Token Efficiency

AI tools like Cursor, Copilot, and Claude read your project files to help you write code. Every file they read costs tokens — the units that determine response speed, API cost, and how much of your project fits in a single conversation.

@farming-labs/docs is built to keep the framework footprint small so more of your token budget goes to your actual documentation content — while still giving you the full flexibility of a modern docs framework.

One Config, Full Control

The core idea: everything about your docs site — theme, colors, typography, sidebar, AI chat, metadata — lives in a single docs.config.ts file.

import { defineDocs } from "@farming-labs/docs";
import { pixelBorder } from "@farming-labs/theme/pixel-border";

export default defineDocs({
  entry: "docs",
  theme: pixelBorder(),
  nav: { title: "My Docs", url: "/docs" },
  ai: { enabled: true, model: "gpt-4o-mini" },
  metadata: {
    titleTemplate: "%s – Docs",
    description: "My documentation site",
  },
});

An AI agent reads this one file and immediately understands your entire docs setup — what theme you're using, where content lives, how AI chat is configured, how pages are titled. There's a provider wrapper (RootProvider) and a docs layout file, but they're minimal one-liners that the CLI generates for you. The real configuration surface is this single file.

This matters because every additional config file, layout wrapper, or routing file is something an AI has to read, reason about, and avoid accidentally breaking. Fewer framework files means more room in the context window for what actually matters: your documentation content.

The CLI Does the Heavy Lifting

You don't have to set any of this up manually. The CLI scaffolds everything in seconds — for both new and existing projects.

Starting from scratch

Use --template to bootstrap a complete project with your framework and theme of choice:

npx @farming-labs/docs@latest init --template next --name my-docs

This creates a new my-docs/ folder with a fully working docs site — config, routes, CSS, sample pages, dependencies installed, dev server running. Pick from next, nuxt, sveltekit, or astro.

Want a specific theme? Add --theme:

npx @farming-labs/docs@latest init --template next --name my-docs --theme pixel-border

That's it — a beautiful themed docs site in one command.

Adding to an existing project

Already have a Next.js, SvelteKit, Astro, or Nuxt project? Just run init inside it:

npx @farming-labs/docs@latest init

The CLI auto-detects your framework from package.json, asks you to pick a theme, generates the config and minimal routing files, installs dependencies, and starts the dev server. Your existing code is untouched — it only adds what's needed for docs.

See the CLI reference for all flags and options.

Why This Matters for AI

Fewer files to reason about

When an AI agent needs to modify your docs setup — change a theme, enable AI chat, adjust the sidebar — it reads your docs.config.ts and makes the change in one place. No hunting through layout files, provider trees, slug handlers, and CSS imports to figure out how everything connects.

Less risk of breaking things

A declarative config file is hard to break. An AI can add ai: { enabled: true } or change theme: darksharp() without worrying about import paths, component hierarchies, or routing logic. The framework handles all of that internally.

Built-in AI Features

Beyond being token-efficient to work with, @farming-labs/docs includes features designed for AI consumption:

llms.txt

Your docs are automatically served in LLM-optimized format — no extra routes needed:

/api/docs?format=llms       → llms.txt (index of all pages)
/api/docs?format=llms-full  → llms-full.txt (full content)

See the llms.txt docs for details.

AI Chat with RAG

Built-in AI chat with retrieval-augmented generation. A simple setup is just two lines:

ai: {
  enabled: true,
  model: "gpt-4o-mini",
}

Need multiple models from different providers? Use the providers map and a model object with a selectable dropdown:

ai: {
  enabled: true,
  providers: {
    openai: {
      baseUrl: "https://api.openai.com/v1",
      apiKey: process.env.OPENAI_API_KEY,
    },
    groq: {
      baseUrl: "https://api.groq.com/openai/v1",
      apiKey: process.env.GROQ_API_KEY,
    },
  },
  model: {
    models: [
      { id: "gpt-4o-mini", label: "GPT-4o mini (fast)", provider: "openai" },
      { id: "gpt-4o", label: "GPT-4o (quality)", provider: "openai" },
      { id: "llama-3.3-70b-versatile", label: "Llama 3.3 70B", provider: "groq" },
    ],
    defaultModel: "gpt-4o-mini",
  },
}

Users get a model selector dropdown in the chat interface. The backend automatically routes each request to the correct provider with the right credentials. No separate API routes, no vector databases, no embedding pipelines — search indexing, context retrieval, and streaming responses are all handled internally.

Works with any OpenAI-compatible provider: OpenAI, Groq, Together, Fireworks, OpenRouter, Ollama, or any vLLM deployment.

See the AI Chat docs for the full configuration reference.

Summary

@farming-labs/docs keeps the framework lean — one config file, minimal routing, CLI-generated scaffolding — so you and your AI tools spend less time on plumbing and more time on content. And it does this without sacrificing flexibility: themes, multi-provider AI chat, search, custom components, and multi-framework support are all still there, just configured from one place.

Edit on GitHubllms.txt llms-full.txt