Add a third-party integrations guide
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Mark Backman
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## Contributing to Pipecat
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**Want to add a new service integration?**
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We encourage community-maintained integrations! Please see our [Third-Party Integration Guide](THIRD_PARTY_INTEGRATIONS.md) for the process and requirements.
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**Want to contribute to Pipecat core?**
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We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
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1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.
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THIRD_PARTY_INTEGRATIONS.md
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THIRD_PARTY_INTEGRATIONS.md
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# Third-Party Integrations Guide
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Pipecat welcomes community-maintained integrations! As our ecosystem grows, we've established a process for third-party developers to create and maintain their own service integrations while ensuring discoverability for the community.
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## Overview
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**What we support:** Third-party maintained integrations that live in separate repositories and are maintained by their authors.
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**What we don't do:** The Pipecat team does not review, test, or maintain third-party integrations. We provide guidance and list approved integrations for discoverability.
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**Why this approach:** This allows the community to move quickly while keeping the Pipecat core team focused on maintaining the framework itself.
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## Requirements for Third-Party Integration Listing
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To be listed as an official third-party integration, your repository must contain:
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### Required Components
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- **Source code** - Complete implementation following Pipecat patterns
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- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
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- **README.md** - Must include:
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- Introduction and explanation of your integration
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- Installation instructions
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- Usage instructions with Pipecat Pipeline
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- How to run your example
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- **LICENSE** - Permissive license (BSD-2 like Pipecat, or equivalent open source terms)
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- **Code documentation** - Source code with docstrings (we recommend following [Pipecat's docstring conventions](https://github.com/pipecat-ai/pipecat/blob/main/CONTRIBUTING.md#docstring-conventions))
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- **Changelog** - Maintain a changelog for version updates
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### Submission Information Required
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When submitting your integration for listing, provide:
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- Service name
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- Service type (STT, LLM, TTS, Image, etc.)
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- Link to your repository
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- GitHub username(s) of maintainers
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## Submission Process
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1. **Create your integration** following the patterns above
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2. **Set up your repository** with all required components
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3. **Join our Discord:** https://discord.gg/pipecat
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4. **Submit for listing** in the `#third-party-integrations` channel with:
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- Service name and type
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- Repository link
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- Maintainer GitHub usernames
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## Integration Patterns and Examples
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### STT (Speech-to-Text) Services
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#### Websocket-based Services
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**Base class:** `STTService`
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**Examples:**
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- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
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- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
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#### File-based Services
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**Base class:** `SegmentedSTTService`
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**Examples:**
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- [RivaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py)
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- [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
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#### Key requirements:
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- STT services should push `InterimTranscriptionFrames` and `TranscriptionFrames`
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- If confidence values are available, filter for values >50% confidence
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### LLM (Large Language Model) Services
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#### OpenAI-Compatible Services
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**Base class:** `OpenAILLMService`
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**Examples:**
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- [AzureLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/azure/llm.py)
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- [GrokLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/grok/llm.py) - Shows overriding the base class where needed
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#### Non-OpenAI Compatible Services
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**Requires:** Full implementation
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**Examples:**
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- [AnthropicLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/anthropic/llm.py)
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- [GoogleLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/llm.py)
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### TTS (Text-to-Speech) Services
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#### AudioContextWordTTSService
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**Use for:** Websocket-based services supporting word/timestamp alignment
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**Example:**
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- [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
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#### InterruptibleTTSService
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**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
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**Example:**
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- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
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#### WordTTSService
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**Use for:** HTTP-based services supporting word/timestamp alignment
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**Example:**
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- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
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#### TTSService
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**Use for:** HTTP-based services without word/timestamp alignment
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**Example:**
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- [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
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#### TTS Considerations:
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- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
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- Handle idle service timeouts with keepalives
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- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
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### Telephony Services
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Pipecat supports telephony provider integration using websocket connections to exchange MediaStreams. These services use a FrameSerializer to serialize and deserialize inputs from the FastAPIWebsocketTransport.
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**Examples:**
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- [Twilio](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/twilio.py)
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- [Telnyx](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/telnyx.py)
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**Considerations:**
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- Include hang-up functionality using the provider's native API, ideally using `aiohttp`
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### Image Generation Services
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**Base class:** `ImageGenService`
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**Examples:**
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- [FalImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/image.py)
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- [GoogleImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/image.py)
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**Requirements:**
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- Must implement `run_image_gen` method returning an `AsyncGenerator`
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## Implementation Guidelines
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### Naming Conventions
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- **STT:** `VendorSTTService`
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- **LLM:** `VendorLLMService`
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- **TTS:**
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- Websocket: `VendorTTSService`
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- HTTP: `VendorHttpTTSService`
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- **Image:** `VendorImageGenService`
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- **Vision:** `VendorVisionService`
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- **Telephony:** `VendorFrameSerializer`
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### Metrics Support
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Enable metrics in your service:
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```python
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def can_generate_metrics(self) -> bool:
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"""Check if this service can generate processing metrics.
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Returns:
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True, as this service supports metrics.
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"""
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return True
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```
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### Dynamic Settings Updates
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STT, LLM, and TTS services support `ServiceUpdateSettingsFrame` for dynamic configuration changes. The base STTService has an `_update_settings()` method that handles settings, and the private `_settings` `Dict` is used to store settings and provide access to the subclass.
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```python
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async def set_language(self, language: Language):
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"""Set the recognition language and reconnect.
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Args:
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language: The language to use for speech recognition.
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"""
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logger.info(f"Switching STT language to: [{language}]")
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self._settings["language"] = language
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await self._disconnect()
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await self._connect()
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```
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Note that, in this example, Deepgram requires the websocket connection be disconnected and reconnected to reinitialize the service with the new value. Consider if your service requires reconnection.
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### Sample Rate Handling
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Sample rates are set via PipelineParams and passed to each frame processor at initialization. The pattern is to _not_ set the sample rate value in the constructor of a given service. Instead, use the `start()` method to initialize sample rates from the frame:
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```python
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async def start(self, frame: StartFrame):
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"""Start the service."""
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await super().start(frame)
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self._settings["output_format"]["sample_rate"] = self.sample_rate
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await self._connect()
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```
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Note that `self.sample_rate` is a `@property` set in the TTSService base class, which provides access to the private sample rate value obtained from the StartFrame.
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### Tracing Decorators
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Use Pipecat's tracing decorators:
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- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
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- **LLM:** `@traced_llm` - decorate the `_process_context()` method
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- **TTS:** `@traced_tts` - decorate the `run_tts()` method
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## Best Practices
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### Packaging and Distribution
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- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
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- Consider releasing to PyPI for easier installation
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- Follow semantic versioning principles
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- Maintain a changelog
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### HTTP Communication
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For REST-based communication, use aiohttp. Pipecat includes this as a required dependency, so using it prevents adding an additional dependency to your integration.
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### Error Handling
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- Wrap API calls in appropriate try/catch blocks
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- Handle rate limits and network failures gracefully
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- Provide meaningful error messages
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### Testing
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- Your foundational example serves as a valuable integration-level test
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- Unit tests are nice to have. As the Pipecat teams provides better guidance, we will encourage unit testing more
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## Disclaimer
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Third-party integrations are community-maintained and not officially supported by the Pipecat team. Users should evaluate these integrations independently. The Pipecat team reserves the right to remove listings that become unmaintained or problematic.
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## Questions?
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Join our Discord community at https://discord.gg/pipecat and post in the `#third-party-integrations` channel for guidance and support.
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For additional questions, you can also reach out to us at pipecat-ai@daily.co.
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