VMware AIops

Author: Wei Zhou, VMware by Broadcom — wei-wz.zhou@broadcom.com This is a community-driven project by a VMware engineer, not an official VMware product. For official VMware developer tools see developer.broadcom.com.

English | 中文

AI-powered VMware vCenter/ESXi VM lifecycle and deployment tool — 60 tools.

Companion skills handle everything else:

Skill	Scope	Install
vmware-monitor	Read-only: inventory, health, alarms, events, metrics	uv tool install vmware-monitor
vmware-storage	Datastores, iSCSI, vSAN management	uv tool install vmware-storage
vmware-vks	Tanzu Namespaces, TKC cluster lifecycle	uv tool install vmware-vks

Need read-only monitoring only? Use VMware-Monitor — zero destructive code in the codebase.

⚡ Quick Investigation Reports (read-only)

Triage → investigate → act, all in one conversation. Five opinionated read-only reports aggregate and correlate server-side and hand back a high-signal result (never raw inventory), so you can decide where to look before changing anything. Each renders a self-contained offline HTML snapshot with --html (no external assets; drill-down detail collapses in native <details>, zero JavaScript). All delegate to the vmware-monitor library using AIops's own vCenter connection.

vmware-aiops attention                            # what needs attention now, all vCenters
vmware-aiops investigate vm web-01 --hours 72     # everything around a VM, then act on it
vmware-aiops investigate vm web-01 --html         # → offline snapshot in ~/vmware-health/

Via MCP these are the tools cluster_health_summary, cross_vcenter_attention, vm_investigation_bundle, host_investigation_bundle, datastore_investigation_bundle. (Requires vmware-monitor installed.)

Quick Install (Recommended)

Works with Claude Code, Cursor, Codex, Gemini CLI, Trae, and 30+ AI agents:

# Via Skills.sh
npx skills add vmware-skills/VMware-AIops

# Via ClawHub
clawhub install @zw008/vmware-aiops

PyPI Install (No GitHub Access Required)

# Install via uv (recommended)
uv tool install vmware-aiops

# Or via pip
pip install vmware-aiops

# China mainland mirror (faster)
pip install vmware-aiops -i https://pypi.tuna.tsinghua.edu.cn/simple

Offline / Air-Gapped Install (from source)

This project uses the modern PEP 517 build system (hatchling), so there is no setup.py by design — that is expected, not a missing file. If you cloned the source and hit ERROR: File "setup.py" or "setup.cfg" not found ... editable mode currently requires a setuptools-based build, your pip is older than 21.3 and cannot do an editable (-e) install with a non-setuptools backend. Editable mode is a developer convenience, not needed to run the tool — do one of:

# From the source tree — a normal (non-editable) install builds a wheel:
pip install .              # NOT  pip install -e .

# ...or upgrade pip first, and editable works too:
pip install --upgrade pip && pip install -e .

For a truly air-gapped host, build the wheels on a connected machine and copy them over — the target then needs no network:

# On a connected machine, collect this package + its dependencies as wheels:
pip wheel . -w dist        # → dist/*.whl   (or: uv build, for just this package)

# Copy dist/ to the air-gapped host, then install offline:
pip install --no-index --find-links dist vmware-aiops

Why this over other VMware MCP servers

Most open-source VMware MCP servers (e.g. bright8192/esxi-mcp-server, giuliolibrando/vmware-vsphere-mcp-server) are single-vCenter VM wrappers: list/power/snapshot a VM, basic monitoring, a confirm=True flag. They explicitly do not cover networking, storage, Kubernetes, ops analytics, load balancing, or compliance — and "logging is documented" is not an audit trail.

This is one skill in an 11-package family that covers the whole estate and runs every tool through a governed harness:

If you only ever power-cycle VMs in one vCenter, a single-file server is fine. If you run a real (regulated, NSX-segmented, multi-domain) VMware estate and need an AI operator an auditor can sign off on, that's what this family is for — see docs/compliance-ready.md.

Capabilities Overview

What This Skill Does

CLI vs MCP: Which Mode to Use

Rule of thumb: Use CLI for cost efficiency and small models. Use MCP for structured automation with large models.

Architecture

User (Natural Language)
  ↓
AI CLI Tool (Claude Code / Gemini / Codex / Aider / Continue / Trae / Kimi)
  ↓ reads SKILL.md / AGENTS.md / rules
  ↓
vmware-aiops CLI
  ↓ pyVmomi (vSphere SOAP API)
  ↓
vCenter Server ──→ ESXi Cluster ──→ VM
    or
ESXi Standalone Host ──→ VM

Version Compatibility

pyVmomi auto-negotiates the API version during SOAP handshake — no manual configuration needed. The same codebase manages 7.0 / 8.0 / 9.0 / 9.1 environments seamlessly.

Official Broadcom References

• SDKs: https://developer.broadcom.com/sdks — VCF Python SDK (recommended for VCF 9+, bundles pyVmomi + vSAN SDK), vSphere Automation SDK for Python (REST APIs)
• REST APIs: https://developer.broadcom.com/xapis — vSphere Automation API, VCF API, SDDC Manager API
• CLI Tools: https://developer.broadcom.com/tools — PowerCLI 9.1, ESXCLI, OVF Tool

Common Workflows

Deploy a Lab Environment

1. Browse datastore for OVA images → vmware-aiops datastore browse <ds> --pattern "*.ova"
2. Deploy VM from OVA → vmware-aiops deploy ova ./image.ova --name lab-vm --datastore ds1
3. Install software inside VM → vmware-aiops vm guest-exec lab-vm --cmd /bin/bash --args "-c 'apt-get install -y nginx'" --user root
4. Create baseline snapshot → vmware-aiops vm snapshot-create lab-vm --name baseline
5. Set TTL for auto-cleanup → vmware-aiops vm set-ttl lab-vm --minutes 480

Batch Clone for Testing

1. Create plan: vm_create_plan with multiple clone + reconfigure steps
2. Review plan with user (shows affected VMs, irreversible warnings)
3. Apply: vm_apply_plan executes sequentially, stops on failure
4. If failed: vm_rollback_plan reverses executed steps
5. Set TTL on all clones for auto-cleanup

Migrate VM to Another Host

1. Check VM info via vmware-monitor → verify power state and current host
2. Migrate: vmware-aiops vm migrate my-vm --to-host esxi-02
3. Verify migration completed

VM Lifecycle

Guest Operations require VMware Tools running inside the guest OS. guest-exec-output auto-detects Linux/Windows shell and captures stdout/stderr.

Plan → Apply (Multi-step Operations)

For complex operations involving 2+ steps or 2+ VMs, use the plan/apply workflow instead of executing individually:

Plans stored in ~/.vmware-aiops/plans/, auto-deleted on success, auto-cleaned after 24h.

VM Deployment & Provisioning

Cluster Management

remove-host requires the host to be in maintenance mode first; the host is moved out of the cluster into the datacenter's host folder as a standalone host.

Alarm Management

Blast radius: vSphere has no per-alarm clear API. alarm reset uses AlarmManager.ClearTriggeredAlarms, which clears all triggered alarms matching the named alarm's entity type (host/VM/all) and current status (red/yellow) — not just the named one. The named alarm is looked up first (typos fail fast), and the output's scope field reports exactly what was cleared. Cleared alarms re-trigger automatically if their underlying condition persists.

Datastore Browser

Scheduled Scanning & Notifications

Safety Features

vCenter vs ESXi Comparison

Inventory, alarms, events, sensors, host services, and scanning are now in vmware-monitor.

What protects you

The table above lists two different surfaces and it is worth being blunt about which protections apply to which, because getting this wrong is worse than having no protection at all — a guardrail you believe in is one you stop compensating for.

On the CLI, a destructive command asks twice and takes no bypass flag, and --dry-run previews any write. That defends a mistyped command typed by a human. It does not defend against an agent, which satisfies both prompts with yes |.

Over MCP, there is no confirmation step at all. All 43 write tools — vm_delete, cluster_delete, vm_guest_exec among them — act on the first call. Seven host-networking and DRS tools take a confirm argument that defaults to a no-write preview, but that is a preview switch, not an approval gate: one more call is all it takes. This is deliberate. A confirmed= handshake was considered in July 2026 and cut, along with the earlier VMWARE_READ_ONLY switch, because neither was a real boundary — the switch was enforced on the MCP path only and any agent with a shell walked around it via the CLI, and a handshake is a speed-bump a model that intends to act steps over.

What actually decides whether a write lands is the vCenter/ESXi service account. Give the skill an account with the privileges the work needs and no more; vCenter refuses the rest itself, on every surface, with no way around it from inside this skill. To run an agent read-only, give it a read-only vCenter role — one decision, enforced where it is made. Every call is then recorded in ~/.vmware/audit.db before the caller sees a result, which is how you find out what happened.

vm_guest_exec is the one to think hardest about. It runs a caller-supplied command inside the guest OS with the credentials handed to it, which the documentation's own example makes root; nothing bounds what the command may be. The guest account is a separate authorization boundary from the vCenter one — a read-only vCenter role does not constrain what this tool does inside a VM. If you do not need guest operations, do not configure guest credentials.

The full inventory of which tools are gated and which are not is in references/capabilities.md, where the numbers are checked against the live tool registry by the test suite rather than maintained by hand.

Troubleshooting

"VM not found" error

VM names are case-sensitive in vSphere. Use exact name from vmware-monitor inventory vms.

Guest exec returns empty output

Use vm_guest_exec_output instead of vm_guest_exec — it auto-captures stdout/stderr. Basic vm_guest_exec only returns exit code.

Deploy OVA times out

Large OVA files (>10GB) may exceed the default 120s timeout. The upload happens via HTTP NFC lease — ensure network between the machine running vmware-aiops and ESXi is stable.

Plan apply fails mid-way

Run vmware-aiops plan list to see failed plan status. Ask user if they want to rollback with vm_rollback_plan. Irreversible steps (delete_vm) are skipped during rollback.

Connection refused / SSL error

1. Verify target is reachable: vmware-aiops doctor
2. For self-signed certs: set verify_ssl: false in config.yaml (lab environments only)

Supported AI Platforms

Platform Comparison

MCP Server Integrations

The vmware-aiops MCP server works with any MCP-compatible agent or tool. Ready-to-use configuration templates are in examples/mcp-configs/.

Xiaoguai (小怪) — a self-hostable, audit-first agent platform (Rust, single binary + embedded SQLite) from the same maintainer. It runs the vmware-aiops MCP server as one of its toolboxes; being both an MCP consumer and an MCP server, its HMAC-chained audit log and human-on-the-loop approval gates line up with this skill's own audit + confirm design. See its MCP integration guide.

Fully local operation (no cloud API required):

# Aider + Ollama + vmware-aiops (via SKILL.md)
aider --conventions skills/vmware-aiops/SKILL.md --model ollama/qwen2.5-coder:32b

# Any MCP agent + local model + vmware-aiops MCP server
# See examples/mcp-configs/ for your agent's config format

Installation

Step 0: Prerequisites

# Python 3.10+ required
python3 --version

# Node.js 18+ required for Gemini CLI and Codex CLI
node --version

Step 1: Clone & Install Python Backend

All platforms share the same Python backend.

git clone https://github.com/vmware-skills/VMware-AIops.git
cd VMware-AIops
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Step 2: Configure

mkdir -p ~/.vmware-aiops
cp config.example.yaml ~/.vmware-aiops/config.yaml
# Edit config.yaml with your vCenter/ESXi targets

Set passwords via .env file (recommended):

# Use the template
cp .env.example ~/.vmware-aiops/.env

# Edit and fill in your passwords, then lock permissions
chmod 600 ~/.vmware-aiops/.env

Security note: Prefer .env file over command-line export to avoid passwords appearing in shell history. The .env file should have chmod 600 (owner-only read/write).

Password environment variable naming convention:

VMWARE_{TARGET_NAME_UPPER}_PASSWORD
# Replace hyphens with underscores, UPPERCASE
# Example: target "home-esxi" → VMWARE_HOME_ESXI_PASSWORD
# Example: target "prod-vcenter" → VMWARE_PROD_VCENTER_PASSWORD

Security Best Practices

• NEVER hardcode passwords in scripts or config files
• NEVER pass passwords as command-line arguments (visible in ps)
• ALWAYS use ~/.vmware-aiops/.env with chmod 600
• ALWAYS configure connections via config.yaml — credentials are loaded from .env automatically
• Config File Contents: config.yaml stores target hostnames, ports, and a reference to the .env file. It does not contain passwords or tokens. All secrets are stored exclusively in .env
• TLS: Enabled by default. Disable only for ESXi hosts with self-signed certificates in isolated lab environments
• Webhook: Disabled by default. When enabled, sends monitoring summaries to your own configured URL only — payloads contain no credentials, IPs, or PII, only aggregated alert metadata. No data sent to third-party services
• Least Privilege: Use a dedicated vCenter service account with minimal permissions. For monitoring-only use cases, prefer the read-only VMware-Monitor
• Prompt Injection Protection: All vSphere-sourced content is truncated, stripped of control characters, and wrapped in boundary markers before output
• Code Review: We recommend reviewing the source code and commit history before deploying in production
• Production Safety: For production environments, use the read-only VMware-Monitor instead. AI agents can misinterpret context and execute unintended destructive operations — real-world incidents have shown that AI-driven infrastructure tools without proper isolation can delete production databases and entire environments. VMware-Monitor eliminates this risk at the code level: no destructive functions exist in its codebase

Step 3: Connect Your AI Tool

Choose one (or more) of the following:

Option A: Claude Code

Method 1: Skills.sh or ClawHub (recommended)

Either installer places the skill in Claude Code's skills directory for you:

npx skills add vmware-skills/VMware-AIops
# or
clawhub install @zw008/vmware-aiops

Method 2: Manual skill install

git clone https://github.com/vmware-skills/VMware-AIops.git
cd VMware-AIops

# Copy the skill into Claude Code's personal skills directory
mkdir -p ~/.claude/skills/vmware-aiops
cp -r skills/vmware-aiops/. ~/.claude/skills/vmware-aiops/

For tool access (not just skill context), also register the MCP server:

claude mcp add vmware-aiops -- vmware-aiops mcp

Restart Claude Code, then:

> Show me all VMs on esxi-lab.example.com

Submit to Official Marketplace

This plugin can also be submitted to the Anthropic official plugin directory for public discovery.

Option B: Gemini CLI

# Install Gemini CLI
npm install -g @google/gemini-cli

# Load the skill as project context (Gemini CLI reads GEMINI.md on startup)
cp skills/vmware-aiops/SKILL.md ./GEMINI.md

For tool access (not just context), register the MCP server in ~/.gemini/settings.json:

{
  "mcpServers": {
    "vmware-aiops": {
      "command": "vmware-aiops",
      "args": ["mcp"],
      "env": { "VMWARE_AIOPS_CONFIG": "~/.vmware-aiops/config.yaml" }
    }
  }
}

Then start Gemini CLI:

gemini
> Show me all VMs on my ESXi host

Option C: OpenAI Codex CLI

# Install Codex CLI
npm i -g @openai/codex
# Or on macOS:
# brew install --cask codex

# Copy skill to Codex skills directory
mkdir -p ~/.codex/skills/vmware-aiops
cp skills/vmware-aiops/SKILL.md ~/.codex/skills/vmware-aiops/SKILL.md

# Copy AGENTS.md to project root
cp skills/vmware-aiops/SKILL.md ./AGENTS.md

Then start Codex CLI:

codex --enable skills
> List all VMs on my ESXi

Option D: Aider (supports local models)

# Install Aider
pip install aider-chat

# Install Ollama for local models (optional)
# macOS:
brew install ollama
ollama pull qwen2.5-coder:32b

# Run with cloud API
aider --conventions skills/vmware-aiops/SKILL.md

# Or with local model via Ollama
aider --conventions skills/vmware-aiops/SKILL.md \
  --model ollama/qwen2.5-coder:32b

Option E: Continue CLI (supports local models)

# Install Continue CLI
npm i -g @continuedev/cli

# Copy rules file
mkdir -p .continue/rules
cp skills/vmware-aiops/SKILL.md .continue/rules/vmware-aiops.md

Configure ~/.continue/config.yaml for local model:

models:
  - name: local-coder
    provider: ollama
    model: qwen2.5-coder:32b

Then:

cn
> Check ESXi health and alarms

Option F: Trae IDE

Copy the rules file to your project's .trae/rules/ directory:

mkdir -p .trae/rules
cp skills/vmware-aiops/SKILL.md .trae/rules/project_rules.md

Trae IDE's Builder Mode reads .trae/rules/ Markdown files at startup.

Note: You can also install Claude Code extension in Trae IDE and use .claude/skills/ format directly.

Option G: Kimi Code CLI

# Copy skill file to Kimi skills directory
mkdir -p ~/.kimi/skills/vmware-aiops
cp skills/vmware-aiops/SKILL.md ~/.kimi/skills/vmware-aiops/SKILL.md

Option H: MCP Server (Glama / Claude Desktop)

The MCP server exposes VMware operations as tools via the Model Context Protocol. Works with any MCP-compatible client (Claude Desktop, Cursor, etc.).

After uv tool install vmware-aiops, start the MCP server with one command (v1.5.15+):

# Recommended — single command, no network re-resolve
vmware-aiops mcp

# With a custom config path
VMWARE_AIOPS_CONFIG=/path/to/config.yaml vmware-aiops mcp

Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "vmware-aiops": {
      "command": "vmware-aiops",
      "args": ["mcp"],
      "env": {
        "VMWARE_AIOPS_CONFIG": "/path/to/config.yaml"
      }
    }
  }
}

# Run without installing (requires PyPI access each launch)
uvx --from vmware-aiops vmware-aiops mcp

# Legacy entry point (still works, kept for backward compatibility)
vmware-aiops-mcp

Option I: Standalone CLI (no AI)

# Already installed in Step 1
source .venv/bin/activate

vmware-aiops vm power-on my-vm --target home-esxi
vmware-aiops deploy ova ./ubuntu.ova --name my-vm --target home-esxi
vmware-aiops datastore browse datastore1 --target home-esxi

Update / Upgrade

Already installed? Re-run the install command for your channel to get the latest version:

Check your current version: vmware-aiops --version

Chinese Cloud Models

For users in China who prefer domestic cloud APIs or have limited access to overseas services.

DeepSeek

Cost-effective, strong coding capability.

# Set DeepSeek API key (get from https://platform.deepseek.com)
export DEEPSEEK_API_KEY="your-key"

# Run with Aider
aider --conventions skills/vmware-aiops/SKILL.md \
  --model deepseek/deepseek-coder

Persistent config ~/.aider.conf.yml:

model: deepseek/deepseek-coder
conventions: skills/vmware-aiops/SKILL.md

Qwen (Alibaba Cloud)

Alibaba Cloud's coding model, free tier available.

# Set DashScope API key (get from https://dashscope.console.aliyun.com)
export DASHSCOPE_API_KEY="your-key"

aider --conventions skills/vmware-aiops/SKILL.md \
  --model qwen/qwen-coder-plus

Or via OpenAI-compatible endpoint:

export OPENAI_API_BASE="https://dashscope.aliyuncs.com/compatible-mode/v1"
export OPENAI_API_KEY="your-dashscope-key"

aider --conventions skills/vmware-aiops/SKILL.md \
  --model qwen-coder-plus-latest

Doubao (ByteDance)

export OPENAI_API_BASE="https://ark.cn-beijing.volces.com/api/v3"
export OPENAI_API_KEY="your-ark-key"

aider --conventions skills/vmware-aiops/SKILL.md \
  --model your-doubao-endpoint-id

With Continue CLI

Configure ~/.continue/config.yaml:

# DeepSeek
models:
  - name: deepseek-coder
    provider: openai-compatible
    apiBase: https://api.deepseek.com/v1
    apiKey: your-deepseek-key
    model: deepseek-coder

# Qwen
models:
  - name: qwen-coder
    provider: openai-compatible
    apiBase: https://dashscope.aliyuncs.com/compatible-mode/v1
    apiKey: your-dashscope-key
    model: qwen-coder-plus-latest

Local Models (Aider + Ollama)

For fully offline operation — no cloud API, no internet, full privacy.

Aider + Ollama + local Qwen/DeepSeek is ideal for air-gapped environments.

Step 1: Install Ollama

# macOS
brew install ollama

# Linux — download from https://ollama.com/download and install manually
# See https://github.com/ollama/ollama for platform-specific instructions

Step 2: Pull a model

Hardware: 32B → ~20GB VRAM (or 32GB RAM for CPU). 7B → 8GB RAM.

Step 3: Run with Aider

pip install aider-chat
ollama serve

# Aider + local Qwen (recommended)
aider --conventions skills/vmware-aiops/SKILL.md \
  --model ollama/qwen2.5-coder:32b

# Aider + local DeepSeek
aider --conventions skills/vmware-aiops/SKILL.md \
  --model ollama/deepseek-coder-v2

# Low-memory option
aider --conventions skills/vmware-aiops/SKILL.md \
  --model ollama/qwen2.5-coder:7b

Persistent config ~/.aider.conf.yml:

model: ollama/qwen2.5-coder:32b
conventions: skills/vmware-aiops/SKILL.md

Local Architecture

User → Aider CLI → Ollama (localhost:11434) → Qwen / DeepSeek local model
  │                                                    ↓
  │                                          reads AGENTS.md instructions
  │                                                    ↓
  └──────────────────────────────→ vmware-aiops CLI ──→ ESXi / vCenter

Tip: Local models are fully offline — perfect for air-gapped environments or strict data compliance.

CLI Reference

# Diagnostics
vmware-aiops doctor                   # Check environment, config, connectivity
vmware-aiops doctor --skip-auth       # Skip vSphere auth check (faster)

# MCP Config Generator
vmware-aiops mcp-config generate --agent goose        # Generate config for Goose
vmware-aiops mcp-config generate --agent claude-code  # Generate config for Claude Code
vmware-aiops mcp-config list                          # List all supported agents

# VM operations
vmware-aiops vm power-on my-vm                                 # Power on
vmware-aiops vm power-off my-vm                                # Graceful shutdown (2x confirm)
vmware-aiops vm power-off my-vm --force                        # Force power off (2x confirm)
vmware-aiops vm create my-new-vm --cpu 4 --memory 8192 --disk 100  # Create VM
vmware-aiops vm delete my-vm --confirm                         # Delete VM (2x confirm)
vmware-aiops vm reconfigure my-vm --cpu 4 --memory 8192        # Reconfigure (2x confirm)
vmware-aiops vm snapshot-create my-vm --name "before-upgrade"  # Create snapshot
vmware-aiops vm snapshot-list my-vm                            # List snapshots
vmware-aiops vm snapshot-revert my-vm --name "before-upgrade"  # Revert snapshot
vmware-aiops vm snapshot-delete my-vm --name "before-upgrade"  # Delete snapshot (waits ≤30 min for consolidation)
vmware-aiops vm snapshot-delete my-vm --name "old-big" --no-wait  # Fire async, return a task id
vmware-aiops vm task-status task-1234                          # Poll an async task by id
vmware-aiops vm clone my-vm --new-name my-vm-clone             # Clone VM
vmware-aiops vm migrate my-vm --to-host esxi-02                # vMotion
vmware-aiops vm set-ttl my-vm --minutes 60                     # Auto-delete in 60 min
vmware-aiops vm cancel-ttl my-vm                               # Cancel TTL
vmware-aiops vm list-ttl                                       # Show all TTLs
vmware-aiops vm clean-slate my-vm --snapshot baseline          # Revert to baseline (2x confirm)

# Guest Operations (requires VMware Tools in guest)
vmware-aiops vm guest-exec my-vm --cmd /bin/bash --args "-c 'whoami'" --user root
vmware-aiops vm guest-upload my-vm --local ./script.sh --guest /tmp/script.sh --user root
vmware-aiops vm guest-download my-vm --guest /var/log/syslog --local ./syslog.txt --user root

# Plan → Apply (multi-step operations)
vmware-aiops plan list                                        # List pending/failed plans

# Deploy
vmware-aiops deploy ova ./ubuntu.ova --name my-vm --datastore ds1      # Deploy from OVA
vmware-aiops deploy template golden-ubuntu --name new-vm               # Deploy from template
vmware-aiops deploy linked-clone --source base-vm --snapshot clean --name test-vm  # Linked clone (seconds)
vmware-aiops deploy iso my-vm --iso "[datastore1] iso/ubuntu-22.04.iso"  # Attach ISO
vmware-aiops deploy mark-template golden-vm                            # Convert VM to template
vmware-aiops deploy batch-clone --source base-vm --count 5 --prefix lab  # Batch clone
vmware-aiops deploy batch deploy.yaml                                  # Batch deploy from YAML spec

# Cluster
vmware-aiops cluster info my-cluster                                   # Cluster details (HA/DRS status)
vmware-aiops cluster create my-cluster --ha --drs                      # Create cluster with HA+DRS
vmware-aiops cluster delete my-cluster                                 # Delete cluster (2x confirm)
vmware-aiops cluster add-host my-cluster --host esxi-03                # Add host to cluster (2x confirm)
vmware-aiops cluster remove-host my-cluster --host esxi-03             # Remove host (2x confirm)
vmware-aiops cluster configure my-cluster --ha --drs                   # Configure HA/DRS (2x confirm)

# Alarm management
vmware-aiops alarm list                                                # List triggered alarms
vmware-aiops alarm acknowledge esxi-01 "Host memory usage"             # Acknowledge alarm
vmware-aiops alarm reset esxi-01 "Host memory usage"                   # Clear alarms (2x confirm; clears ALL matching entity type + status)

# Datastore (browse and scan only — iSCSI/vSAN moved to vmware-storage)
vmware-aiops datastore browse datastore1 --path "iso/"                 # Browse datastore
vmware-aiops datastore scan-images --target home-esxi                  # Scan all datastores for images

# Scan
vmware-aiops scan now              # One-time scan

# Daemon
vmware-aiops daemon start          # Start scanner
vmware-aiops daemon status         # Check status
vmware-aiops daemon stop           # Stop daemon

# Companion skills for other operations:
#   vmware-monitor: inventory, alarms, events, sensors
#   vmware-storage: datastores, iSCSI, vSAN
#   vmware-vks:     Tanzu/TKC cluster lifecycle

Configuration

See config.example.yaml for all options.

Project Structure

VMware-AIops/
├── skills/                        # Skills index (npx skills add)
│   └── vmware-aiops/
│       ├── SKILL.md               # Slimmed-down skill (progressive disclosure)
│       └── references/            # Detailed docs loaded on-demand
│           ├── capabilities.md    # Full capabilities tables
│           ├── cli-reference.md   # Complete CLI reference
│           └── setup-guide.md     # Install, security, AI platforms
├── vmware_aiops/                  # Python backend
│   ├── config.py                  # YAML + .env config
│   ├── connection.py              # Multi-target pyVmomi
│   ├── cli/                       # Typer CLI (double confirm)
│   ├── ops/                       # Operations
│   │   ├── inventory.py           # VMs, hosts, datastores, clusters
│   │   ├── health.py              # Alarms, events, sensors
│   │   ├── vm_lifecycle.py        # VM CRUD, snapshots, clone, migrate
│   │   ├── vm_deploy.py           # OVA, template, linked clone, batch deploy
│   │   └── datastore_browser.py   # Datastore browsing, image discovery
│   ├── scanner/                   # Log scanning daemon
│   ├── notify/                    # Notifications (JSONL + webhook)
│   └── mcp_server/                # MCP server wrapper
│       ├── server.py              # FastMCP server with tools
│       └── __main__.py
├── examples/mcp-configs/          # MCP client config templates
├── tests/                         # Test suite
├── smithery.yaml                  # Smithery marketplace config
├── RELEASE_NOTES.md
├── config.example.yaml
└── pyproject.toml

API Coverage

Built on pyVmomi (vSphere Web Services API / SOAP).

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Troubleshooting & Contributing

If you encounter any errors or issues, please send the error message, logs, or screenshots to zhouwei008@gmail.com. Contributions are welcome — feel free to join us in maintaining and improving this project!

License

MIT