Update quickstart, make it deployable

examples/quickstart/Dockerfile

@@ -0,0 +1,7 @@
+FROM dailyco/pipecat-base:latest
+
+COPY ./requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY ./bot.py bot.py

examples/quickstart/README.md

@@ -4,15 +4,10 @@ Run your first Pipecat bot in under 5 minutes. This example creates a voice AI b
 
 ## Prerequisites
 
-### Python 3.10+
+### Environment
 
-Pipecat requires Python 3.10 or newer. Check your version:
+- Python 3.10 or later
-
+- [uv](https://docs.astral.sh/uv/getting-started/installation/) package manager installed
-```bash
-python --version
-```
-
-If you need to upgrade Python, we recommend using a version manager like `uv` or `pyenv`.
 
 ### AI Service API keys
 
@@ -26,26 +21,21 @@ Have your API keys ready. We'll add them to your `.env` shortly.
 
 ## Setup
 
-1. Set up a virtual environment
+### Set up your environment and dependencies
 
-From the `examples/quickstart` directory, run:
+From `examples/quickstart`, run:
 
 ```bash
-python -m venv .venv
+uv sync --extra webrtc \
-source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+    --extra daily \
+    --extra silero \
+    --extra deepgram \
+    --extra openai \
+    --extra cartesia \
+    --extra runner
 ```
 
-> Using `uv`? Create your venv using: `uv venv && source .venv/bin/activate`.
+### Configure environment variables
-
-2. Install dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-> Using `uv`? Install requirements using: `uv pip install -r requirements.txt`.
-
-3. Configure environment variables
 
 Create a `.env` file:
 
@@ -55,25 +45,110 @@ cp env.example .env
 
 Then, add your API keys:
 
-```
+```ini
 DEEPGRAM_API_KEY=your_deepgram_api_key
 OPENAI_API_KEY=your_openai_api_key
 CARTESIA_API_KEY=your_cartesia_api_key
 ```
 
-4. Run the example
+## Run your bot locally
-
-Run your bot using:
 
 ```bash
-python bot.py
+uv run bot.py
 ```
 
-> Using `uv`? Run your bot using: `uv run bot.py`.
-
 **Open http://localhost:7860 in your browser** and click `Connect` to start talking to your bot.
 
-> 💡 First run note: The initial startup may take ~10 seconds as Pipecat downloads required models, like the Silero VAD model.
+> 💡 First run note: The initial startup may take ~20 seconds as Pipecat downloads required models and imports.
+
+## Deploy to Pipecat Cloud (Optional)
+
+Pipecat Cloud is a managed platform for hosting and scaling your Pipecat bots in production.
+
+[Sign up](https://pipecat.daily.co/sign-up) for a Pipecat Cloud account and deploy your bot in minutes.
+
+### Setup
+
+#### Install `pipecatcloud`
+
+The `pipecatcloud` CLI manages your Pipecat deployments, secrets, and organization. Install in your virtual enviroment:
+
+```bash
+uv add pipecatcloud
+```
+
+> 💡 Tip: You can run the `pipecatcloud` CLI using the `pcc` alias.
+
+#### Docker
+
+Pipecat Cloud expects a built Docker image that includes the agent code and all dependencies. You need to:
+
+1. Install [Docker](https://www.docker.com/) on your system.
+2. Create a container registry account. We'll use [Docker Hub](https://hub.docker.com/) in this example.
+
+### Configure pcc-deploy.toml
+
+The `pcc-deploy.toml` file is the spec for your Pipecat Cloud deployment.
+
+```ini
+agent_name = "quickstart"
+image = "your_username/quickstart:0.1"
+secret_set = "quickstart-secrets"
+
+[scaling]
+	min_agents = 1
+```
+
+Details:
+
+- `agent_name`: the name of your Pipecat Cloud agent
+- `image`: your Docker Hub username and image tag (image_name:version) to run
+- `secret_set`: environment variable secrets that you can use in your bot file
+- `min_agents`: the number of reserve instances you'll run. We'll start with 1 to ensure you get an instant start
+
+> 💡 Tip: [Set up `image_credentials`](https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets) in your TOML file for authenticated image pulls
+
+### Configure secrets
+
+Store your secrets in Pipecat Cloud and use the environment variable keys in your bot file. To create secrets, run:
+
+```bash
+uv run pcc secrets set quickstart-secrets --file .env
+```
+
+This command creates a new secret set called `quickstart-secrets`. This value must match the `secret_set` in your TOML file. The `--file` arg points your `.env` file. This will add all environment variables from the file to the secret set.
+
+## Build and push your Docker image
+
+Pipecat Cloud expects a built Docker image that includes the agent code and all dependencies. You can build, tag, and push your Docker image using the included `build.sh` shell script:
+
+1. Login to Docker Hub:
+
+   ```bash
+   docker login
+   ```
+
+2. Update the `VERSION`, `DOCKER_USERNAME`, and `AGENT_NAME` to match your TOML file.
+3. Run the script:
+
+   ```bash
+   ./build.sh
+   ```
+
+## Deploy your agent
+
+You're ready to deploy your agent. Use the following command to deploy your agent according to the spec in your `pcc-deploy.toml` file:
+
+```bash
+uv run pcc deploy
+```
+
+## Connect to your agent
+
+The Pipecat Cloud dashboard has a Sandbox which makes it easy to talk to your agent. In your [Pipecat Cloud dashboard](https://pipecat.daily.co/), select your `quickstart` agent > Sandbox.
+
+- Accept the browser prompt for device access.
+- Click `Connect` to start your agent.
 
 ## Troubleshooting
 

examples/quickstart/bot.py

@@ -7,15 +7,13 @@
 """Pipecat Quickstart Example.
 
 The example runs a simple voice AI bot that you can connect to using your
-browser and speak with it.
+browser and speak with it. You can also deploy this bot to Pipecat Cloud.
 
 Required AI services:
 - Deepgram (Speech-to-Text)
 - OpenAI (LLM)
 - Cartesia (Text-to-Speech)
 
-The example connects between client and server using a P2P WebRTC connection.
-
 Run the bot using::
 
     python bot.py
@@ -27,7 +25,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 print("🚀 Starting Pipecat bot...")
-print("⏳ Loading AI models (30-40 seconds first run, <2 seconds after)\n")
+print("⏳ Loading models and imports (20 seconds first run only)\n")
 
 logger.info("Loading Silero VAD model...")
 from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -40,15 +38,12 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
 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.services.daily import DailyParams
-logger.info("✅ Pipeline components loaded")
-
-logger.info("Loading WebRTC transport...")
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 
 logger.info("✅ All components loaded successfully!")
 
@@ -121,14 +116,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 async def bot(runner_args: RunnerArguments):
     """Main bot entry point for the bot starter."""
 
-    transport = SmallWebRTCTransport(
+    transport_params = {
-        params=TransportParams(
+        "daily": lambda: DailyParams(
             audio_in_enabled=True,
             audio_out_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
         ),
-        webrtc_connection=runner_args.webrtc_connection,
+        "webrtc": lambda: TransportParams(
-    )
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    }
+
+    transport = await create_transport(runner_args, transport_params)
 
     await run_bot(transport, runner_args)
 

examples/quickstart/build.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+set -e
+
+VERSION="0.1"
+DOCKER_USERNAME="your_username"
+AGENT_NAME="quickstart"
+
+# Build the Docker image with the correct context
+echo "Building Docker image..."
+docker build --platform=linux/arm64 -t "$DOCKER_USERNAME/$AGENT_NAME:$VERSION" -t "$DOCKER_USERNAME/$AGENT_NAME:latest" .
+
+# Push the Docker images
+echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:$VERSION..."
+docker push "$DOCKER_USERNAME/$AGENT_NAME:$VERSION"
+
+echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:latest..."
+docker push "$DOCKER_USERNAME/$AGENT_NAME:latest"
+
+echo "Successfully built and pushed $DOCKER_USERNAME/$AGENT_NAME:$VERSION and $DOCKER_USERNAME/$AGENT_NAME:latest"

examples/quickstart/env.example

@@ -1,3 +1,6 @@
 DEEPGRAM_API_KEY=your_deepgram_api_key
 OPENAI_API_KEY=your_openai_api_key
 CARTESIA_API_KEY=your_cartesia_api_key
+
+# Optional: Connect via Daily WebRTC locally
+DAILY_API_KEY=your_daily_api_key

examples/quickstart/pcc-deploy.toml

@@ -0,0 +1,6 @@
+agent_name = "quickstart"
+image = "your_username/quickstart:0.1"
+secret_set = "quickstart-secrets"
+
+[scaling]
+	min_agents = 1

examples/quickstart/requirements.txt

@@ -1 +1,2 @@
-pipecat-ai[webrtc,silero,deepgram,openai,cartesia,runner]>=0.0.77
+pipecat-ai[webrtc,websocket,daily,silero,deepgram,openai,cartesia,runner]>=0.0.77
+pipecatcloud>=0.2.1