How to Add AI Agents to Docusaurus
Add an AI agent to your Docusaurus docs so it answers from your own content with citations. Here is the setup and the right injection point.
Adding an AI agent to a Docusaurus site lets visitors ask questions and get answers drawn from your own docs, with a citation to the exact page — and the setup takes minutes if you inject the widget the Docusaurus way. Done right, it survives the static build and stays current as your docs change.
Why Docusaurus docs benefit
Docusaurus is built for documentation, which means it's exactly the kind of content an AI agent shines on. Readers arrive with a specific question; search returns a list of pages and leaves them to dig. An agent reads your docs and answers the question directly, citing the page so the reader can verify it. The better your docs are structured — clear headings, focused pages — the better the agent retrieves them. See the documentation use case.
Step 1: Get your site key and index your docs
First, create an account and grab your publishable site key. Then point the platform at your Docusaurus site to crawl, or connect your GitHub repo directly. The platform chunks and embeds your content so it can be retrieved at answer time. Set a re-crawl schedule so each release keeps the agent current.
Step 2: Inject the widget the Docusaurus way
This is the step people get wrong. A raw embed pasted into MDX can be stripped by the static build. Instead, register the AIML.chat embed through a Docusaurus client module or the scripts configuration in docusaurus.config, which loads it on every page reliably. Keep your publishable key in an environment variable rather than committing it. The Docusaurus setup skill walks through this.
Step 3: Configure the agent
Choose a technical agent so it answers precisely for developers, returning the relevant snippet and citing the page. Give it a grounded prompt: answer only from your indexed docs, cite sources, and say so when the answer isn't there rather than guessing. This grounding is what keeps it trustworthy on a docs site where accuracy matters. See what is RAG for why grounding works.
Step 4: Structure content for retrieval
Retrieval favors well-organized docs. Keep each page focused on one topic, use clear headings, lead with the answer, and prefer descriptive titles over clever ones. If a page is so long it can't be cleanly chunked, split it. These small habits noticeably improve how well the agent answers, and they make your docs better for human readers too.
Step 5: Watch the gaps
Once live, the analytics show which questions readers ask and which the agent couldn't answer well. That last list is a precise docs backlog — write those pages, re-crawl, and the agent improves. Your support load becomes a roadmap.
What readers get
The result is a docs site that answers questions instead of just hosting pages: instant, cited responses, around the clock, in the reader's language. Developers get unblocked without filing a ticket, and you see exactly where your documentation falls short. See the Docusaurus integration for platform details.
Getting started
Add an agent to your docs on the AIML.chat free plan: index your site, register the embed, and configure the agent. Compare tiers on the pricing page, and read how to add AI to MkDocs for another framework.
Will the static build strip the widget?+
Not if you register it through a Docusaurus client module or the scripts config rather than pasting a raw embed into MDX — that survives the build.
Can it index my GitHub repo?+
Yes — index the rendered docs site or your repo, and schedule a re-crawl so each release keeps the agent current.
Will it answer accurately?+
Yes, when grounded — it answers only from your indexed docs and cites the page, and says so when the answer isn't there.
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