Deploy

Deploy your smart contracts effortlessly to various networks with the AlgoKit Deploy feature. This feature is essential for automation in CI/CD pipelines and for seamless deployment to various Algorand network environments.

Usage

$ algokit deploy [OPTIONS] [ENVIRONMENT_NAME]

This command deploys smart contracts from an AlgoKit compliant repository to the specified network.

Options

• --command, -C TEXT: Specifies a custom deploy command. If this option is not provided, the deploy command will be loaded from the .algokit.toml file.
• --interactive / --non-interactive, --ci: Enables or disables the interactive prompt for mnemonics. When the CI environment variable is set, it defaults to non-interactive.
• --path, -P DIRECTORY: Specifies the project directory. If not provided, the current working directory will be used.
• --deployer: Specifies the deployer alias. If not provided and if the deployer is specified in .algokit.toml file its mnemonic will be prompted.
• --dispenser: Specifies the dispenser alias. If not provided and if the dispenser is specified in .algokit.toml file its mnemonic will be prompted.
• -h, --help: Show this message and exit.

Environment files

AlgoKit deploy employs both a general and network-specific environment file strategy. This allows you to set environment variables that are applicable across all networks and others that are specific to a given network.

The general environment file (.env) should be placed at the root of your project. This file will be used to load environment variables that are common across deployments to all networks.

For each network you're deploying to, you can optionally have a corresponding .env.[network_name] file. This file should contain environment variables specific to that network. Network-specific environment variables take precedence over general environment variables.

The directory layout would look like this:

.
├── ... (your project files and directories)
├── .algokit.toml # Configuration file for AlgoKit
├── .env # (OPTIONAL) General environment variables common across all deployments
└── .env.[{mainnet|testnet|localnet|betanet|custom}] # (OPTIONAL) Environment variables specific to deployments to a network

⚠️ Please note that creating .env and .env.[network_name] files is only necessary if you're deploying to a custom network or if you want to override the default network configurations provided by AlgoKit. AlgoKit comes with predefined configurations for popular networks like TestNet, MainNet, BetaNet, or AlgoKit's LocalNet.

The logic for loading environment variables is as follows:

• If a .env file exists, the environment variables contained in it are loaded first.
• If a .env.[network_name] file exists, the environment variables in it are loaded, overriding any previously loaded values from the .env file for the same variables.

AlgoKit Configuration File

AlgoKit uses a configuration file called .algokit.toml in the root of your project. The configuration file can be created using the algokit init command. This file will define the deployment commands for the various network environments that you want to target.

Here's an example of what the .algokit.toml file might look like. When deploying it will prompt for the DEPLOYER_MNEMONIC secret unless it is already defined as an environment variable or is deploying to localnet.

[algokit]
min_version = "v{lastest_version}"

[deploy]
command = "poetry run python -m smart_contracts deploy"
environment_secrets = [
  "DEPLOYER_MNEMONIC",
]

[deploy.localnet]
environment_secrets = []

The command key under each [deploy.{network_name}] section should contain a string that represents the deployment command for that particular network. If a command key is not provided in a network-specific section, the command from the general [deploy] section will be used.

The environment_secrets key should contain a list of names of environment variables that should be treated as secrets. This can be defined in the general [deploy] section, as well as in the network-specific sections. The environment-specific secrets will be added to the general secrets during deployment.

The [algokit] section with the min_version key allows you to specify the minimum version of AlgoKit that the project requires.

This way, you can define common deployment logic and environment secrets in the [deploy] section, and provide overrides or additions for specific environments in the [deploy.{environment_name}] sections.

Deploying to a Specific Network

The command requires a ENVIRONMENT argument, which specifies the network environment to which the smart contracts will be deployed. Please note, the environment argument is case-sensitive.

Example:

$ algokit deploy testnet

This command deploys the smart contracts to the testnet.

Custom Project Directory

By default, the deploy command looks for the .algokit.toml file in the current working directory. You can specify a custom project directory using the --project-dir option.

Example:

$ algokit deploy testnet --project-dir="path/to/project"

Custom Deploy Command

You can provide a custom deploy command using the --custom-deploy-command option. If this option is not provided, the deploy command will be loaded from the .algokit.toml file.

Example:

$ algokit deploy testnet --custom-deploy-command="your-custom-command"

CI Mode

By using the --ci or --non-interactive flag, you can skip the interactive prompt for mnemonics.

This is useful in CI/CD environments where user interaction is not possible. When using this flag, you need to make sure that the mnemonics are set as environment variables.

Example:

$ algokit deploy testnet --ci

Example of a Full Deployment

$ algokit deploy testnet --custom-deploy-command="your-custom-command"

This example shows how to deploy smart contracts to the testnet using a custom deploy command. This also assumes that .algokit.toml file is present in the current working directory, and .env.testnet file is present in the current working directory and contains the required environment variables for deploying to TestNet environment.

Further Reading

For in-depth details, visit the deploy section in the AlgoKit CLI reference documentation.