VMware-AIops
The VMware-AIops server provides AI-powered VMware vCenter/ESXi management with tools spanning VM lifecycle, deployment, guest operations, cluster management, and monitoring.
Inventory & Monitoring:
List VMs (power state, CPU, memory, guest OS, IP), ESXi hosts, datastores, clusters, active alarms, and recent events
Get detailed VM info (CPU, memory, disks, NICs, snapshots)
VM Lifecycle Management:
Power on/off (graceful or forced), suspend, reset, delete, reconfigure VMs
Create, list, revert, and delete snapshots
Clone/migrate VMs (vMotion), set/cancel VM Time-To-Live (TTL) for auto-cleanup, and perform "clean slate" revert operations
VM Deployment & Provisioning:
Deploy VMs from OVA files, vSphere templates, or linked clones from snapshots
Attach ISOs to VM CD-ROM drives; convert powered-off VMs to vSphere templates
Batch Operations:
Batch clone multiple VMs from a gold image (full or linked clones)
Batch deploy VMs from a YAML specification file (clone, template, linked clone, OVA, or empty VM)
Datastore Management:
Browse datastore files/folders and scan for deployable images (OVA/ISO/OVF/VMDK) with local registry caching
Cluster Management:
Create/delete clusters, add/remove hosts, configure HA/DRS settings
Guest Operations:
Execute commands inside guest OS, upload/download files (requires VMware Tools)
Multi-step Plan/Apply Workflow:
Create, review, apply, and optionally roll back complex multi-step operations
Scheduled Scanning & Notifications:
Background daemon periodically scans for alarms, events, and host logs, with configurable severity thresholds and webhook notifications
Safety Features:
Dry-run mode, double confirmation for destructive operations, audit logging, input validation, secure credential handling, and prompt injection protection
Multi-target Support:
Operate across multiple vCenter/ESXi targets with configurable connection settings
Supports sending automated notifications, monitoring alerts, and scan logs to Discord channels via webhooks.
Provides tools for managing vSphere Kubernetes Service (VKS), including listing Tanzu clusters, checking node status, and scaling worker nodes.
Supports sending automated notifications, monitoring alerts, and scan logs to Slack channels via webhooks.
Enables comprehensive management of vCenter and ESXi environments, including VM lifecycle operations, vSAN health monitoring, hardware sensor reporting, and datastore browsing.
VMware AIops
English | 中文
AI-powered VMware vCenter/ESXi VM lifecycle and deployment tool — 31 tools across 6 categories.
Companion skills handle everything else:
Skill
Scope
Install
Read-only: inventory, health, alarms, events, metrics
uv tool install vmware-monitorDatastores, iSCSI, vSAN management
uv tool install vmware-storageTanzu Namespaces, TKC cluster lifecycle
uv tool install vmware-vksNeed read-only monitoring only? Use VMware-Monitor — 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-aiopsPyPI 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/simpleClaude Code Plugin Install
# Add marketplace
/plugin marketplace add zw008/VMware-AIops
# Install plugin
/plugin install vmware-ops
# Use the skill
/vmware-ops:vmware-aiopsCapabilities Overview
What This Skill Does
Category | Tools | Count |
VM Lifecycle | power on/off, TTL auto-delete, clean slate | 6 |
Deployment | OVA, template, linked clone, batch clone/deploy | 8 |
Guest Ops | exec commands, upload/download files, provision | 5 |
Plan/Apply | multi-step planning with rollback | 4 |
Cluster | create, delete, HA/DRS config, add/remove hosts | 6 |
Datastore | browse files, scan for images | 2 |
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 many 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 |
Monitoring / storage / K8s | Companion skills |
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 ──→ VMVersion Compatibility
vSphere Version | Support | Notes |
8.0 / 8.0U1-U3 | ✅ Full |
|
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.
Common Workflows
Deploy a Lab Environment
Browse datastore for OVA images →
vmware-aiops datastore browse <ds> --pattern "*.ova"Deploy VM from OVA →
vmware-aiops deploy ova ./image.ova --name lab-vm --datastore ds1Install software inside VM →
vmware-aiops vm guest-exec lab-vm --cmd /bin/bash --args "-c 'apt-get install -y nginx'" --user rootCreate baseline snapshot →
vmware-aiops vm snapshot-create lab-vm --name baselineSet TTL for auto-cleanup →
vmware-aiops vm set-ttl lab-vm --minutes 480
Batch Clone for Testing
Create plan:
vm_create_planwith multiple clone + reconfigure stepsReview plan with user (shows affected VMs, irreversible warnings)
Apply:
vm_apply_planexecutes sequentially, stops on failureIf failed:
vm_rollback_planreverses executed stepsSet TTL on all clones for auto-cleanup
Migrate VM to Another Host
Check VM info via
vmware-monitor→ verify power state and current hostMigrate:
vmware-aiops vm migrate my-vm --to-host esxi-02Verify migration completed
VM Lifecycle
Operation | Command | Confirmation | vCenter | ESXi |
Power On |
| — | ✅ | ✅ |
Graceful Shutdown |
| Double | ✅ | ✅ |
Force Power Off |
| Double | ✅ | ✅ |
Reset |
| — | ✅ | ✅ |
Suspend |
| — | ✅ | ✅ |
Create VM |
| — | ✅ | ✅ |
Delete VM |
| Double | ✅ | ✅ |
Reconfigure |
| Double | ✅ | ✅ |
Create Snapshot |
| — | ✅ | ✅ |
List Snapshots |
| — | ✅ | ✅ |
Revert Snapshot |
| — | ✅ | ✅ |
Delete Snapshot |
| — | ✅ | ✅ |
Clone VM |
| — | ✅ | ✅ |
vMotion |
| — | ✅ | ❌ |
Set TTL |
| — | ✅ | ✅ |
Cancel TTL |
| — | ✅ | ✅ |
List TTLs |
| — | ✅ | ✅ |
Clean Slate |
| Double | ✅ | ✅ |
Guest Exec |
| — | ✅ | ✅ |
Guest Exec (with output) |
| — | ✅ | ✅ |
Guest Upload |
| — | ✅ | ✅ |
Guest Download |
| — | ✅ | ✅ |
Guest Operations require VMware Tools running inside the guest OS.
guest-exec-outputauto-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:
Step | What Happens |
1. Create Plan | AI calls |
2. Review | AI shows plan to user: steps, affected VMs, irreversible warnings |
3. Apply |
|
4. Rollback (if failed) | Asks user whether to rollback, then |
Plans stored in ~/.vmware-aiops/plans/, auto-deleted on success, auto-cleaned after 24h.
VM Deployment & Provisioning
Operation | Command | Speed | vCenter | ESXi |
Deploy from OVA |
| Minutes | ✅ | ✅ |
Deploy from Template |
| Minutes | ✅ | ✅ |
Linked Clone |
| Seconds | ✅ | ✅ |
Attach ISO |
| Instant | ✅ | ✅ |
Convert to Template |
| Instant | ✅ | ✅ |
Batch Clone |
| Minutes | ✅ | ✅ |
Batch Deploy (YAML) |
| Auto | ✅ | ✅ |
Cluster Management
Operation | Command | Confirmation | vCenter | ESXi |
Cluster Info |
| — | ✅ | ❌ |
Create Cluster |
| — | ✅ | ❌ |
Delete Cluster |
| Double | ✅ | ❌ |
Add Host |
| Double | ✅ | ❌ |
Remove Host |
| Double | ✅ | ❌ |
Configure HA/DRS |
| Double | ✅ | ❌ |
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 |
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 |
Webhook | Slack, Discord, or any HTTP endpoint |
Daemon Management |
|
Safety Features
Feature | Details |
Dry-Run Mode |
|
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 |
Input Validation | VM name, CPU (1-128), memory (128-1048576 MB), disk (1-65536 GB) validated |
Password Protection |
|
SSL Self-signed Support |
|
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) |
vCenter vs ESXi Comparison
Capability | vCenter | ESXi Standalone |
vMotion migration | ✅ | ❌ |
Cross-host clone | ✅ | ❌ |
Cluster management | ✅ | ❌ |
All VM lifecycle ops | ✅ | ✅ |
OVA/Template/Linked Clone deploy | ✅ | ✅ |
Datastore browsing & image scan | ✅ | ✅ |
Snapshots | ✅ | ✅ |
Guest operations | ✅ | ✅ |
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
Verify target is reachable:
vmware-aiops doctorFor self-signed certs: set
disableSslCertValidation: truein config.yaml (lab environments only)
Supported AI Platforms
Platform | Status | Config File | AI Model |
Claude Code | ✅ Native Skill |
| Anthropic Claude |
Gemini CLI | ✅ Extension |
| Google Gemini |
OpenAI Codex CLI | ✅ Skill + AGENTS.md |
| OpenAI GPT |
Aider | ✅ Conventions |
| Any (cloud + local) |
Continue CLI | ✅ Rules |
| Any (cloud + local) |
Trae IDE | ✅ Rules |
| Claude/DeepSeek/GPT-4o/Doubao |
Kimi Code CLI | ✅ Skill |
| Moonshot Kimi |
MCP Server | ✅ MCP Protocol |
| 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 | 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 |
✅ Ollama, LM Studio | |||
✅ Fully offline | |||
✅ Ollama, vLLM | |||
VS Code Copilot | — | ||
— | |||
Continue | ✅ Ollama | ||
Claude Code | — | — |
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 formatInstallation
Step 0: Prerequisites
# Python 3.10+ required
python3 --version
# Node.js 18+ required for Gemini CLI and Codex CLI
node --versionStep 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 targetsSet 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/.envSecurity note: Prefer
.envfile over command-lineexportto avoid passwords appearing in shell history. The.envfile should havechmod 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_PASSWORDSecurity Best Practices
NEVER hardcode passwords in scripts or config files
NEVER pass passwords as command-line arguments (visible in
ps)ALWAYS use
~/.vmware-aiops/.envwithchmod 600ALWAYS configure connections via
config.yaml— credentials are loaded from.envautomaticallyConfig File Contents:
config.yamlstores target hostnames, ports, and a reference to the.envfile. It does not contain passwords or tokens. All secrets are stored exclusively in.envTLS: 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-opsThen use:
/vmware-ops:vmware-aiops
> Show me all VMs on esxi-lab.example.comMethod 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-aiopsSubmit 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-AIopsThen start Gemini CLI:
gemini
> Show me all VMs on my ESXi hostOption 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.mdThen start Codex CLI:
codex --enable skills
> List all VMs on my ESXiOption 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:32bOption 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.mdConfigure ~/.continue/config.yaml for local model:
models:
- name: local-coder
provider: ollama
model: qwen2.5-coder:32bThen:
cn
> Check ESXi health and alarmsOption 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.mdTrae 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.mdOption 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 via uvx (recommended — works with uv tool install)
uvx --from vmware-aiops vmware-aiops-mcp
# With a custom config path
VMWARE_AIOPS_CONFIG=/path/to/config.yaml uvx --from vmware-aiops vmware-aiops-mcpClaude Desktop config (claude_desktop_config.json):
{
"mcpServers": {
"vmware-aiops": {
"command": "uvx",
"args": ["--from", "vmware-aiops", "vmware-aiops-mcp"],
"env": {
"VMWARE_AIOPS_CONFIG": "/path/to/config.yaml"
}
}
}
}Install via Smithery:
npx -y @smithery/cli install @zw008/VMware-AIops --client claudeOption 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-esxiUpdate / Upgrade
Already installed? Re-run the install command for your channel to get the latest version:
Install Channel | Update Command |
ClawHub |
|
Skills.sh |
|
Claude Code Plugin |
|
Git clone |
|
uv |
|
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-coderPersistent config ~/.aider.conf.yml:
model: deepseek/deepseek-coder
conventions: codex-skill/AGENTS.mdQwen (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-plusOr 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-latestDoubao (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-idWith 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-latestLocal 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 instructionsStep 2: Pull a model
Model | Command | Size | Note |
Qwen 2.5 Coder 32B |
| ~20GB | Best local coding model |
Qwen 2.5 Coder 7B |
| ~4.5GB | Low-memory option |
DeepSeek Coder V2 |
| ~8.9GB | Strong reasoning |
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:7bPersistent config ~/.aider.conf.yml:
model: ollama/qwen2.5-coder:32b
conventions: codex-skill/AGENTS.mdLocal Architecture
User → Aider CLI → Ollama (localhost:11434) → Qwen / DeepSeek local model
│ ↓
│ reads AGENTS.md instructions
│ ↓
└──────────────────────────────→ vmware-aiops CLI ──→ ESXi / vCenterTip: 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
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)
# 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 lifecycleConfiguration
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 |
|
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 # 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.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.tomlAPI Coverage
Built on pyVmomi (vSphere Web Services API / SOAP).
API Object | Usage |
| VM lifecycle, snapshots, clone, migrate |
| ESXi host info, sensors, services |
| Storage capacity, type, accessibility |
| File browsing, image discovery (ISO/OVA/VMDK) |
| OVA import and deployment |
| Cluster, DRS, HA |
| Network listing |
| Active alarm monitoring |
| Event/log queries |
Related Projects
Skill | Scope | Tools | Install |
Read-only monitoring, alarms, events | 8 |
| |
VM lifecycle, deployment, guest ops, cluster, datastore browse | 31 |
| |
Datastores, iSCSI, vSAN | 11 |
| |
Tanzu Namespaces, TKC cluster lifecycle | 20 |
|
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
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