# Contentrain

> Contentrain is a Git-native content governance layer for AI-built products. Developers start with MIT CLI, MCP, query SDK, rules, skills, and types packages. Teams add Studio for structured editing, review, roles, and Git-backed operations, with media, delivery, APIs, and remote MCP available when the plan and deployment support them.

## Core Positioning

- Agent generates. Human approves. Git records every change.
- Content lives in Git as structured JSON and Markdown instead of hidden CMS state.
- AI agents operate through bounded MCP tools, rules, skills, validation, and reviewable branches.
- Studio adds structured editing, team review, roles, and Git-backed operations; managed media, delivery, APIs, and remote MCP depend on plan and deployment capabilities.

## Primary URLs

- Website: https://contentrain.io
- Studio: https://studio.contentrain.io
- AI package docs: https://ai.contentrain.io
- Studio docs: https://docs.contentrain.io
- GitHub organization: https://github.com/Contentrain

## Product Pages

- https://contentrain.io/developers - Start with MCP, CLI, SDK, rules, and skills: Developers get a local-first content stack: CLI setup, MCP tools, rules, skills, validation, generated SDK access, and a clear path into Studio when teams need review.
- https://contentrain.io/enterprise - Govern AI content on your infrastructure: Enterprise Contentrain is for teams that need governed AI content operations on controlled infrastructure with licensed Studio capabilities, review controls, delivery surfaces, and operational support.
- https://contentrain.io/integrations - Works with your agent, framework, and deployment path.: Contentrain stores content as plain JSON and Markdown in Git, exposes MCP tools to agents, and provides generated query access plus eligible CDN and API delivery paths.
- https://contentrain.io/docs/mcp-connector - Connect AI clients to governed Contentrain projects: Use Studio's remote MCP Connector to let supported AI clients read and update structured project content through OAuth-scoped, Git-backed operations.
- https://contentrain.io/normalize - Turn hardcoded strings into governed content: Normalize scans existing code, extracts hardcoded UI text into structured content, patches reuse points, and turns AI-generated copy into a governed workflow.
- https://contentrain.io/open-source - MIT packages and an AGPL Studio core: Contentrain provides six MIT-licensed developer packages for local content work. Studio Community is AGPL-3.0, while separately licensed enterprise modules cover additional managed and organizational capabilities.
- https://contentrain.io/playbooks - Run content governance as a repeatable operating system: Playbooks turn Contentrain adoption into repeatable workflows for AI agents, developers, editors, migrations, and Studio operations.
- https://contentrain.io/pricing - Free open-source packages. Studio plans for teams.: Start free with the MIT packages. Add Studio when content becomes a team workflow, then choose Starter, Pro, or Enterprise by seats, usage, delivery, and deployment needs.
- https://contentrain.io/security - Bounded AI operations with Git as the audit layer: Contentrain security is built around bounded agent operations, Git auditability, branch review, role-scoped Studio access, provider boundaries, encrypted keys, and self-managed deployment paths.
- https://contentrain.io/self-hosted - Run Contentrain Studio on infrastructure you control: Run the AGPL Studio Community edition on infrastructure you control, keep content in Git, and add Enterprise terms or capabilities when organizational requirements call for them.
- https://contentrain.io/studio - The team operating surface for governed content in Git: Studio gives teams structured editing, chat-assisted content work, roles, and review over Git-backed projects. Media, CDN, forms, webhooks, APIs, and remote MCP appear when the edition, plan, provider, and deployment support them.
- https://contentrain.io/templates - Start with structured content instead of scattered copy.: Starter templates pair modern frameworks with Contentrain content models, generated clients, and repeatable workflows.

## Solution Pages

- https://contentrain.io/solutions/agencies - Repeatable content infrastructure for every client project: Agencies can start each client with the same Git-native content workflow, template-driven models, Studio review, and delivery surface.
- https://contentrain.io/solutions/ai-native-teams - Govern the content your AI coding workflow creates: AI coding makes product surfaces faster to ship, but it also spreads copy, labels, docs, and locale strings across components. Contentrain gives that output structure, review, and reuse.
- https://contentrain.io/solutions/content-teams - Let non-developers change content without losing Git control: Editors, marketers, translators, and reviewers can work through Studio while developers keep branches, diffs, validation, and source-of-truth control.
- https://contentrain.io/solutions/platform-teams - One content contract for web, docs, mobile, and APIs: Platform teams can standardize content operations around plain JSON, generated query access, Git provider workflows, and eligible API or CDN delivery.

## Use Case Pages

- https://contentrain.io/use-cases/ai-content-governance - Give AI agents bounded content operations: Agents can create, update, translate, search, and validate content through deterministic tools instead of editing source strings directly.
- https://contentrain.io/use-cases/git-native-cms - Use Git as the storage and audit layer for content: Content stays in your repository as JSON and Markdown. Branches, commits, diffs, and merge rules remain visible.
- https://contentrain.io/use-cases/hardcoded-strings - Extract hardcoded UI strings before they become product debt: Find strings across components, classify them, create content models, and prepare translation-ready output in Git.
- https://contentrain.io/use-cases/localization - Make localization a content workflow, not grep-and-replace: Extract strings into locale-aware models, copy locales, translate content, and keep parity visible through validation and health checks.

## Comparison Pages

- https://contentrain.io/compare/contentful - Contentrain vs Contentful: Compare Contentful's proprietary cloud database approach with Contentrain's Git-native content governance layer for AI-native teams.
- https://contentrain.io/compare/directus - Contentrain vs Directus: Compare Directus's sql database approach with Contentrain's Git-native content governance layer for AI-native teams.
- https://contentrain.io/compare/payload - Contentrain vs Payload: Compare Payload's database-backed application approach with Contentrain's Git-native content governance layer for AI-native teams.
- https://contentrain.io/compare/sanity - Contentrain vs Sanity: Compare Sanity's content lake and groq approach with Contentrain's Git-native content governance layer for AI-native teams.
- https://contentrain.io/compare/strapi - Contentrain vs Strapi: Compare Strapi's relational database approach with Contentrain's Git-native content governance layer for AI-native teams.

## Playbooks

- https://contentrain.io/playbooks/ai-agent-content-governance - Govern AI agents that create and edit product content: A practical operating model for giving AI coding agents content authority without giving them uncontrolled repository access.
- https://contentrain.io/playbooks/content-editor-workflow - Give editors Studio workflows without losing Git control: A workflow for letting editors, reviewers, and marketers change structured content while engineering keeps schemas, diffs, validation, and release discipline.
- https://contentrain.io/playbooks/developer-implementation - Implement Git-native content in a modern app: A developer-first path for adding models, content, validation, generated SDK access, and local agent workflows to a Nuxt, Next, Astro, SvelteKit, Vite, or Node project.
- https://contentrain.io/playbooks/normalize-migration - Migrate hardcoded strings into governed content: A migration path for extracting AI-generated UI copy, labels, empty states, and page text from source files into structured Contentrain content.
- https://contentrain.io/playbooks/studio-adoption - Adopt Studio when content work becomes a team operation: A practical path from local open-source packages to Studio team workflows, eligible managed operations, and self-managed or Enterprise deployment.

## Best Entry Points By Intent

- Developer evaluating the stack: https://contentrain.io/developers
- AI agent governance: https://contentrain.io/playbooks/ai-agent-content-governance
- Hardcoded string extraction: https://contentrain.io/normalize
- Content editor workflow: https://contentrain.io/playbooks/content-editor-workflow
- Studio workflows and deployment options: https://contentrain.io/studio
- Contentrain MCP Connector for remote AI clients: https://contentrain.io/docs/mcp-connector
- Vendor comparison: https://contentrain.io/compare/contentful

## Full AI Context

- https://contentrain.io/llms-full.txt