VMware AIops

DevOps

by zw008

基于 AI 的 VMware vCenter/ESXi 监控与运维工具集,内含 20 个 MCP 工具,帮助排障、巡检和自动化操作。

什么是 VMware AIops

基于 AI 的 VMware vCenter/ESXi 监控与运维工具集,内含 20 个 MCP 工具,帮助排障、巡检和自动化操作。

README

<!-- mcp-name: io.github.zw008/vmware-aiops -->

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:

SkillScopeInstall
vmware-monitorRead-only: inventory, health, alarms, events, metricsuv tool install vmware-monitor
vmware-storageDatastores, iSCSI, vSAN managementuv tool install vmware-storage
vmware-vksTanzu Namespaces, TKC cluster lifecycleuv tool install vmware-vks

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

ClawHub Skills.sh Claude Code Marketplace License: MIT

Quick Install (Recommended)

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

bash
# Via Skills.sh
npx skills add zw008/VMware-AIops

# Via ClawHub
clawhub install vmware-aiops

PyPI Install (No GitHub Access Required)

bash
# 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

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

# Install plugin
/plugin install vmware-ops

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

Capabilities Overview

What This Skill Does

CategoryToolsCount
VM Lifecyclepower on/off, TTL auto-delete, clean slate6
DeploymentOVA, template, linked clone, batch clone/deploy8
Guest Opsexec commands, upload/download files, provision5
Plan/Applymulti-step planning with rollback4
Clustercreate, delete, HA/DRS config, add/remove hosts6
Datastorebrowse files, scan for images2

CLI vs MCP: Which Mode to Use

ScenarioRecommendedWhy
Local/small models (Ollama, Qwen <32B)CLI~2K tokens context vs ~10K for MCP; small models struggle with many tool schemas
Token-sensitive workflowsCLISKILL.md + Bash tool = minimal overhead
Cloud models (Claude, GPT-4o)EitherBoth work; MCP gives structured JSON I/O
Automated pipelines / Agent chainingMCPType-safe parameters, structured output, no shell parsing
Monitoring / storage / K8sCompanion skillsSee vmware-monitor, vmware-storage, vmware-vks

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

Architecture

code
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 VersionSupportNotes
8.0 / 8.0U1-U3✅ FullCreateSnapshot_Task deprecated → use CreateSnapshotEx_Task
7.0 / 7.0U1-U3✅ FullAll APIs supported
6.7✅ CompatibleBackward-compatible, tested
6.5✅ CompatibleBackward-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

  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

OperationCommandConfirmationvCenterESXi
Power Onvm power-on <name>
Graceful Shutdownvm power-off <name>Double
Force Power Offvm power-off <name> --forceDouble
Resetvm reset <name>
Suspendvm suspend <name>
Create VMvm create <name> --cpu --memory --disk
Delete VMvm delete <name>Double
Reconfigurevm reconfigure <name> --cpu --memoryDouble
Create Snapshotvm snapshot-create <name> --name <snap>
List Snapshotsvm snapshot-list <name>
Revert Snapshotvm snapshot-revert <name> --name <snap>
Delete Snapshotvm snapshot-delete <name> --name <snap>
Clone VMvm clone <name> --new-name <new>
vMotionvm migrate <name> --to-host <host>
Set TTLvm set-ttl <name> --minutes <n>
Cancel TTLvm cancel-ttl <name>
List TTLsvm list-ttl
Clean Slatevm clean-slate <name> [--snapshot baseline]Double
Guest Execvm guest-exec <name> --cmd /bin/bash --args "..."
Guest Exec (with output)vm guest-exec-output <name> --cmd "df -h"
Guest Uploadvm guest-upload <name> --local f.sh --guest /tmp/f.sh
Guest Downloadvm guest-download <name> --guest /var/log/syslog --local ./syslog

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:

StepWhat Happens
1. Create PlanAI calls vm_create_plan — validates actions, checks targets in vSphere, generates plan with rollback info
2. ReviewAI shows plan to user: steps, affected VMs, irreversible warnings
3. Applyvm_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.

VM Deployment & Provisioning

OperationCommandSpeedvCenterESXi
Deploy from OVAdeploy ova <path> --name <vm>Minutes
Deploy from Templatedeploy template <tmpl> --name <vm>Minutes
Linked Clonedeploy linked-clone --source <vm> --snapshot <snap> --name <new>Seconds
Attach ISOdeploy iso <vm> --iso "[ds] path/to.iso"Instant
Convert to Templatedeploy mark-template <vm>Instant
Batch Clonedeploy batch-clone --source <vm> --count <n>Minutes
Batch Deploy (YAML)deploy batch spec.yamlAuto

Cluster Management

OperationCommandConfirmationvCenterESXi
Cluster Infocluster info <name>
Create Clustercluster create <name> [--ha] [--drs]
Delete Clustercluster delete <name>Double
Add Hostcluster add-host <cluster> --host <host>Double
Remove Hostcluster remove-host <cluster> --host <host>Double
Configure HA/DRScluster configure <name> [--ha/--no-ha] [--drs/--no-drs]Double

Datastore Browser

FeaturevCenterESXiDetails
Browse FilesList files/folders in any datastore path
Scan ImagesDiscover ISO, OVA, OVF, VMDK across all datastores

Scheduled Scanning & Notifications

FeatureDetails
DaemonAPScheduler-based, configurable interval (default 15 min)
Multi-target ScanSequentially scan all configured vCenter/ESXi targets
Scan ContentAlarms + Events + Host logs (hostd, vmkernel, vpxd)
Log AnalysisRegex pattern matching: error, fail, critical, panic, timeout, corrupt
Structured LogJSONL output to ~/.vmware-aiops/scan.log
WebhookSlack, Discord, or any HTTP endpoint
Daemon Managementdaemon start/stop/status, PID file, graceful shutdown

Safety Features

FeatureDetails
Dry-Run Mode--dry-run on any destructive command prints exact API calls without executing
Plan → Confirm → Execute → LogStructured workflow: show current state, confirm changes, execute, audit log
Double ConfirmationAll destructive ops (power-off, delete, reconfigure, snapshot-revert/delete, clone, migrate) require 2 sequential confirmations — no bypass flags
Rejection LoggingDeclined confirmations are recorded in the audit trail
Audit TrailAll operations logged to ~/.vmware-aiops/audit.log (JSONL) with before/after state
Input ValidationVM 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 SupportdisableSslCertValidation — only for ESXi with self-signed certs in isolated labs; production should use CA-signed certificates
Prompt Injection ProtectionvSphere event messages and host logs are truncated, stripped of control characters, and wrapped in boundary markers before output
Webhook Data ScopeSends notifications to user-configured URLs only — no third-party services by default
Task WaitingAll async operations wait for completion and report result
State ValidationPre-operation checks (VM exists, power state correct)

vCenter vs ESXi Comparison

CapabilityvCenterESXi 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

  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

PlatformStatusConfig FileAI Model
Claude Code✅ Native Skillskills/vmware-aiops/SKILL.mdAnthropic Claude
Gemini CLI✅ Extensiongemini-extension/GEMINI.mdGoogle Gemini
OpenAI Codex CLI✅ Skill + AGENTS.mdcodex-skill/AGENTS.mdOpenAI GPT
Aider✅ Conventionscodex-skill/AGENTS.mdAny (cloud + local)
Continue CLI✅ Rulescodex-skill/AGENTS.mdAny (cloud + local)
Trae IDE✅ Rulestrae-rules/project_rules.mdClaude/DeepSeek/GPT-4o/Doubao
Kimi Code CLI✅ Skillkimi-skill/SKILL.mdMoonshot Kimi
MCP Server✅ MCP Protocolmcp_server/Any MCP client
Python CLI✅ StandaloneN/AN/A

Platform Comparison

FeatureClaude CodeGemini CLICodex CLIAiderContinueTrae IDEKimi CLI
Cloud AIAnthropicGoogleOpenAIAnyAnyMultiMoonshot
Local modelsOllamaOllama
Skill systemSKILL.mdExtensionSKILL.mdRulesRulesSKILL.md
MCP supportNativeNativeVia SkillsThird-partyNative
Free tier60 req/minSelf-hostedSelf-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 / ToolLocal Model SupportConfig TemplateIntegration Guide
Goose✅ Ollama, LM Studiogoose.jsonGuide
LocalCowork✅ Fully offlinelocalcowork.jsonGuide
mcp-agent✅ Ollama, vLLMmcp-agent.yamlGuide
VS Code Copilotvscode-copilot.jsonGuide
Cursorcursor.jsonGuide
Continue✅ Ollamacontinue.yamlGuide
Claude Codeclaude-code.json

Fully local operation (no cloud API required):

bash
# 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

bash
# 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.

bash
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

bash
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):

bash
# 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:

code
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:

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

Then use:

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

Method 2: Local install

bash
# 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:

code
/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

bash
# 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:

code
gemini
> Show me all VMs on my ESXi host

Option C: OpenAI Codex CLI

bash
# 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:

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

Option D: Aider (supports local models)

bash
# 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)

bash
# 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:

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

Then:

bash
cn
> Check ESXi health and alarms

Option F: Trae IDE

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

bash
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

bash
# 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.).

bash
# 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-mcp

Claude Desktop config (claude_desktop_config.json):

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

Install via Smithery:

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

Option I: Standalone CLI (no AI)

bash
# 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:

Install ChannelUpdate Command
ClawHubclawhub install vmware-aiops
Skills.shnpx skills add zw008/VMware-AIops
Claude Code Plugin/plugin marketplace add zw008/VMware-AIops
Git clonecd VMware-AIops && git pull origin main && uv pip install -e .
uvuv 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.

bash
# 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:

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

Qwen (Alibaba Cloud)

Alibaba Cloud's coding model, free tier available.

bash
# 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:

bash
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)

bash
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:

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

bash
# 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

ModelCommandSizeNote
Qwen 2.5 Coder 32Bollama pull qwen2.5-coder:32b~20GBBest local coding model
Qwen 2.5 Coder 7Bollama pull qwen2.5-coder:7b~4.5GBLow-memory option
DeepSeek Coder V2ollama pull deepseek-coder-v2~8.9GBStrong reasoning
CodeLlama 34Bollama pull codellama:34b~19GBMeta coding model

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

Step 3: Run with Aider

bash
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:

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

Local Architecture

code
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

bash
# 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 lifecycle

Configuration

See config.example.yaml for all options.

SectionKeyDefaultDescription
targetsnameFriendly name
targetshostvCenter/ESXi hostname or IP
targetstypevcentervcenter or esxi
targetsport443Connection port
targetsverify_sslfalseSSL certificate verification
scannerinterval_minutes15Scan frequency
scannerseverity_thresholdwarningMin severity: critical/warning/info
scannerlookback_hours1How far back to scan
scannerlog_types[vpxd, hostd, vmkernel]Log sources
notifylog_file~/.vmware-aiops/scan.logJSONL log output
notifywebhook_urlWebhook endpoint (Slack, Discord, etc.)

Project Structure

code
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.toml

API Coverage

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

API ObjectUsage
vim.VirtualMachineVM lifecycle, snapshots, clone, migrate
vim.HostSystemESXi host info, sensors, services
vim.DatastoreStorage capacity, type, accessibility
vim.host.DatastoreBrowserFile browsing, image discovery (ISO/OVA/VMDK)
vim.OvfManagerOVA import and deployment
vim.ClusterComputeResourceCluster, DRS, HA
vim.NetworkNetwork listing
vim.alarm.AlarmManagerActive alarm monitoring
vim.event.EventManagerEvent/log queries

Related Projects

SkillScopeToolsInstall
vmware-monitorRead-only monitoring, alarms, events8uv tool install vmware-monitor
vmware-aiopsVM lifecycle, deployment, guest ops, cluster, datastore browse31uv tool install vmware-aiops
vmware-storageDatastores, iSCSI, vSAN11uv tool install vmware-storage
vmware-vksTanzu Namespaces, TKC cluster lifecycle20uv tool install vmware-vks

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

常见问题

VMware AIops 是什么?

基于 AI 的 VMware vCenter/ESXi 监控与运维工具集,内含 20 个 MCP 工具,帮助排障、巡检和自动化操作。

相关 Skills

可观测性设计

by alirezarezvani

Universal
热门

面向生产系统规划可落地的可观测性体系,串起指标、日志、链路追踪与 SLI/SLO、错误预算、告警和仪表盘设计,适合搭建监控平台与优化故障响应。

把监控、日志、链路追踪串起来,帮助团队从设计阶段构建可观测性,排障更快、系统演进更稳。

DevOps
未扫描9.0k

资深开发运维

by alirezarezvani

Universal
热门

覆盖 CI/CD 流水线生成、Terraform 基建脚手架和自动化部署,适合在 AWS、GCP、Azure 上搭建云原生发布流程,管理 Docker/Kubernetes 基础设施并持续优化交付。

把CI/CD、基础设施即代码、容器与监控串成一条交付链,尤其适合AWS/GCP/Azure多云团队高效落地。

DevOps
未扫描9.0k

环境密钥管理

by alirezarezvani

Universal
热门

统一梳理dev/staging/prod的.env和密钥流程,自动生成.env.example、校验必填变量、扫描Git历史泄漏,并联动Vault、AWS SSM、1Password、Doppler完成轮换。

统一管理环境变量、密钥与配置,减少泄露和部署混乱,安全治理与团队协作一起做好,DevOps 场景很省心。

DevOps
未扫描9.0k

相关 MCP Server

kubefwd

编辑精选

by txn2

热门

kubefwd 是让 AI 帮你批量转发 Kubernetes 服务到本地的开发神器。

微服务开发者最头疼的本地调试问题,它一键搞定——自动分配 IP 避免端口冲突,还能用自然语言查询状态。但依赖 AI 工作流,纯命令行爱好者可能觉得不够直接。

DevOps
4.1k

Cloudflare

编辑精选

by Cloudflare

热门

Cloudflare MCP Server 是让你用自然语言管理 Workers、KV 和 R2 等云资源的工具。

这个工具解决了开发者频繁切换控制台和文档的痛点,特别适合那些在 Cloudflare 上部署无服务器应用、需要快速调试或管理配置的团队。不过,由于它依赖多个子服务器,初次设置可能有点繁琐,建议先从 Workers Bindings 这类核心功能入手。

DevOps
3.6k

Terraform

编辑精选

by hashicorp

Terraform MCP Server 是让 AI 助手直接操作 Terraform Registry 和 HCP Terraform 的桥梁。

如果你经常在 Terraform 里翻文档找模块配置,这个服务器能省不少时间——直接问 Claude 就能生成准确的代码片段。最适合管理多云基础设施的团队,但注意它目前只适合本地使用,别在生产环境里暴露 HTTP 端点。

DevOps
1.3k

评论