vmware-aiops 1.1.0

VMware vCenter/ESXi AI-powered monitoring and operations tool

VMware AIops

English | 中文

AI-powered VMware vCenter/ESXi monitoring and operations tool.

Need read-only monitoring only? See VMware-Monitor — an independent repository with code-level safety (zero destructive code in the codebase).

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

Claude Code Plugin Install

# Add marketplace
/plugin marketplace add zw008/VMware-AIops

# Install plugin
/plugin install vmware-ops

# Use the skill
/vmware-ops:vmware-aiops

---

Capabilities Overview

CLI vs MCP: Which Mode to Use

Scenario	Recommended	Why
Local/small models (Ollama, Qwen <32B)	CLI	~2K tokens context vs ~10K for MCP; small models struggle with 31 tool schemas
Token-sensitive workflows	CLI	SKILL.md + Bash tool = minimal overhead
Cloud models (Claude, GPT-4o)	Either	Both work; MCP gives structured JSON I/O
Automated pipelines / Agent chaining	MCP	Type-safe parameters, structured output, no shell parsing

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

vSphere Version	Support	Notes
8.0 / 8.0U1-U3	✅ Full	CreateSnapshot_Task deprecated → use CreateSnapshotEx_Task
7.0 / 7.0U1-U3	✅ Full	All APIs supported
6.7	✅ Compatible	Backward-compatible, tested
6.5	✅ Compatible	Backward-compatible, tested

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

1. Inventory

Feature	vCenter	ESXi	Details
List VMs	✅	✅	Name, power state, CPU, memory, guest OS, IP
List Hosts	✅	⚠️ Self only	CPU cores, memory, ESXi version, VM count, uptime
List Datastores	✅	✅	Capacity, free/used, type (VMFS/NFS), usage %
List Clusters	✅	❌	Host count, DRS/HA status
List Networks	✅	✅	Network name, associated VM count

2. Health & Monitoring

Feature	vCenter	ESXi	Details
Active Alarms	✅	✅	Severity, alarm name, entity, timestamp
Event/Log Query	✅	✅	Filter by time range (--hours), severity level; 50+ event types
Hardware Sensors	✅	✅	Temperature, voltage, fan status
Host Services	✅	✅	hostd, vpxa, etc. running/stopped

Monitored Event Types:

Category	Events
VM Failures	VmFailedToPowerOnEvent, VmDiskFailedEvent, VmFailoverFailed
Host Issues	HostConnectionLostEvent, HostShutdownEvent, HostIpChangedEvent
Storage	DatastoreCapacityIncreasedEvent, NASDatastoreEvent, SCSI high latency
HA/DRS	DasHostFailedEvent, DrsVmMigratedEvent, DrsSoftRuleViolationEvent
Auth	UserLoginSessionEvent, BadUsernameSessionEvent

3. VM Lifecycle

Operation	Command	Confirmation	vCenter	ESXi
Power On	vm power-on <name>	—	✅	✅
Graceful Shutdown	vm power-off <name>	Double	✅	✅
Force Power Off	vm power-off <name> --force	Double	✅	✅
Reset	vm reset <name>	—	✅	✅
Suspend	vm suspend <name>	—	✅	✅
VM Info	vm info <name>	—	✅	✅
Create VM	vm create <name> --cpu --memory --disk	—	✅	✅
Delete VM	vm delete <name>	Double	✅	✅
Reconfigure	vm reconfigure <name> --cpu --memory	Double	✅	✅
Create Snapshot	vm snapshot-create <name> --name <snap>	—	✅	✅
List Snapshots	vm snapshot-list <name>	—	✅	✅
Revert Snapshot	vm snapshot-revert <name> --name <snap>	—	✅	✅
Delete Snapshot	vm snapshot-delete <name> --name <snap>	—	✅	✅
Clone VM	vm clone <name> --new-name <new>	—	✅	✅
vMotion	vm migrate <name> --to-host <host>	—	✅	❌
Set TTL	vm set-ttl <name> --minutes <n>	—	✅	✅
Cancel TTL	vm cancel-ttl <name>	—	✅	✅
List TTLs	vm list-ttl	—	✅	✅
Clean Slate	vm clean-slate <name> [--snapshot baseline]	Double	✅	✅
Guest Exec	vm guest-exec <name> --cmd /bin/bash --args "..."	—	✅	✅
Guest Upload	vm guest-upload <name> --local f.sh --guest /tmp/f.sh	—	✅	✅
Guest Download	vm guest-download <name> --guest /var/log/syslog --local ./syslog	—	✅	✅

Guest Operations require VMware Tools running inside the guest OS.

Plan → Apply (Multi-step Operations)

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

Step	What Happens
1. Create Plan	AI calls vm_create_plan — validates actions, checks targets in vSphere, generates plan with rollback info
2. Review	AI shows plan to user: steps, affected VMs, irreversible warnings
3. Apply	vm_apply_plan executes sequentially; stops on failure
4. Rollback (if failed)	Asks user whether to rollback, then vm_rollback_plan reverses executed steps (irreversible steps skipped)

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

4. VM Deployment & Provisioning

Operation	Command	Speed	vCenter	ESXi
Deploy from OVA	deploy ova <path> --name <vm>	Minutes	✅	✅
Deploy from Template	deploy template <tmpl> --name <vm>	Minutes	✅	✅
Linked Clone	deploy linked-clone --source <vm> --snapshot <snap> --name <new>	Seconds	✅	✅
Attach ISO	deploy iso <vm> --iso "[ds] path/to.iso"	Instant	✅	✅
Convert to Template	deploy mark-template <vm>	Instant	✅	✅
Batch Clone	deploy batch-clone --source <vm> --count <n>	Minutes	✅	✅
Batch Deploy (YAML)	deploy batch spec.yaml	Auto	✅	✅

5. Datastore Browser

Feature	vCenter	ESXi	Details
Browse Files	✅	✅	List files/folders in any datastore path
Scan Images	✅	✅	Discover ISO, OVA, OVF, VMDK across all datastores
Local Cache	✅	✅	Registry at ~/.vmware-aiops/image_registry.json

6. Scheduled Scanning & Notifications

Feature	Details
Daemon	APScheduler-based, configurable interval (default 15 min)
Multi-target Scan	Sequentially scan all configured vCenter/ESXi targets
Scan Content	Alarms + Events + Host logs (hostd, vmkernel, vpxd)
Log Analysis	Regex pattern matching: error, fail, critical, panic, timeout, corrupt
Structured Log	JSONL output to ~/.vmware-aiops/scan.log
Webhook	Slack, Discord, or any HTTP endpoint
Daemon Management	daemon start/stop/status, PID file, graceful shutdown

7. Safety Features

Feature	Details
Dry-Run Mode	--dry-run on any destructive command prints exact API calls without executing
Plan → Confirm → Execute → Log	Structured workflow: show current state, confirm changes, execute, audit log
Double Confirmation	All destructive ops (power-off, delete, reconfigure, snapshot-revert/delete, clone, migrate) require 2 sequential confirmations — no bypass flags
Rejection Logging	Declined confirmations are recorded in the audit trail
Audit Trail	All operations logged to ~/.vmware-aiops/audit.log (JSONL) with before/after state
Input Validation	VM name, CPU (1-128), memory (128-1048576 MB), disk (1-65536 GB) validated
Password Protection	.env file loading with permission check; never in shell history
SSL Self-signed Support	disableSslCertValidation — only for ESXi with self-signed certs in isolated labs; production should use CA-signed certificates
Prompt Injection Protection	vSphere event messages and host logs are truncated, stripped of control characters, and wrapped in boundary markers before output
Webhook Data Scope	Sends notifications to user-configured URLs only — no third-party services by default
Task Waiting	All async operations wait for completion and report result
State Validation	Pre-operation checks (VM exists, power state correct)

8. vSAN Management

Feature	Details
Health Check	Cluster-wide health summary, per-group test results
Capacity	Total/free/used capacity with projections
Disk Groups	Cache SSD + capacity disks per host
Performance	IOPS, latency, throughput per cluster/host/VM

Requires pyVmomi 8.0.3+ (vSAN SDK merged). For older versions, install the standalone vSAN Management SDK.

9. Aria Operations / VCF Operations

Feature	Details
Historical Metrics	Time-series CPU, memory, disk, network with months of history
Anomaly Detection	ML-based dynamic baselines and anomaly alerts
Capacity Planning	What-if analysis, time-to-exhaustion, forecasting
Right-sizing	CPU/memory recommendations per VM
Intelligent Alerts	Root cause analysis, remediation recommendations

REST API at /suite-api/. Auth: vRealizeOpsToken. Rebranded as VCF Operations in VCF 9.0.

10. vSphere Kubernetes Service (VKS)

Feature	Details
List Clusters	Tanzu Kubernetes clusters with phase status
Cluster Health	InfrastructureReady, ControlPlaneAvailable, WorkersAvailable conditions
Scale Workers	Adjust MachineDeployment replicas
Node Status	Machine status, ready/unhealthy counts

Kubernetes-native API via kubectl/kubeconfig. VKS 3.6+ uses Cluster API specification.

11. vCenter vs ESXi Comparison

Capability	vCenter	ESXi Standalone
Full cluster inventory	✅	❌ Single host only
DRS/HA management	✅	❌
vMotion migration	✅	❌
Cross-host clone	✅	❌
All VM lifecycle ops	✅	✅
OVA/Template/Linked Clone deploy	✅	✅
Datastore browsing & image scan	✅	✅
Alarms & events	✅	✅
Hardware sensors	✅	✅
Host services	✅	✅
Snapshots	✅	✅
Scheduled scanning	✅	✅

---

Supported AI Platforms

Platform	Status	Config File	AI Model
Claude Code	✅ Native Skill	skills/vmware-aiops/SKILL.md	Anthropic Claude
Gemini CLI	✅ Extension	gemini-extension/GEMINI.md	Google Gemini
OpenAI Codex CLI	✅ Skill + AGENTS.md	codex-skill/AGENTS.md	OpenAI GPT
Aider	✅ Conventions	codex-skill/AGENTS.md	Any (cloud + local)
Continue CLI	✅ Rules	codex-skill/AGENTS.md	Any (cloud + local)
Trae IDE	✅ Rules	trae-rules/project_rules.md	Claude/DeepSeek/GPT-4o/Doubao
Kimi Code CLI	✅ Skill	kimi-skill/SKILL.md	Moonshot Kimi
MCP Server	✅ MCP Protocol	mcp_server/	Any MCP client
Python CLI	✅ Standalone	N/A	N/A

Platform Comparison

Feature	Claude Code	Gemini CLI	Codex CLI	Aider	Continue	Trae IDE	Kimi CLI
Cloud AI	Anthropic	Google	OpenAI	Any	Any	Multi	Moonshot
Local models	—	—	—	Ollama	Ollama	—	—
Skill system	SKILL.md	Extension	SKILL.md	—	Rules	Rules	SKILL.md
MCP support	Native	Native	Via Skills	Third-party	Native	—	—
Free tier	—	60 req/min	—	Self-hosted	Self-hosted	—	—

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/.

Agent / Tool	Local Model Support	Config Template	Integration Guide
Goose	✅ Ollama, LM Studio	goose.json	Guide
LocalCowork	✅ Fully offline	localcowork.json	Guide
mcp-agent	✅ Ollama, vLLM	mcp-agent.yaml	Guide
VS Code Copilot	—	vscode-copilot.json	Guide
Cursor	—	cursor.json	—
Continue	✅ Ollama	continue.yaml	Guide
Claude Code	—	claude-code.json	—

Fully local operation (no cloud API required):

# Aider + Ollama + vmware-aiops (via AGENTS.md)
aider --conventions codex-skill/AGENTS.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 (Marketplace)

Method 1: Marketplace (recommended)

In Claude Code, run:

/plugin marketplace add zw008/VMware-AIops
/plugin install vmware-ops

Then use:

/vmware-ops:vmware-aiops
> Show me all VMs on esxi-lab.example.com

Method 2: Local install

# Clone and symlink
git clone https://github.com/zw008/VMware-AIops.git
ln -sf $(pwd)/VMware-AIops ~/.claude/plugins/marketplaces/vmware-aiops

# Register marketplace
python3 -c "
import json, pathlib
f = pathlib.Path.home() / '.claude/plugins/known_marketplaces.json'
d = json.loads(f.read_text()) if f.exists() else {}
d['vmware-aiops'] = {
    'source': {'source': 'github', 'repo': 'zw008/VMware-AIops'},
    'installLocation': str(pathlib.Path.home() / '.claude/plugins/marketplaces/vmware-aiops')
}
f.write_text(json.dumps(d, indent=2))
"

# Enable plugin
python3 -c "
import json, pathlib
f = pathlib.Path.home() / '.claude/settings.json'
d = json.loads(f.read_text()) if f.exists() else {}
d.setdefault('enabledPlugins', {})['vmware-ops@vmware-aiops'] = True
f.write_text(json.dumps(d, indent=2))
"

Restart Claude Code, then:

/vmware-ops:vmware-aiops

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

# Install the extension from the cloned repo
gemini extensions install ./gemini-extension

# Or install directly from GitHub
# gemini extensions install https://github.com/zw008/VMware-AIops

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 codex-skill/SKILL.md ~/.codex/skills/vmware-aiops/SKILL.md

# Copy AGENTS.md to project root
cp codex-skill/AGENTS.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 codex-skill/AGENTS.md

# Or with local model via Ollama
aider --conventions codex-skill/AGENTS.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 codex-skill/AGENTS.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 trae-rules/project_rules.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 kimi-skill/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.).

# Run directly
python -m mcp_server

# Or via the installed entry point
vmware-aiops-mcp

# With a custom config path
VMWARE_AIOPS_CONFIG=/path/to/config.yaml python -m mcp_server

Claude Desktop config (claude_desktop_config.json):

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

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 inventory vms --target home-esxi
vmware-aiops health alarms --target home-esxi
vmware-aiops vm power-on my-vm --target home-esxi

---

Update / Upgrade

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

Install Channel	Update Command
ClawHub	clawhub install vmware-aiops
Skills.sh	npx skills add zw008/VMware-AIops
Claude Code Plugin	/plugin marketplace add zw008/VMware-AIops
Git clone	cd VMware-AIops && git pull origin main && uv pip install -e .
uv	uv tool install vmware-aiops --force

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 codex-skill/AGENTS.md \
  --model deepseek/deepseek-coder

Persistent config ~/.aider.conf.yml:

model: deepseek/deepseek-coder
conventions: codex-skill/AGENTS.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 codex-skill/AGENTS.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 codex-skill/AGENTS.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 codex-skill/AGENTS.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

Model	Command	Size	Note
Qwen 2.5 Coder 32B	ollama pull qwen2.5-coder:32b	~20GB	Best local coding model
Qwen 2.5 Coder 7B	ollama pull qwen2.5-coder:7b	~4.5GB	Low-memory option
DeepSeek Coder V2	ollama pull deepseek-coder-v2	~8.9GB	Strong reasoning
CodeLlama 34B	ollama pull codellama:34b	~19GB	Meta coding 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 codex-skill/AGENTS.md \
  --model ollama/qwen2.5-coder:32b

# Aider + local DeepSeek
aider --conventions codex-skill/AGENTS.md \
  --model ollama/deepseek-coder-v2

# Low-memory option
aider --conventions codex-skill/AGENTS.md \
  --model ollama/qwen2.5-coder:7b

Persistent config ~/.aider.conf.yml:

model: ollama/qwen2.5-coder:32b
conventions: codex-skill/AGENTS.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

# Inventory
vmware-aiops inventory vms                          # List VMs
vmware-aiops inventory vms --limit 10 --sort-by memory_mb  # Top 10 VMs by memory
vmware-aiops inventory vms --power-state poweredOn  # Only powered-on VMs
vmware-aiops inventory hosts --target prod-vcenter  # List hosts
vmware-aiops inventory datastores                   # List datastores
vmware-aiops inventory clusters                     # List clusters

# Health
vmware-aiops health alarms                                # Active alarms
vmware-aiops health events --hours 24 --severity warning  # Recent events

# VM operations
vmware-aiops vm info my-vm                                     # VM details
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
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

# Datastore
vmware-aiops datastore browse datastore1 --path "iso/"                 # Browse datastore
vmware-aiops datastore scan-images --target home-esxi                  # Scan all datastores for images
vmware-aiops datastore images --type iso                               # List cached 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

# vSAN
vmware-aiops vsan health [--target prod-vcenter]                  # vSAN health
vmware-aiops vsan capacity [--target prod-vcenter]                # vSAN capacity
vmware-aiops vsan disks [--target prod-vcenter]                   # Disk groups
vmware-aiops vsan performance [--hours 1] [--target prod-vcenter] # Performance

# Aria Operations / VCF Operations
vmware-aiops ops alerts [--severity critical]                     # Intelligent alerts
vmware-aiops ops metrics <resource-name> [--hours 24]             # Time-series metrics
vmware-aiops ops recommendations [--target prod-vcenter]          # Right-sizing
vmware-aiops ops capacity <cluster-name>                          # Capacity planning

# vSphere Kubernetes Service (VKS)
vmware-aiops vks clusters [--namespace default]                   # List K8s clusters
vmware-aiops vks health <cluster-name>                            # Cluster health
vmware-aiops vks scale <machine-deployment> --replicas <n>        # Scale workers
vmware-aiops vks nodes <cluster-name>                             # Node status

---

Configuration

See config.example.yaml for all options.

Section	Key	Default	Description
targets	name	—	Friendly name
targets	host	—	vCenter/ESXi hostname or IP
targets	type	vcenter	vcenter or esxi
targets	port	443	Connection port
targets	verify_ssl	false	SSL certificate verification
scanner	interval_minutes	15	Scan frequency
scanner	severity_threshold	warning	Min severity: critical/warning/info
scanner	lookback_hours	1	How far back to scan
scanner	log_types	[vpxd, hostd, vmkernel]	Log sources
notify	log_file	~/.vmware-aiops/scan.log	JSONL log output
notify	webhook_url	—	Webhook endpoint (Slack, Discord, etc.)

---

Project Structure

VMware-AIops/
├── .claude-plugin/                # Claude Code marketplace manifest
│   └── marketplace.json
├── plugins/                       # Claude Code plugin
│   └── vmware-ops/
│       ├── .claude-plugin/
│       │   └── plugin.json
│       └── skills/
│           └── vmware-aiops/
│               └── SKILL.md       # Full operations skill
├── skills/                        # Skills index (npx skills add)
│   └── vmware-aiops/
│       └── SKILL.md
├── vmware_aiops/                  # Python backend
│   ├── config.py                  # YAML + .env config
│   ├── connection.py              # Multi-target pyVmomi
│   ├── cli.py                     # 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)
├── gemini-extension/              # Gemini CLI extension
│   ├── gemini-extension.json
│   └── GEMINI.md
├── codex-skill/                   # Codex + Aider + Continue
│   ├── SKILL.md
│   └── AGENTS.md
├── trae-rules/                    # Trae IDE rules
│   └── project_rules.md
├── kimi-skill/                    # Kimi Code CLI skill
│   └── SKILL.md
├── mcp_server/                    # MCP server wrapper
│   ├── server.py                  # FastMCP server with tools
│   └── __main__.py
├── smithery.yaml                  # Smithery marketplace config
├── RELEASE_NOTES.md
├── config.example.yaml
└── pyproject.toml

API Coverage

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

API Object	Usage
vim.VirtualMachine	VM lifecycle, snapshots, clone, migrate
vim.HostSystem	ESXi host info, sensors, services
vim.Datastore	Storage capacity, type, accessibility
vim.host.DatastoreBrowser	File browsing, image discovery (ISO/OVA/VMDK)
vim.OvfManager	OVA import and deployment
vim.ClusterComputeResource	Cluster, DRS, HA
vim.Network	Network listing
vim.alarm.AlarmManager	Active alarm monitoring
vim.event.EventManager	Event/log queries

Related Projects

Project	Description	Install
VMware-Monitor	Read-only monitoring — code-level enforced safety, zero destructive operations	clawhub install vmware-monitor
VMware-AIops (this repo)	Full AI-powered operations — monitoring + VM lifecycle management	clawhub install vmware-aiops

Choosing between them: Use VMware-Monitor if you only need read-only monitoring with zero risk of accidental changes. Use VMware-AIops if you need full operations (create, delete, power, snapshot, clone, migrate).

---

Troubleshooting & Contributing

If you encounter any errors or issues, please send the error message, logs, or screenshots to [e-mail]. Contributions are welcome — feel free to join us in maintaining and improving this project!

License

MIT