> ## Documentation Index > Fetch the complete documentation index at: https://docs.contextual.ai/llms.txt > Use this file to discover all available pages before exploring further. # Documentation Agent > The agent powering agentic search on this site: try it in the search bar ## Where to find it The documentation agent powers the chat experience on this very site. You can use it in two places: * **Top:** The search bar at the top of the page ("Ask Contextual AI...") — type your question and send. * **Right:** The chat panel persists on the right after you run your first query — use it to run multi-turn conversations. When you ask a question, **Mintlify's built-in search** first surfaces **keyword-based results** so you get quick, relevant links. In parallel, the **agent runs the longer-running task**: it retrieves across the documentation, runs multi-step research (Agentic Search), and streams back a detailed, cited answer. You get both instant keyword hits and a synthesized response. **GitHub repo:** The sync code for keeping a Contextual AI datastore in sync with your docs is public: [ContextualAI/datastore-sync](https://github.com/ContextualAI/datastore-sync)—see the section below for an overview. ## Why this agent ### (and why not generic LLMs or basic RAG) **Use cases:** This is the kind of agent we use for **customer engineering** and **self-serve users**. We believe that **all docs need agents**, and **all websites need agents**, because people would rather prompt than search for info. If you ask a **generic LLM** and point it at the docs page, you tend to get **more general, less relevant** responses (based on qualitative review). Our agent gives **very specific, detailed** answers; the docs provided to it **more precisely constrain** the information it can use. In short: it's a **better docs agent**, and you can set one up for yourself in **hours**. Compared with web search, **basic RAG** or no citations: * **Citations are key** — People often want to **read the source doc** after the initial response: to verify, go deeper, or share the link. This agent cites every factual claim and adds a References section with doc URLs. * **Multi-step retrieval** — Single-shot RAG can miss context or mix in irrelevant chunks; Agentic Search does breadth-then-depth over the docs and produces more precise answers. * **Grounded generation** — Responses are generated only from retrieved content, reducing hallucination. We've already seen users rely on it for **troubleshooting** and support-style questions—before we had even written about it—because they can trust the references and follow them into the docs. ## Challenges we solved with this agent Running a docs agent well comes with a few realities we've designed for: * **Multimodal content** — Documentation often has important information in **images** (diagrams, screenshots, UI). Our pipeline handles multimodal content state-of-the-art: we parse and index both text and images so the agent can retrieve and use information from figures, screenshots, and diagrams, not just body text. * **Keeping the agent up to date** — Docs change **many times a day**. The agent must stay in sync with the latest content. You can do that with [Contextual AI connectors](/connectors/overview) (e.g. Confluence, SharePoint, Google Drive), or—for GitHub-backed docs—with the **code we wrote and share**: [**datastore-sync**](https://github.com/ContextualAI/datastore-sync). It syncs your repo (and optionally your website) into a Contextual AI datastore via webhooks and incremental updates so the agent always has fresh content. * **Citations** — As above: citations aren't just for trust; they let users **open the source** and read the full doc after the initial response. * **Repo vs. web:** Unlike searching docs via the web, our documentation lives in our **GitHub repo** and we sync only what's published (e.g. pages in nav, `docs.json`). We may have **outdated or unlisted pages** in the repo that still exist but aren't findable on the site—our agent **won't surface those**, because they're not in the synced datastore. LLMs that use web search can sometimes surface those older or hidden pages; our agent deliberately reflects only the docs we publish, so answers stay aligned with what we actually ship. ## How it works 1. **Your documentation as data:** These docs are ingested into a Contextual AI datastore, chunked and indexed. They're kept in sync so when content is updated, the agent has the latest info (here, via sync code we share; see below). 2. **Keyword search + agent:** Mintlify's built-in search returns keyword-based results immediately. The agent then runs the [Agentic Search](/how-to-guides/agent-composer-templates#agentic-search) template—multi-turn research over the datastore—and streams a response with citations and a References section. 3. **Grounded generation:** Answers are generated only from the research step, with citations in `[n]()` format and links to the documentation URLs. ## Try it Use the search bar at the top and ask a question about Contextual AI. ## Build your own: sync + agent We share the building blocks so you can run a documentation agent on your own content. ### datastore-sync Open-source repo that keeps a Contextual AI datastore in sync with your docs (and optionally your website). We use it for this site: [**ContextualAI/datastore-sync**](https://github.com/ContextualAI/datastore-sync). * **GitHub docs sync:** Automatically syncs MDX/MD and OpenAPI specs from a GitHub repo (e.g. Mintlify docs). Incremental sync (commit SHAs, tree diffs), published-content filtering via `docs.json`, and OpenAPI parsing into per-endpoint markdown with human-readable URLs. Compiles MDX to clean PDFs before ingestion for better RAG quality. * **Website sync (optional):** Firecrawl-based sync for other website content, with sitemap crawling and diffing (agent using this section coming soon) * **State:** Redis-backed state (or local JSON in dev); Vercel-ready with webhook endpoints (e.g. `POST /sync/github` for GitHub push events). Use it to feed an agent with your published documentation so it stays up to date as you push changes. ## Next steps * [Agent Composer Documentation](/quickstarts/agent-composer) — Create and configure agents in the UI. * [Agentic Search](/how-to-guides/agent-composer-templates#agentic-search) — Template used by this documentation agent. * [Connectors](/connectors/overview) — Keep datastores in sync with Confluence, SharePoint, Drive, and more. * [Template Catalog](/examples/templates-catalog) — Other templates (Basic Search, Enterprise, etc.).