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The Knowledge Base is how your agent answers with your facts: uploaded files, structured docs, and pages from websites you allow. Answers can include source attribution so visitors see where information came from.

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Two steps: add content, then link to an agent

You can link multiple knowledge bases to one agent. Removing a link from an agent does not delete the org’s knowledge base; it only stops this agent from using it.

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File uploads (PDF and Markdown)

  1. Go to Knowledge Base in the sidebar (not the Agent editor). Upload PDF or Markdown there.
  2. The system extracts text, splits it into searchable chunks, and indexes them for retrieval.
  3. Wait until processing finishes before linking and testing—large PDFs can take longer.
  4. Open the Agent editor → Knowledge and link this knowledge base to the agent(s) that should use it.
Tips
  • Use clear headings in Markdown so chunks stay meaningful.
  • For citations, add a metadata block to Markdown files (see Source attribution below).

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Website and URL sources (web crawling)

Configure web crawl and URLs in the Knowledge Base section first, same as files. After the source is ready, link that knowledge base to your agent in the Agent editor so answers can use the crawled pages.

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What you can configure (typical options)

Operational notes
  • Very large sites may be subject to per-organization crawl limits and pagination in the UI so you can browse long URL lists.
  • After a successful sync, search engines may be notified of changes where that feature is enabled—useful when public URLs change often.
If you only need a one-off import, you can still add a single URL without turning on aggressive schedules.

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Source attribution

When the agent answers from your knowledge base, it can cite sources. That builds trust and helps visitors verify facts.

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Adding metadata to Markdown

At the end of your Markdown file, add a ## metadata: heading followed by a JSON object (exact layout can depend on your Markdown tooling; the goal is sourceURL and title for citations):

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How retrieval works (RAG)

We use Retrieval-Augmented Generation (RAG):
  1. User asks — e.g. “How do I reset my password?”
  2. Search — The system searches your knowledge (files + web chunks) for the most relevant snippets.
  3. Augment — Those snippets are added to the model prompt with guardrails.
  4. Generate — The model answers using that context—reducing hallucinations relative to a blank model with no knowledge.