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llms.txt Content

# Decantr > AI Frontend Governance for production codebases touched by AI agents. Decantr is the governance layer between product intent and AI-edited frontend code — giving coding assistants typed contracts, route-scoped context, graph-backed health evidence, and repair prompts so UI changes stay coherent instead of drifting prompt by prompt. The model still writes the code; Decantr defines the Contract, Context, and Evidence loop around it. Decantr separates design governance into **DNA** (durable visual axioms: theme, spacing, motion, accessibility, personality), **Blueprint** (product topology: sections, page routes, shells, layouts, features), and the emerging typed Contract graph that agents can query directly. Strict where it should be strict, flexible where it should be flexible. ## Docs - [Homepage](https://decantr.ai/): What Decantr is and why AI-edited frontend code needs a governance layer. - [Sitemap](https://decantr.ai/sitemap.xml): Crawl inventory for public Decantr docs, schemas, references, and release notes. - [Design contract basics](https://decantr.ai/guides/design-contract-basics.md): Short explanation of Essence, DNA, Blueprint, context files, and the Decantr loop. - [Existing app adoption](https://decantr.ai/guides/existing-apps.md): Brownfield attach path for adding Decantr to an existing app without rewriting the runtime. - [Monorepos](https://decantr.ai/guides/monorepos.md): Root install, app-scoped attach, workspace health, pinned CI, and what to commit in monorepos. - [Project Health CI](https://decantr.ai/guides/project-health-ci.md): Installing and running the Decantr CI gate locally, in GitHub Actions, or in generic internal pipelines. - [AI assistant setup](https://decantr.ai/guides/ai-assistant-setup.md): Using Decantr with MCP-compatible assistants and file-based CLI context. - [Typed graph agent playbook](https://decantr.ai/guides/typed-graph-agent-playbook.md): Operational flow for Contract capsules, route graph context, nod