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Instead of paying for cloud AI APIs, all inference runs locally on a GPU accelerated VM. CrewAI orchestrates multiple specialized agents that analyze infrastructure alerts, determine root causes, and generate structured incident reports automatically.\n\nThis is a real working system built on real homelab hardware, not a demo or tutorial project.\n\n---\n\n## Architecture\n\n```\nBiggie Proxmox Node\n└── VM 300: ai-agent-lab (<ai-agent-vm-ip>)\n    ├── Ubuntu 24.04\n    ├── Python 3.12\n    ├── CrewAI 1.14.4\n    └── NOC Analyst Crew\n         ├── Agent 1: Infrastructure Analyst\n         ├── Agent 2: Root Cause Analyst\n         └── Agent 3: NOC Documentation Writer\n\nBigWorld Proxmox Node\n└── VM 400: ai-inference (<ai-inference-vm-ip>)\n    ├── Ubuntu 24.04\n    ├── NVIDIA GeForce GTX 1060 6GB (VFIO Passthrough)\n    ├── Nvidia Driver 535.288.01\n    ├── CUDA 12.2\n    └── Ollama GPU accelerated\n         └── mistral:7b at 40-50 tokens per second\n```\n\n---\n\n## Hardware\n\n| Component | Spec |\n|-----------|------|\n| Proxmox Cluster | 6 node cluster |\n| AI Agent Node | Biggie Intel Pentium Silver J5005 8GB RAM |\n| AI Inference Node | BigWorld GPU VM GTX 1060 6GB |\n| GPU | NVIDIA GeForce GTX 1060 6GB ZOTAC |\n| CUDA | 12.2 |\n| K3s Cluster | 12-node Raspberry Pi cluster |\n| Network | UniFi VLAN segmented |\n| Storage | TrueNAS |\n| Tunneling | Cloudflare Tunnels + Nginx Proxy Manager |\n\n---\n\n## Stack\n\n| Component | Role | Location |\n|-----------|------|----------|\n| Proxmox VE 8.4 | Hypervisor | BigWorld + Biggie |\n| Ubuntu 24.04 | Guest OS | Both VMs |\n| VFIO | GPU Passthrough | BigWorld host |\n| Nvidia Driver 535 | GPU driver | ai-inference VM |\n| CUDA 12.2 | GPU compute | ai-inference VM |\n| Ollama | LLM inference server | ai-inference VM |\n| mistral:7b | Language model | ai-inference VM |\n| Python 3.12 | Runtime | ai-agent-lab VM |\n| CrewAI 1.14.4 | Multi-agent framework | ai-agent-lab VM |\n\n---\n\n## NOC Analyst Crew\n\nThe first crew is a 3 agent NOC analyst pipeline.\n\n### Agents\n\n**Infrastructure Analyst**\n- Reads raw alert data\n- Identifies symptoms and affected systems\n- Outputs structured symptom list\n\n**Root Cause Analyst**\n- Takes Infrastructure Analyst findings\n- Determines most likely root cause\n- Assesses cluster impact\n- Recommends immediate actions\n\n**NOC Documentation Writer**\n- Takes all findings\n- Produces formatted incident report\n- Follows NOC reporting standards\n\n### Agent Handoff Flow\n\n```\nAlert Input\n    |\n    v\nInfrastructure Analyst\n    | symptom list\n    v\nRoot Cause Analyst\n    | root cause + recommendations\n    v\nNOC Documentation Writer\n    |\n    v\nStructured Incident Report\n```\n\n---\n\n## Sample Output\n\n```\nNOC Incident Report\n\nIncident Summary\nIncident ID: INC-20230167-AffectedNodes\nStatus: Open\nImpact Level: High\n\nAffected Systems\n1. K3s-Node-4 Unreachable for 5 minutes\n2. Remaining K3s Nodes CPU spiked to 85 percent\n3. Prometheus Scrape Target Down on K3s-Node-4\n4. K3s-Node-5 and K3s-Node-6 Pod rescheduling active\n\nRoot Cause\nNetwork connectivity failure on K3s-Node-4 causing pod rescheduling\nand CPU overload on remaining nodes.\n\nNext Actions\n1. Run network diagnostics on K3s-Node-4\n2. Scale down non critical workloads to reduce CPU pressure\n3. Monitor remaining nodes and restore K3s-Node-4 when resolved\n\n\nPriority Level\nHIGH - Immediate action required\n```\n\n---\n\n## Quick Start\n\n**Prerequisites**\n- Proxmox VE 8.x\n- A VM with Nvidia GPU passthrough configured\n- Ollama installed and running with GPU support\n- Python 3.12 or higher\n\n**Clone the repo**\n\n```bash\ngit clone https://github.com/ced4568/ced-noc-ai-agents.git\ncd ced-noc-ai-agents\n```\n\n**Set up Python environment**\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\npip install --upgrade pip\npip install crewai crewai-tools\n```\n\n**Configure your Ollama endpoint**\n\nEdit `crews/noc_crew.py` and update `base_url` to your Ollama IP:\n\n```python\nbase_url=\"http://<ai-inference-vm-ip>:11434\"\n```\n\n**Pull the model on your Ollama server**\n\n```bash\nollama pull mistral:7b\n```\n\n**Run the NOC crew**\n\n```bash\nsource .venv/bin/activate\npython crews/noc_crew.py\n```\n\n---\n\n## GPU Passthrough Setup\n\nFull step by step guide is in `docs/gpu-passthrough-setup.md`\n\nCovers:\n- IOMMU verification\n- VFIO module configuration\n- GPU device ID binding\n- nouveau blacklisting\n- VM creation settings\n- kvm=off requirement for Nvidia\n- Nvidia driver install inside VM\n- Ollama network binding configuration\n\n---\n\n## Roadmap\n\n- [ ] Connect agents to live Prometheus API\n- [ ] Connect agents to live Grafana alerts\n- [ ] Add K3s cluster monitoring agent\n- [ ] Add Proxmox node health agent\n- [ ] Slack and Discord alert output\n- [ ] Automated runbook generation\n- [ ] Web dashboard for incident reports\n- [ ] Integrate with Uptime Kuma webhooks\n- [ ] Add memory persistence between crew runs\n- [ ] Multi-crew orchestration\n- [ ] Upgrade BigWorld to a more powerful server\n\n---\n\n## Related Projects\n\n| Project | Description |\n|---------|-------------|\n| [ceds-observability-stack](https://github.com/ced4568/ceds-observability-stack) | Prometheus, Grafana, Node Exporter, Blackbox Exporter, Uptime Kuma |\n\n---\n\n## About\n\nBuilt as part of Ced's Home Lab  a 6 node Proxmox cluster, 12 node Raspberry Pi K3s cluster, TrueNAS storage, UniFi networking, and full observability stack.\n\nThis lab directly supports my work as a DevOps and Cloud Infrastructure Engineer and serves as a portfolio of real infrastructure engineering skills.\n\nGitHub: https://github.com/ced4568\nDomain: cedshomelab.com\n","readmeExcerpt":"Ced's NOC AI Agents A self hosted multi agent AI system for Network Operations Center automation, built on a Proxmox homelab with GPU accelerated local LLM inference. 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