# ADL CLI _A command-line interface for generating enterprise-ready A2A (Agent-to-Agent) servers from Agent Definition Language (ADL) files._ > ⚠️ **Early Development Warning**: This project is in its early stages of development. Breaking changes are expected and acceptable until we reach a stable version. Use with caution in production environments. [![Go Version](https://img.shields.io/badge/go-1.25+-blue.svg?style=flat-square&logo=go)](https://golang.org) [![License](https://img.shields.io/badge/license-MIT-green.svg?style=flat-square)](LICENSE) [![Build Status](https://img.shields.io/github/actions/workflow/status/inference-gateway/adl-cli/ci.yml?style=flat-square&logo=github)](https://github.com/inference-gateway/adl-cli/actions) [![Go Report Card](https://goreportcard.com/badge/github.com/inference-gateway/adl-cli?style=flat-square)](https://goreportcard.com/report/github.com/inference-gateway/adl-cli) [![Release](https://img.shields.io/github/v/release/inference-gateway/adl-cli?style=flat-square&logo=github)](https://github.com/inference-gateway/adl-cli/releases)

## Table of Contents - [Overview](#overview) - [Installation](#installation) - [Quick Install (Recommended)](#quick-install-recommended) - [From Source](#from-source) - [Using Go Install](#using-go-install) - [Pre-built Binaries](#pre-built-binaries) - [Quick Start](#quick-start) - [Usage](#usage) - [Commands](#commands) - [Init Command](#init-command) - [Generate Command](#generate-command) - [Agent Definition Language (ADL)](#agent-definition-language-adl) - [Generated Project Structure](#generated-project-structure) - [Sandbox Environments](#sandbox-environments) - [Enterprise Features](#enterprise-features) - [Artifacts Support](#artifacts-support) - [GitHub Issue Templates](#github-issue-templates) - [Examples](#examples) - [Template System & Architecture](#template-system--architecture) - [Customizing Generation with .adl-ignore](#customizing-generation-with-adl-ignore) - [Configurable Acronyms](#configurable-acronyms) - [Post-Generation Hooks](#post-generation-hooks) - [Development](#development) - [Roadmap](#roadmap) - [License](#license) - [Support](#support) ## Overview The ADL CLI helps you build enterprise-ready A2A agents quickly by generating complete project scaffolding from YAML-based Agent Definition Language (ADL) files. It eliminates boilerplate code and ensures consistent patterns across your agent implementations. ### Key Features - 🚀 **Rapid Development** - Generate complete projects in seconds - 📋 **Schema-Driven** - Use YAML Agent Definition Language files (ADL) to define your agents - 🎯 **Enterprise Ready** - Single unified template with AI integration and enterprise features - 🔐 **Enterprise Features** - Authentication, SCM integration, and audit logging - 🛠️ **Smart Ignore** - Protect your implementations with .adl-ignore files - ✅ **Validation** - Built-in ADL schema validation - 🛠️ **Interactive Setup** - Guided project initialization with extensive CLI options - 🔗 **Structured Services** - Type-safe dependency injection with interfaces and factory functions - ⚙️ **Configuration Management** - Automatic environment variable mapping with proper naming conventions - 🔧 **CI/CD Generation** - Automatic GitHub Actions workflows with semantic-release CD pipelines - 🏗️ **Sandbox Environments** - Flox and DevContainer support for isolated development - 🎣 **Post-Generation Hooks** - Customize build, format, and test commands after generation - 🤖 **Multi-Provider AI** - OpenAI, Anthropic, DeepSeek, Ollama, Google, Mistral, and Groq support - 📁 **Artifacts Support** - Integrated filesystem and MinIO object storage for artifact management ## Installation ### Quick Install (Recommended) Use our install script to automatically download and install the latest binary: ```bash curl -fsSL https://raw.githubusercontent.com/inference-gateway/adl-cli/main/install.sh | bash ``` Or download and run the script manually: ```bash wget https://raw.githubusercontent.com/inference-gateway/adl-cli/main/install.sh chmod +x install.sh ./install.sh ``` **Install Options:** - Install specific version: `./install.sh --version v1.0.0` - Custom install directory: `INSTALL_DIR=~/bin ./install.sh` - Show help: `./install.sh --help` ### From Source ```bash git clone https://github.com/inference-gateway/adl-cli.git cd adl-cli go install . ``` ### Using Go Install ```bash go install github.com/inference-gateway/adl-cli@latest ``` ### Pre-built Binaries Download pre-built binaries from the [releases page](https://github.com/inference-gateway/adl-cli/releases). ## 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 for your implementations: ```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 ``` ## Usage ### Commands | Command | Description | | --------------------- | ------------------------------------------------------------------ | | `adl init [name]` | Create ADL manifest file interactively with options | | `adl generate` | Generate project code from ADL file with CI/CD and sandbox support | | `adl validate [file]` | Validate an ADL file against the complete schema | ### Init Command The `adl init` command provides a interactive wizard for creating ADL manifest files: ```bash # Interactive ADL manifest creation adl init my-weather-agent # Use defaults for all prompts adl init my-agent --defaults # Non-interactive with specific configuration adl init my-agent \ --name "Weather Agent" \ --description "Provides weather information" \ --provider openai \ --model gpt-4o-mini \ --language go \ --flox ``` #### Init Command Options The init command supports extensive configuration options: **Project Settings:** - `--defaults` - Use default values for all prompts - `--path` - Project directory path - `--name` - Agent name - `--description` - Agent description - `--version` - Agent version **Agent Configuration:** - `--type` - Agent type (`ai-powered`/`minimal`) - `--provider` - AI provider (`openai`/`anthropic`/`deepseek`/`ollama`/`google`/`mistral`/`groq`) - `--model` - AI model name - `--system-prompt` - System prompt for the agent - `--max-tokens` - Maximum tokens (integer) - `--temperature` - Temperature (0.0-2.0) **Capabilities:** - `--streaming` - Enable streaming responses - `--notifications` - Enable push notifications - `--history` - Enable state transition history **Server Configuration:** - `--port` - Server port (integer) - `--debug` - Enable debug mode **Language-Specific Options:** - `--language` - Programming language (`go`/`rust`, TypeScript support planned) **Go Options:** - `--go-module` - Go module path (e.g., `github.com/user/project`) - `--go-version` - Go version (e.g., `1.25`) **Rust Options:** - `--rust-package-name` - Rust package name - `--rust-version` - Rust version (e.g., `1.88`) - `--rust-edition` - Rust edition (e.g., `2024`) **TypeScript Options:** - `--typescript-name` - TypeScript package name **Environment Options:** - `--flox` - Enable Flox environment - `--devcontainer` - Enable DevContainer environment ### Generate Command ```bash # Generate project from ADL file adl generate --file agent.yaml --output ./test-my-agent # Overwrite existing files (respects .adl-ignore) adl generate --file agent.yaml --output ./test-my-agent --overwrite # Generate with CI workflow configuration adl generate --file agent.yaml --output ./test-my-agent --ci # Generate with AI assistant instructions and claude-code integration adl generate --file agent.yaml --output ./test-my-agent --ai # Generate with CloudRun deployment configuration adl generate --file agent.yaml --output ./test-my-agent --deployment cloudrun # Generate with CloudRun deployment and CD pipeline adl generate --file agent.yaml --output ./test-my-agent --deployment cloudrun --cd ``` #### Generate Flags | Flag | Description | | ------------------ | ------------------------------------------------------------------------------------------ | | `--file`, `-f` | ADL file to generate from (default: "agent.yaml") | | `--output`, `-o` | Output directory for generated code (default: ".") | | `--template`, `-t` | Template to use (default: "minimal") | | `--overwrite` | Overwrite existing files (respects .adl-ignore) | | `--ci` | Generate CI workflow configuration (GitHub Actions) | | `--cd` | Generate CD pipeline configuration with semantic-release | | `--deployment` | Generate deployment configuration (`kubernetes`, `cloudrun`) | | `--ai` | Generate AI assistant instructions (CLAUDE.md) and add claude-code to sandbox environments | **CI Generation Features:** - **Automatic Provider Detection**: Detects GitHub from ADL `spec.scm.provider` (GitLab support planned) - **Language-Specific Workflows**: Tailored CI configurations for Go, Rust, and TypeScript - **Version Integration**: Uses language versions from ADL configuration - **Task Integration**: Leverages generated Taskfile for consistent build processes - **Caching**: Includes service caching for faster builds **CD Generation Features:** - **Semantic Release Integration**: Automatic versioning based on conventional commits - **Multi-Language Support**: Builds and tests for Go, Rust, and TypeScript projects - **Container Publishing**: Builds and pushes Docker images to GitHub Container Registry - **Manual Dispatch**: CD workflow triggered manually via GitHub Actions - **Changelog Generation**: Automatic CHANGELOG.md generation with release notes - **GitHub Releases**: Creates GitHub releases with appropriate tagging - **Deployment Integration**: Supports automatic deployment to Kubernetes and Cloud Run after successful releases **AI Integration Features:** The `--ai` flag enables enhanced development experience with AI assistant capabilities: - **CLAUDE.md Generation**: Creates AI assistant instructions tailored to your agent - Project-specific guidelines based on your ADL configuration - Language-specific development patterns and best practices - Skills implementation guidance with TODO placeholders context - Testing strategies and development workflow recommendations - **Claude Code Integration**: Automatically adds claude-code to sandbox environments - DevContainer integration for seamless AI-assisted development - Flox environment integration with claude-code tooling - Improved development experience with AI pair programming capabilities **Deployment Generation Features:** The `--deployment` flag generates platform-specific deployment configurations: - **CloudRun Deployment**: Creates a `deploy` task in the root `Taskfile.yml` for gcloud deployment - Supports both Google Container Registry (GCR) and GitHub Container Registry (GHCR) - Configurable resources (CPU, memory), scaling (min/max instances), and service options - Uses direct gcloud commands for truly serverless deployment (no Kubernetes required) - Automatic container building with Docker or Cloud Build integration - **Kubernetes Deployment**: Creates `k8s/deployment.yaml` with standard Kubernetes manifests - Production-ready configurations with resource limits and health checks - ConfigMap and Secret integration for environment variables - Service and Ingress configurations for load balancing ## Agent Definition Language (ADL) ADL files use YAML to define your agent's configuration, capabilities, and tools. ### Example ADL File ```yaml apiVersion: adl.dev/v1 kind: Agent metadata: name: weather-agent description: "Provides weather information for cities worldwide" version: "1.0.0" spec: capabilities: streaming: true pushNotifications: false stateTransitionHistory: false agent: provider: "" # Choose: openai, anthropic, deepseek, ollama, google, mistral, groq model: "" # Specify default model name for chosen provider systemPrompt: "You are a helpful weather assistant." maxTokens: 4096 temperature: 0.7 skills: - name: get_weather description: "Get current weather for a city" schema: type: object properties: city: type: string description: "City name" country: type: string description: "Country code" required: - city server: port: 8080 debug: false language: go: module: "github.com/example/weather-agent" version: "1.25" acronyms: ["api", "json", "xml"] # Optional: Custom acronyms for better code generation ``` ### ADL Schema The complete ADL schema includes: - **metadata**: Agent name, description, and version - **capabilities**: Streaming, notifications, state history - **config**: Structured configuration sections with environment variable mapping - **services**: Service services with interfaces, factories, and type definitions - **agent**: AI provider configuration (OpenAI, Anthropic, DeepSeek, Ollama, Google, Mistral, Groq) - **skills**: Function definitions with complex JSON schemas, validation, and service injection support - **server**: HTTP server configuration with authentication support - **language**: Programming language-specific settings (Go, Rust, TypeScript) and configurable acronyms - **scm**: Source control management configuration (GitHub, GitLab) - **sandbox**: Development environment configuration (Flox, DevContainer) - **deployment**: Platform-specific deployment configuration (Kubernetes, Cloud Run) ### Complete ADL Example ```yaml apiVersion: adl.dev/v1 kind: Agent metadata: name: advanced-agent description: "Enterprise agent with full feature set" version: "1.0.0" spec: capabilities: streaming: true pushNotifications: true stateTransitionHistory: true agent: provider: openai model: gpt-4o-mini systemPrompt: | You are a helpful assistant with enterprise capabilities. Always prioritize security and compliance. maxTokens: 8192 temperature: 0.3 config: database: connectionString: "postgresql://user:pass@localhost:5432/db" maxConnections: "10" timeout: "30s" notifications: slackWebhook: "https://hooks.slack.com/services/..." emailApiKey: "your-email-api-key" retryAttempts: "3" services: database: type: service interface: DatabaseService factory: NewDatabaseService description: PostgreSQL database service for persistent storage notifications: type: service interface: NotificationService factory: NewNotificationService description: Multi-channel notification service skills: - name: query_database description: "Execute database queries with validation" inject: - logger - database schema: type: object properties: query: type: string description: "SQL query to execute" table: type: string description: "Target table name" limit: type: integer description: "Result limit" maximum: 1000 required: [query, table] - name: send_notification description: "Send multi-channel notifications" inject: - logger - notifications schema: type: object properties: recipient: type: string description: "Recipient identifier" message: type: string description: "Message content" priority: type: string enum: ["low", "medium", "high", "critical"] channel: type: string enum: ["email", "slack", "teams", "webhook"] required: [recipient, message, priority, channel] server: port: 8443 debug: false auth: enabled: true language: go: module: "github.com/company/advanced-agent" version: "1.25" scm: provider: github url: "https://github.com/company/advanced-agent" deployment: type: cloudrun cloudrun: image: registry: gcr.io repository: advanced-agent tag: latest useCloudBuild: true resources: cpu: "2" memory: 1Gi scaling: minInstances: 1 maxInstances: 100 concurrency: 1000 service: timeout: 3600 allowUnauthenticated: false serviceAccount: advanced-agent@PROJECT_ID.iam.gserviceaccount.com executionEnvironment: gen2 environment: LOG_LEVEL: info ENVIRONMENT: production sandbox: flox: enabled: true ``` ## Service Injection & Configuration Management The ADL CLI provides a sophisticated service injection system with structured configuration management. This system improves testability, separation of concerns, and provides type-safe configuration with environment variable mapping. ### Structured Service System Define services with explicit types, interfaces, and factory functions. The system supports both built-in services (like logger) and custom service services: ```yaml spec: config: googleCalendar: scopes: "https://www.googleapis.com/auth/calendar" credentialsPath: "/secrets/credentials.json" cache: ttl: "3600" maxEntries: "1000" services: googleCalendar: type: service interface: CalendarService factory: NewCalendarService description: Google Calendar API service for managing calendar events cache: type: service interface: CacheRepository factory: NewCacheRepository description: High-performance caching layer for API responses skills: - name: create_event description: "Create a new calendar event" inject: - logger # Built-in, always available - googleCalendar # Custom service - cache # Custom service schema: type: object properties: title: type: string description: "Event title" start: type: string description: "Start time (ISO 8601)" required: [title, start] ``` ### Configuration Management The configuration system generates type-safe structs with automatic environment variable mapping: **Generated Configuration (`config/config.go`):** ```go type Config struct { // Core application settings Environment string `env:"ENVIRONMENT"` // A2A configuration A2A serverConfig.Config `env:",prefix=A2A_"` // Custom configuration sections Cache CacheConfig `env:",prefix=CACHE_"` GoogleCalendar GoogleCalendarConfig `env:",prefix=GOOGLE_CALENDAR_"` } type GoogleCalendarConfig struct { CredentialsPath string `env:"CREDENTIALS_PATH"` Scopes string `env:"SCOPES"` } type CacheConfig struct { MaxEntries string `env:"MAX_ENTRIES"` Ttl string `env:"TTL"` } ``` **Environment Variables:** - `GOOGLE_CALENDAR_CREDENTIALS_PATH="/secrets/google-creds.json"` - `GOOGLE_CALENDAR_SCOPES="https://www.googleapis.com/auth/calendar"` - `CACHE_MAX_ENTRIES="1000"` - `CACHE_TTL="3600"` ### Service Architecture The service injection system generates: 1. **Built-in Logger**: Automatically available as `*zap.Logger` without declaration 2. **Type-Safe Configuration**: Structured config with environment variable mapping 3. **Service Interfaces**: Custom service packages with interface definitions 4. **Factory Functions**: Constructor functions that receive logger and configuration 5. **Automatic Registration**: Services are automatically wired into skills 6. **File Protection**: Generated service files are automatically added to `.adl-ignore` ### Generated Structure ``` my-agent/ ├── config/ │ └── config.go # Type-safe configuration with env mapping ├── internal/ │ ├── logger/ │ │ └── logger.go # Built-in logger factory │ ├── googleCalendar/ │ │ └── googleCalendar.go # Calendar service with interface │ └── cache/ │ └── cache.go # Cache service with interface ├── skills/ │ ├── create_event.go # Skills with injected services │ └── list_events.go └── .adl-ignore # Protects custom implementations ``` ### Generated Service Code Each service generates a package with interface and factory: **Example `internal/googleCalendar/googleCalendar.go`:** ```go type CalendarService interface { // TODO: Define your CalendarService interface methods CreateEvent(ctx context.Context, event *Event) error ListEvents(ctx context.Context, query *Query) ([]*Event, error) } type calendarService struct { logger *zap.Logger config *config.Config } func NewCalendarService(logger *zap.Logger, cfg *config.Config) (CalendarService, error) { // TODO: Implement CalendarService initialization return &calendarService{ logger: logger, config: cfg, }, nil } ``` ### Skill Integration Skills automatically receive injected services as constructor parameters: **Example `skills/create_event.go`:** ```go type CreateEventSkill struct { logger *zap.Logger calendar googleCalendar.CalendarService cache cache.CacheRepository } func NewCreateEventSkill(logger *zap.Logger, calendar googleCalendar.CalendarService, cache cache.CacheRepository) *CreateEventSkill { return &CreateEventSkill{ logger: logger, calendar: calendar, cache: cache, } } ``` ### Benefits - **Type Safety**: Structured configuration with compile-time validation - **Environment Variables**: Automatic mapping with proper naming conventions - **Interface-Based Design**: Testable services with clear contracts - **Separation of Concerns**: Configuration separate from service definitions - **Language Agnostic**: Works across Go, Rust, and planned TypeScript support - **Hot Reload**: Configuration changes via environment variables - **Security**: No secrets in code, environment-based configuration - **Scalability**: Easy to add new services and configuration sections ### Best Practices 1. **Configuration**: Use environment variables for secrets and environment-specific values 2. **Interfaces**: Define clear interfaces for testability and modularity 3. **Factory Functions**: Initialize services with proper error handling 4. **Logging**: Use the injected logger for consistent log formatting 5. **Testing**: Create mock implementations of service interfaces 6. **Documentation**: Document interface methods and configuration options ## Generated Project Structure The ADL CLI generates project scaffolding tailored to your chosen language: ### Go Project Structure ``` my-go-agent/ ├── main.go # Main server setup ├── go.mod # Go module definition ├── config/ │ └── config.go # Centralized application configuration ├── internal/ │ └── logger/ │ └── logger.go # Built-in logger factory ├── skills/ # Skill implementations directory │ ├── query_database.go # Individual skill files (TODO placeholders) │ └── send_notification.go ├── Taskfile.yml # Development tasks (build, test, lint) ├── Dockerfile # Container configuration ├── .adl-ignore # Files to protect from regeneration ├── .well-known/ │ └── agent-card.json # Agent capabilities (auto-generated) ├── .github/ # GitHub-specific configurations │ ├── workflows/ # Generated when using --ci flag │ │ ├── ci.yml # GitHub Actions CI workflow │ │ └── cd.yml # GitHub Actions CD workflow (with --cd flag) │ └── ISSUE_TEMPLATE/ # Generated when issue_templates: true │ ├── bug_report.md # Bug report template │ ├── feature_request.md # Feature request template │ └── refactor_request.md # Refactoring request template ├── .releaserc.yaml # Semantic-release configuration (with --cd flag) ├── k8s/ │ └── deployment.yaml # Kubernetes deployment manifest ├── cloudrun/ │ └── deploy.sh # CloudRun deployment script (with --deployment cloudrun) ├── .flox/ # Generated when sandbox: flox │ ├── env/manifest.toml │ ├── env.json │ ├── .gitignore │ └── .gitattributes ├── .gitignore # Standard Git ignore patterns ├── .gitattributes # Git attributes configuration ├── .editorconfig # Editor configuration ├── CLAUDE.md # AI assistant instructions (generated with --ai flag) └── README.md # Project documentation with setup instructions ``` ### Rust Project Structure ``` my-rust-agent/ ├── src/ │ ├── main.rs # Main application entry point │ └── skills/ # Skill implementations directory │ ├── mod.rs # Module declarations │ ├── query_database.rs # Individual skill implementations │ └── send_notification.rs ├── Cargo.toml # Rust package configuration ├── Taskfile.yml # Development tasks ├── Dockerfile # Rust-optimized container ├── .adl-ignore # Protection configuration ├── .well-known/ │ └── agent-card.json # Agent capabilities ├── .github/workflows/ # CI configuration (with --ci) │ ├── ci.yml # Rust-specific CI workflow │ └── cd.yml # GitHub Actions CD workflow (with --cd flag) ├── .releaserc.yaml # Semantic-release configuration (with --cd flag) ├── k8s/ │ └── deployment.yaml # Kubernetes deployment ├── cloudrun/ │ └── deploy.sh # CloudRun deployment script (with --deployment cloudrun) ├── CLAUDE.md # AI assistant instructions (generated with --ai flag) └── README.md # Documentation ``` ### Universal Generated Files All projects include these essential files regardless of language: - **`.well-known/agent-card.json`** - A2A agent discovery and capabilities manifest - **`Taskfile.yml`** - Unified task runner configuration for build, test, lint, run - **`Dockerfile`** - Language-optimized container configuration - **`k8s/deployment.yaml`** - Kubernetes deployment manifest - **`deploy` task in `Taskfile.yml`** - CloudRun deployment task (when using `--deployment cloudrun`) - **`.adl-ignore`** - Protects user implementations from overwrite - **CI Workflows** - When using `--ci` flag, generates GitHub Actions workflows: - **GitHub Actions**: `.github/workflows/ci.yml` - **GitLab CI**: `.gitlab-ci.yml` (planned, not yet implemented) - **CD Workflows** - When using `--cd` flag, generates continuous deployment: - **GitHub Actions**: `.github/workflows/cd.yml` - **Semantic Release**: `.releaserc.yaml` - **Development Environment** - Based on `sandbox` configuration: - **Flox**: `.flox/` directory with environment configuration when `sandbox.flox.enabled: true` - **DevContainer**: `.devcontainer/devcontainer.json` when `sandbox.devcontainer.enabled: true` - **AI Assistant Instructions** - When using `--ai` flag: - **CLAUDE.md**: AI assistant instructions tailored to your agent configuration ### CI Integration When using the `--ci` flag, the ADL CLI generates GitHub Actions workflows for your project: ```bash # Generate project with CI workflow adl generate --file agent.yaml --output ./test-my-agent --ci ``` This creates a GitHub Actions workflow (`.github/workflows/ci.yml`) that includes: - **Automated Testing**: Runs all tests on every push and pull request - **Code Quality**: Format checking and linting - **Multi-Environment**: Supports main and develop branches - **Caching**: Go module caching for faster builds - **Task Integration**: Uses the generated Taskfile for consistent build steps The generated workflow automatically detects your Go version from the ADL file and configures the appropriate environment. ### CD Integration The ADL CLI can generate continuous deployment (CD) pipelines with semantic release automation: ```bash # Generate project with CD pipeline adl generate --file agent.yaml --output ./test-my-agent --cd ``` This creates a complete CD setup including: - **`.releaserc.yaml`** - Semantic-release configuration with conventional commits - **`.github/workflows/cd.yml`** - GitHub Actions CD workflow with manual dispatch The generated CD pipeline includes: - **Semantic Versioning**: Automatic version bumping based on conventional commit messages - **Release Automation**: Creates GitHub releases with generated release notes - **Container Publishing**: Builds and publishes Docker images to GitHub Container Registry - **Multi-Platform Builds**: Supports both AMD64 and ARM64 architectures - **Language Detection**: Automatically configures build steps based on your project language - **Change Detection**: Only publishes releases when there are changes to release #### CD Workflow Features **Manual Trigger**: The CD workflow uses `workflow_dispatch` for controlled releases: ```bash # Trigger via GitHub CLI gh workflow run cd.yml # Or trigger via GitHub Actions UI ``` **Conventional Commits Support**: The pipeline recognizes these commit types for versioning: - `feat:` - Minor version bump (new features) - `fix:` - Patch version bump (bug fixes) - `refactor:`, `perf:`, `ci:`, `docs:`, `style:`, `test:`, `build:`, `chore:` - Patch version bump **Container Registry**: Published images are available at: ``` ghcr.io/your-org/your-agent:latest ghcr.io/your-org/your-agent:v1.0.0 ghcr.io/your-org/your-agent:1.0 ``` ## CloudRun Deployment The ADL CLI provides native support for deploying A2A agents to Google Cloud Run, offering a truly serverless deployment experience without Kubernetes complexity. ### CloudRun Configuration Configure CloudRun deployment in your ADL file: ```yaml spec: deployment: type: cloudrun cloudrun: image: registry: gcr.io # gcr.io or ghcr.io repository: my-agent # Repository name tag: latest # Image tag useCloudBuild: true # Use Cloud Build or local Docker resources: cpu: "2" # CPU allocation (0.1 to 8) memory: 1Gi # Memory limit (128Mi to 32Gi) scaling: minInstances: 0 # Minimum instances (0 to 1000) maxInstances: 100 # Maximum instances (1 to 1000) concurrency: 1000 # Max concurrent requests per instance service: timeout: 3600 # Request timeout in seconds allowUnauthenticated: true # Allow public access serviceAccount: my-agent@PROJECT_ID.iam.gserviceaccount.com executionEnvironment: gen2 # gen1 or gen2 environment: # Custom environment variables LOG_LEVEL: info ENVIRONMENT: production ``` ### Container Registry Options **Google Container Registry (GCR):** ```yaml image: registry: gcr.io repository: my-project/my-agent useCloudBuild: true # Automatically build and push ``` **GitHub Container Registry (GHCR):** ```yaml image: registry: ghcr.io repository: myorg/my-agent useCloudBuild: false # Skip Cloud Build, use pre-built image ``` ### Generated Deployment Script When using `--deployment cloudrun`, the ADL CLI generates a `deploy` task in the `Taskfile.yml` that: - **Validates Environment**: Checks for required `PROJECT_ID` and `REGION` variables - **Container Building**: Uses Docker locally or Cloud Build based on configuration - **Direct gcloud Deployment**: Uses `gcloud run deploy` for serverless deployment - **Configuration Summary**: Displays all deployment settings for verification ### CloudRun Deployment Workflow ```bash # 1. Generate project with CloudRun deployment adl generate --file agent.yaml --output ./my-agent --deployment cloudrun # 2. Set required environment variables export PROJECT_ID="my-gcp-project" export REGION="us-central1" # 3. Deploy to CloudRun cd my-agent task deploy ``` ### CloudRun with CI/CD Generate CloudRun deployment with continuous deployment: ```bash adl generate --file agent.yaml --deployment cloudrun --cd ``` This creates: - **CD Workflow**: Automatically deploys to CloudRun after releases - **Environment Integration**: Uses GitHub secrets for GCP authentication - **Multi-Environment Support**: Deploy to different regions/projects **Required GitHub Secrets:** - `GCP_SA_KEY`: Service account key JSON - `GCP_PROJECT_ID`: Google Cloud project ID - `GCP_REGION`: Deployment region (e.g., us-central1) ### CloudRun Benefits - **Truly Serverless**: No Kubernetes clusters or infrastructure management - **Auto-Scaling**: Scale to zero when idle, scale up automatically under load - **Pay-per-Use**: Only pay for actual request processing time - **Global Edge**: Deploy to multiple regions with traffic management - **Integrated Monitoring**: Built-in logging, metrics, and tracing - **Custom Domains**: HTTPS support with automatic SSL certificates ### Example ADL Files The CLI includes CloudRun example files: ```bash # Validate CloudRun examples adl validate examples/cloudrun-agent.yaml adl validate examples/cloudrun-ghcr-agent.yaml # Generate CloudRun projects adl generate --file examples/cloudrun-agent.yaml --output ./cloudrun-test adl generate --file examples/cloudrun-ghcr-agent.yaml --output ./ghcr-test ``` ## Sandbox Environments The ADL CLI supports multiple development environments for isolated, reproducible development: ### Flox Environment Configure Flox for your project by adding to your ADL file: ```yaml spec: sandbox: flox: enabled: true ``` Generated files: - `.flox/env/manifest.toml` - Flox environment manifest with language-specific services - `.flox/env.json` - Environment configuration - `.flox/.gitignore` - Flox-specific ignore patterns - `.flox/.gitattributes` - Git attributes for Flox files ### DevContainer Environment Configure DevContainer for your project: ```yaml spec: sandbox: devcontainer: enabled: true ``` Generated files: - `.devcontainer/devcontainer.json` - VS Code DevContainer configuration with language support ### Multiple Environment Support You can enable multiple sandbox environments simultaneously: ```yaml spec: sandbox: flox: enabled: true devcontainer: enabled: true ``` This generates both Flox and DevContainer configurations, allowing developers to choose their preferred environment. ### Benefits of Sandbox Environments - **Reproducible Development** - Consistent environments across team members - **Isolated Dependencies** - No conflicts with system-wide installations - **Language-Specific Tooling** - Pre-configured with appropriate development tools - **CI/CD Integration** - Matches production environment characteristics ## Enterprise Features ### Authentication Configuration Enable server authentication in your ADL file: ```yaml spec: server: port: 8443 debug: false auth: enabled: true ``` This generates enterprise-ready authentication scaffolding in your project. ### SCM Integration Configure source control management for automatic CI/CD provider detection: ```yaml spec: scm: provider: github # gitlab support planned url: "https://github.com/company/my-agent" github_app: false # optional: enable GitHub App for CD issue_templates: true # optional: generate GitHub issue templates ``` **Features:** - **Automatic CI Detection** - Generates appropriate workflows based on SCM provider - **Repository Integration** - Links generated projects to source control - **Workflow Optimization** - SCM-specific optimizations and best practices - **GitHub App Support** - Enhanced security for enterprise CD pipelines - **Issue Templates** - Generate GitHub issue templates for standardized bug reports and feature requests #### GitHub App Integration For enterprise environments, you can enable GitHub App-based CD deployment for enhanced security: ```yaml spec: scm: provider: github url: "https://github.com/company/my-agent" github_app: true ``` **GitHub App CD Benefits:** - **Enhanced Security** - App tokens are automatically revoked after pipeline execution - **Enterprise Compliance** - Keeps main branch protected from direct pushes - **Bot Identity** - Release operations performed by dedicated bot account - **Audit Trail** - Clear attribution of automated actions **Required GitHub Secrets:** - `BOT_GH_APP_ID` - Your GitHub App ID - `BOT_GH_APP_PRIVATE_KEY` - Your GitHub App private key When `github_app: true` is set, the generated CD pipeline will use GitHub App authentication instead of the default `GITHUB_TOKEN`, providing better security isolation for release management. ### AI Provider Support The ADL CLI supports multiple AI providers including OpenAI, Anthropic, DeepSeek, Ollama (for local LLMs), Google AI, Mistral, and Groq. Each provider requires appropriate API keys to be configured as environment variables. See the ADL examples above for configuration details. ## Artifacts Support Enable artifacts support to allow your agent to create, store, and manage files and resources: ```yaml spec: artifacts: enabled: true ``` Configure storage via environment variables (see generated README for A2A_ARTIFACT_* variables). Supports both filesystem and MinIO/S3 storage backends. **Examples:** - `examples/go-agent-artifacts-filesystem.yaml` - Filesystem storage example - `examples/go-agent-artifacts-minio.yaml` - MinIO storage example ## GitHub Issue Templates The ADL CLI can automatically generate GitHub issue templates for your agent projects, providing standardized forms for bug reports, feature requests, and refactoring tasks: ```yaml spec: scm: provider: github url: "https://github.com/company/my-agent" issue_templates: true # Enable issue template generation ``` When `issue_templates: true` is set, the following templates are generated in `.github/ISSUE_TEMPLATE/`: - **`bug_report.md`** - Structured bug reporting with severity levels, reproduction steps, and environment details - **`feature_request.md`** - Feature proposals with use case descriptions and acceptance criteria - **`refactor_request.md`** - Code improvement requests with motivation and impact analysis **Issue Template Features:** - **Agent Context** - Templates include agent name and version from your ADL metadata - **Structured Sections** - Consistent formatting for better issue triage and tracking - **GitHub Integration** - Automatic labels and assignees configured in frontmatter - **Severity Levels** - Priority classification for bug reports (critical, high, medium, low) - **Environment Info** - Sections for capturing logs, system details, and configurations ## Examples The CLI includes example ADL files in the `examples/` directory: ```bash # Validate examples adl validate examples/go-agent.yaml adl validate examples/rust-agent.yaml adl validate examples/github-app-agent.yaml adl validate examples/cloudrun-agent.yaml adl validate examples/cloudrun-ghcr-agent.yaml # Generate from examples adl generate --file examples/go-agent.yaml --output ./test-go-agent adl generate --file examples/rust-agent.yaml --output ./test-rust-agent adl generate --file examples/github-app-agent.yaml --output ./test-github-app-agent --cd adl generate --file examples/cloudrun-agent.yaml --output ./test-cloudrun-agent --deployment cloudrun adl generate --file examples/cloudrun-ghcr-agent.yaml --output ./test-ghcr-agent --deployment cloudrun # Generate with CI/CD pipeline adl generate --file examples/github-app-agent.yaml --output ./enterprise-agent --ci --cd adl generate --file examples/cloudrun-agent.yaml --output ./cloudrun-enterprise --deployment cloudrun --cd ``` **Example ADL Files:** - `go-agent.yaml` - Basic Go agent with multiple skills and capabilities - `rust-agent.yaml` - Rust agent with enterprise features - `github-app-agent.yaml` - Enterprise agent with GitHub App CD integration - `cloudrun-agent.yaml` - CloudRun deployment with Google Container Registry - `cloudrun-ghcr-agent.yaml` - CloudRun deployment with GitHub Container Registry ## Template System & Architecture The ADL CLI uses a sophisticated template system that generates language-specific projects: ### Language Detection The generator automatically detects your target language from the ADL file: ```go // Automatic detection based on spec.language configuration func DetectLanguageFromADL(adl *schema.ADL) string { if adl.Spec.Language.Go != nil { return "go" } if adl.Spec.Language.Rust != nil { return "rust" } if adl.Spec.Language.TypeScript != nil { return "typescript" } return "go" // default } ``` ### File Mapping System Each language has its own file mapping that determines what gets generated: **Go Projects:** - `main.go` → Go main server setup - `skills/{skillname}.go` → Individual skill implementations - `go.mod` → Go module configuration - Language-specific Dockerfile and CI configurations **Rust Projects:** - `src/main.rs` → Rust main application - `src/skills/{skillname}.rs` → Skill implementations - `src/skills/mod.rs` → Module declarations - `Cargo.toml` → Rust package configuration **Universal Files:** - `Taskfile.yml` → Development task runner - `.well-known/agent-card.json` → A2A capabilities manifest - `k8s/deployment.yaml` → Kubernetes deployment - CI workflows and sandbox configurations ### Template Context All templates receive a rich context object: ```go type Context struct { ADL *schema.ADL // Complete ADL configuration Metadata GeneratedMetadata // Generation metadata Language string // Detected language } ``` This allows templates to access any ADL configuration and generate language-appropriate code. ## Customizing Generation with .adl-ignore The ADL CLI automatically creates a `.adl-ignore` file during project generation to protect files containing TODO implementations. This file works similar to `.gitignore` and prevents important implementation files from being overwritten during subsequent generations. ### Automatically Protected Files When you generate a project, implementation files are automatically added to `.adl-ignore` to protect your business logic from being overwritten during regeneration. You can control which additional files are generated or updated by editing the `.adl-ignore` file: ```bash # .adl-ignore # Skip Docker-related files if you have custom containerization Dockerfile docker-compose.yml # Skip Kubernetes manifests if you use different deployment tools k8s/ # Skip specific generated files you want to customize middleware.go auth.go # Skip build configuration if you have custom setup Taskfile.yml ``` ### .adl-ignore Patterns - Use `#` for comments - Use `/` at the end to match directories - Use `*` for wildcards - Exact file paths or glob patterns - Protects files during all `generate` operations ### Common Use Cases - **Custom Deployment**: Skip `Dockerfile`, `k8s/`, `docker-compose.yml` - **Custom Build**: Skip `Taskfile.yml`, `Makefile` - **Custom Auth**: Skip `auth.go`, `middleware.go` - **Custom Documentation**: Skip `README.md` ## Development ### Prerequisites - Go 1.25+ - [Task](https://taskfile.dev/) (optional, for using Taskfile commands) ### Building from Source ```bash git clone https://github.com/inference-gateway/adl-cli.git cd adl-cli # Install dependencies go mod download # Build task build # Run tests task test # Format code task fmt # Lint task lint ``` ### Testing ```bash # Run tests task test # Test with coverage task test:coverage # Test all examples task examples:test # Generate all examples task examples:generate ``` ### Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Add tests for new functionality 5. Run `task ci` to ensure everything passes 6. Submit a pull request ## Configurable Acronyms The ADL CLI includes support for configurable acronyms to improve code generation readability. This feature helps generate more readable function and struct names by properly capitalizing acronyms in generated code. ### How It Works Define custom acronyms in your ADL file's `spec.acronyms` field. These acronyms will be properly capitalized when generating identifiers in your code. ### Configuration ```yaml spec: language: go: module: "github.com/company/my-agent" version: "1.25" acronyms: ["n8n", "xml", "mqtt", "iot", "uuid"] ``` ### Generated Code Examples **Without custom acronyms:** - `get_n8n_docs` → `GetN8nDocsSkill` - `process_xml_data` → `ProcessXmlDataSkill` **With custom acronyms:** - `get_n8n_docs` → `GetN8NDocsSkill` - `process_xml_data` → `ProcessXMLDataSkill` ### Default Acronyms The following acronyms are recognized by default: - **Common**: id, api, url, uri, json, xml, sql, html, css, js, ui, uuid - **Network**: http, https, tcp, udp, ip, dns, tls, ssl - **Tech**: cpu, gpu, ram, io, os, db Your custom acronyms extend these defaults and take precedence over them. ## Post-Generation Hooks The ADL CLI supports custom post-generation hooks that run automatically after project generation. These hooks allow you to execute commands like formatting, linting, testing, or custom setup scripts. ### Default Hooks Each language has sensible defaults: **Go Projects:** - `go fmt ./...` - Format all Go source files - `go mod tidy` - Download dependencies and clean up go.mod **Rust Projects:** - `cargo fmt` - Format all Rust source files - `cargo check` - Check the project for errors ### Custom Hooks You can customize or extend the default behavior by adding a `hooks` section to your ADL file: ```yaml apiVersion: adl.dev/v1 kind: Agent metadata: name: my-agent spec: # ... other configuration ... # Custom post-generation hooks hooks: post: - "go fmt ./..." - "go mod tidy" - "go vet ./..." - "go test -short ./..." - "golangci-lint run --fix" ``` ### Hooks Behavior - **Override Defaults**: When you specify custom hooks, they completely replace the language defaults - **Command Execution**: Commands run in the generated project directory - **Error Handling**: Failed commands show warnings but don't stop generation - **Sequential Execution**: Commands run in the order specified - **Shell Support**: Commands are executed through the system shell ### Example Configurations **Extended Go Development:** ```yaml hooks: post: - "go mod download" # Download dependencies first - "go generate ./..." # Generate code if needed - "gofumpt -l -w ." # Improved formatting - "golangci-lint run --fix" # Lint and auto-fix - "go test -race -short ./..." # Run tests - "go build -v ./..." # Verify build works ``` **Rust with Additional Tools:** ```yaml hooks: post: - "cargo fmt" - "cargo clippy --fix --allow-dirty" - "cargo check --all-targets" - "cargo test --lib" ``` **TypeScript/Node.js:** ```yaml hooks: post: - "npm install" - "npm run format" - "npm run lint:fix" - "npm run type-check" - "npm test" ``` ### Best Practices - **Keep hooks fast** - Avoid long-running commands that slow down generation - **Use error-tolerant commands** - Commands should gracefully handle missing tools - **Order matters** - Place dependencies first (e.g., `npm install` before `npm run lint`) - **Document requirements** - Note any required tools in your project README ## Roadmap ### Language Support The ADL CLI currently supports Go and Rust, with plans to expand to additional programming languages: #### ✅ Currently Supported - **Go** - Full support with templates for main.go, go.mod, and tools - **Rust** - Full support with templates for main.rs, Cargo.toml, and tools #### 🚧 Planned Support - **TypeScript/Node.js** - Template structure exists but templates not yet implemented - Complete A2A agent generation with Express.js framework planned - AI-powered agents with OpenAI/Anthropic integration - Enterprise features (auth, metrics, logging) - Docker and Kubernetes deployment configs - **Python** - Rapid prototyping and AI-first development - FastAPI-based server generation - Rich AI ecosystem integration - Jupyter notebook support for development #### 🔮 Future Considerations - **Java/Kotlin** - Enterprise JVM support - **C#/.NET** - Microsoft ecosystem integration - **Swift** - Apple ecosystem and server-side Swift ### Template Enhancements - **Multi-language projects** - Generate polyglot agents with language-specific microservices - **Custom templates** - User-defined project templates and scaffolding - **Plugin system** - Extensible architecture for custom generators - **Cloud-native templates** - Serverless (AWS Lambda, Vercel) and edge deployment support ### Contribute to the Roadmap We welcome community input on our roadmap! Please: - 💡 Suggest new languages or frameworks via [Issues](https://github.com/inference-gateway/adl-cli/issues) - 🤝 Contribute implementations for new languages (see [Contributing Guide](CONTRIBUTING.md)) ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Support - 📖 [Documentation](https://docs.inference-gateway.com) - 💬 [Discussions](https://github.com/inference-gateway/adl-cli/discussions) - 🐛 [Issues](https://github.com/inference-gateway/adl-cli/issues) --- > 🤖 Powered by the [Inference Gateway ecosystem](https://github.com/inference-gateway/)