Pipecat Cloud + LMNT Quickstart

Prerequisites

System requirements

• Python 3.10+
• Linux, MacOS, or Windows Subsystem for Linux (WSL)
• Docker installed on your system
• Access to a container registry (e.g., Docker Hub, GitHub Container Registry, etc.)

Install Docker

Docker Desktop is recommended for development environments as it provides a comprehensive GUI and tools.

Set up a container registry

1. 1
   Create a Docker Hub account

   If you don't already have a Docker Hub account, create one for free.

   While other container registries will work, this guide uses Docker Hub in its examples.

2. 2
   Log in to Docker

   After creating your account, log in via your terminal:

   docker login

   Learn about different Docker login options in the official documentation.

3. 3
   No repository creation needed yet

   Unlike some services, with Docker Hub you don't need to create a repository in advance.

   When you push your first image later in this guide, a repository will be automatically created with the name you specify.

Create service accounts

The Pipecat Cloud starter agent uses OpenAI for LLM inference and LMNT for text-to-speech. You will need API keys for both services.

• OpenAI API key
• LMNT API key

Create your starter project

A bare-bones voice AI agent template is available to help you get started. You'll need to set up your Python environment and authenticate with Pipecat Cloud before initializing the starter project.

First, create a new directory for your project:

mkdir pipecat-cloud-starter && cd pipecat-cloud-starter

Configure your Python environment

We recommend using a virtual environment to isolate your project dependencies:

Set up Pipecat Cloud

1. 1
   Create a Pipecat Cloud account

   Create an account at pipecat.daily.co.

   You must have valid billing information associated with your account in order to deploy agents.

2. 2
   Install Pipecat Cloud CLI

   With your environment activated, install the Pipecat Cloud CLI:

   pip install pipecatcloud

   The Pipecat Cloud CLI can be used with either pipecatcloud or pcc as the command prefix.

3. 3
   Authenticate

   Run pcc auth login to authenticate via your browser (if this doesn't work, try python -m pipecatcloud auth login.)

Initialize the starter project

Now, clone the starter project.

git clone https://github.com/lmnt-com/pipecat-cloud-starter.git

This command downloads a template that includes everything you need to build and deploy your first voice AI agent.

Project structure

The starter project includes these key files:

• bot.py: Python entry-point containing your Pipecat agent pipeline
• Dockerfile: Dockerfile for building the agent container
• requirements.txt: Python dependencies used by your agent code
• pcc-deploy.toml: Pipecat Cloud deployment configuration file (optional)

Configure to run locally (optional)

You can test your agent locally before deploying to Pipecat Cloud:

# Set environment variables with your API keys
export LMNT_API_KEY="your_lmnt_key"
export DAILY_API_KEY="your_daily_key"
export OPENAI_API_KEY="your_openai_key"

First install requirements:

pip install -r requirements.txt

Then, launch the bot.py script locally:

LOCAL_RUN=1 python bot.py

Deploy the agent

Pipecat Cloud expects a built Docker image that includes the agent code and all dependencies.

Build and push your Docker image

Build your Docker image

From within your project directory, run Docker build:

docker build --platform=linux/arm64 -t lmnt-agent:latest .

This command builds a Docker image named lmnt-agent with the tag latest from the current directory.

Tag your Docker image

Using the Docker image you just built, tag it with your Docker Hub username and a version number. The tag should be [your-username]/[image-name]:[version-number].

docker tag lmnt-agent:latest your-username/lmnt-agent:0.1

Push your Docker image

After tagging your image, you can push it to your Docker Hub registry:

docker push your-username/lmnt-agent:0.1

Add secrets

Secrets are a secure way to manage sensitive information such as API keys, passwords, and other credentials.

The starter project requires the following API keys:

• OpenAI API key
• LMNT API key
• Daily API key (automatically provided through your Pipecat Cloud account)

Pipecat Cloud organizes your secrets into secret sets. This allows you to re-use the same set of secrets across multiple agents within your organization.

Creating a secret set from a file (recommended)

The starter project includes an env.example file that you can use as a template. Create a copy of this file and add your actual API keys:

# Copy the example file
cp env.example .env
 
# Edit the file with your API keys
# LMNT_API_KEY=your_lmnt_key
# OPENAI_API_KEY=your_openai_key

Then, create a secret set from this file:

pcc secrets set lmnt-agent-secrets --file .env

Create a deployment

The CLI deploy command requires three key pieces of information:

1. The name of your agent on Pipecat Cloud
2. The repository and tag of your Docker image
3. The secrets set to use for environment variables

Let's deploy the agent using your pushed image:

pcc deploy lmnt-agent your-username/lmnt-agent:0.1 --secrets lmnt-agent-secrets

When you run this command, you'll be asked to confirm your deployment configuration before proceeding.

For more deployment configuration options, see the deploy reference docs.

Check the status of your deployment

Assuming the deployment was successful, you can check the status of your agent using the CLI:

# Deployment status
pcc agent status lmnt-agent
 
# List deployment history
pcc agent deployments lmnt-agent

Scale the deployment

Right now, your deployment has been made with the default runtime configuration.

This means that your agent defaults to "scale-to-zero", with no minimum agent instances to serve on-demand session requests.

If you were to attempt to connect with your agent now, it's likely you'd encounter a cold start while the agent spins up. Cold starts typically take around 10 seconds.

To avoid this, you can scale your deployment to a minimum of one instance:

pcc deploy lmnt-agent your-username/lmnt-agent:0.1 --min-agents 1

This will provide you with one warm instance ready to serve any active sessions.

Start an active session

Now that your agent has been deployed, you can start an active session to interact with it.

Creating active sessions requires passing a valid API key for your namespace or organization. This is used to authenticate the request and prevent unauthorized access.

Create a public access key

pcc organizations keys create

Running this command will ask if you'd like to set this key as your default. Doing so will associate the created key with your current namespace, allowing you to omit the --api-key flag in future requests.

Next set the key to be used with your agent:

pcc organizations keys use

When prompted, select your agent from the list.

Talk to your agent

The starter project is configured to use Daily as a WebRTC transport. Pipecat Cloud has a direct integration with Daily, meaning you are issued a Daily API key when you create an account.

pcc agent start lmnt-agent --use-daily

This command will start a new active session with your agent. Since you are using Daily, you will be given a URL in the terminal to open in your browser. This will open a new tab where you can interact with your agent.

For more information on starting sessions, see Active Sessions.

Monitor and Troubleshoot

Once your agent is deployed, you can use the following commands to monitor its status and troubleshoot any issues:

# Check deployment status
pcc agent status lmnt-agent
 
# View deployment logs
pcc agent logs lmnt-agent

These commands provide visibility into your agent's operation and can help diagnose problems if your agent isn't functioning as expected.

Next Steps

Congratulations! You have successfully deployed your first agent w/ LMNT TTS to Pipecat Cloud.