"Infrastructure that converses, learns, and evolves autonomously"
Create a homelab where AI serves as the primary operator interface, managing wishlist prioritization, workflow orchestration, and continuous optimization while leveraging your existing excellent infrastructure foundation.
- AI-First Operations: Conversational interface as primary control plane
- Task-Agnostic Framework: Handle any future requirement through universal AI processing
- Professional Workflow: GitHub issues → AI analysis → implementation → documentation → commit
- Autonomous Evolution: AI manages its own improvement post-Phase 1
- Existing Infrastructure Respect: Build upon MAAS + Proxmox + K3s + FluxCD foundation
- ✅ Preferred: Python, KRO, K3s, GitOps, local LLMs
- ❌ Avoided: Terraform, Ansible (complex HCL/YAML configuration)
- 🎯 Target: 90% uptime tolerance, cost-effective, learning-focused
┌─────────────────────────────────────────────────────────────┐
│ MANDATORY Professional Workflow (Every Operation) │
│ ├── GitHub Issue Creation (AI auto-creates for all tasks) │
│ ├── AI Analysis & ROI Calculation │
│ ├── Human Approval Gate (go/no-go decision) │
│ ├── Automated Implementation with Progress Tracking │
│ ├── Documentation Update (MANDATORY - Never skip) │
│ ├── Testing & Validation │
│ └── Commit with "fixes #issue" + Issue Auto-Closure │
│
│ CRITICAL: No infrastructure change without this workflow │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ MAAS + Proxmox + K3s Foundation (Keep & Enhance) │
│ ├── MAAS: Bare metal provisioning, DNS, DHCP │
│ ├── Proxmox: VM/LXC management, 2.5GbE networking │
│ ├── K3s Cluster: Container orchestration │
│ ├── FluxCD: GitOps deployment automation │
│ └── Hardware: still-fawn (RTX 3070), chief-horse, etc. │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ KRO Workflow State Management (Replaces Terraform/Ansible)│
│ ├── Physical Infrastructure Workflows │
│ │ ├── Storage expansion (procurement → install → config)│
│ │ ├── Hardware upgrades (order → receive → deploy) │
│ │ └── Network changes (physical + software coordination)│
│ ├── Software Deployment Workflows │
│ │ ├── K3s service deployments with best practices │
│ │ ├── LXC container provisioning │
│ │ └── Configuration management │
│ └── Hybrid Workflows (Physical + Virtual + Cloud) │
│ ├── GPU passthrough setup │
│ ├── Cross-node service migration │
│ └── Backup orchestration (local + cloud) │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Hybrid AI Engine (Privacy-First Routing) │
│ ├── Local LLM (Ollama on RTX 3070) │
│ │ ├── Sensitive operations (configs, security analysis)│
│ │ ├── Real-time decisions (monitoring, alerts) │
│ │ ├── Offline operations (core functionality) │
│ │ └── Homelab-specific fine-tuned models │
│ ├── Cloud LLM Integration (OpenAI, Anthropic) │
│ │ ├── Complex reasoning (architecture decisions) │
│ │ ├── Latest knowledge (research, best practices) │
│ │ ├── Code generation and analysis │
│ │ └── Specialized tasks (image processing, etc.) │
│ └── AI Router (Privacy + Cost + Latency Optimization) │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Conversational Infrastructure Interface │
│ ├── MCP Server (Primary Interface - Phase 1 Priority) │
│ │ ├── Universal task processor (any homelab operation) │
│ │ ├── Wishlist management and ROI analysis │
│ │ ├── KRO workflow generation │
│ │ └── Professional workflow integration │
│ ├── Python Orchestrator (Enhanced Existing Code) │
│ │ ├── Proxmox VM/LXC management │
│ │ ├── K3s cluster operations │
│ │ ├── MAAS device registration │
│ │ └── Service deployment automation │
│ └── AI-Driven Capability Expansion │
│ ├── Dynamic handler generation │
│ ├── Self-improving automation │
│ └── Learning from outcomes │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ SINGLE SOURCE OF TRUTH: Grafana-First Alerting │
│ ├── Grafana Alerts (PRIMARY - 100% of alerts) │
│ │ ├── Prometheus metrics (infrastructure, services) │
│ │ ├── AI Anomaly Detection Models (MANDATORY) │
│ │ │ ├── Resource usage pattern analysis │
│ │ │ ├── Service performance degradation detection │
│ │ │ ├── Network behavior anomaly identification │
│ │ │ └── Predictive failure analysis │
│ │ ├── Home Assistant integration (IoT + environmental) │
│ │ ├── Custom business logic alerts │
│ │ └── Alert routing to AI decision engine │
│ ├── Uptime Kuma (BACKUP ONLY - when Grafana fails) │
│ │ ├── Simple HTTP/TCP checks │
│ │ ├── Basic service availability monitoring │
│ │ ├── Emergency notification via separate channels │
│ │ └── Grafana health monitoring (monitors the monitor) │
│ └── AI Alert Processing Engine │
│ ├── Real-time alert correlation and deduplication │
│ ├── Intelligent noise reduction (ML-based filtering) │
│ ├── Auto-remediation decision making │
│ ├── Escalation path determination │
│ └── Learning from alert resolution outcomes │
└─────────────────────────────────────────────────────────────┘
Goal: Establish conversational AI interface Scope: Convert existing Python orchestrator to MCP server with hybrid LLM routing
Critical Changes Required:
# Convert existing orchestrator to MCP-compatible
class HomelabMCPServer(MCPServer):
def __init__(self):
self.local_llm = OllamaClient("http://still-fawn.homelab:11434")
self.cloud_llm = AnthropicClient() # or OpenAI
self.orchestrator = ProxmoxOrchestrator() # existing code
self.ai_router = PrivacyFirstRouter()
@mcp_tool("process_any_task")
async def universal_task_handler(self, task_description: str):
"""Handle any homelab task through AI analysis"""
return await self.ai_engine.process_task(task_description)Goal: AI manages entire workflow autonomously Scope: AI-driven wishlist management, priority calculation, workflow generation
AI Autonomous Loop:
1. AI Maintains Wishlist
├── Your explicit requests
├── Discovered optimization opportunities
├── Predicted future needs
└── Community trends research
2. AI Calculates ROI (Bang-for-Buck)
├── Time savings potential
├── Cost optimization impact
├── Learning/experimentation value
├── Implementation complexity
└── Risk assessment
3. AI Generates Implementation Plan
├── Creates GitHub issue automatically
├── Generates KRO workflow definition
├── Identifies human assistance needs
└── Estimates timeline and resources
4. AI Requests Human Approval
├── Presents ROI justification
├── Explains implementation approach
├── Highlights manual tasks required
└── Asks for go/no-go decision
5. AI Executes with Assistance
├── Autonomous execution where possible
├── Human assistance requests as needed
├── Real-time progress updates
└── Automatic rollback on failures
6. AI Documents & Learns
├── Auto-updates documentation
├── Commits changes with proper messages
├── Closes GitHub issues
├── Updates ROI models based on outcomes
└── Improves future predictions
Goal: Self-improving infrastructure with predictive capabilities Scope: AI-driven infrastructure evolution, proactive optimization, community contribution
apiVersion: kro.run/v1alpha1
kind: ResourceGraphDefinition
metadata:
name: storage-expansion-workflow
spec:
resources:
# Step 1: AI Analysis & Procurement (CONCRETE EXAMPLE)
- name: capacity-analysis
type: ai-task
dependencies: []
analysis_output: "Current ZFS pool 78% full. Recommend 2TB NVMe SSD for $150. ROI: 6 months extended capacity"
- name: hardware-procurement
type: external-task
dependencies: [capacity-analysis]
waitForHuman: true # Waits for you to buy/install disk
procurement_details:
recommended_part: "Samsung 980 PRO 2TB NVMe SSD"
estimated_cost: "$150"
vendor_links: ["amazon.com/dp/B08GLX7TNT", "newegg.com/samsung-980-pro-2tb"]
installation_guide: "docs/hardware/nvme-installation.md"
# Step 2: Physical Installation Validation
- name: disk-detection
type: system-check
dependencies: [hardware-procurement]
validation_commands: ["lsblk", "dmesg | grep nvme", "smartctl -a /dev/nvme1n1"]
# Step 3: Software Configuration
- name: zfs-pool-expansion
type: proxmox-task
dependencies: [disk-detection]
commands: ["zpool add tank nvme1n1", "zfs set compression=lz4 tank"]
- name: k3s-storage-update
type: kubernetes-task
dependencies: [zfs-pool-expansion]
actions: ["update-storageclass-capacity", "migrate-pvc-if-needed"]
# Step 4: Validation & Documentation
- name: capacity-validation
type: ai-validation
dependencies: [k3s-storage-update]
success_criteria: ["zpool status healthy", "available space > 2TB", "all PVCs accessible"]
- name: documentation-update
type: git-commit
dependencies: [capacity-validation]
files_to_update: ["docs/infrastructure/storage.md", "INFRASTRUCTURE.md"]Every service deployment follows this AI-automated pattern:
# KRO ResourceGraphDefinition Template
apiVersion: kro.run/v1alpha1
kind: ResourceGraphDefinition
metadata:
name: homelab-service-template
spec:
resources:
# Professional workflow
- name: github-issue
type: github-integration
# Infrastructure preparation
- name: resource-analysis
type: ai-analysis
dependencies: [github-issue]
# Network configuration
- name: dns-entry
type: opnsense-dns
dependencies: [resource-analysis]
pattern: "{{.service_name}}.homelab → LoadBalancer IP"
# Service deployment
- name: k3s-deployment
type: kubernetes-deployment
dependencies: [dns-entry]
- name: traefik-ingress
type: ingress-route
dependencies: [k3s-deployment]
# Monitoring setup
- name: grafana-alerts
type: monitoring-config
dependencies: [traefik-ingress]
- name: uptime-kuma-backup
type: backup-monitoring
dependencies: [grafana-alerts]
# AI configuration (Specific Examples)
- name: ai-service-config
type: ai-configuration
dependencies: [uptime-kuma-backup]
examples:
home_assistant: "Configure chatbot with personality, create automations for common tasks, set up voice commands"
paperless_ngx: "Configure AI document classification, set up OCR with local LLM, create smart filing rules"
ollama: "Optimize model loading for RTX 3070, configure context window, set up API rate limiting"
bruno: "Create API test collections for all homelab services, set up automated test scheduling"
prompt: "AI auto-configure {{.service_name}} with homelab-optimized settings and integrations"
# Documentation & closure
- name: documentation-update
type: auto-documentation
dependencies: [ai-service-config]
- name: github-close
type: github-integration
dependencies: [documentation-update]Tier 1 (High ROI):
- Tailscale Integration: Secure remote access automation
- Continue.dev + Local LLM: AI-assisted coding with RTX 3070
- Paperless-NGX + AI Search: Document management with local LLM
AI Autonomous Processing:
AI Analysis: "Tailscale integration ranks #1"
├── ROI Score: 9.2/10 (high time savings, security improvement)
├── Complexity: 6/10 (straightforward API integration)
├── Dependencies: None (can implement immediately)
├── Resource Impact: Minimal (no new hardware needed)
└── Learning Value: 8/10 (zero-trust networking principles)
AI Generated Plan:
├── GitHub Issue: "Implement Tailscale exit node automation #124"
├── KRO Workflow: tailscale-integration.yaml
├── Implementation Steps: [5 automated, 1 manual approval needed]
├── Timeline: 2 hours total, 15 minutes human time
└── Success Metrics: Remote access latency <50ms, 100% uptime
Human Decision Required: Approve tailscale-exit-node on still-fawn? [Y/n]
- MAAS: Continue using for bare metal provisioning, enhance with AI device management
- Proxmox: Keep VM/LXC management, add AI-driven resource optimization
- K3s + FluxCD: Maintain GitOps workflow, enhance with KRO abstractions
- Python Orchestrator: Evolve into MCP server, maintain existing logic
- 2.5GbE Network: Optimize with AI-driven traffic analysis
- KRO: Replace Terraform/Ansible complexity with Kubernetes-native workflows
- Local LLM: Ollama on RTX 3070 for privacy-sensitive operations
- MCP Server: Universal conversational interface to infrastructure
- AI Router: Intelligent routing between local and cloud LLMs
- MCP server responds to conversational infrastructure requests
- Local LLM handles privacy-sensitive operations (configs, security)
- Cloud LLM integration for complex reasoning tasks
- Basic task automation through conversational interface
- AI autonomously manages 80% of wishlist items
- 90% reduction in manual deployment time
- Professional documentation maintained automatically
- ROI predictions improve 10% quarterly through learning
- Handles any task type through universal AI processing
- Scales from current 4-node setup to 50+ nodes
- Maintains 90% uptime target with intelligent monitoring
- Cost-effective operation through local LLM optimization
- Convert Python orchestrator to MCP server
- Implement hybrid LLM routing (RTX 3070 + cloud)
- Add conversational interface via Claude Code
- Professional workflow integration (GitHub issues → commits)
After Phase 1, AI determines all subsequent implementations:
- AI prioritizes wishlist items based on ROI analysis
- AI generates KRO workflows for selected tasks
- AI requests human approval for implementation
- AI executes, documents, and learns from outcomes
- AI continuously improves prioritization and execution
Key Insight: Architecture provides framework; AI determines specific implementations based on evolving needs, technology changes, and learned outcomes.
Goal: Python orchestrator + MCP server becomes the standard homelab automation tool
Open Source Strategy:
Phase 1: Internal Development
├── Develop and refine MCP server on personal homelab
├── Create comprehensive KRO workflow library
├── Document best practices and lessons learned
└── Validate with diverse homelab scenarios
Phase 2: Community Release
├── Open source Python orchestrator + MCP server
├── Publish KRO workflow templates on GitHub
├── Create homelab automation framework documentation
├── Establish community contribution guidelines
└── Build plugin ecosystem for different homelab setups
Phase 3: Ecosystem Growth
├── Integration with popular homelab tools (Proxmox, TrueNAS, etc.)
├── Cloud provider integrations (for hybrid homelab setups)
├── Hardware vendor partnerships (pre-configured automation)
├── Conference presentations and community building
└── Standardization across homelab community
Value Proposition for Community:
- Reduce Learning Curve: Conversational interface eliminates complex configuration
- Best Practices Built-In: Automated deployment follows established patterns
- Hardware Agnostic: Works with any homelab hardware combination
- Privacy First: Local LLM processing for sensitive operations
- Professional Grade: GitHub workflow and documentation standards
Year 1: Foundation & Community Adoption
├── MCP server maturity and stability
├── KRO workflow library expansion
├── Community feedback integration
└── Plugin ecosystem development
Year 2: Multi-Homelab Federation
├── Cross-homelab resource sharing protocols
├── Distributed AI processing capabilities
├── Federated monitoring and alerting
└── Collaborative learning between homelabs
Year 3: Advanced Intelligence
├── Specialized edge AI hardware optimization
├── Predictive infrastructure lifecycle management
├── Autonomous infrastructure evolution
├── Integration with emerging homelab technologies
└── Industry-standard homelab automation platform
AI assistants working with this architecture MUST follow these precision requirements:
Never Make Up:
- Service names, entity IDs, or API endpoints
- User interface elements or menu paths
- Configuration syntax or parameters
- File paths or system commands
Always Verify:
- Use exact terminology from the user's environment
- Ask for confirmation of UI elements before referencing them
- Check actual available options before suggesting them
- Verify entity names exist before using them
Example Correct Approach:
Assistant: "What tabs do you see under Developer Tools?"
User: "I see Actions, States, Events..."
Assistant: "Go to Developer Tools → Actions and search for 'llm'"
This prevents frustration and builds trust through precision.
This architecture transforms your existing excellent homelab foundation into an AI-first, conversational, self-managing system that respects your technology preferences while providing unlimited extensibility through AI-driven evolution.