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2 Commits

Author SHA1 Message Date
Mark Backman
240780f80e docs: add changelog for #4072 2026-03-18 10:20:22 -04:00
Mark Backman
ac0643f82f feat: add WakePhraseUserFrameFilter for aggregator-level wake phrase gating
Add a new user frame filter concept that runs inside the aggregator,
before VAD processing. This blocks transcriptions, VAD events, and
interruptions until a wake phrase is detected, solving ordering
conflicts that prevented wake phrase detection from working as a
user turn start strategy.

- Add BaseUserFrameFilter base class and WakePhraseUserFrameFilter
  with LISTENING/INACTIVE state machine, timeout, and single_activation
- Add user_frame_filters param to LLMUserAggregatorParams with
  _maybe_filter_frame() running after mute check, before VAD
- Deprecate WakeCheckFilter in favor of the new filter
- Update 10-wake-phrase.py example
2026-03-18 10:20:22 -04:00
713 changed files with 29199 additions and 28079 deletions

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

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

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

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

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

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

File diff suppressed because it is too large Load Diff

62
CHANGELOG.md.template Normal file
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@@ -0,0 +1,62 @@
# Changelog
All notable changes to the **<project name>** SDK will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
Please make sure to add your changes to the appropriate categories:
## [Unreleased]
### Added
<!-- for new functionality -->
- n/a
### Changed
<!-- for changed functionality -->
- n/a
### Deprecated
<!-- for soon-to-be removed functionality -->
- n/a
### Removed
<!-- for removed functionality -->
- n/a
### Fixed
<!-- for fixed bugs -->
- n/a
### Performance
<!-- for performance-relevant changes -->
- n/a
### Security
<!-- for security-relevant changes -->
- n/a
### Other
<!-- for everything else -->
- n/a
## [0.1.0] - YYYY-MM-DD
Initial release.

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

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@@ -23,7 +23,7 @@ Create your integration following the patterns and examples shown in the "Integr
Your repository must contain these components:
- **Source code** - Complete implementation following Pipecat patterns
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples))
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
- **README.md** - Must include:
- Introduction and explanation of your integration
- Installation instructions
@@ -65,25 +65,12 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
#### Websocket-based Services
**Base class:** `WebsocketSTTService`
**Use for:** Services where you manage the websocket connection directly. Combines `STTService` with `WebsocketService` for automatic reconnection and keepalive support.
**Examples:**
- [CartesiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/stt.py)
- [ElevenLabsRealtimeSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/stt.py)
#### SDK-based Streaming Services
**Base class:** `STTService`
**Use for:** Streaming services where the provider's Python SDK manages the connection internally.
**Examples:**
- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
- [GoogleSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/stt.py)
- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
#### File-based Services
@@ -121,59 +108,55 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
#### 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
- `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.
- **Context aggregation:** Implement context aggregation to collect user and assistant content:
- Aggregators come in pairs with a `user()` instance and `assistant()` instance
- Context must adhere to the `LLMContext` universal format
- Aggregators should handle adding messages, function calls, and images to the context
### TTS (Text-to-Speech) Services
#### WebsocketTTSService
#### AudioContextWordTTSService
**Use for:** Websocket-based streaming services (with or without word timestamps)
**Use for:** Websocket-based services supporting word/timestamp alignment
**Examples:**
**Example:**
- [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)
**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
**Example:**
- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
#### WordTTSService
**Use for:** HTTP-based services supporting word/timestamp alignment
**Example:**
- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
#### TTSService
**Use for:** HTTP-based services (word timestamps are supported in the base class)
**Use for:** HTTP-based services without word/timestamp alignment
**Examples:**
**Example:**
- [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
- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
- Handle idle service timeouts with keepalives
- TTS services push both audio (`TTSAudioRawFrame`) and text (`TTSTextFrame`) frames
- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
### Telephony Serializers
@@ -217,25 +200,14 @@ Vision services process images and provide analysis such as descriptions, object
#### 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`
- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
- The method processes the latest image in the context and yields frames with analysis results
- Typically yields `TextFrame` objects containing descriptions or answers
## 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:**
@@ -409,7 +381,7 @@ Note that `self.sample_rate` is a `@property` set in the TTSService base class,
Use Pipecat's tracing decorators:
- **STT:** `@traced_stt` - decorate `_handle_transcription(self, transcript, is_final, language)` (the standard method name convention)
- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
- **LLM:** `@traced_llm` - decorate the `_process_context()` method
- **TTS:** `@traced_tts` - decorate the `run_tts()` method
@@ -417,9 +389,8 @@ Use Pipecat's tracing decorators:
### Packaging and Distribution
- Name your package `pipecat-{vendor}` (see [Naming Conventions](#naming-conventions))
- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
- Publish to PyPI for easier installation
- Consider releasing to PyPI for easier installation
- Follow semantic versioning principles
- Maintain a changelog
@@ -432,15 +403,17 @@ For REST-based communication, use aiohttp. Pipecat includes this as a required d
- Wrap API calls in appropriate try/catch blocks
- Handle rate limits and network failures gracefully
- Provide meaningful error messages
- When errors occur, raise exceptions AND push errors to notify the pipeline:
- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
```python
from pipecat.frames.frames import ErrorFrame
try:
# Your API call
result = await self._make_api_call()
except Exception as e:
# Push error upstream to notify the pipeline
await self.push_error(f"{self} error: {e}", exception=e)
# Push error frame to pipeline
await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
# Raise or handle as appropriate
raise
```

View File

@@ -8,7 +8,7 @@
**Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
> Want to dive right in? Run `pipecat init quickstart` or follow the [quickstart guide](https://docs.pipecat.ai/getting-started/quickstart).
> Want to dive right in? Try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
## 🚀 What You Can Build
@@ -65,10 +65,6 @@ claude plugin marketplace add pipecat-ai/skills
and install any of the available plugins.
### 🧩 Community Integrations
Build and share your own Pipecat service integrations! Browse existing [community integrations](https://docs.pipecat.ai/server/services/community-integrations) or check out our [guide](COMMUNITY_INTEGRATIONS.md) to create your own.
### 📺️ Pipecat TV Channel
Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
@@ -79,26 +75,25 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/simple-chatbot/image.png" width="400" /></a>&nbsp;
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
<br/>
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/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>
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>&nbsp;
<a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
</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) |
| Category | Services |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox), |
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
| Serializers | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage) |
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/video/lemonslice), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/google-imagen), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) |
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
@@ -142,15 +137,15 @@ You can get started with Pipecat running on your local machine, then move your a
## 🧪 Code examples
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples) — small snippets that build on each other, introducing one or two concepts at a time
- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — 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
**Minimum Python Version:** 3.10
**Recommended Python Version:** 3.12
### Setup Steps
@@ -166,6 +161,7 @@ You can get started with Pipecat running on your local machine, then move your a
```bash
uv sync --group dev --all-extras \
--no-extra gstreamer \
--no-extra krisp \
--no-extra local \
```

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@@ -0,0 +1 @@
- Changed tool result JSON serialization to use `ensure_ascii=False`, preserving UTF-8 characters instead of escaping them. This reduces context size and token usage for non-English languages.

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@@ -0,0 +1 @@
- `OpenAIRealtimeSTTService`'s `noise_reduction` parameter is now part of `OpenAIRealtimeSTTSettings`, making it runtime-updatable via `STTUpdateSettingsFrame`. The direct `noise_reduction` init argument is deprecated as of 0.0.106.

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- Updated `sarvamai` dependency from `0.1.26a2` (alpha) to `0.1.26` (stable release).

1
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@@ -0,0 +1 @@
- Fixed an issue where the default model for `OpenAILLMService` and `AzureLLMService` was mistakenly reverted to `gpt-4o`. The defaults are now restored to `gpt-4.1`.

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@@ -0,0 +1 @@
- `SimliVideoService` now extends `AIService` instead of `FrameProcessor`, aligning it with the HeyGen and Tavus video services. It supports `SimliVideoService.Settings(...)` for configuration and uses `start()`/`stop()`/`cancel()` lifecycle methods. Existing constructor usage (`api_key`, `face_id`, etc.) remains unchanged.

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@@ -0,0 +1 @@
- `SimliVideoService.InputParams` is deprecated. Use the direct constructor parameters `max_session_length`, `max_idle_time`, and `enable_logging` instead.

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@@ -0,0 +1 @@
- Added optional `service` field to `ServiceUpdateSettingsFrame` (and its subclasses `LLMUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `STTUpdateSettingsFrame`) to target a specific service instance. When `service` is set, only the matching service applies the settings; others forward the frame unchanged. This enables updating a single service when multiple services of the same type exist in the pipeline.

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@@ -0,0 +1 @@
- Added `sip_provider` and `room_geo` parameters to `configure()` in the Daily runner. These convenience parameters let callers specify a SIP provider name and geographic region directly without manually constructing `DailyRoomProperties` and `DailyRoomSipParams`.

1
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@@ -0,0 +1 @@
- Fixed a race condition where `EndTaskFrame` could cause the pipeline to shut down before in-flight frames (e.g. LLM function call responses) finished processing. `EndTaskFrame` and `StopTaskFrame` now flow through the pipeline as `ControlFrame`s, ensuring all pending work is flushed before shutdown begins. `CancelTaskFrame` and `InterruptionTaskFrame` remain immediate (`SystemFrame`).

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@@ -0,0 +1 @@
- Fixed `TTSService` potentially canceling in-flight audio during shutdown. The stop sequence now waits for all queued audio contexts to finish processing before canceling the stop frame task.

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@@ -0,0 +1 @@
- Fixed `ParallelPipeline` dropping or misordering frames during lifecycle synchronization. Buffered frames are now flushed in the correct order relative to synchronization frames (`StartFrame` goes first, `EndFrame`/`CancelFrame` go after), and frames added to the buffer during flush are also drained.

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@@ -0,0 +1 @@
- Added `PerplexityLLMAdapter` that automatically transforms conversation messages to satisfy Perplexity's stricter API constraints (strict role alternation, no non-initial system messages, last message must be user/tool). Previously, certain conversation histories could cause Perplexity API errors that didn't occur with OpenAI (`PerplexityLLMService` subclasses `OpenAILLMService` since Perplexity uses an OpenAI-compatible API).

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@@ -0,0 +1 @@
- Deprecated `LocalSmartTurnAnalyzerV2` and `LocalCoreMLSmartTurnAnalyzer`. Use `LocalSmartTurnAnalyzerV3` instead. Instantiating these analyzers will now emit a `DeprecationWarning`.

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@@ -0,0 +1 @@
- Update `pipecat-ai-small-webrtc-prebuilt` to `2.4.0`.

1
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@@ -0,0 +1 @@
- Fixed `Language` enum values (e.g. `Language.ES`) not being converted to service-specific codes when passed via `settings=Service.Settings(language=Language.ES)` at init time. This caused API errors (e.g. 400 from Rime) because the raw enum was sent instead of the expected language code (e.g. `"spa"`). Runtime updates via `UpdateSettingsFrame` were unaffected. The fix centralizes conversion in the base `TTSService` and `STTService` classes so all services handle this consistently.

1
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@@ -0,0 +1 @@
- Fixed `DeepgramSTTService` ignoring the `base_url` scheme when using `ws://` or `http://`. Previously these were silently overwritten with `wss://` / `https://`, breaking air-gapped or private deployments that don't use TLS. All scheme choices (`wss://`, `https://`, `ws://`, `http://`, or bare hostname) are now respected.

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@@ -0,0 +1 @@
- Bumped PyJWT minimum version from 2.10.1 to 2.12.0 in the `livekit` extra to address CVE-2026-32597 (GHSA-752w-5fwx-jx9f), where PyJWT <= 2.11.0 accepted unknown `crit` header extensions.

1
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@@ -0,0 +1 @@
- Fixed `LLMSwitcher.register_function()` and `register_direct_function()` not accepting or forwarding the `timeout_secs` parameter.

1
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@@ -0,0 +1 @@
Fixed `SonioxSTTService` and `OpenAIRealtimeSTTService` crash when language parameters contain plain strings instead of `Language` enum values.

1
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@@ -0,0 +1 @@
- Added DTMF input event support to the Daily transport. Incoming DTMF tones are now received via Daily's `on_dtmf_event` callback and pushed into the pipeline as `InputDTMFFrame`, enabling bots to react to keypad presses from phone callers.

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@@ -0,0 +1 @@
- Updated `daily-python` dependency to 0.25.0.

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@@ -0,0 +1 @@
- Added `enable_dialout` parameter to `configure()` in `pipecat.runner.daily` to support dial-out rooms. Also narrowed misleading `Optional` type hints and deduplicated token expiry calculation.

1
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@@ -0,0 +1 @@
- Fixed premature user turn stops caused by late transcriptions arriving between turns. A stale transcript from the previous turn could persist into the next turn and trigger a stop before the current turn's real transcript arrived. Stop strategies are now reset at both turn start and turn stop to prevent state from leaking across turn boundaries.

1
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@@ -0,0 +1 @@
- Fixed raw language strings like `"de-DE"` silently failing when passed to TTS/STT services (e.g. ElevenLabs producing no audio). Raw strings now go through the same `Language` enum resolution as enum values, so regional codes like `"de-DE"` are properly converted to service-expected formats like `"de"`. Unrecognized strings log a warning instead of failing silently.

1
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@@ -0,0 +1 @@
- Fixed Deepgram STT list-type settings (`keyterm`, `keywords`, `search`, `redact`, `replace`) being stringified instead of passed as lists to the SDK, which caused them to be sent as literal strings (e.g. `"['pipecat']"`) in the WebSocket query params.

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@@ -0,0 +1 @@
- Added `WakePhraseUserFrameFilter`, a new aggregator-level filter that gates user interaction behind a wake phrase. Unlike the previous `WakeCheckFilter` (which only filtered transcriptions), this filter runs inside the aggregator before VAD processing, blocking transcriptions, VAD events, and interruptions until the wake phrase is spoken. Configure via the new `user_frame_filters` parameter on `LLMUserAggregatorParams`. Also added `BaseUserFrameFilter` base class for building custom filters.

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@@ -0,0 +1 @@
- Deprecated `WakeCheckFilter`. Use `WakePhraseUserFrameFilter` from `pipecat.turns.user_filter` instead, which runs inside the aggregator and blocks both transcriptions and VAD events.

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

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

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@@ -1,16 +1,8 @@
#!/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
uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
# Check if sphinx-build is available
if ! uv run sphinx-build --version &> /dev/null; then
@@ -22,7 +14,8 @@ fi
rm -rf _build
echo "Building documentation..."
uv run sphinx-build -b html -d _build/doctrees . _build/html $SPHINX_OPTS
# Build docs matching ReadTheDocs configuration
uv run sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
if [ $? -eq 0 ]; then
echo "Documentation built successfully!"

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@@ -4,19 +4,6 @@ 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")
@@ -61,6 +48,8 @@ autodoc_default_options = {
# Mock imports for optional dependencies
autodoc_mock_imports = [
# Krisp - has build issues on some platforms
"pipecat_ai_krisp",
"krisp",
"krisp_audio",
# System-specific GUI libraries
"_tkinter",
@@ -89,6 +78,16 @@ autodoc_mock_imports = [
"einops",
"intel_extension_for_pytorch",
"huggingface_hub",
# riva dependencies
"riva",
"riva.client",
"riva.client.Auth",
"riva.client.ASRService",
"riva.client.StreamingRecognitionConfig",
"riva.client.RecognitionConfig",
"riva.client.AudioEncoding",
"riva.client.proto.riva_tts_pb2",
"riva.client.SpeechSynthesisService",
# MLX dependencies (Apple Silicon specific)
"mlx",
"mlx_whisper", # Note: might need underscore format too
@@ -99,6 +98,7 @@ autodoc_mock_imports = [
"cartesia",
"camb",
"sarvamai",
"openpipe",
"openai.types.beta.realtime",
"langchain_core",
"langchain_core.messages",
@@ -110,8 +110,6 @@ autodoc_mock_imports = [
"fastapi.middleware",
"fastapi.responses",
"uvicorn",
# Deepgram dependencies
"deepgram",
]
# HTML output settings
@@ -138,8 +136,6 @@ def import_core_modules():
"pipecat.runner",
"pipecat.serializers",
"pipecat.transcriptions",
"pipecat.turns",
"pipecat.extensions",
"pipecat.utils",
]
@@ -184,6 +180,7 @@ def setup(app):
logger.info(f"Source directory: {source_dir}")
excludes = [
str(project_root / "src/pipecat/pipeline/to_be_updated"),
str(project_root / "src/pipecat/examples"),
str(project_root / "src/pipecat/tests"),
"**/test_*.py",

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

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

View File

@@ -1,150 +1,31 @@
# Pipecat Examples
This directory contains examples showing how to build voice and multimodal agents with Pipecat.
This directory contains examples to help you learn how to build with Pipecat.
## Setup
## Getting Started
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.
New to Pipecat? Start here:
> **Run from root directory**: Make sure you are running the steps from the root directory.
- **[Quickstart](quickstart/)** - Get your first voice AI bot running in 5 minutes _(coming soon)_
- **[Client/Server Web](client-server-web/)** - Learn to build web applications with Pipecat's client SDKs _(coming soon)_
- **[Phone Bot with Twilio](phone-bot-twilio/)** - Connect your bot to a phone number _(coming soon)_
> **Using local audio?**: The `LocalAudioTransport` requires a system dependency for `portaudio`. Install the dependency to use the transport.
## Foundational Examples
2. Copy the [`env.example`](../env.example) file and add API keys for services you plan to use:
Single-file examples that introduce core Pipecat concepts one at a time. These examples:
```bash
cp env.example .env
# Edit .env with your API keys
```
- Build on each other progressively
- Focus on specific features or integrations
- Are used for testing with every Pipecat release
3. Run any example:
See the **[Foundational Examples README](foundational/)** for the complete list.
```bash
uv run python getting-started/01-say-one-thing.py
```
## More Advanced Examples
4. Open the web interface at http://localhost:7860/client/ and click "Connect"
Ready to explore complex use cases? Visit **[pipecat-examples](https://github.com/pipecat-ai/pipecat-examples)** for:
## 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).
- Production-ready applications
- Multi-platform client implementations
- Telephony integrations
- Multimodal and creative applications
- Deployment and monitoring examples

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@@ -1,204 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""TTSSpeakFrame timing and transcript ordering example.
Demonstrates two common patterns for injecting hardcoded speech into a voice
agent without breaking the LLM context / transcript ordering:
1. Intro / pre-roll. Say something before the agent speaks, and make sure
it shows up in the LLM context in the right place.
2. Tool-call filler. Say something while a function call is in flight,
without the filler audio overlapping the post-tool LLM response and
without the filler text landing on the wrong turn in the transcript.
Key techniques shown:
- ``TTSSpeakFrame(text, append_to_context=True)`` — the TTS service commits
the spoken text to the assistant aggregator after the audio drains, so
turn ordering in the transcript matches the audio.
- ``pause_frame_processing=True`` on the TTS service — stops the TTS from
processing the next LLM response while the filler is still speaking,
which is what keeps the audio and the transcript aligned during tool
calls.
- A system-prompt nudge asking the LLM not to acknowledge before a tool
call, so you don't get double acknowledgements (one from the LLM, one
from ``on_function_calls_started``).
Notes:
- Do NOT call ``asyncio.sleep`` to add pauses around TTS. Use
``FrameProcessorPauseFrame`` / ``FrameProcessorResumeUrgentFrame`` if you
need a synthetic gap. ``asyncio.sleep`` does not interact with the
frame-processing system and will desync audio and transcript.
- The base ``TTSService`` defaults ``pause_frame_processing`` to ``False``.
Many wrappers (ElevenLabs, Rime, Deepgram, Groq, Azure, ...) hardcode it
to ``True`` in their ``super().__init__()`` calls, so you don't need to
opt in. ``OpenAITTSService`` inherits the base default (``False``), so we
pass it explicitly below.
- ``CartesiaTTSService`` is the odd one: it hardcodes
``pause_frame_processing=False`` AND does not accept the kwarg via the
constructor (you'll get ``TypeError: got multiple values for keyword
argument 'pause_frame_processing'``). If you're on Cartesia, set it after
construction: ``tts._pause_frame_processing = True``.
Requirements:
- OpenAI API key
Environment variables (.env):
OPENAI_API_KEY=...
"""
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.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.openai.stt import OpenAISTTService
from pipecat.services.openai.tts import OpenAITTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
SYSTEM_INSTRUCTION = """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. Keep responses brief.
IMPORTANT: When you are about to call a tool, do NOT say an acknowledgement like \
"Let me check on that" or "One moment" before the call. The system plays its own \
filler audio while the tool runs, so if you also acknowledge you will be heard twice."""
async def fetch_weather_from_api(params: FunctionCallParams):
await params.result_callback({"conditions": "sunny", "temperature": "75"})
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 TTSSpeakFrame timing demo")
stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"))
# pause_frame_processing=True keeps filler audio and the post-tool LLM
# response from overlapping. OpenAI TTS inherits the base default of False,
# so we opt in explicitly here.
tts = OpenAITTSService(
api_key=os.getenv("OPENAI_API_KEY"),
settings=OpenAITTSService.Settings(voice="ballad"),
pause_frame_processing=True,
)
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
settings=OpenAILLMService.Settings(system_instruction=SYSTEM_INSTRUCTION),
)
llm.register_function("get_current_weather", fetch_weather_from_api)
# Tool-call filler. Fires once per function-call batch. append_to_context=True
# makes the filler text show up in the transcript in the correct turn order,
# because the TTS service commits it only after the audio drains.
@llm.event_handler("on_function_calls_started")
async def on_function_calls_started(service, function_calls):
await tts.queue_frame(TTSSpeakFrame("Gotcha, one sec.", append_to_context=True))
weather_function = FunctionSchema(
name="get_current_weather",
description="Get the current weather for a location",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
},
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),
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
# Intro / pre-roll. append_to_context=True makes this line land in the
# LLM context before the first user message, in the correct turn order.
# No LLMFullResponseStart/End wrap needed.
await tts.queue_frame(
TTSSpeakFrame(
"Hi, I'm Paul, your virtual agent. Ask me about the weather anywhere.",
append_to_context=True,
)
)
# Kick off the LLM so it's ready to respond to the first user turn.
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()

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

View File

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

View File

@@ -0,0 +1,64 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
from dotenv import load_dotenv
from loguru import logger
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.runner.livekit import configure
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
(url, token, room_name) = await configure()
transport = LiveKitTransport(
url=url,
token=token,
room_name=room_name,
params=LiveKitParams(audio_out_enabled=True),
)
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
settings=CartesiaTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
),
)
runner = PipelineRunner()
task = PipelineTask(Pipeline([tts, transport.output()]))
# Register an event handler so we can play the audio when the
# participant joins.
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant_id):
await asyncio.sleep(1)
await task.queue_frame(
TTSSpeakFrame(
"Hello there! How are you doing today? Would you like to talk about the weather?"
)
)
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

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

View File

@@ -60,7 +60,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
context = LLMContext()
context.add_message({"role": "developer", "content": "Say hello to the world."})
context.add_message({"role": "user", "content": "Say hello to the world."})
await task.queue_frames([LLMContextFrame(context), EndFrame()])
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

View File

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

View File

@@ -109,9 +109,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
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."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -92,7 +92,7 @@ async def main():
await transport.capture_participant_transcription(participant["id"])
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])

View File

@@ -16,12 +16,11 @@ from pipecat.frames.frames import (
Frame,
LLMContextFrame,
LLMFullResponseStartFrame,
OutputImageRawFrame,
TextFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.sync_parallel_pipeline import FrameOrder, SyncParallelPipeline
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.sentence import SentenceAggregator
@@ -31,7 +30,6 @@ from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
from pipecat.services.fal.image import FalImageGenService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.tts_service import TextAggregationMode
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
@@ -46,18 +44,6 @@ class MonthFrame(DataFrame):
return f"{self.name}(month: {self.month})"
class MarkImageForPlaybackSync(FrameProcessor):
"""Marks output image frames to be synchronized with audio playback."""
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, OutputImageRawFrame):
frame.sync_with_audio = True
await self.push_frame(frame, direction)
class MonthPrepender(FrameProcessor):
def __init__(self):
super().__init__()
@@ -115,10 +101,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
settings=CartesiaHttpTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
),
# No need to aggregate by sentences (the default), as we already know we're getting full sentences
# (Otherwise the service will unnecessarily wait for follow-up input to confirm the sentence is complete,
# which, sadly, actually breaks the synchronization mechanism)
text_aggregation_mode=TextAggregationMode.TOKEN,
)
imagegen = FalImageGenService(
@@ -137,26 +119,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
# that, each pipeline runs concurrently and `SyncParallelPipeline` will
# wait for the input frame to be processed.
#
# We use `FrameOrder.PIPELINE` so that each synchronized batch of output
# frames is pushed in the order the pipelines are listed: image first,
# then audio. This ensures the transport receives the image before the
# audio frames it should accompany.
#
# Note that `SyncParallelPipeline` requires the last processor in each
# of the pipelines to be synchronous. In this case, we use
# `FalImageGenService` and `CartesiaHttpTTSService` which make HTTP
# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
# requests and wait for the response.
pipeline = Pipeline(
[
llm, # LLM
sentence_aggregator, # Aggregates LLM output into full sentences
SyncParallelPipeline( # Run pipelines in parallel aggregating the result
[
imagegen, # Generate image
MarkImageForPlaybackSync(), # Mark image as needing sync w/audio during playback
],
[month_prepender, tts], # Create "Month: sentence" and output audio
frame_order=FrameOrder.PIPELINE,
[imagegen], # Generate image
),
transport.output(), # Transport output
]

View File

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

View File

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

View File

@@ -119,8 +119,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
)
image_sync_aggregator = ImageSyncAggregator(
os.path.join(os.path.dirname(__file__), "..", "assets", "speaking.png"),
os.path.join(os.path.dirname(__file__), "..", "assets", "waiting.png"),
os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
)
pipeline = Pipeline(

View File

@@ -103,7 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])

View File

@@ -98,9 +98,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -148,7 +148,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message({"role": "developer", "content": "Say a short hello to the user."})
context.add_message({"role": "user", "content": "Say a short hello to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -128,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message({"role": "developer", "content": "Say a short hello to the user."})
context.add_message({"role": "user", "content": "Say a short hello to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -8,9 +8,9 @@
import os
from dotenv import load_dotenv
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from loguru import logger

View File

@@ -109,9 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -104,7 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])

View File

@@ -114,9 +114,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -106,9 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -100,9 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -108,7 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])

View File

@@ -100,9 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -102,9 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -102,9 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -106,9 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -108,9 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -4,7 +4,9 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import time
from dotenv import load_dotenv
from loguru import logger
@@ -23,7 +25,7 @@ from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.responses.llm import OpenAIResponsesHttpLLMService
from pipecat.services.openpipe.llm import OpenPipeLLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -60,9 +62,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
),
)
llm = OpenAIResponsesHttpLLMService(
timestamp = int(time.time())
llm = OpenPipeLLMService(
api_key=os.getenv("OPENAI_API_KEY"),
settings=OpenAIResponsesHttpLLMService.Settings(
openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
tags={"conversation_id": f"pipecat-{timestamp}"},
settings=OpenPipeLLMService.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.",
),
)
@@ -98,9 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -104,7 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])

View File

@@ -114,9 +114,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -109,9 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -99,9 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -95,9 +95,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -151,7 +151,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
LLMMessagesAppendFrame(
messages=[
{
"role": "developer",
"role": "user",
"content": f"Greet the user and introduce yourself. Don't use emojis.",
}
],

View File

@@ -66,7 +66,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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.",
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.",
),
)
@@ -101,9 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -13,6 +13,9 @@ Features showcased:
- Gemini LLM for conversation and image generation
- Google TTS and STT
Run with:
python examples/foundational/07n-interruptible-gemini-image.py
Make sure to set your environment variables:
export GOOGLE_API_KEY=your_api_key_here
"""
@@ -121,9 +124,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation with a styled introduction
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -128,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
# Kick off the conversation
context.add_message(
{
"role": "developer",
"role": "user",
"content": "You are an AI assistant. You can help with a variety of tasks. Introduce yourself and ask the user what they would like to know.",
}
)

View File

@@ -112,9 +112,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -112,9 +112,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -153,9 +153,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -102,9 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -104,7 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=KrispVivaTurn())]
),
vad_analyzer=SileroVADAnalyzer(), # or KrispVivaVadAnalyzer
vad_analyzer=SileroVADAnalyzer(),
),
)
@@ -134,9 +134,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -10,6 +10,7 @@ import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.filters.krisp_filter import KrispFilter
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
@@ -23,7 +24,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.mistral.tts import MistralTTSService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
@@ -31,21 +32,23 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
audio_in_filter=KrispFilter(),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
audio_in_filter=KrispFilter(),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
audio_in_filter=KrispFilter(),
),
}
@@ -55,10 +58,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = MistralTTSService(
api_key=os.getenv("MISTRAL_API_KEY"),
settings=MistralTTSService.Settings(
voice="c69964a6-ab8b-4f8a-9465-ec0925096ec8",
tts = DeepgramTTSService(
api_key=os.getenv("DEEPGRAM_API_KEY"),
settings=DeepgramTTSService.Settings(
voice="aura-helios-en",
),
)
@@ -78,7 +81,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt,
stt, # STT
user_aggregator, # User responses
llm, # LLM
tts, # TTS
@@ -100,9 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -107,7 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])

View File

@@ -99,9 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -95,9 +95,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -269,9 +269,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

View File

@@ -100,9 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")

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