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 zw008/VMware-AIops

# Via ClawHub
clawhub install 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.

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 disableSslCertValidation: true 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/zw008/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 zw008/VMware-AIops
# or
clawhub install vmware-aiops

Method 2: Manual skill install

git clone https://github.com/zw008/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 (Smithery / 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

Install via Smithery:

npx -y @smithery/cli install @zw008/VMware-AIops --client claude

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).

Related Projects

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