amdf 0.1.1

Transform Kubernetes CRDs into type-safe KCL schemas and developer-friendly blueprints

AMDF - Agnostic Multi-cloud Delivery Framework

Transform any Kubernetes CRD into type-safe KCL schemas and developer-friendly blueprints

Quick Start{ .md-button .md-button--primary } Examples{ .md-button }

Why AMDF?

Direct interaction with Kubernetes Custom Resource Definitions (CRDs) presents critical challenges:

• ❌ Security & Compliance Risks Without strict schemas, misconfigurations are easy to commit and hard to validate.

• ❌ Developer Velocity Bottleneck Writing thousands of lines of YAML manually is slow, error-prone, and hard to reuse across teams.

• ❌ Inefficient Feedback Loops Engineers waste valuable cycles waiting for CI/CD pipelines to fail, rather than catching configuration errors instantly in the IDE.

The AMDF Solution

AMDF automatically transforms complex CRDs into simple, validated interfaces:

graph LR
    A[Complex CRD] --> B[AMDF] --> C[Type-Safe Schema] --> D[Simple Blueprint]
    style A fill:#ffcccc
    style D fill:#ccffcc

What You Get

Before AMDF	After AMDF
Complex CRD Manifests	Streamlined Blueprints
Runtime validation errors	Compile-time type checking
Provider-specific patterns	Unified interface
Manual documentation	Auto-generated examples

Core Capabilities

🔍 Universal Discovery

Automatically finds and catalogs all CRDs in your cluster

🏗️ Smart Generation

• The Library Model (Schema) - Complete, type-safe representations of the raw CRD.

• The Developer Interface (Blueprint) - A concise, easy-to-read module that exposes only essential configuration.

🤖 AI-Enhanced Experience

Built-in AI assistant explains generated code and provides usage examples via Ollama integration.

🔌 Multiple Interfaces

• CLI Tool - Direct command-line usage
• MCP Server - Integration with AI development tools
• Guided Mode - Interactive wizard for beginners

Architecture

AMDF follows a clean, modular design:

graph TB
    subgraph "User Interfaces"
        CLI[CLI Commands]
        MCP[MCP Server]
        GUI[Guided Mode]
    end

    subgraph "Core Engine"
        DISC[CRD Discovery]
        GEN[Schema Generator]
        BP[Blueprint Creator]
    end

    subgraph "Output"
        SCHEMA[KCL Schemas]
        BLUE[KCL Blueprints]
    end

    CLI --> DISC
    MCP --> DISC
    GUI --> DISC
    DISC --> GEN
    GEN --> BP
    GEN --> SCHEMA
    BP --> BLUE

Real-World Impact

Platform Teams

Transform hundreds of CRDs into consistent, validated schemas that development teams can actually use.

DevOps Engineers

Eliminate configuration drift and deployment failures with compile-time validation.

Application Developers

Focus on business logic instead of learning complex CRD specifications.

Getting Started

Installation

pip install amdf

Basic Workflow

# Discover what's available
amdf list-crds --filter aws

# Generate schemas
amdf generate instances.ec2.aws.upbound.io

# Use the blueprint
import library.blueprints.Instance

server = Instance.InstanceBlueprint {
    _metadataName = "web-server"
    _instanceType = "t3.medium"
}

Interactive Mode

# Guided experience with AI explanations
amdf guided --ai-model qwen3-coder:30b

Ecosystem Integration

AMDF orchestrates a best-in-class stack to deliver infrastructure as code:

• KCL (Configuration Engine) Provides the modeling language, validation logic, and schema generation capabilities.

• CNCF Ecosystem (Target Resource Model) Compatible with any Kubernetes CRD, like as Crossplane, ACK, , KRO, Config Connector, and custom operators.

• Kubernetes (Control Plane) Serves as the universal API and reconciliation engine that AMDF extends.

• Ollama (Local Intelligence) Powers the integrated AI assistant for offline, privacy-focused schema explanations and guidance.

• Model Context Protocol (MCP) (Agentic Interface) Exposes AMDF as a standardized server, allowing AI clients to directly interact with, query, and generate infrastructure.