KubeRocketAI

AI-as-Code framework for structuring AI-assisted software development. Define AI agents as version-controlled Markdown files with YAML frontmatter.

Problem

You've figured out how to make AI agents understand your project. You've crafted the perfect prompts, built context-aware agents, and your AI-generated code actually works. Now what?

The real challenge isn't creating smart agents—it's scaling that success across your team, projects, and workflow.

What Happens When It Actually Works

• "How do I share this with my team?" - You've spent weeks perfecting an agent configuration, but there's no clean way to distribute it beyond copy-pasting prompts in Slack
• "Which version was working?" - Your agent setup evolved over time, but when something breaks, you can't roll back to the version that was working last week
• "I need this in my IDE, not just ChatGPT" - Your perfect brainstorming agent lives in a web interface, but you need it integrated into your actual development workflow
• "Can we run this in CI?" - Your agents work great for interactive development, but you need them for code review, documentation generation, and automated analysis
• "How do we maintain this across 10 repositories?" - Each project needs slight variations, but managing separate configurations becomes a nightmare

The Scaling Problem

• No Distribution System: Agents remain isolated to individuals or require manual sharing through ad-hoc methods
• Version Control Gap: Agent configurations evolve without proper versioning, making it impossible to track what works and what doesn't
• Platform Lock-in: Agents tied to specific tools can't be moved, adapted, or integrated into different parts of your workflow
• Maintenance Overhead: As your agent library grows, keeping configurations synchronized across projects becomes unmanageable
• Quality Assurance: No systematic way to validate that agents work correctly before deploying them across teams or into production workflows
• Context Fragmentation: Web-based AI tools lose critical project context, forcing manual re-explanation of architecture, patterns, and organizational standards for each interaction

Solution

KubeRocketAI brings the proven "Infrastructure-as-Code" model to AI agent management. Teams define agents in version-controlled Markdown files that integrate seamlessly with existing development workflows, enabling the same declarative approach developers expect from modern CI/CD pipelines.

Key Value Propositions

1. Agent-as-Code Distribution: Version-control and share your proven agent configurations just like you do with Infrastructure-as-Code
2. Multi-Platform Injection: Deploy the same agent definition to your IDE, CI pipeline, bundled for brainstorming, or wherever you need AI assistance
3. Built-in Validation: Validate agent configurations and dependencies before deployment, ensuring consistent behavior across environments
4. Context-Preserving Bundling: Package agents with full project context for web chat tools, maintaining architectural knowledge and organizational standards across platforms
5. Scalable Team Workflows: Maintain agent libraries across multiple projects and repositories without configuration hell

Smart Context Bundling

Two deployment modes for different contexts:

IDE Integration: Limited, Focused Context

Deploy agents directly into your development environment with project-specific context optimized for coding tasks.

Web Chat: Full Context for Strategic Work

Bundle agents with complete project assets for brainstorming and requirements elicitation in high-context models (ChatGPT, Claude, Gemini Pro).

# IDE: Focused development context
krci-ai install --ide=cursor

# Web Chat: Complete project context for strategic sessions
krci-ai bundle --all --output brainstorm-context.md

High Level Project Diagram

This diagram illustrates the AI-as-Code approach for AI agents, showing how KubeRocketAI enables declarative AI-as-Code management within existing developer workflows.

graph TD
    subgraph "Local Developer Environment"
        Developer["👨‍💻 Developer<br/>Uses existing tools"]
        CLI["🛠️ krci-ai CLI<br/>📦 Embedded Framework Assets<br/>🔧 AI-as-Code Management"]
        IDE["🎨 AI-Powered IDE<br/>Native Integration<br/>(No plugins required)"]
        LocalFramework["📁 ./krci-ai/<br/>🔗 Declarative AI Agents<br/>📋 Extracted + Local"]
        TargetProject["💻 Target Project<br/>🔀 Git Repository"]
    end

    subgraph "Internet/Cloud (Post-MVP)"
        GoldenRepo["🏢 Golden Source<br/>🔗 Git Repository<br/>🤖 AI-as-Code<br/>🔮 Future Enhancement"]
    end

    Developer --> CLI
    Developer --> IDE
    CLI -->|"📦 Extract embedded assets<br/>Offline operation"| LocalFramework
    IDE -.->|"📖 Reads declarative configs<br/>Native filesystem access"| LocalFramework
    LocalFramework --> TargetProject
    GoldenRepo -.->|"🔮 Post-MVP: Remote updates<br/>Community contributions"| CLI
    TargetProject -.->|"🔄 Future: Contribute back<br/>Local customizations"| GoldenRepo

    style CLI fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    style IDE fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    style GoldenRepo fill:#f0f0f0,stroke:#999999,stroke-width:1px,stroke-dasharray: 5 5
    style LocalFramework fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px

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Installation

macOS - Homebrew (Recommended)

# Add the KubeRocketCI tap
brew tap KubeRocketCI/homebrew-tap

# Install krci-ai
brew install krci-ai

Linux - Direct Download

# Download and extract latest release
curl -L "https://github.com/KubeRocketCI/kuberocketai/releases/latest/download/krci-ai_Linux_x86_64.tar.gz" | tar -xz
chmod +x krci-ai
sudo mv krci-ai /usr/local/bin/

Windows - Direct Download

1. Download the latest release: krci-ai_Windows_x86_64.zip
2. Extract the zip file
3. Add the krci-ai.exe to your PATH or move it to a directory in your PATH

Build from Source

git clone https://github.com/KubeRocketCI/kuberocketai.git
cd kuberocketai
make build

Update & Uninstall

# Update via Homebrew
brew update && brew upgrade krci-ai

# Uninstall via Homebrew
brew uninstall krci-ai

Usage

Quick Start

# Install framework with IDE integration
krci-ai install --ide=cursor

# Validate your agent configurations
krci-ai validate --all

# Create context-aware bundles for web chat tools
krci-ai bundle --all --output project-context.md

# List available agents
krci-ai list agents

# Install with all IDE integrations
krci-ai install --all

Success Metrics

KubeRocketAI succeeds when you can scale your AI workflow like you scale your infrastructure:

• Agent Reusability: Deploy proven configurations across multiple projects without manual setup
• Quality Assurance: Validate agent configurations before deployment to catch issues early
• Version Control Integration: Track what works, roll back when things break, collaborate on improvements
• Platform Flexibility: Use the same agent definitions for IDE development, CI automation, and brainstorming sessions

Our goal: Turn your AI agent expertise into scalable, maintainable infrastructure.

Target Users

Teams That Have Figured Out AI, But Need to Scale It

You'll love this if you:

• Have created effective AI agents but struggle to share them across your team
• Want to version-control your agent configurations just like you do with your infrastructure
• Need validation and quality assurance for agents before deploying them across projects
• Need the same agents working in multiple contexts: IDE, CI/CD, brainstorming, code review
• Maintain multiple repositories and are tired of keeping agent configurations in sync
• Have proven AI workflows but no systematic way to scale them across projects

Perfect for:

• Senior AI Engineers: Have mastered individual agent creation, now need enterprise-scale management
• DevOps-Minded Teams: Want to apply Infrastructure-as-Code principles to AI agent management
• Multi-Project Teams: Need consistent AI capabilities across different repositories and contexts
• AI-First Organizations: Ready to systematize their AI workflows beyond individual experimentation

Documentation

📚 Complete documentation available at /docs

• Quick Start Guide - Get running in 3 minutes
• Core Concepts - Understand AI-as-Code principles
• Architecture Overview - System design and components

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

Apache-2.0 License - see LICENSE for details.