`. They are designed to serve as a quick references for building different kinds of AI apps. But the examples also build on one another, so it can be really helpful to walk through them in order.
+
+To start, you can learn about the overall structure of the examples in [01 - Say One Thing](01-say-one-thing.md).

env.example

@@ -1,217 +0,0 @@
-# AI-COUSTICS
-AICOUSTICS_LICENSE_KEY=...
-
-# Anthropic
-ANTHROPIC_API_KEY=...
-
-# Assembly AI
-ASSEMBLYAI_API_KEY=...
-
-# Async
-ASYNCAI_API_KEY=...
-ASYNCAI_VOICE_ID=...
-
-# AWS
-AWS_SECRET_ACCESS_KEY=...
-AWS_ACCESS_KEY_ID=...
-AWS_REGION=...
-
-# Azure
-AZURE_SPEECH_REGION=...
-AZURE_SPEECH_API_KEY=...
-
-AZURE_CHATGPT_API_KEY=...
-AZURE_CHATGPT_ENDPOINT=https://...
-AZURE_CHATGPT_MODEL=...
-
-AZURE_REALTIME_API_KEY=...
-AZURE_REALTIME_BASE_URL=...
-
-AZURE_DALLE_API_KEY=...
-AZURE_DALLE_ENDPOINT=https://...
-AZURE_DALLE_MODEL=...
-
-# Camb.ai
-CAMB_API_KEY=...
-
-# Cartesia
-CARTESIA_API_KEY=...
-CARTESIA_VOICE_ID=...
-
-# Cerebras
-CEREBRAS_API_KEY=...
-
-# Daily
-DAILY_API_KEY=...
-DAILY_ROOM_URL=https://...
-
-# Deepgram
-DEEPGRAM_API_KEY=...
-SAGEMAKER_STT_ENDPOINT_NAME=...
-SAGEMAKER_TTS_ENDPOINT_NAME=...
-
-# DeepSeek
-DEEPSEEK_API_KEY=...
-
-# ElevenLabs
-ELEVENLABS_API_KEY=...
-ELEVENLABS_VOICE_ID=...
-
-# Fal
-FAL_KEY=...
-
-# Fireworks
-FIREWORKS_API_KEY=...
-
-# Fish Audio
-FISH_API_KEY=...
-
-# Gladia
-GLADIA_API_KEY=...
-GLADIA_REGION=...
-
-# Google
-GOOGLE_API_KEY=...
-GOOGLE_VERTEX_TEST_CREDENTIALS=...
-GOOGLE_CLOUD_PROJECT_ID=...
-GOOGLE_CLOUD_LOCATION=...
-GOOGLE_TEST_CREDENTIALS=...
-
-# Gradium
-GRAPDIUM_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Heygen
-HEYGEN_API_KEY=...
-HEYGEN_LIVE_AVATAR_API_KEY=...
-
-# Hume
-HUME_API_KEY=...
-HUME_VOICE_ID=...
-
-# Inworld
-INWORLD_API_KEY=...
-
-# Krisp
-KRISP_MODEL_PATH=...
-
-# Krisp Viva
-KRISP_VIVA_API_KEY=...
-KRISP_VIVA_FILTER_MODEL_PATH=...
-KRISP_VIVA_TURN_MODEL_PATH=...
-
-# LemonSlice
-LEMONSLICE_API_KEY=...
-LEMONSLICE_AGENT_ID=...
-
-# LiveKit
-LIVEKIT_API_KEY=...
-LIVEKIT_API_SECRET=...
-
-# LMNT
-LMNT_API_KEY=...
-LMNT_VOICE_ID=...
-
-# MiniMax
-MINIMAX_API_KEY=...
-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=...
-
-# OpenRouter
-OPENROUTER_API_KEY=...
-
-# Perplexity
-PERPLEXITY_API_KEY=...
-
-# Picovoice Koala
-KOALA_ACCESS_KEY=...
-
-# Piper
-PIPER_BASE_URL=...
-
-# Plivo
-PLIVO_AUTH_ID=...
-PLIVO_AUTH_TOKEN=...
-
-# Qwen
-QWEN_API_KEY=...
-
-# Resemble AI
-RESEMBLE_API_KEY=
-RESEMBLE_VOICE_UUID=
-
-# Rime
-RIME_API_KEY=...
-RIME_VOICE_ID=...
-
-# SambaNova
-SAMBANOVA_API_KEY=...
-
-# Sarvam AI
-SARVAM_API_KEY=...
-
-# Sentry
-SENTRY_DSN=...
-
-# Simli
-SIMLI_API_KEY=...
-SIMLI_FACE_ID=...
-
-# Smallest
-SMALLEST_API_KEY=...
-
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=...
-FAL_SMART_TURN_API_KEY=...
-
-# Soniox
-SONIOX_API_KEY=...
-
-# Speechmatics
-SPEECHMATICS_API_KEY=...
-
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
-
-# Telnyx
-TELNYX_API_KEY=...
-TELNYX_ACCOUNT_SID=...
-
-# Together.ai
-TOGETHER_API_KEY=...
-
-# Twilio
-TWILIO_ACCOUNT_SID=...
-TWILIO_AUTH_TOKEN=...
-
-# Ultravox Realtime
-ULTRAVOX_API_KEY=...
-
-# WhatsApp
-WHATSAPP_TOKEN=...
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
-WHATSAPP_PHONE_NUMBER_ID=...
-WHATSAPP_APP_SECRET=...
-
-# xAI / Grok
-XAI_API_KEY=...

examples/README.md

@@ -1,150 +0,0 @@
-# Pipecat Examples
-
-This directory contains examples showing how to build voice and multimodal agents with Pipecat.
-
-## 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. Run any example:
-
-   ```bash
-   uv run python getting-started/01-say-one-thing.py
-   ```
-
-4. 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 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

@@ -1,155 +0,0 @@
-#
-# 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.audio.mixers.soundfile_mixer import SoundfileMixer
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, MixerEnableFrame, MixerUpdateSettingsFrame
-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
-
-load_dotenv(override=True)
-
-OFFICE_SOUND_FILE = os.path.join(
-    os.path.dirname(__file__), "../assets", "office-ambience-24000-mono.mp3"
-)
-
-# 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_out_mixer=SoundfileMixer(
-            sound_files={"office": OFFICE_SOUND_FILE},
-            default_sound="office",
-            volume=2.0,
-        ),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        audio_out_mixer=SoundfileMixer(
-            sound_files={"office": OFFICE_SOUND_FILE},
-            default_sound="office",
-            volume=2.0,
-        ),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        audio_out_mixer=SoundfileMixer(
-            sound_files={"office": OFFICE_SOUND_FILE},
-            default_sound="office",
-            volume=2.0,
-        ),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    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(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, participant):
-        # Show how to use mixer control frames.
-        logger.info(f"Listening for background sound for a bit...")
-        await asyncio.sleep(5.0)
-        logger.info(f"Reducing volume...")
-        await task.queue_frame(MixerUpdateSettingsFrame({"volume": 0.5}))
-        await asyncio.sleep(5.0)
-        logger.info(f"Disabling background sound for a bit...")
-        await task.queue_frame(MixerEnableFrame(False))
-        await asyncio.sleep(5.0)
-        logger.info(f"Re-enabling background sound and starting bot...")
-        await task.queue_frame(MixerEnableFrame(True))
-        # 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/audio/audio-recording.py

@@ -1,206 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Audio Recording Example with Pipecat.
-
-This example demonstrates how to record audio from a conversation between a user and an AI assistant,
-saving both merged and individual audio tracks. It showcases the AudioBufferProcessor's capabilities
-to handle both combined and separate audio streams.
-
-The example:
-    1. Sets up a basic conversation with an AI assistant
-    2. Records the entire conversation
-    3. Saves three separate WAV files:
-        - A merged recording of both participants
-        - Individual recording of user audio
-        - Individual recording of assistant audio
-
-Requirements:
-    - OpenAI API key (for GPT-4)
-    - Cartesia API key (for text-to-speech)
-    - Daily API key (for video/audio transport)
-
-    Environment variables (.env file):
-        OPENAI_API_KEY=your_openai_key
-        CARTESIA_API_KEY=your_cartesia_key
-        DAILY_API_KEY=your_daily_key
-        DEEPGRAM_API_KEY=your_deepgram_key
-
-The recordings will be saved in a 'recordings' directory with timestamps:
-    recordings/
-        merged_20240315_123456.wav  (Combined audio)
-        user_20240315_123456.wav    (User audio only)
-        bot_20240315_123456.wav     (Bot audio only)
-
-Note:
-    This example requires the AudioBufferProcessor with track-specific audio support,
-    which provides both 'on_audio_data' and 'on_track_audio_data' events for
-    handling merged and separate audio tracks respectively.
-"""
-
-import datetime
-import io
-import os
-import wave
-
-import aiofiles
-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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-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)
-
-
-async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
-    """Save audio data to a WAV file."""
-    if len(audio) > 0:
-        with io.BytesIO() as buffer:
-            with wave.open(buffer, "wb") as wf:
-                wf.setsampwidth(2)
-                wf.setnchannels(num_channels)
-                wf.setframerate(sample_rate)
-                wf.writeframes(audio)
-            async with aiofiles.open(filename, "wb") as file:
-                await file.write(buffer.getvalue())
-        logger.info(f"Audio saved to {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,
-    ),
-    "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"), audio_passthrough=True)
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",
-        ),
-    )
-
-    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.",
-        ),
-    )
-
-    # Create audio buffer processor
-    audiobuffer = AudioBufferProcessor()
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            audiobuffer,  # Add audio buffer to pipeline
-            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")
-        # Start recording audio
-        await audiobuffer.start_recording()
-        # Start conversation - empty prompt to let LLM follow system instructions
-        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()
-
-    # Handler for merged audio
-    @audiobuffer.event_handler("on_audio_data")
-    async def on_audio_data(buffer, audio, sample_rate, num_channels):
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"recordings/merged_{timestamp}.wav"
-        os.makedirs("recordings", exist_ok=True)
-        await save_audio_file(audio, filename, sample_rate, num_channels)
-
-    # Handler for separate tracks
-    @audiobuffer.event_handler("on_track_audio_data")
-    async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-        os.makedirs("recordings", exist_ok=True)
-
-        # Save user audio
-        user_filename = f"recordings/user_{timestamp}.wav"
-        await save_audio_file(user_audio, user_filename, sample_rate, 1)
-
-        # Save bot audio
-        bot_filename = f"recordings/bot_{timestamp}.wav"
-        await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
-
-    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/audio/audio-sound-effects.py

@@ -1,178 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-import wave
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    Frame,
-    LLMContextFrame,
-    LLMFullResponseEndFrame,
-    OutputAudioRawFrame,
-    TTSSpeakFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import 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.processors.logger import FrameLogger
-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)
-
-
-sounds = {}
-sound_files = ["ding1.wav", "ding2.wav"]
-
-script_dir = os.path.dirname(__file__)
-
-for file in sound_files:
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, "assets", file)
-    # Get the filename without the extension to use as the dictionary key
-    filename = os.path.splitext(os.path.basename(full_path))[0]
-    # Open the image and convert it to bytes
-    with wave.open(full_path) as audio_file:
-        sounds[file] = OutputAudioRawFrame(
-            audio_file.readframes(-1), audio_file.getframerate(), audio_file.getnchannels()
-        )
-
-
-class OutboundSoundEffectWrapper(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, LLMFullResponseEndFrame):
-            await self.push_frame(sounds["ding1.wav"])
-            # In case anything else downstream needs it
-            await self.push_frame(frame, direction)
-        else:
-            await self.push_frame(frame, direction)
-
-
-class InboundSoundEffectWrapper(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, LLMContextFrame):
-            await self.push_frame(sounds["ding2.wav"])
-            # In case anything else downstream needs it
-            await self.push_frame(frame, direction)
-        else:
-            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"))
-
-    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.",
-        ),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-    out_sound = OutboundSoundEffectWrapper()
-    in_sound = InboundSoundEffectWrapper()
-    fl = FrameLogger("LLM Out")
-    fl2 = FrameLogger("Transcription In")
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            in_sound,
-            fl2,
-            llm,
-            fl,
-            tts,
-            out_sound,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        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_frame(TTSSpeakFrame("Hi, I'm listening!"))
-        await transport.send_audio(sounds["ding1.wav"])
-
-    @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/context-summarization/context-summarization-dedicated-llm.py

@@ -1,238 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example demonstrating advanced context summarization configuration.
-
-This example shows how to customize context summarization with:
-- A dedicated cheap/fast LLM for generating summaries (Gemini Flash)
-- A custom summary message template (XML tags)
-- A custom summarization prompt
-- A summarization timeout
-- The on_summary_applied event for observability
-"""
-
-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_context_summarizer import SummaryAppliedEvent
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMAssistantAggregatorParams,
-    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.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.context.llm_context_summarization import (
-    LLMAutoContextSummarizationConfig,
-    LLMContextSummaryConfig,
-)
-
-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,
-    ),
-}
-
-# Custom summarization prompt tailored to the application
-CUSTOM_SUMMARIZATION_PROMPT = """Summarize this conversation, preserving:
-- Key decisions and agreements
-- Important facts and user preferences
-- Any pending action items or unresolved questions
-
-Be concise. Use clear, factual statements grouped by topic.
-Omit greetings, small talk, and resolved tangents."""
-
-
-# Tool functions for the LLM
-async def get_current_weather(params: FunctionCallParams):
-    """Get the current weather."""
-    logger.info("Tool called: get_current_weather")
-    await asyncio.sleep(1)  # Simulate some processing
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info("Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    system_prompt = """You are a helpful LLM in a voice call. Your goal is to demonstrate your
-                    capabilities in a succinct way. Your output will be spoken aloud, so avoid
-                    special characters that can't easily be spoken, such as emojis or bullet points.
-                    Respond to what the user said in a creative and helpful way.
-                    You have access to tools to get the current weather - use them when relevant.
-                    When you see a <context_summary> block, it contains a compressed summary
-                    of earlier conversation. Use it as reference but don't mention it to the user.
-                    """
-
-    # Primary LLM for conversation (could be any provider)
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction=system_prompt,
-        ),
-    )
-
-    # Dedicated cheap/fast LLM for summarization only
-    summarization_llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(
-            model="gemini-2.5-flash",
-        ),
-    )
-
-    # Register tool functions
-    llm.register_function("get_current_weather", get_current_weather)
-
-    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)
-
-    # Create aggregators with custom summarization
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-        assistant_params=LLMAssistantAggregatorParams(
-            enable_auto_context_summarization=True,
-            auto_context_summarization_config=LLMAutoContextSummarizationConfig(
-                # Trigger thresholds (low values to demonstrate quickly)
-                max_context_tokens=1000,
-                max_unsummarized_messages=10,
-                summary_config=LLMContextSummaryConfig(
-                    # Summary generation
-                    target_context_tokens=800,
-                    min_messages_after_summary=2,
-                    summarization_prompt=CUSTOM_SUMMARIZATION_PROMPT,
-                    # Custom summary format - wrap in XML tags so the system
-                    # prompt can identify summaries vs. live conversation
-                    summary_message_template="<context_summary>\n{summary}\n</context_summary>",
-                    # Use a dedicated cheap LLM for summarization instead of
-                    # the primary conversation model
-                    llm=summarization_llm,
-                    # Cancel summarization if it takes longer than 60 seconds
-                    summarization_timeout=60.0,
-                ),
-            ),
-        ),
-    )
-
-    # Listen for summarization events
-    @assistant_aggregator.event_handler("on_summary_applied")
-    async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent):
-        logger.info(
-            f"Context summarized: {event.original_message_count} messages -> "
-            f"{event.new_message_count} messages "
-            f"({event.summarized_message_count} summarized, "
-            f"{event.preserved_message_count} preserved)"
-        )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info("Client connected")
-        # Kick off the conversation.
-        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("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/context-summarization/context-summarization-google.py

@@ -1,199 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example demonstrating context summarization feature.
-
-This example shows how to enable and configure context summarization to automatically
-compress conversation history when token limits are approached. It also demonstrates
-that summarization correctly handles function calls, preserving incomplete function
-call sequences.
-"""
-
-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_context_summarizer import SummaryAppliedEvent
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMAssistantAggregatorParams,
-    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
-from pipecat.utils.context.llm_context_summarization import (
-    LLMAutoContextSummarizationConfig,
-    LLMContextSummaryConfig,
-)
-
-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,
-    ),
-}
-
-
-# Tool functions for the LLM
-async def get_current_weather(params: FunctionCallParams):
-    """Get the current time in a readable format."""
-    logger.info("Tool called: get_current_weather")
-    await asyncio.sleep(1)  # Simulate some processing
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info("Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        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. Respond to what the user said in a creative, helpful, and brief way. You have access to tools to get the current weather - use them when relevant.",
-        ),
-    )
-
-    # Register tool functions
-    llm.register_function("get_current_weather", get_current_weather)
-
-    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)
-
-    # Create aggregators with summarization enabled
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-        assistant_params=LLMAssistantAggregatorParams(
-            enable_auto_context_summarization=True,
-            # Optional: customize context summarization behavior
-            # Using low limits to demonstrate the feature quickly
-            auto_context_summarization_config=LLMAutoContextSummarizationConfig(
-                max_context_tokens=1000,  # Trigger summarization at 1000 tokens
-                max_unsummarized_messages=10,  # Or when 10 new messages accumulate
-                summary_config=LLMContextSummaryConfig(
-                    target_context_tokens=800,  # Target context size for the summarization
-                    min_messages_after_summary=2,  # Keep last 2 messages uncompressed
-                ),
-            ),
-        ),
-    )
-
-    # Listen for summarization events
-    @assistant_aggregator.event_handler("on_summary_applied")
-    async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent):
-        logger.info(
-            f"Context summarized: {event.original_message_count} messages -> "
-            f"{event.new_message_count} messages "
-            f"({event.summarized_message_count} summarized, "
-            f"{event.preserved_message_count} preserved)"
-        )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info("Client connected")
-        # Kick off the conversation.
-        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("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/context-summarization/context-summarization-manual-openai.py

@@ -1,173 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example demonstrating manual context summarization via a function call.
-
-This example shows how to trigger context summarization on demand rather than
-automatically. The user can ask the bot to "summarize the conversation" and the
-bot will call a function that pushes an LLMSummarizeContextFrame into the
-pipeline, causing the LLM service to compress the conversation history.
-
-Unlike example 54, automatic summarization is NOT enabled here. Summarization
-only happens when the user explicitly requests it through the function call.
-"""
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, LLMSummarizeContextFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def summarize_conversation(params: FunctionCallParams):
-    """Trigger manual context summarization via a pipeline frame."""
-    logger.info("Tool called: summarize_conversation")
-    await params.result_callback({"status": "summarization_requested"})
-    await params.llm.queue_frame(LLMSummarizeContextFrame())
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info("Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    system_prompt = """You are a helpful LLM in a voice call. Your goal is to demonstrate your
-                    capabilities in a succinct way. Your output will be spoken aloud, so avoid
-                    special characters that can't easily be spoken, such as emojis or bullet points.
-                    Respond to what the user said in a creative and helpful way.
-                    If the user asks you to summarize the conversation, call the
-                    summarize_conversation function. After summarization, briefly acknowledge
-                    that the conversation history has been compressed.
-                    """
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction=system_prompt,
-        ),
-    )
-
-    llm.register_function("summarize_conversation", summarize_conversation)
-
-    summarize_function = FunctionSchema(
-        name="summarize_conversation",
-        description=(
-            "Summarize and compress the conversation history. "
-            "Call this when the user asks you to summarize the conversation "
-            "or when you want to free up context space."
-        ),
-        properties={},
-        required=[],
-    )
-    tools = ToolsSchema(standard_tools=[summarize_function])
-
-    context = LLMContext(tools=tools)
-
-    # Automatic summarization is NOT enabled here (enable_auto_context_summarization
-    # defaults to False). The summarizer is still created internally so that
-    # LLMSummarizeContextFrame frames pushed via the function call are handled.
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(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("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("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/context-summarization/context-summarization-openai.py

@@ -1,199 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example demonstrating context summarization feature.
-
-This example shows how to enable and configure context summarization to automatically
-compress conversation history when token limits are approached. It also demonstrates
-that summarization correctly handles function calls, preserving incomplete function
-call sequences.
-"""
-
-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_context_summarizer import SummaryAppliedEvent
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMAssistantAggregatorParams,
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.context.llm_context_summarization import (
-    LLMAutoContextSummarizationConfig,
-    LLMContextSummaryConfig,
-)
-
-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,
-    ),
-}
-
-
-# Tool functions for the LLM
-async def get_current_weather(params: FunctionCallParams):
-    """Get the current time in a readable format."""
-    logger.info("Tool called: get_current_weather")
-    await asyncio.sleep(1)  # Simulate some processing
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info("Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        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. You have access to tools to get the current weather - use them when relevant.",
-        ),
-    )
-
-    # Register tool functions
-    llm.register_function("get_current_weather", get_current_weather)
-
-    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)
-
-    # Create aggregators with summarization enabled
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-        assistant_params=LLMAssistantAggregatorParams(
-            enable_auto_context_summarization=True,
-            # Optional: customize context summarization behavior
-            # Using low limits to demonstrate the feature quickly
-            auto_context_summarization_config=LLMAutoContextSummarizationConfig(
-                max_context_tokens=1000,  # Trigger summarization at 1000 tokens
-                max_unsummarized_messages=10,  # Or when 10 new messages accumulate
-                summary_config=LLMContextSummaryConfig(
-                    target_context_tokens=800,  # Target context size for the summarization
-                    min_messages_after_summary=2,  # Keep last 2 messages uncompressed
-                ),
-            ),
-        ),
-    )
-
-    # Listen for summarization events
-    @assistant_aggregator.event_handler("on_summary_applied")
-    async def on_summary_applied(aggregator, summarizer, event: SummaryAppliedEvent):
-        logger.info(
-            f"Context summarized: {event.original_message_count} messages -> "
-            f"{event.new_message_count} messages "
-            f"({event.summarized_message_count} summarized, "
-            f"{event.preserved_message_count} preserved)"
-        )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info("Client connected")
-        # Kick off the conversation.
-        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("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/features/features-before-and-after-events.py

@@ -1,148 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-from dataclasses import dataclass
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import DataFrame, 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
-
-load_dotenv(override=True)
-
-
-@dataclass
-class CustomBeforeProcessFrame(DataFrame):
-    pass
-
-
-@dataclass
-class CustomAfterPushFrame(DataFrame):
-    pass
-
-
-# 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(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.output().event_handler("on_before_process_frame")
-    async def transport_on_before_process_frame(transport, frame):
-        if isinstance(frame, CustomBeforeProcessFrame):
-            logger.info("*** CUSTOM FRAME RECEIVED. Bot is about to talk, let's do something!")
-
-    @transport.output().event_handler("on_after_push_frame")
-    async def transport_on_after_push_frame(transport, frame):
-        if isinstance(frame, CustomAfterPushFrame):
-            logger.info("*** CUSTOM FRAME RECEIVED. Bot has stopped talking, let's do something!")
-
-    @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."}
-        )
-        # Custom frames are pushed in order so they can be used for synchronization purposes.
-        await task.queue_frames([CustomBeforeProcessFrame(), LLMRunFrame(), CustomAfterPushFrame()])
-
-    @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/features/features-concurrent-llm-evaluation.py

@@ -1,173 +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 LLMRunFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.audio.vad_processor import VADProcessor
-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.groq.llm import GroqLLMService
-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_turn_processor import UserTurnProcessor
-from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
-
-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
-        ),
-    )
-
-    openai_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.",
-        ),
-    )
-
-    groq_llm = GroqLLMService(
-        api_key=os.getenv("GROQ_API_KEY"),
-        settings=GroqLLMService.Settings(
-            system_instruction="You are a very helpful assistant. Your goal is to demonstrate your capabilities in detail in a creative and helpful way.",
-        ),
-    )
-
-    openai_context = LLMContext()
-    groq_context = LLMContext()
-
-    # We use an external VADProcessor because the UserTurnProcessor is shared
-    # across multiple parallel aggregators. The VADProcessor emits
-    # VADUserStartedSpeakingFrame and VADUserStoppedSpeakingFrame which the
-    # UserTurnProcessor needs to manage turn lifecycle.
-    vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
-
-    # We use this external user turn processor. This processor will push
-    # UserStartedSpeakingFrame and UserStoppedSpeakingFrame as well as
-    # interruptions. This can be used in advanced cases when there are multiple
-    # aggregators in the pipeline.
-    user_turn_processor = UserTurnProcessor()
-
-    # We use external user turn strategies for both aggregators since the turn
-    # management is done by the common UserTurnProcessor.
-    openai_context_aggregator = LLMContextAggregatorPair(
-        openai_context,
-        user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
-    )
-    groq_context_aggregator = LLMContextAggregatorPair(
-        groq_context,
-        user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            vad_processor,
-            user_turn_processor,
-            ParallelPipeline(
-                [
-                    openai_context_aggregator.user(),  # User responses
-                    openai_llm,  # LLM
-                    tts,  # TTS (bot will speak the chosen language)
-                    transport.output(),  # Transport bot output
-                    openai_context_aggregator.assistant(),  # Assistant spoken responses
-                ],
-                [
-                    groq_context_aggregator.user(),  # User responses
-                    groq_llm,  # LLM
-                    groq_context_aggregator.assistant(),  # Assistant 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.
-        openai_context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        groq_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/features/features-concurrent-llm-rtvi-ignored-sources.py

@@ -1,184 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""RTVIObserver ignored sources example.
-
-This example shows how to suppress RTVI messages from a specific pipeline
-processor so that secondary branches don't leak events to the client.
-
-"""
-
-import os
-
-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.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.audio.vad_processor import VADProcessor
-from pipecat.processors.frameworks.rtvi import RTVIObserverParams
-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_turn_processor import UserTurnProcessor
-from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
-
-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("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
-        ),
-    )
-
-    # Main LLM — drives the conversation. Its RTVI events reach the client.
-    main_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.",
-        ),
-    )
-
-    # Evaluator LLM — silently grades the user's message in the background.
-    # Its RTVI events will be suppressed so the client is unaware of this branch.
-    evaluator_llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        name="EvaluatorLLM",
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a silent quality evaluator. When given a user message, respond with a single JSON object: {'score': <1-5>, 'reason': '<brief reason>'}. Do not respond conversationally.",
-        ),
-    )
-
-    main_context = LLMContext()
-    evaluator_context = LLMContext()
-
-    # We use an external VADProcessor because the UserTurnProcessor is shared
-    # across multiple parallel aggregators. The VADProcessor emits
-    # VADUserStartedSpeakingFrame and VADUserStoppedSpeakingFrame which the
-    # UserTurnProcessor needs to manage turn lifecycle.
-    vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
-
-    # We use this external user turn processor. This processor will push
-    # UserStartedSpeakingFrame and UserStoppedSpeakingFrame as well as
-    # interruptions. This can be used in advanced cases when there are multiple
-    # aggregators in the pipeline.
-    user_turn_processor = UserTurnProcessor()
-
-    # We use external user turn strategies for both aggregators since the turn
-    # management is done by the common UserTurnProcessor.
-    main_context_aggregator = LLMContextAggregatorPair(
-        main_context,
-        user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
-    )
-    evaluator_context_aggregator = LLMContextAggregatorPair(
-        evaluator_context,
-        user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            vad_processor,
-            user_turn_processor,
-            ParallelPipeline(
-                # Main branch: speaks to the user.
-                [
-                    main_context_aggregator.user(),
-                    main_llm,
-                    tts,
-                    transport.output(),
-                    main_context_aggregator.assistant(),
-                ],
-                # Evaluator branch: silent background scoring, no audio output.
-                [
-                    evaluator_context_aggregator.user(),
-                    evaluator_llm,
-                    evaluator_context_aggregator.assistant(),
-                ],
-            ),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        rtvi_observer_params=RTVIObserverParams(ignored_sources=[evaluator_llm]),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info("Client connected")
-        main_context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        evaluator_context.add_message(
-            {"role": "developer", "content": "Ready to evaluate user messages."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info("Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,168 +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.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
-
-load_dotenv(override=True)
-
-
-def format_metrics(metrics, indent=0):
-    lines = []
-    tab = "\t" * indent
-
-    for metric in metrics:
-        lines.append(tab + type(metric).__name__)
-        for field, value in vars(metric).items():
-            if hasattr(value, "__dict__") and not isinstance(
-                value, (str, int, float, bool, type(None))
-            ):
-                lines.append(f"{tab}\t{field}={type(value).__name__}")
-                for k, v in vars(value).items():
-                    lines.append(f"{tab}\t\t{k}={repr(v)}")
-            else:
-                lines.append(f"{tab}\t{field}={repr(value)}")
-
-    return "\n".join(lines)
-
-
-class MetricsFrameLogger(FrameProcessor):
-    """MetricsFrameLogger formats and logs all MetericsFrames"""
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, MetricsFrame):
-            logger.info(f"{frame.name}\n    {format_metrics(frame.data)}")
-            await self.push_frame(frame, direction)
-
-        # ALWAYS push all frames
-        else:
-            # SUPER IMPORTANT: always push every frame!
-            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,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_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(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    metrics_frame_processor = MetricsFrameLogger()
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-            metrics_frame_processor,  # pretty print metrics frames
-        ]
-    )
-
-    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.
-        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/features/features-gpu-container-local-bot.py

@@ -1,165 +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 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 (
-    DailyOutputTransportMessageFrame,
-    DailyOutputTransportMessageUrgentFrame,
-    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 = DeepgramTTSService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-asteria-en",
-        ),
-        base_url="http://0.0.0.0:8080",
-    )
-
-    llm = OpenAILLMService(
-        # To use OpenAI
-        # api_key=os.getenv("OPENAI_API_KEY"),
-        # Or, to use a local vLLM (or similar) api server
-        settings=OpenAILLMService.Settings(
-            model="meta-llama/Meta-Llama-3-8B-Instruct",
-            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.",
-        ),
-        base_url="http://0.0.0.0:8000/v1",
-    )
-
-    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,
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    # When the first participant joins, the bot should introduce itself.
-    @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()])
-
-    # Handle "latency-ping" messages. The client will send app messages that look like
-    # this:
-    #   { "latency-ping": { ts: <client-side timestamp> }}
-    #
-    # We want to send an immediate pong back to the client from this handler function.
-    # Also, we will push a frame into the top of the pipeline and send it after the
-    #
-    @transport.event_handler("on_app_message")
-    async def on_app_message(transport, message, sender):
-        try:
-            if "latency-ping" in message:
-                logger.debug(f"Received latency ping app message: {message}")
-                ts = message["latency-ping"]["ts"]
-                # Send immediately
-                await task.queue_frame(
-                    DailyOutputTransportMessageUrgentFrame(
-                        message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
-                    )
-                )
-                # And push to the pipeline for the Daily transport.output to send
-                await task.queue_frame(
-                    DailyOutputTransportMessageFrame(
-                        message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                        participant_id=sender,
-                    )
-                )
-        except Exception as e:
-            logger.debug(f"message handling error: {e} - {message}")
-
-    @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/features/features-live-translation.py

@@ -1,142 +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 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.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_start import TranscriptionUserTurnStartStrategy
-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="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9",  # Spanish-speaking Lady
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a live translation assistant. Your sole purpose is to translate English text into Spanish. When you receive English text from the user, immediately translate it into natural, fluent Spanish. Do not add explanations, commentary, or extra information—only provide the Spanish translation of the text you receive.",
-        ),
-    )
-
-    context = LLMContext()
-
-    # We use the TranscriptionUserTurnStartStrategy to start a new user turn
-    # every time a transcription is received. We disable interruptions, so the
-    # user can continue speaking while the bot is transcribing, without
-    # interrupting the bot.
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            user_turn_strategies=UserTurnStrategies(
-                start=[TranscriptionUserTurnStartStrategy(enable_interruptions=False)],
-            ),
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS (bot will speak the chosen language)
-            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")
-        await task.queue_frames(
-            [
-                TTSSpeakFrame(
-                    text="Hello, welcome to live translation. Everything you say will be automatically translated to Spanish. Let's begin!",
-                    append_to_context=True,
-                ),
-            ]
-        )
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=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/features/features-pattern-pair-voice-switching.py

@@ -1,252 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Pattern Pair Voice Switching Example with Pipecat.
-
-This example demonstrates how to use the PatternPairAggregator to dynamically switch
-between different voices in a storytelling application. It showcases how pattern matching
-can be used to control TTS behavior in streaming text from an LLM.
-
-The example:
-    1. Sets up a storytelling bot with three distinct voices (narrator, male, female)
-    2. Uses pattern pairs (<voice>name</voice>) to trigger voice switching
-    3. Processes the patterns in real-time as text streams from the LLM
-    4. Removes the pattern tags before sending text to TTS
-
-The PatternPairAggregator:
-    - Buffers text until complete patterns are detected
-    - Identifies content between start/end pattern pairs
-    - Triggers callbacks when patterns are matched
-    - Processes patterns that may span across multiple text chunks
-    - Returns processed text at sentence boundaries
-
-Requirements:
-    - OpenAI API key
-    - Cartesia API key (for text-to-speech)
-    - Daily API key (for video/audio transport)
-
-    Environment variables (.env file):
-        OPENAI_API_KEY=your_openai_key
-        CARTESIA_API_KEY=your_cartesia_key
-        DAILY_API_KEY=your_daily_key
-
-Note:
-    This example shows one application of PatternPairAggregator (voice switching),
-    but the same approach can be used for various pattern-based text processing needs,
-    such as formatting instructions, command recognition, or structured data extraction.
-"""
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, TTSUpdateSettingsFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-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
-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.utils.text.pattern_pair_aggregator import (
-    MatchAction,
-    PatternMatch,
-    PatternPairAggregator,
-)
-
-load_dotenv(override=True)
-
-
-# Define voice IDs
-VOICE_IDS = {
-    "narrator": "c45bc5ec-dc68-4feb-8829-6e6b2748095d",  # Narrator voice
-    "female": "71a7ad14-091c-4e8e-a314-022ece01c121",  # Female character voice
-    "male": "7cf0e2b1-8daf-4fe4-89ad-f6039398f359",  # Male character voice
-}
-
-# 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")
-
-    # Create pattern pair aggregator for voice switching
-    llm_text_aggregator = PatternPairAggregator()
-
-    # Add pattern for voice switching
-    llm_text_aggregator.add_pattern(
-        type="voice",
-        start_pattern="<voice>",
-        end_pattern="</voice>",
-        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:
-            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}")
-
-    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"],
-        ),
-        skip_aggregator_types=["voice"],  # Skip voice tags in TTS speech
-    )
-
-    # System prompt for storytelling with voice switching
-    system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.
-
-You have three voices to use, but each has a specific purpose:
-
-<voice>narrator</voice>
-This is the default narrator voice. Use this for all narration, descriptions, and non-dialogue text.
-
-<voice>female</voice>
-Use this ONLY for direct speech by female characters (just the quoted text).
-
-<voice>male</voice>
-Use this ONLY for direct speech by male characters (just the quoted text).
-
-IMPORTANT: Switch back to narrator voice immediately after character dialogue.
-
-Here's an EXAMPLE of correct voice usage:
-
-<voice>narrator</voice>
-Sarah spotted her old friend across the café. She couldn't believe her eyes.
-
-<voice>female</voice>
-"Jacob! It's been so long!"
-
-<voice>narrator</voice>
-Sarah exclaimed, jumping up from her seat with a radiant smile.
-
-<voice>male</voice>
-"Sarah, is it really you? I can't believe it!"
-
-<voice>narrator</voice>
-Jacob replied, grinning widely as he walked over to her. The two friends embraced warmly, as if trying to make up for all the years spent apart.
-
-<voice>female</voice>
-"What are you doing in town? Last I heard you were in Seattle."
-
-<voice>narrator</voice>
-She asked, gesturing for him to join her at the table.
-
-FOLLOW THESE RULES:
-1. Always begin with the narrator voice
-2. Only use character voices for the EXACT words they speak (in quotes)
-3. SWITCH BACK to narrator voice for speech tags and all other text
-4. Begin by asking what kind of story the user would like to hear
-5. Create engaging dialogue with distinct characters
-
-Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue."""
-
-    # Initialize LLM
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction=system_prompt,
-        ),
-    )
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    # Create pipeline
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            llm_text_processor,
-            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")
-        # Start conversation - empty prompt to let LLM follow system instructions
-        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/features/features-service-switcher.py

@@ -1,200 +0,0 @@
-#
-# 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, ManuallySwitchServiceFrame
-from pipecat.pipeline.llm_switcher import LLMSwitcher
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.service_switcher import ServiceSwitcher
-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.stt import CartesiaSTTService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# "Classic" function
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-# "Direct" function
-async def get_restaurant_recommendation(params: FunctionCallParams, location: str):
-    """
-    Get a restaurant recommendation.
-
-    Args:
-        location (str): The city and state, e.g. "San Francisco, CA".
-    """
-    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")
-
-    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"],
-    )
-
-    stt_cartesia = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
-    stt_deepgram = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-    # Uses ServiceSwitcherStrategyManual by default
-    stt_switcher = ServiceSwitcher(services=[stt_cartesia, stt_deepgram])
-
-    tts_cartesia = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-    tts_deepgram = DeepgramTTSService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-2-helena-en",
-        ),
-    )
-    # Uses ServiceSwitcherStrategyManual by default
-    tts_switcher = ServiceSwitcher(services=[tts_cartesia, tts_deepgram])
-
-    system_prompt = "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."
-
-    llm_openai = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(system_instruction=system_prompt),
-    )
-    llm_google = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(system_instruction=system_prompt),
-    )
-    # Uses ServiceSwitcherStrategyManual by default
-    llm_switcher = LLMSwitcher(llms=[llm_openai, llm_google])
-    # Register a "classic" function
-    llm_switcher.register_function("get_current_weather", fetch_weather_from_api)
-    # Register a "direct" function
-    llm_switcher.register_direct_function(get_restaurant_recommendation)
-
-    tools = ToolsSchema(standard_tools=[weather_function, get_restaurant_recommendation])
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt_switcher,
-            user_aggregator,  # User responses
-            llm_switcher,  # LLM
-            tts_switcher,  # 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": "developer", "content": "Please introduce yourself to the user."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-        await asyncio.sleep(15)
-        print(f"Switching to {stt_deepgram}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=stt_deepgram)])
-        await asyncio.sleep(15)
-        print(f"Switching to {llm_google}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=llm_google)])
-        await asyncio.sleep(15)
-        print(f"Switching to {tts_deepgram}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=tts_deepgram)])
-
-    @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/features/features-switch-languages.py

@@ -1,190 +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 Frame, LLMRunFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.filters.function_filter import FunctionFilter
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-class SwitchLanguage(ParallelPipeline):
-    def __init__(self):
-        self._current_language = "English"
-
-        english_tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-        )
-
-        spanish_tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9",  # Spanish-speaking Lady
-            ),
-        )
-
-        super().__init__(
-            # English
-            [FunctionFilter(self.english_filter), english_tts],
-            # Spanish
-            [FunctionFilter(self.spanish_filter), spanish_tts],
-        )
-
-    @property
-    def current_language(self):
-        return self._current_language
-
-    async def switch_language(self, params: FunctionCallParams):
-        self._current_language = params.arguments["language"]
-        await params.result_callback(
-            {"voice": f"Your answers from now on should be in {self.current_language}."}
-        )
-
-    async def english_filter(self, _: Frame) -> bool:
-        return self.current_language == "English"
-
-    async def spanish_filter(self, _: Frame) -> bool:
-        return self.current_language == "Spanish"
-
-
-# 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"),
-        settings=DeepgramSTTService.Settings(
-            language="multi",
-        ),
-    )
-
-    tts = SwitchLanguage()
-
-    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. You can speak the following languages: 'English' and 'Spanish'.",
-        ),
-    )
-    llm.register_function("switch_language", tts.switch_language)
-
-    switch_language_function = FunctionSchema(
-        name="switch_language",
-        description="Switch to another language when the user asks you to",
-        properties={
-            "language": {
-                "type": "string",
-                "description": "The language the user wants you to speak",
-            },
-        },
-        required=["language"],
-    )
-    tools = ToolsSchema(standard_tools=[switch_language_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,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS (bot will speak the chosen language)
-            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": "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}.",
-            }
-        )
-        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/features/features-switch-voices.py

@@ -1,200 +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 Frame, LLMRunFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.filters.function_filter import FunctionFilter
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-class SwitchVoices(ParallelPipeline):
-    def __init__(self):
-        self._current_voice = "News Lady"
-
-        news_lady = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="bf991597-6c13-47e4-8411-91ec2de5c466",  # Newslady
-            ),
-        )
-
-        british_lady = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-        )
-
-        barbershop_man = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
-            ),
-        )
-
-        super().__init__(
-            # News Lady voice
-            [FunctionFilter(self.news_lady_filter), news_lady],
-            # British Reading Lady voice
-            [FunctionFilter(self.british_lady_filter), british_lady],
-            # Barbershop Man voice
-            [FunctionFilter(self.barbershop_man_filter), barbershop_man],
-        )
-
-    @property
-    def current_voice(self):
-        return self._current_voice
-
-    async def switch_voice(self, params: FunctionCallParams):
-        self._current_voice = params.arguments["voice"]
-        await params.result_callback(
-            {
-                "voice": f"You are now using your {self.current_voice} voice. Your responses should now be as if you were a {self.current_voice}."
-            }
-        )
-
-    async def news_lady_filter(self, _: Frame) -> bool:
-        return self.current_voice == "News Lady"
-
-    async def british_lady_filter(self, _: Frame) -> bool:
-        return self.current_voice == "British Lady"
-
-    async def barbershop_man_filter(self, _: Frame) -> bool:
-        return self.current_voice == "Barbershop Man"
-
-
-# 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 = SwitchVoices()
-
-    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 and helpful way. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
-        ),
-    )
-    llm.register_function("switch_voice", tts.switch_voice)
-
-    switch_voice_function = FunctionSchema(
-        name="switch_voice",
-        description="Switch your voice only when the user asks you to",
-        properties={
-            "voice": {
-                "type": "string",
-                "description": "The voice the user wants you to use",
-            },
-        },
-        required=["voice"],
-    )
-    tools = ToolsSchema(standard_tools=[switch_voice_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 responses
-            llm,  # LLM
-            tts,  # TTS with switch voice functionality
-            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": "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}.",
-            }
-        )
-        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/features/features-user-email-gathering.py

@@ -1,161 +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.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def store_user_emails(params: FunctionCallParams):
-    print(f"User emails: {params.arguments}")
-
-
-# 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"))
-
-    # Cartesia offers a `<spell></spell>` tags that we can use to ask the user
-    # to confirm the emails.
-    # (see https://docs.cartesia.ai/build-with-sonic/formatting-text-for-sonic/spelling-out-input-text)
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    # Rime offers a function `spell()` that we can use to ask the user
-    # to confirm the emails.
-    # (see https://docs.rime.ai/api-reference/spell)
-    # tts = RimeHttpTTSService(
-    #     api_key=os.getenv("RIME_API_KEY", ""),
-    #     settings=RimeTTSSettings(
-    #         voice="eva",
-    #     ),
-    #     aiohttp_session=session,
-    # )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You need to gather a valid email or emails from the user. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. If the user provides one or more email addresses confirm them with the user. Enclose all emails with <spell> tags, for example <spell>a@a.com</spell>.",
-        ),
-    )
-    # 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("store_user_emails", store_user_emails)
-
-    store_emails_function = FunctionSchema(
-        name="store_user_emails",
-        description="Store user emails when confirmed",
-        properties={
-            "emails": {
-                "type": "array",
-                "description": "The list of user emails",
-                "items": {"type": "string"},
-            },
-        },
-        required=["emails"],
-    )
-    tools = ToolsSchema(standard_tools=[store_emails_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")
-        # Start conversation - empty prompt to let LLM follow system instructions
-        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/features/features-voicemail-detection.py

@@ -1,145 +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.extensions.voicemail.voicemail_detector import VoicemailDetector
-from pipecat.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_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
-
-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.",
-        ),
-    )
-    classifier_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    voicemail = VoicemailDetector(llm=classifier_llm)
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            voicemail.detector(),  # Voicemail detection — between STT and User context aggregator
-            user_aggregator,
-            llm,
-            tts,
-            voicemail.gate(),  # TTS gating — Immediately after the TTS service
-            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")
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    @voicemail.event_handler("on_conversation_detected")
-    async def on_conversation_detected(processor):
-        logger.info("Conversation detected!")
-
-    @voicemail.event_handler("on_voicemail_detected")
-    async def on_voicemail_detected(processor):
-        logger.info("Voicemail detected! Leaving a message...")
-
-        # Push frames using standard Pipecat pattern
-        await processor.push_frame(
-            TTSSpeakFrame(
-                "Hello, this is Jamie calling about your appointment. Please call me back at 555-0123 when you get this."
-            )
-        )
-
-        # NOTE: A common pattern is to end pipeline after the voicemail is left.
-        # Uncomment the following line to end the pipeline after leaving the voicemail.
-        # await processor.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
-
-    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/features/features-wake-phrase.py

@@ -1,147 +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 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_start import WakePhraseUserTurnStartStrategy
-from pipecat.turns.user_turn_strategies import (
-    UserTurnStrategies,
-    default_user_turn_start_strategies,
-)
-
-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"),
-        settings=DeepgramSTTService.Settings(
-            keyterm=["pipecat"],
-        ),
-    )
-
-    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(
-                start=[
-                    WakePhraseUserTurnStartStrategy(
-                        phrases=["pipecat"],
-                        # Timeout before wake phrase must be spoken again
-                        timeout=5.0,
-                    ),
-                    *default_user_turn_start_strategies(),
-                ]
-            ),
-            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": "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-stream.py

@@ -1,210 +0,0 @@
-#
-# 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

@@ -1,180 +0,0 @@
-#
-# 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

@@ -1,192 +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, 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.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
-
-load_dotenv(override=True)
-
-
-async def fetch_user_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
-        ),
-    )
-
-    # Anthropic for vision analysis
-    llm = AnthropicLLMService(
-        api_key=os.getenv("ANTHROPIC_API_KEY"),
-        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 are able to describe images from the user camera.",
-        ),
-    )
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    @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."))
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_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=[fetch_image_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,  # 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: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        # Set the participant ID in the image requester
-        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-anthropic.py

@@ -1,165 +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.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 get_weather(params: FunctionCallParams):
-    location = params.arguments["location"]
-    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-# 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"),
-        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.",
-        ),
-    )
-    llm.register_function("get_weather", get_weather)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    weather_function = FunctionSchema(
-        name="get_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    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-aws-video.py

@@ -1,197 +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, 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.aws.llm import AWSBedrockLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-async def fetch_user_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
-        ),
-    )
-
-    # AWS for vision analysis
-    llm = AWSBedrockLLMService(
-        aws_region="us-west-2",
-        settings=AWSBedrockLLMService.Settings(
-            model="us.anthropic.claude-sonnet-4-6",
-            # Note: usually, prefer providing latency="optimized" param.
-            # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
-            # which we need for image input.
-            temperature=0.8,
-            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 are able to describe images from the user camera.",
-        ),
-    )
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    @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."))
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_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=[fetch_image_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,  # 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: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        # Set the participant ID in the image requester
-        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 briefly; don't mention the camera. 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-aws.py

@@ -1,174 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-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.aws.llm import AWSBedrockLLMService
-from pipecat.services.aws.stt import AWSTranscribeSTTService
-from pipecat.services.aws.tts import AWSPollyTTSService
-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"})
-
-
-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 = AWSTranscribeSTTService()
-
-    tts = AWSPollyTTSService(
-        region="us-west-2",  # only specific regions support generative TTS
-        settings=AWSPollyTTSService.Settings(
-            voice="Joanna",
-            engine="generative",
-            rate="1.1",
-        ),
-    )
-
-    llm = AWSBedrockLLMService(
-        aws_region="us-west-2",
-        settings=AWSBedrockLLMService.Settings(
-            model="us.anthropic.claude-sonnet-4-6",
-            temperature=0.8,
-            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)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-    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()

examples/function-calling/function-calling-azure.py

@@ -1,157 +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_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.azure.llm import AzureLLMService
-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):
-    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 = AzureLLMService(
-        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-        settings=AzureLLMService.Settings(
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-            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)
-
-    @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])
-    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/function-calling/function-calling-cerebras.py

@@ -1,166 +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_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.cerebras.llm import CerebrasLLMService
-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):
-    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 = CerebrasLLMService(
-        api_key=os.getenv("CEREBRAS_API_KEY"),
-        settings=CerebrasLLMService.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 have one functions available:
-
-1. get_current_weather is used to get current weather information.
-
-Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference.
-
-Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
-
-    Respond to what the user said in a creative and helpful 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)
-
-    @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])
-
-    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/function-calling/function-calling-deepseek.py

@@ -1,167 +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_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.deepseek.llm import DeepSeekLLMService
-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 = DeepSeekLLMService(
-        api_key=os.getenv("DEEPSEEK_API_KEY"),
-        settings=DeepSeekLLMService.Settings(
-            model="deepseek-chat",
-            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 have one functions available:
-
-1. get_current_weather is used to get current weather information.
-
-Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference.
-
-Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
-
-    Respond to what the user said in a creative and helpful 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)
-
-    @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])
-
-    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/function-calling/function-calling-direct.py

@@ -1,158 +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.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_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def get_current_weather(params: FunctionCallParams, location: str, format: str):
-    """
-    Get the current weather.
-
-    Args:
-        location (str): The city and state, e.g. "San Francisco, CA".
-        format (str): The temperature unit to use. Must be either "celsius" or "fahrenheit". Infer this from the user's location.
-    """
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def get_restaurant_recommendation(params: FunctionCallParams, location: str):
-    """
-    Get a restaurant recommendation.
-
-    Args:
-        location (str): The city and state, e.g. "San Francisco, CA".
-    """
-    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 = 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.",
-        ),
-    )
-
-    # 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_direct_function(get_current_weather)
-    llm.register_direct_function(get_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."))
-
-    tools = ToolsSchema(standard_tools=[get_current_weather, get_restaurant_recommendation])
-
-    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/function-calling/function-calling-fireworks.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 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.fireworks.llm import FireworksLLMService
-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 = FireworksLLMService(
-        api_key=os.getenv("FIREWORKS_API_KEY"),
-        settings=FireworksLLMService.Settings(
-            model="accounts/fireworks/models/gpt-oss-20b",
-            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)
-
-    # Disabling for now, as it ends up tripping up the model in this example
-    # ("let me check on that" ends up at the end of the context, which the
-    # model erroneously treats as a nudge to call the tool again; the
-    # ensuing inference then yields wonky results).
-    # @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])
-    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()

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

@@ -1,214 +0,0 @@
-#
-# 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

@@ -1,256 +0,0 @@
-#
-# 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

@@ -1,165 +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_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.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.google.vertex.llm import GoogleVertexLLMService
-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 = GoogleVertexLLMService(
-        credentials=os.getenv("GOOGLE_VERTEX_TEST_CREDENTIALS"),
-        project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
-        location=os.getenv("GOOGLE_CLOUD_LOCATION"),
-        settings=GoogleVertexLLMService.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": "developer",
-            "content": "Start a conversation with 'Hey there' to get the current weather.",
-        },
-    ]
-
-    context = LLMContext(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/function-calling/function-calling-google-video.py

@@ -1,192 +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, 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 fetch_user_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
-        ),
-    )
-
-    # Google Gemini model for vision analysis
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        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. Respond to what the user said in a creative, helpful, and brief way. You are able to describe images from the user camera.",
-        ),
-    )
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    @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."))
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_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=[fetch_image_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,  # 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: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        # Set the participant ID in the image requester
-        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.py

@@ -1,248 +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, 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):
-    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"),
-        settings=GoogleLLMService.Settings(
-            system_instruction=system_prompt,
-        ),
-    )
-    llm.register_function("get_weather", get_weather)
-    llm.register_function("get_image", get_image)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
-
-    weather_function = FunctionSchema(
-        name="get_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    get_image_function = FunctionSchema(
-        name="get_image",
-        description="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-grok.py

@@ -1,157 +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.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()

examples/function-calling/function-calling-groq.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.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_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.groq.llm import GroqLLMService
-from pipecat.services.groq.stt import GroqSTTService
-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 = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = GroqLLMService(
-        api_key=os.getenv("GROQ_API_KEY"),
-        settings=GroqLLMService.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)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
-
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function])
-    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/function-calling/function-calling-mistral.py

@@ -1,168 +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.llm_service import FunctionCallParams
-from pipecat.services.mistral.llm import MistralLLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-# We 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 = MistralLLMService(
-        api_key=os.getenv("MISTRAL_API_KEY"),
-        settings=MistralLLMService.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)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-    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/function-calling/function-calling-moondream-video.py

@@ -1,230 +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 (
-    Frame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMRunFrame,
-    TextFrame,
-    TTSSpeakFrame,
-    UserImageRequestFrame,
-)
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import 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,
-    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.llm_service import FunctionCallParams
-from pipecat.services.moondream.vision import MoondreamService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-async def fetch_user_image(params: FunctionCallParams):
-    """Fetch the user image.
-
-    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. 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. In this case, we don't want the requested
-    # image to be added to the context because we will process it with
-    # Moondream. 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=False,
-            function_name=params.function_name,
-            tool_call_id=params.tool_call_id,
-            result_callback=params.result_callback,
-        ),
-        FrameDirection.UPSTREAM,
-    )
-
-
-class MoondreamTextFrameWrapper(FrameProcessor):
-    """Wraps Moondream-provided TextFrames with LLM response start/end frames.
-
-    This processor detects TextFrames and automatically wraps them with
-    LLMFullResponseStartFrame and LLMFullResponseEndFrame to provide proper
-    response boundaries for downstream processors.
-    """
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        # If we receive a TextFrame, wrap it with response start/end frames
-        if isinstance(frame, TextFrame):
-            await self.push_frame(LLMFullResponseStartFrame(), direction)
-            await self.push_frame(frame, direction)
-            await self.push_frame(LLMFullResponseEndFrame(), direction)
-        else:
-            # For all other frames, just pass them through
-            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,
-        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
-        ),
-    )
-
-    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. You are able to describe images from the user camera.",
-        ),
-    )
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    @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."))
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_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=[fetch_image_function])
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    # If you run into weird description, try with use_cpu=True
-    moondream = MoondreamService()
-
-    # Wrap TextFrames with LLM response start/end frames, which makes Moondream
-    # output be treated like LLM responses for the purpose of context
-    # aggregation. Without this, the assistant context aggregator would ignore
-    # Moondream output (if the TTS service is disabled).
-    moondream_text_wrapper = MoondreamTextFrameWrapper()
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            ParallelPipeline(
-                [llm],  # LLM
-                [moondream, moondream_text_wrapper],
-            ),
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        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)
-
-        # Set the participant ID in the image requester
-        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()