zmesh-cli 0.1.0

The AI-first Backend as a Service — Database, Edge Functions, Storage, AI Gateway from your terminal.

⚡ zMesh

The AI-First Backend as a Service — From Your Terminal

Database • Edge Functions • Storage • AI Gateway • Auth — One CLI.

Website · Docs · Dashboard

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What is zMesh?

zMesh is an AI-first Backend as a Service that gives you a full backend — PostgreSQL database, serverless edge functions, file storage, AI gateway, authentication — without writing a single line of infrastructure code.

This CLI lets you manage everything from your terminal.

pip install zmesh-cli

Quick Start

# Authenticate
zmesh login

# Create a new project
zmesh init --name "My App"

# Create a table
zmesh database query "CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name TEXT NOT NULL,
  email TEXT UNIQUE
)"

# Deploy an edge function
zmesh functions deploy ./api.js

# Upload a file
zmesh storage upload ./logo.png --path assets/logo.png

That's it. Your backend is live.

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Features

🗄️ Database Full PostgreSQL — create tables, run queries, migrate schemas, seed data. zmesh database tables zmesh database query "SELECT * FROM users" zmesh database migrate ./schema.sql zmesh database seed ./data.json	⚡ Edge Functions Deploy JavaScript or Python functions with built-in DB access. zmesh functions deploy ./handler.js zmesh functions invoke my-func --payload '{}' zmesh functions logs my-func
📦 Storage Upload, download, and manage files on Cloudflare R2. zmesh storage upload ./image.png zmesh storage list --prefix photos/ zmesh storage download photos/img.png	🤖 AI Gateway Multi-provider AI — OpenAI, Anthropic, Google, Groq, Mistral, DeepSeek. # Manage via Dashboard or API # Chat, Embeddings, Vector Store (pgvector) # Schema & RLS generation from prompts

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Commands

Authentication & Projects

Command	Description
zmesh login	Authenticate with email/password
zmesh logout	Clear stored credentials
zmesh whoami	Show current user & linked project
zmesh projects	List all your projects
zmesh init --name "App"	Create & link a new project
zmesh link <project-id>	Link to an existing project
zmesh unlink	Unlink current directory

Database

Command	Description
zmesh database tables	List all tables
zmesh database query "<sql>"	Execute raw SQL
zmesh database query --file schema.sql	Run SQL from file
zmesh database migrate <file>	Run a migration script
zmesh database seed <file>	Seed data from JSON

Edge Functions

Command	Description
zmesh functions list	List all deployed functions
zmesh functions deploy <file>	Deploy a JS or Python function
zmesh functions invoke <slug>	Invoke with optional --payload
zmesh functions logs <slug>	View invocation history
zmesh functions delete <slug>	Remove a function

Deploy with options:

zmesh functions deploy ./send-email.js \
  --name send-email \
  --description "Transactional email sender" \
  --entry-point handler \
  --env SMTP_HOST=smtp.example.com \
  --timeout 10000

Storage

Command	Description
zmesh storage list	List files (with --bucket, --prefix)
zmesh storage upload <file>	Upload with --path and --bucket
zmesh storage download <path>	Download to --output
zmesh storage delete <path>	Remove a file

Environment Variables

Command	Description
zmesh env --list	List all env vars
zmesh env --set KEY=value	Set an env var
zmesh env --unset KEY	Remove an env var

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Edge Functions

zMesh's edge functions run in a custom-built sandboxed runtime — no AWS Lambda, no third-party FaaS. Each function gets isolated subprocess execution with OS-level resource limits and a pre-injected zmesh SDK with direct database access.

JavaScript

// get-users.js
exports.handler = async (payload, zmesh) => {
  const users = await zmesh.db.query(
    'SELECT * FROM users WHERE active = $1', [true]
  );

  const newLog = await zmesh.db.insert('audit_log', {
    action: 'list_users',
    count: users.length
  });

  const secret = zmesh.env.API_SECRET;

  return { users, logged: true };
};

Python

# get-users.py
def handler(payload, zmesh):
    users = zmesh.db.query(
        "SELECT * FROM users WHERE active = %s", (True,)
    )

    zmesh.db.insert("audit_log", {
        "action": "list_users",
        "count": len(users)
    })

    return {"users": users}

SDK Reference

Method	Description
zmesh.db.query(sql, params)	Execute SQL, return rows
zmesh.db.queryOne(sql, params)	Execute SQL, return single row
zmesh.db.insert(table, data)	Insert a record
zmesh.db.select(table, where, params)	Select with conditions
zmesh.db.update(table, data, where, params)	Update matching rows
zmesh.db.remove(table, where)	Delete matching rows
zmesh.env.KEY	Access environment variable
zmesh.projectId	Current project ID

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End-to-End Example

Build a complete blog API in under 2 minutes:

# 1. Create project
zmesh init --name "Blog API"

# 2. Create schema
zmesh database query "
CREATE TABLE posts (
  id SERIAL PRIMARY KEY,
  title TEXT NOT NULL,
  body TEXT,
  author TEXT,
  created_at TIMESTAMP DEFAULT NOW()
)"

# 3. Write a function
cat > get-posts.js << 'EOF'
exports.handler = async (payload, zmesh) => {
  const posts = await zmesh.db.query(
    'SELECT * FROM posts ORDER BY created_at DESC LIMIT $1',
    [payload.limit || 10]
  );
  return { posts };
};
EOF

# 4. Deploy
zmesh functions deploy ./get-posts.js

# 5. Test
zmesh functions invoke get-posts --payload '{"limit": 5}'

# 6. Call from your app
curl -X POST https://api.zmesh.in/api/functions/get-posts \
  -H "x-api-key: zb_your_key" \
  -H "Content-Type: application/json" \
  -d '{"limit": 5}'

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Configuration

Location	Purpose
~/.zmesh/config.json	Global — API URL, auth token
.zmesh/config.json	Local — linked project & org
.zmesh/.env	Function environment variables

Self-Hosted

zmesh login --api-url https://api.yourdomain.com/v1

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Requirements

• Python 3.11+
• A zMesh account

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Links

🌐 Website	zmesh.in
📖 Docs	zmesh.in/docs
🖥️ Dashboard	app.zmesh.in
📧 Support	[e-mail]

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MIT License