Getting Started

Contentrain AI is an open-source, repo-native content governance stack. Your AI agent creates, extracts, and updates content through Model Context Protocol (MCP) tools, while Contentrain ensures that content is structured, validated, reviewed, and deliverable to any platform.

Prerequisites

• Node.js 22+
• Git
• An MCP-compatible AI agent (Claude Code, Cursor, Windsurf, or similar)

Choose Your Path

Existing Project

# This is the main wedge: rescue hardcoded strings
npx contentrain init
# Then tell your agent: "Scan my project and extract all hardcoded strings"

New Project

# Start with structured content from day one
npx contentrain init

Quick Start: Existing Project

Already have hardcoded strings scattered across your codebase? Start here.

npx contentrain init
npx contentrain serve --stdio

Then tell your agent:

Scan my project and extract all hardcoded UI strings into structured content.

Typical outcome:

Agent scans 47 files → finds 523 strings → classifies → creates models → writes content → proposes source patches

Review the extracted content and branch diffs locally, then continue with the full Normalize Flow.

Quick Start: New Project

1. Initialize

npx contentrain init

This creates .contentrain/ in your project root with configuration, model definitions, and content directories. If the directory is not a git repo, it runs git init automatically.

2. Connect your AI agent

Add the Contentrain MCP server to your IDE:

Claude Code (.mcp.json)

{
  "mcpServers": {
    "contentrain": {
      "command": "npx",
      "args": ["contentrain", "serve", "--stdio"]
    }
  }
}

Cursor (.cursor/mcp.json)

{
  "mcpServers": {
    "contentrain": {
      "command": "npx",
      "args": ["contentrain", "serve", "--stdio"]
    }
  }
}

Windsurf

{
  "mcpServers": {
    "contentrain": {
      "command": "npx",
      "args": ["contentrain", "serve", "--stdio"]
    }
  }
}

3. Create a content model

Tell your agent:

Create a hero section model with title, subtitle, and CTA fields

The agent calls contentrain_model_save and creates:

.contentrain/
  models/hero-section.json
  content/marketing/hero-section/en.json

4. Add content

Add content to the hero section: title is "Build faster with AI",
subtitle is "Content governance for any platform"

5. Use your content

The content is plain JSON — you can read it directly from any language or platform:

{
  "title": "Build faster with AI",
  "subtitle": "Content governance for any platform"
}

For TypeScript projects, generate a typed SDK client for convenience:

npx contentrain generate

import { singleton } from '#contentrain'

const hero = singleton('hero-section').locale('en').get()
console.log(hero.title) // "Build faster with AI"

SDK is optional

The generated TypeScript SDK provides type-safe queries, but the content files are plain JSON and Markdown. Any platform that reads JSON — Go, Python, Swift, Kotlin, Rust — can consume your content directly.

6. Review with the local UI

npx contentrain serve

Open http://localhost:3333 to browse models, content, validation results, and pending branches.

Team Workflows

When the local CLI and MCP flow are not enough, Contentrain Studio adds the team web layer:

• workspace and project management
• role-based access and review responsibilities
• chat-first content operations
• branch and diff review
• media management
• CDN delivery for non-web platforms

Connect your local project to Studio with two commands:

contentrain studio login
contentrain studio connect

The connect command detects your git remote, verifies GitHub App installation, scans for .contentrain/ configuration, and creates the project — all in one interactive flow. See CLI Studio Integration for details.

The Content Pipeline

Every operation follows the same governance pipeline:

Agent generates → MCP validates → Human reviews → Git commits → Content delivered

• Agent decides what to create (your AI, your choice — BYOA)
• MCP enforces structure, validation, and canonical serialization
• Human reviews and approves through the local UI or Studio
• Git stores everything — full history, rollback, audit trail
• Content is delivered as plain JSON/Markdown to any platform

Packages

All packages are published on npm:

Package	Description	Install
contentrain	CLI (init, serve, generate, validate)	npx contentrain init
@contentrain/mcp	15 MCP tools for AI agents	pnpm add @contentrain/mcp
@contentrain/query	TypeScript query SDK (optional)	pnpm add @contentrain/query
@contentrain/types	Shared TypeScript types	pnpm add @contentrain/types
@contentrain/rules	AI agent quality rules	pnpm add @contentrain/rules
@contentrain/skills	AI agent workflow procedures	pnpm add @contentrain/skills

Starter Templates

Want to skip setup? Start from a production-ready template with content models, SDK client, and framework patterns pre-configured:

Template	Framework	Use Case
astro-blog	Astro	Blog / editorial
astro-landing	Astro	Landing page
next-commerce	Next.js	E-commerce
next-saas-dashboard	Next.js	SaaS dashboard
nuxt-saas	Nuxt	SaaS marketing
sveltekit-editorial	SvelteKit	Editorial
vitepress-docs	VitePress	Documentation

See all 10 templates on GitHub

What's Next?

• Core Concepts — Models, content kinds, domains, and the governance architecture
• Ecosystem Map — How AI packages and Studio fit together
• MCP Tools — All 15 tools available to your agent
• Normalize Flow — Extract hardcoded strings from existing code
• i18n Workflow — Add languages to your content
• Framework Integration — Platform-specific setup patterns

Contentrain Studio

Contentrain Studio is the open-core team operations surface for Git-native structured content. Teams can self-host the AGPL core or use a managed Pro/Enterprise offering when they want web-based collaboration, review, media, and CDN delivery on top of the same content model.