Move the LocalSmartTurnAnalyzerV3 import from module level into
__post_init__ so that importing user_turn_strategies no longer eagerly
loads the transformers package. This eliminates the spurious PyTorch
was not found warning for users who don't use the smart turn analyzer.
Adds a TTS service that connects to Deepgram models deployed on AWS
SageMaker endpoints via HTTP/2 bidirectional streaming. Supports the
Deepgram TTS protocol (Speak, Flush, Clear, Close) over the BiDi
client, with interruption handling and per-turn TTFB metrics.
Updates the example and env.example with separate STT/TTS endpoint names.
Eliminate custom _emit_stt_ttfb_metric and manual timestamp tracking in
STTService by reusing FrameProcessor's start_ttfb_metrics/stop_ttfb_metrics
with new start_time/end_time parameters. This keeps the chronological
start→stop ordering and removes _speech_end_time and _last_transcription_time
state from STTService.
Remove the deprecation warning and __post_init__ override. Also fix the
default value for remote_participants to use field(default_factory=dict)
instead of None.
Add write_transport_frame() hook to BaseOutputTransport so subclasses
can handle custom frame types that flow through the audio queue. Add
DailySIPTransferFrame and DailySIPReferFrame as DataFrame subclasses
that queue with audio, ensuring SIP operations execute only after the
bot finishes its current utterance. Override write_transport_frame in
DailyOutputTransport to dispatch these frames to the existing
sip_call_transfer() and sip_refer() client methods.
Also switch DailyOutputTransport.send_message error handling from
logger.error to push_error for consistency.
RTVIObserver previously filtered out all upstream frames to avoid
duplicate messages from broadcasted frames. This caused upstream-only
frames to be silently ignored. Instead, add a `broadcasted` field to
the Frame base class that is set by broadcast_frame() and
broadcast_frame_instance(), and only skip upstream copies of
broadcasted frames.
The CI was failing because the runner's package index was stale,
causing a 404 when fetching libasound2-dev (a dependency of
portaudio19-dev). Running apt-get update first refreshes the index.
Change the version specifier from `>=0.2.8` to
`~=0.2.8` for the `speechmatics-voice` package.
This ensures compatibility with future patch
versions while preventing potential breaking
changes from minor updates.
Use client_req_id-based multiplexing instead of disconnecting and
reconnecting the websocket on every interruption. This follows the
same pattern used by Cartesia, ElevenLabs, and other services via
AudioContextWordTTSService.
Key changes:
- Base class: InterruptibleWordTTSService -> AudioContextWordTTSService
- Add close_ws_on_eos: False to setup message to keep connection alive
- Add client_req_id to text, end_of_stream messages for demultiplexing
- Route audio via append_to_audio_context() instead of push_frame()
- Silently drop messages for cancelled/unknown contexts on interruption
- Add _handle_interruption() that resets context without reconnecting
- Remove no-op push_frame() override
Always create UserIdleController (timeout=0 means disabled), removing
all Optional guards. Add UserIdleTimeoutUpdateFrame to allow changing
the idle timeout at runtime.
Replace the continuous heartbeat-based timer (UserSpeakingFrame/BotSpeakingFrame
+ asyncio.Event loop) with a simple one-shot timer that starts when
BotStoppedSpeakingFrame is received and cancels on UserStartedSpeakingFrame or
BotStartedSpeakingFrame. This eliminates false idle triggers caused by gaps
between the user finishing speaking and the bot starting to speak (LLM/TTS
latency).
Guard the timer start with two conditions to prevent false triggers:
- User turn in progress: during interruptions, BotStoppedSpeaking arrives
while the user is still speaking mid-turn.
- Function calls in progress: FunctionCallsStarted arrives before
BotStoppedSpeaking because the bot speaks concurrently with the function
call starting, so the timer must wait for the result and subsequent bot
response.
The outer try/except in each service decorator caught both tracing
setup errors and application errors from the wrapped function. If the
function itself raised (e.g. LLM rate limit, TTS timeout), the
exception was caught and the function was called a second time.
Fix by tracking whether the original function was called via a
fn_called flag. If the function was already called, re-raise the
exception instead of falling back to untraced re-execution.
Adds a Claude Code skill that analyzes the current branch diff against
main, maps changed source files to their doc pages, and makes targeted
updates to Configuration, InputParams, Usage, Notes, and Event Handlers
sections.
The class_decorators.py module (Traceable, @traceable, @traced) is not
used anywhere in the codebase. Mark it deprecated and fix the misleading
comment in service_decorators.py that referenced it as if it were active.
Consolidate _tracing_enabled and _tracing_context from LLMService,
STTService, and TTSService into the shared AIService base class.
Extract _get_turn_context() helper in service_decorators.py to
encapsulate the repeated pattern across all traced decorators.
Add model-specific params (arcana: repetition_penalty, temperature, top_p;
mistv2: no_text_normalization, save_oovs, segment) with dynamic query param
building via _build_settings(). Model/voice/param changes now trigger
WebSocket reconnection since all settings are URL query params.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This allows non-websocket STT services (like SarvamSTTService, which uses
the Sarvam Python SDK for connection management) to reuse the same keepalive
pattern. Subclasses override _send_keepalive() and _is_keepalive_ready() for
their specific protocol.
ConversationContextProvider and TurnContextProvider were singletons that
stored tracing context as class-level state. When two PipelineTask instances
ran concurrently, they would overwrite each other's context, causing service
spans to attach to the wrong pipeline's turn span.
Replace both singletons with a single TracingContext object owned by each
PipelineTask, threaded to services via StartFrame.
The Grok API now returns prefixed voice names (e.g. "human_Ara") in
session.updated events, causing Pydantic validation errors. Widen the
voice field type from GrokVoice to GrokVoice | str to accept both
user-facing names and server-returned values.
Extract the procedural PR workflow into an actionable skill that can be
invoked with /pr-submit. CLAUDE.md is better suited for project context
and conventions, not step-by-step procedures.
Expand the Pull Requests section with detailed step-by-step instructions
including branch naming, commit guidance, changelog generation, and PR
description updates.
OpenAI's AsyncStream uses close() while async generators (e.g. from
OpenPipe) use aclose(). Replace direct async-with on the stream with a
helper that handles both protocols.
Adds opt-in keepalive_timeout and keepalive_interval params to
WebsocketSTTService. When enabled, a background task sends silent audio
(or a service-specific protocol message) when the connection has been
idle, preventing server-side timeout disconnects.
Subclasses override _send_keepalive(silence) to wrap the silence in
their wire format. The default sends raw PCM bytes.
Enables keepalive for ElevenLabs (10s), Gladia (20s), and Soniox (1s),
replacing their per-service custom keepalive tasks.
Add a `service` field so the frame targets a specific service, allowing
ServiceSwitcher.push_frame to consume it only when the targeted service
matches the active service. STTService and test mocks now push the frame
downstream after handling instead of silently consuming it.
The default stop strategy changed to TurnAnalyzerUserTurnStopStrategy,
which requires actual audio analysis. Use SpeechTimeoutUserTurnStopStrategy
explicitly since this test is not testing turn detection.
Change the default user turn stop strategy from
TranscriptionUserTurnStopStrategy to TurnAnalyzerUserTurnStopStrategy
with LocalSmartTurnAnalyzerV3. Also reduce AUDIO_INPUT_TIMEOUT_SECS
from 1.0 to 0.5 and remove its debug log.
- Make ServiceSwitcherStrategy inherit from BaseObject with properties
for services and active_service, and move initial service selection
into the base class
- Add on_service_switched event to ServiceSwitcherStrategy
- handle_frame now returns the switched-to service (or None), allowing
ServiceSwitcher to swallow ManuallySwitchServiceFrame on switch and
request metadata from the new active service
- Override push_frame to suppress RequestMetadataFrame and
ServiceMetadataFrame from inactive services
- Remove ServiceSwitcherFilter and ServiceSwitcherFilterFrame in favor
of plain FunctionFilter instances with closures that check the
strategy's active service directly
- FunctionFilter: add FilterType alias
- FunctionFilter: when direction is None, frames in both directions
are filtered instead of just one
- Add docstrings to ServiceSwitcher and its components
Refactor TranscriptionUserTurnStopStrategy and TurnAnalyzerUserTurnStopStrategy
to use VADUserStoppedSpeakingFrame as the ground truth for when speech ended,
rather than triggering timeouts from transcription frames.
RTVIObserver now skips upstream frames to prevent duplicate RTVI messages
when frames are broadcast in both directions. Also changed
FunctionCallCancelFrame to use broadcast_frame for consistency with
other function call frames.
Processors inside parallel sub-pipelines can push frames during
StartFrame/EndFrame/CancelFrame processing. Previously these frames
could escape the ParallelPipeline before all branches finished
processing the lifecycle frame. Now they are buffered and flushed
after synchronization completes.
Add tests for the event-based interruption completion: complete() sets
the event, complete() is safe without an event, the event fires at
the pipeline sink, and a warning is logged when the frame is blocked.
Also remove the unconditional await after the timeout so the function
returns instead of hanging when complete() is never called.
Move the interruption wait event from per-processor instance state to
the frame itself. The event is created in
push_interruption_task_frame_and_wait(), threaded through
InterruptionTaskFrame → InterruptionFrame, and set when the frame
reaches the pipeline sink. This scopes the event to each interruption
flow rather than sharing mutable state on the processor.
Also adds a 2s timeout warning to help diagnose cases where
InterruptionFrame.complete() is never called.
This adds user-to-bot response latency tracking to OpenTelemetry spans:
- Created UserBotLatencyObserver as a reusable component for tracking
user-to-bot response latency
- Records the value as an attribute on turn spans (turn.user_bot_latency_seconds)
- Updated TurnTraceObserver to use UserBotLatencyObserver, following the same pattern as TurnTrackingObserver
- Updated PipelineTask to automatically create and wire UserBotLatencyObserver
when tracing is enabled (same as TurnTrackingObserver)
Tests cover:
- No messages received (raises ValueError)
- One message received (logs warning, continues)
- Two messages received (normal operation)
- All telephony providers (Twilio, Telnyx, Plivo, Exotel)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Handle WebSocket disconnections gracefully when telephony providers send
fewer messages than expected. Adds explicit StopAsyncIteration handling
for both first and second message retrieval.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
NLTK's sent_tokenize() only supports ~15 European languages and defaults to
English. For Japanese, Chinese, Korean, Hindi, Arabic, and other non-Latin
languages, NLTK fails to recognize sentence boundaries like 。?! causing
text to accumulate until flush instead of being emitted sentence-by-sentence.
Add a fallback in match_endofsentence() that scans for unambiguous non-Latin
sentence-ending punctuation when NLTK fails to split the text. Latin
punctuation (. ! ? ; …) is excluded from the fallback since NLTK handles
those correctly and they can be ambiguous (abbreviations, decimals, etc.).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
broadcast_frame() expects a frame class and kwargs, but the three
websocket input transports (fastapi, client, server) were incorrectly
passing a frame instance. This would cause a TypeError at runtime when
an InputTransportMessageFrame was received.
Incorporates latest changes from main branch including:
- AIC filter and VAD updates
- STT service improvements
- Base serializer changes
- Various bug fixes
If `enable_rtvi` is enabled (enabled by default) and RTVI processor will be
added automatically to the pipeline. Also, and RTVI observer will be
registered.
Process audio as soon as we receive it from the generator. Previously, we were
reading from the generator and adding elements into a queue until there was no
more data, then we would process the queue.
If AIService subclasses implement start()/stop()/cancel() and exception are not
handled, execution will not continue and therefore the originator frames will
not be pushed. This would cause the pipeline to not be started (i.e. StartFrame
would not be pushed downstream) or stopped properly.
Issues:
- After disconnecting, we were prematurely sending audio messages using the new prompt and content names, before the new prompt and content were created
- We weren't properly sending system instruction and conversation history messages to Nova Sonic with `"interactive": false`
The underlying issue was related to the fact that we were sending audio to Grok before we had configured the Grok session with our default input sample rate (16000), so Grok was interpreting those initial audio chunks as having its default sample rate (24000). We didn't see this issue when using the Daily transport simply because in our test environments Daily took a smidge longer than a reflexive (localhost) pure WebRTC connection, so we would only send audio to Grok *after* we had configured the Grok session with the desired sample rate.
Extract dictionary value to local variable and check for None before
accessing cancel_on_interruption attribute, since the dictionary values
are typed as Optional[FunctionCallInProgressFrame].
If we aggregate transcriptions we will get incorrect interruptions. For example,
if we have a strategy with min_words=3 and we say "One" and pause, then "Two"
and pause and then "Three", this would trigger the start of the turn when it
shouldn't. We should only look at the incoming transcription text and don't
aggregate it with the previous.
- Replace aiohttp with camb SDK (AsyncCambAI client)
- Add support for passing existing SDK client instance
- Simplify API: no longer requires aiohttp_session parameter
- Update example to use simplified initialization
- Rewrite tests to mock SDK client instead of HTTP servers
- Add --voice-id CLI argument to example (default: 2681)
- Remove test_camb_quick.py from examples/ (tests belong in tests/)
- Update docstring with new usage
Gemini expects parallel function calls to be passed in as a single multi-part `Content` block. This is important because only one of the function calls in a batch of parallel function calls gets a thought signature—if they're passed in as separate `Content` blocks, there'd be one or more missing thought signatures, which would result in a Gemini error.
Add support for Gladia's speech_start/speech_end events to emit
UserStartedSpeakingFrame and UserStoppedSpeakingFrame frames.
When enable_vad=True in GladiaInputParams:
- speech_start triggers interruption and pushes UserStartedSpeakingFrame
- speech_end pushes UserStoppedSpeakingFrame
- Tracks speaking state to prevent duplicate events
This allows using Gladia's built-in VAD instead of a separate VAD
in the pipeline.
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
**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`)
- **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
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.
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
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.
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
- **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:
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, create it 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]
-`guides/learn/speech-to-text.mdx` — Updated code example (renamed `old_param` → `new_param`)
### 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
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 --no-extra krisp
# 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
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.
- **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).
- **Type hints**: Required for complex async code.
### Docstring Example
```python
classMyService(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.
- Introducing user turn strategies. User turn strategies indicate when the user turn starts or stops. In conversational agents, these are often referred to as start/stop speaking or turn-taking plans or policies.
User turn start strategies indicate when the user starts speaking (e.g. using VAD events or when a user says one or more words).
User turn stop strategies indicate when the user stops speaking (e.g. using an end-of-turn detection model or by observing incoming transcriptions).
A list of strategies can be specified for both strategies; strategies are evaluated in order until one evaluates to true.
- ⚠️ `TransportParams.turn_analyzer` is deprecated and might result in unexpected behavior, use `LLMUserAggregator`'s new `user_turn_strategies` parameter instead.
- The `LLMUserAggregatorParams` and `LLMAssistantAggregatorParams` classes in `pipecat.processors.aggregators.llm_response` are now deprecated. Use the new universal `LLMContext` and `LLMContextAggregatorPair` instead.
-`pipecat.audio.interruptions.MinWordsInterruptionStrategy` is deprecated. Use `pipecat.turns.user_start.MinWordsUserTurnStartStrategy` with `LLMUserAggregator`'s new `user_turn_strategies` parameter instead.
-`TranscriptionFrame` and `InterimTranscriptionFrame` produced by `DailyTransport` now include the transport source (i.e., the originating audio track).
-`OpenAILLMContext` and its associated things (context aggregators, etc.) are now deprecated in favor of the universal `LLMContext` and its associated things.
From the developer's point of view, switching to using `LLMContext` machinery will usually be a matter of going from this:
- Added a new `AudioContextTTSService` to the TTS service base classes. The `AudioContextWordTTSService` now inherits from `AudioContextTTSService` and `WebsocketWordTTSService`.
- Introducing user mute strategies. User mute strategies indicate when user input should be muted based on the current system state.
In conversational agents, user mute strategies are used to prevent user input from interrupting bot speech, tool execution, or other critical system operations.
A list of strategies can be specified; all strategies are evaluated for every frame so that each strategy can maintain its internal state. A user frame is muted if any of the configured strategies indicates it should be muted.
Available user mute strategies:
*`FirstSpeechUserMuteStrategy`
*`MuteUntilFirstBotCompleteUserMuteStrategy`
*`AlwaysUserMuteStrategy`
*`FunctionCallUserMuteStrategy`
User mute strategies replace the legacy `STTMuteFilter` and provide a more flexible and composable approach to muting user input.
User mute strategies are configured when setting up the `LLMContextAggregatorPair`. For example:
```python
context_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
user_mute_strategies=[
FirstSpeechUserMuteStrategy(),
]
),
)
```
In order to use user mute strategies you should update to the new universal `LLMContext` and `LLMContextAggregatorPair`.
-`PipelineParams.allow_interruptions` is now deprecated, use `LLMUserAggregator`'s new parameter `user_turn_strategies` instead. For example, to disable interruptions but still get user turns you can do:
- Updated `DeepgramSTTService` to push user started/stopped speaking and interruption frames when `vad_enabled` is set to true. This centralizes the frames into the service, removing the need to have your application code handle Deepgram's events and push these frames.
- Added `enable_interruptions` constructor argument to all user turn strategies. This tells the `LLMUserAggregator` to push or not push an `InterruptionFrame`.
- Added `52-live-transcription.py` foundational example demonstrating live transcription and translation from English to Spanish. In this example, the bot is not interruptible: as the user continues speaking, English transcriptions are queued, and the bot continuously translates and speaks each queued sentence in Spanish without being interrupted by new user speech.
- Updated `SpeechmaticsSTTService` for version `0.0.99+`:
- Fixed `SpeechmaticsSTTService` to listen for `VADUserStoppedSpeakingFrame` in order to finalize transcription.
- Default to `TurnDetectionMode.FIXED` for Pipecat-controlled end of turn detection.
- Only emit VAD + interruption frames if VAD is enabled within the plugin (modes other than `TurnDetectionMode.FIXED` or `TurnDetectionMode.EXTERNAL`).
- Added encoding validation to `DeepgramTTSService` to prevent unsupported encodings from reaching the API. The service now raises `ValueError` at initialization with a clear error message.
- Fixed an issue with function calling where a handler failing to invoke its result callback could leave the context stuck in IN_PROGRESS, causing LLM inference for subsequent function call results to block while waiting on the unresolved call.
- Added `pronunciation_dict_id` parameter to `CartesiaTTSService.InputParams` and `CartesiaHttpTTSService.InputParams` to support Cartesia's pronunciation dictionary feature for custom pronunciations.
- Added `UserTurnProcessor`, a frame processor built on `UserTurnController` that pushes `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` frames and interruptions based on the controller's user turn strategies.
- Added `UserTurnController` to manage user turns. It emits `on_user_turn_started`, `on_user_turn_stopped`, and `on_user_turn_stop_timeout` events, and can be integrated into processors to detect and handle user turns. `LLMUserAggregator` and `UserTurnProcessor` are implemented using this controller.
- Added `should_interrupt` property to `DeepgramFluxSTTService`, `DeepgramSTTService`, and `SpeechmaticsSTTService` to configure whether the bot should be interrupted when the external service detects user speech.
-`TranscriptProcessor` and related data classes and frames (`TranscriptionMessage`, `ThoughtTranscriptionMessage`, `TranscriptionUpdateFrame`) are deprecated. Use `LLMUserAggregator`'s and `LLMAssistantAggregator`'s new events (`on_user_turn_stopped` and `on_assistant_turn_stopped`) instead.
- Added a new foundational example `28-user-assistant-turns.py` that shows how to use the new `LLMUserAggregator` and `LLMAssistantAggregator` events to gather a conversation transcript.
- Deprecated support for the `vad_events``LiveOptions` in `DeepgramSTTService`. Instead, use a local Silero VAD for VAD events. Additionally, deprecated `should_interrupt` which will be removed along with `vad_events` support in a future release.
- Added support for setting up a pipeline task from external files. You can now register custom pipeline task setup files by setting the `PIPECAT_SETUP_FILES` environment variable. This variable should contain a colon-separated list of Python files (e.g. `export PIPECAT_SETUP_FILES="setup1.py:setup.py:..."`). Each file must define a function with the following signature:
- Fixed timing issue in `BaseOutputTransport` where the bot speaking flag was set after awaiting, allowing the event loop to re-enter the method before the guard was set.
- Fixed race condition where `RTVIObserver` could send messages before `DailyTransport` join completed. Outbound messages are now queued & delivered after the transport is ready.
- Added `"timestampTransportStrategy": "ASYNC"` to `InworldAITTSService`. This allows timestamps info to trail audio chunks arrival, resulting in much better first audio chunk latency
- ⚠️ `RimeTTSService` now defaults to `model="arcana"` and the `wss://users-ws.rime.ai/ws3` endpoint. `InputParams` defaults changed from mistv2-specific values to `None` — only explicitly-set params are sent as query params.
-`AICFilter` now shares read-only AIC models via a singleton `AICModelManager` in `aic_filter.py`.
- Multiple filters using the same model path or `(model_id, model_download_dir)` share one loaded model, with reference counting and concurrent load deduplication.
- Model file I/O runs off the event loop so the filter does not block.
- Added `write_transport_frame()` hook to `BaseOutputTransport` allowing transport subclasses to handle custom frame types that flow through the audio queue.
- Added `DailySIPTransferFrame` and `DailySIPReferFrame` to the Daily transport. These frames queue SIP transfer and SIP REFER operations with audio, so the operation executes only after the bot finishes its current utterance.
- Fixed context ID reuse issue in `ElevenLabsTTSService`, `InworldTTSService`, `RimeTTSService`, `CartesiaTTSService`, `AsyncAITTSService`, and `PlayHTTTSService`. Services now properly reuse the same context ID across multiple `run_tts()` invocations within a single LLM turn, preventing context tracking issues and incorrect lifecycle signaling.
- Moved STT keepalive mechanism from `WebsocketSTTService` to the `STTService` base class, allowing any STT service (not just websocket-based ones) to use idle-connection keepalive via the `keepalive_timeout` and `keepalive_interval` parameters.
- Improved audio context management in `AudioContextTTSService` by moving context ID tracking to the base class and adding `reuse_context_id_within_turn` parameter to control concurrent TTS request handling.
- Deprecated unused `Traceable`, `@traceable`, `@traced`, and `AttachmentStrategy` in `pipecat.utils.tracing.class_decorators`. This module will be removed in a future release.
- Fixed tracing service decorators executing the wrapped function twice when the function itself raised an exception (e.g., LLM rate limit, TTS timeout).
- Fixed `UserIdleController` false idle triggers caused by gaps between user and bot activity frames. The idle timer now starts only after `BotStoppedSpeakingFrame` and is suppressed during active user turns and function calls.
- Added `UserIdleTimeoutUpdateFrame` to enable or disable user idle detection at runtime by updating the timeout dynamically.
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