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

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The ADL CLI is a powerful command-line tool designed to rapidly scaffold and manage enterprise-ready AI Agents. It leverages the YAML-based Agent Definition Language (ADL) to generate complete project structures, eliminating boilerplate and ensuring consistent patterns. This tool significantly accelerates the development of AI agents powered by the A2A (Agent-to-Agent) protocol.

GitHub: https://github.com/inference-gateway/adl-cli
OSRepos URL: https://osrepos.com/repo/inference-gateway-adl-cli

## Summary

The ADL CLI is a powerful command-line tool designed to rapidly scaffold and manage enterprise-ready AI Agents. It leverages the YAML-based Agent Definition Language (ADL) to generate complete project structures, eliminating boilerplate and ensuring consistent patterns. This tool significantly accelerates the development of AI agents powered by the A2A (Agent-to-Agent) protocol.

## Topics

- a2a
- ai-agents
- cli-tool
- scaffolding
- go
- containerization
- enterprise-ai
- developer-tools

## Repository Information

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## Content

## 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):**

bash
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:

bash
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:

bash
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:**

    bash
    # 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.

    go
    // 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:**

    bash
    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](https://github.com/inference-gateway/adl-cli){:target="_blank"}
*   **Documentation**: [ADL CLI Documentation](https://docs.inference-gateway.com){:target="_blank"}
*   **Discussions**: [GitHub Discussions](https://github.com/inference-gateway/adl-cli/discussions){:target="_blank"}
*   **Issues**: [GitHub Issues](https://github.com/inference-gateway/adl-cli/issues){:target="_blank"}