---
title: How to add llms.txt to Next.js
description: Add llms.txt, full-site context, and per-page Markdown endpoints to a Next.js App Router project and verify the production result.
canonical_url: https://next-ai-ready.vercel.app/en/docs/guides/nextjs-llms-txt
url: https://next-ai-ready.vercel.app/en/docs/guides/nextjs-llms-txt
last_updated: 2026-08-02
updated: 2026-08-02
author: next-ai-ready team
summary: Add llms.txt, full-site context, and per-page Markdown endpoints to a Next.js App Router project and verify the production result.
topics: [nextjs, llms.txt, markdown, tutorial]
---

# How to add llms.txt to Next.js

The smallest implementation is a hand-written `public/llms.txt`. That is enough for a small,
rarely changing site. Use a build integration when the file must stay synchronized with many
pages, page summaries, Markdown representations, structured data, or agent tools.

This guide adds those interfaces to an existing Next.js App Router project without changing its UI.

Using a documentation framework? Follow the dedicated [Nextra 4 guide](./nextra-ai-ready) or
[Fumadocs guide](./fumadocs-ai-ready) for content paths, plugin composition, and deployment limits.

## See a production example

This documentation site uses the same integration described below:

- [Production llms.txt](https://next-ai-ready.vercel.app/llms.txt)
- [This installation page as Markdown](https://next-ai-ready.vercel.app/en/docs/installation.md)
- [Production OpenAPI document](https://next-ai-ready.vercel.app/openapi.json)
- [Production tool manifest](https://next-ai-ready.vercel.app/tools.json)
- [Source for the deployed documentation site](https://github.com/mustcanbedo/next-ai-ready/tree/main/examples/docs-site)

The production site also passes all 25 checks in the pinned Vercel Agent Readability CLI baseline.
That score measures technical readability, not search position or citation.

## 1. Install the package

From an existing Next.js App Router project:

```bash
pnpm add next-ai-ready zod@^4
pnpm exec next-ai-ready init
```

`init` creates explicit route handlers, an AI-ready configuration file, a starter action, and the
Next.js rewrite configuration. Existing page components remain unchanged.

## 2. Add AI-readable content

Create an MDX source such as `content/about.mdx`:

```markdown
---
title: About Acme
summary: Acme helps support teams find verified product answers.
author: Acme team
updatedAt: 2026-08-02
questions:
  - q: What does Acme do?
    a: Acme helps support teams search verified product documentation.
---

# About Acme

Acme gives support teams one searchable source for verified product answers.
```

Clear titles, summaries, authorship, freshness, and direct answers are useful to people as well as
AI consumers. Do not add claims or FAQ answers that the visible page cannot support.

## 3. Generate the interfaces

```bash
pnpm exec next-ai-ready build
pnpm exec next-ai-ready doctor --score
```

The build produces discovery files and the semantic graph used by the runtime handlers. A basic
deployment exposes:

- `/llms.txt` for concise site discovery.
- `/llms-full.txt` for combined site context.
- `/<page>.md` for clean page retrieval.
- `/sitemap.md` for agent-readable navigation.
- `/openapi.json` and `/tools.json` for declared capabilities.
- `/api/mcp` when you choose to expose MCP resources and tools.

The setup is ready when `doctor` reports zero errors and `llms.txt` lists the intended pages.
Warnings describe optional quality or production settings that still need attention.

## 4. Verify the deployed site

After deploying, inspect the real public responses rather than relying only on build artifacts:

```bash
pnpm exec next-ai-ready audit https://example.com --version 3
```

Also open `https://example.com/llms.txt` and one real page such as
`https://example.com/about.md`. Confirm that canonical URLs point to the production origin and that
normal browser pages still return HTML.

## Add callable actions later

`llms.txt` and Markdown solve the first problem: helping AI systems discover and read content.
Callable actions are a separate production decision. Add them only when an agent needs to perform a
specific operation, then configure authentication, input validation, and audit logging.

## What this does not guarantee

Technical AI-readiness cannot guarantee that an AI product will crawl, index, rank, quote, or cite a
page. Treat these endpoints as reliable machine-facing access to good source content, then measure
actual visits, retrievals, citations, and business outcomes separately.