ADL CLI: Scaffold Enterprise-Ready AI Agents with A2A Protocol

Introduction

The ADL CLI is a robust command-line interface designed to streamline the development and management of enterprise-ready AI Agents. It leverages the Agent Definition Language (ADL), a YAML-based specification, to generate comprehensive project scaffolding. This tool significantly reduces boilerplate code and enforces consistent architectural patterns for AI agents powered by the innovative A2A (Agent-to-Agent) protocol.

Why Use ADL CLI & Key Features

ADL CLI accelerates the creation of sophisticated AI agents by providing a schema-driven approach to project generation. It ensures that agents are built with enterprise-grade features and best practices from the outset.

Key features include:

• Rapid Development: Generate complete projects in seconds, allowing developers to focus on business logic rather than setup.
• Schema-Driven: Define agents using intuitive YAML-based Agent Definition Language (ADL) files, ensuring clarity and consistency.
• Enterprise Ready: Built-in support for authentication, SCM integration, audit logging, and other features crucial for production environments.
• Multi-Provider AI: Seamless integration with leading AI providers such as OpenAI, Anthropic, Google, Groq, Mistral, DeepSeek, Cohere, Cloudflare, Moonshot, Ollama, Ollama Cloud, and Nvidia.
• CI/CD Generation: Automatic generation of GitHub Actions workflows for Continuous Integration and Continuous Deployment, including semantic release pipelines.
• Sandbox Environments: Support for isolated development environments like Flox and DevContainers, ensuring reproducible setups.
• OpenTelemetry Instrumentation: Opt-in tracing and metrics via OpenTelemetry for enhanced observability of agent behavior.
• Service Injection & Configuration Management: A sophisticated system for type-safe dependency injection and structured configuration, with automatic environment variable mapping.

Installation

The recommended way to install ADL CLI is via npm or npx, which provides a convenient wrapper that downloads the native binary on first use.

Using npm / npx (Recommended):

npx @inference-gateway/adl-cli init my-agent
npx @inference-gateway/adl-cli generate --file agent.yaml --output ./agent
npx @inference-gateway/adl-cli validate agent.yaml

Alternatively, install it globally:

npm install -g @inference-gateway/adl-cli
adl --help

Using the Install Script:

For a direct binary download and installation, use the provided install script:

curl -fsSL https://raw.githubusercontent.com/inference-gateway/adl-cli/main/install.sh | bash

Examples

Getting started with ADL CLI is straightforward. You can initialize a new agent project and generate its code with just a few commands.

Quick Start:

1. Initialize a New Project:

# Interactive project setup - creates ADL manifest
adl init my-weather-agent

# Generate project code from the manifest
adl generate --file agent.yaml --output ./test-my-agent

2. Implement Your Business Logic:
The generated project includes TODO placeholders where you can add your specific agent logic.

// TODO: Implement weather API logic
func GetWeatherTool(ctx context.Context, args map[string]any) (string, error) {
    city := args["city"].(string)
    // TODO: Replace with actual weather API call
    return fmt.Sprintf(`{"city": "%s", "temp": "22°C"}`, city), nil
}

3. Build and Run:

cd test-weather-agent
task build
task run

For more comprehensive examples, explore the examples/ directory in the GitHub repository, which showcases various agent configurations and deployment scenarios.

Links

• GitHub Repository: inference-gateway/adl-cli
• Documentation: ADL CLI Documentation
• Discussions: GitHub Discussions
• Issues: GitHub Issues