Collection of self-contained real-time voice and video AI demo applications built with dailyai.

Each project has its own set of dependencies and configuration variables. This repo intentionally avoids shared code across projects — you can grab whichever demo folder you want to work with as a starting point.

We recommend you start with a virtual environment:

python -m venv venv

source venv/bin/activate

cd simple-chatbot

pip install -r requirements.txt

Next, follow the steps in the README for each demo.

ℹ️ Make sure you pip install -r requirements.txt for each demo project, so you can be sure to have the necessary service dependencies that extend the functionality of Daily AI. You can read more about the framework architecture here.

The Daily AI repo has a wide array of feature-specific demos and foundational examples, you can find them here.

For each of these demos we've included a Dockerfile. Out of the box, this should provide everything needed to get the respective demo running on a VM:

docker build username/app:tag .

docker run -p 7860:7860 --env-file ./.env username/app:tag

docker push ...

If you're working with a custom UI (such as with the Storytelling Chatbot), it's important to ensure your deployment platform supports HTTPS, as accessing user devices such as mics and webcams requires SSL.

If you try to run a custom UI without SSL, you may see an error in the console telling you that navigator is undefined, or no devices are available.

Yes, kind of.

These demos attempt to keep things simple and are unopinionated regarding environment or scalability.

We're using FastAPI to spawn a subprocess for the bots / agents — useful for small tests, but not so great for production grade apps with many concurrent users. You can see how this works in each project's start endpoint in server.py.

Creating virtualized worker pools and on-demand instances is out of scope for these examples, but we have shared various implementation ideas here.

For projects that have CUDA as a requirement, such as Moondream Chatbot, be sure to deploy to a GPU-powered platform (such as fly.io or Runpod.)

Voice Activity Detection — very important for knowing when a user has finished speaking to your bot. If you are not using press-to-talk, and want Daily AI to detect when the user has finished talking, VAD is an essential component for a natural feeling conversation.

Daily AI makes use of WebRTC VAD by default when using the Daily transport layer. Optionally, you can use Silero VAD for improved accuracy at the cost of higher CPU usage.

You can enable Silero like so:

pip install dailyai[silero]

Installing Silero will override the default VAD implementation, which can be manually toggled on and off like so:

transport = DailyTransport(
    room_url,
    token,
    "Bot Name",
    ...
    vad_enabled=True #Note: True by default
)

The first time your run your bot with Silero, startup may take a while whilst it downloads and caches the model in the background. You can check the progress of this in the console.

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