dockai-cli 2.4.3

The Customizable AI Dockerfile Generation Framework

DockAI

AI-Powered Dockerfile Generation Framework

Generate production-ready Dockerfiles from first principles using AI agents

Quick Start • Features • Docs • CI/CD • Contributing

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🎯 What is DockAI?

DockAI is an agentic AI framework that analyzes your codebase and generates optimized, production-ready Dockerfiles. Unlike template-based tools, DockAI uses first-principles reasoning to understand your application and create Dockerfiles from scratch—handling everything from standard stacks to legacy systems.

pip install dockai-cli
dockai build /path/to/project

That's it. DockAI handles the rest.

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✨ Features

🧠 First-Principles AI No templates. Analyzes file structures, dependencies, and code patterns to deduce the optimal containerization strategy.	🔄 Self-Correcting Workflow Builds and tests Dockerfiles in a sandbox. If something fails, AI reflects, learns, and retries with a new approach.
🛡️ Security-First Built-in Trivy integration scans for vulnerabilities. Enforces non-root users, minimal base images, and hardened configs.	🤖 10 Specialized Agents Each agent handles a specific task: analysis, planning, generation, review, and more. All fully customizable.
⚡ Multi-Provider LLMs Supports OpenAI, Azure, Gemini, Anthropic, and Ollama. Mix and match providers per agent (e.g., OpenAI for analysis, Ollama for generation).	🔧 Fully Customizable Override prompts, instructions, and model selection per agent. Use .dockai files for repo-specific configs.
📦 Smart Registry Integration Automatically validates base images against Docker Hub, GCR, Quay, and GHCR. Prioritizes small, secure variants like alpine and slim.	🚀 Performance Optimized Intelligent caching prevents redundant network calls. Semantic version sorting ensures you always get the latest stable releases.

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🚀 Three Ways to Use DockAI

DockAI is designed to fit into any workflow, whether you are a developer, a DevOps engineer, or an AI user.

1. The CLI (For Developers)

Perfect for running locally on your machine.

# Install
pip install dockai-cli

# Run
dockai build .

2. GitHub Actions (For CI/CD)

Automate Dockerfile generation in your pipelines.

steps:
  - uses: actions/checkout@v3
  - uses: itzzjb/dockai@v2
    with:
      openai_api_key: ${{ secrets.OPENAI_API_KEY }}

3. MCP Server (For AI Agents)

Use DockAI directly inside Claude Desktop, Cursor, or any MCP-compliant tool.

1. Install dockai-cli.
2. Configure your MCP client:

{
  "mcpServers": {
    "dockai": {
      "command": "python",
      "args": ["-m", "dockai.core.mcp_server"]
    }
  }
}

3. Ask your AI: "Analyze this project and generate a Dockerfile for it."

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Configuration

Create a .env file:

# Required: Choose your LLM provider and add the API key
OPENAI_API_KEY=sk-your-api-key

# Optional: Use a different provider (openai, azure, gemini, anthropic, ollama)
# DOCKAI_LLM_PROVIDER=openai

Usage

# Generate Dockerfile for your project
dockai build /path/to/project

# With verbose output
dockai build /path/to/project --verbose

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🏗️ How It Works

graph TD
    Start([▶ Start]) --> Scan[📂 Scanner]
    Scan --> Analyze[🧠 Analyzer]
    Analyze --> Read[📖 Reader]
    Read --> Health[🏥 Health Detector]
    Health --> Ready[⏱️ Readiness Detector]
    Ready --> Plan[📝 Planner]
    Plan --> Generate[⚙️ Generator]
    Generate --> Review[🔒 Security Reviewer]
    
    Review -- Secure --> Validate[✅ Validator]
    Review -- Issues & Can Retry --> Reflect[🤔 Reflector]
    Review -- Critical & Max Retries --> Fail([❌ Fail])
    
    Validate -- Success --> End([🏁 Finish])
    Validate -- Failure --> Reflect
    
    Reflect --> Increment[🔄 Increment Retry]
    
    Increment -- Fix Code --> Generate
    Increment -- New Strategy --> Plan
    Increment -- Re-Analyze --> Analyze
    Increment -- Max Retries --> Fail

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🤖 The 10 AI Agents

Agent	Role	Model Type
Analyzer	Project discovery & stack detection	Fast
Planner	Strategic build planning	Fast
Generator	Dockerfile creation	Powerful
Generator (Iterative)	Debugging failed Dockerfiles	Powerful
Reviewer	Security audit & hardening	Fast
Reflector	Failure analysis & learning	Powerful
Health Detector	Health endpoint discovery	Fast
Readiness Detector	Startup pattern analysis	Fast
Error Analyzer	Error classification	Fast
Iterative Improver	Targeted fix application	Powerful

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⚙️ Configuration

Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key	Required*
GOOGLE_API_KEY	Google Gemini API key	Required*
ANTHROPIC_API_KEY	Anthropic Claude API key	Required*
AZURE_OPENAI_API_KEY	Azure OpenAI API key	Required*
OLLAMA_BASE_URL	Ollama Base URL	http://localhost:11434
DOCKAI_LLM_PROVIDER	Provider (openai, azure, gemini, anthropic, ollama)	openai
MAX_RETRIES	Maximum retry attempts	3
DOCKAI_SKIP_SECURITY_SCAN	Skip Trivy scanning	false
DOCKAI_TRUNCATION_ENABLED	Enable file truncation	false
DOCKAI_TOKEN_LIMIT	Token limit for auto-truncation	100000
DOCKAI_MAX_FILE_CHARS	Max chars per file (when truncating)	200000
DOCKAI_MAX_FILE_LINES	Max lines per file (when truncating)	5000

*Only one API key required for your chosen provider.

Repository-Level Configuration

Create a .dockai file in your project root:

[instructions_analyzer]
This is a Django application with Celery workers.

[instructions_generator]
Use gunicorn as the WSGI server.
Run database migrations at container start.

[instructions_reviewer]
All containers must run as non-root (UID >= 10000).

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🔗 GitHub Actions

name: Auto-Dockerize

on:
  push:
    branches: [main]

jobs:
  dockai:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: itzzjb/dockai@v2
        with:
          openai_api_key: ${{ secrets.OPENAI_API_KEY }}

Multi-Provider Example

- uses: itzzjb/dockai@v2
  with:
    llm_provider: gemini
    google_api_key: ${{ secrets.GOOGLE_API_KEY }}
    max_retries: 5
    strict_security: true

Mixed Provider Example

Use Ollama locally for most tasks, but OpenAI for complex analysis:

# .env
DOCKAI_LLM_PROVIDER=ollama
DOCKAI_MODEL_ANALYZER=openai/gpt-4o-mini

See GitHub Actions Guide for all options.

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📖 Documentation

Document	Description
Getting Started	Installation, configuration, first run
Architecture	Deep dive into the internal design
Configuration	Full reference for env vars and inputs
Customization	Tuning agents for your organization
API Reference	Module and function documentation
GitHub Actions	CI/CD integration guide
MCP Server	AI Agent integration guide
FAQ	Frequently asked questions

💡 MCP Support: Expose DockAI as a Model Context Protocol server for use in any MCP client.

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🛠️ Tech Stack

Technology	Purpose
Python 3.10+	Core runtime
LangGraph	Stateful agent workflow orchestration
LangChain	LLM provider integration
Pydantic	Structured output validation
Rich + Typer	Beautiful CLI interface
Trivy	Security vulnerability scanning

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🤝 Contributing

Contributions are welcome! Feel free to open issues and pull requests.

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📄 License

MIT License - see LICENSE for details.

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_{Built with ❤️ by itzzjb}