Library

Give the agent documents it can search, from Markdown or public URLs, with a vector store.

Give the agent documents it can search, so it answers questions about your product and not only your API.

createAgent({
  // ...
  knowledge: {
    embeddings: { model: "text-embedding-3-small" },
    sources: [{ id: "guide", title: "Invoicing guide", content: markdownString }],
    urls: ["https://docs.example.com/payment-terms", { url: "https://example.com/shipping", title: "Shipping" }],
  },
  vectorStore, // optional: pgvector, Vectorize, ...
});

The agent gets a search_knowledge tool. Answers show a Sources card, with links only for URLs you configured.

Behavior#

• Passages reach the model as labelled untrusted data and never reach the browser.
• URLs come only from config. They must be public http(s) and follow only same-host redirects. maxSourceBytes caps their size.
• URL sources refresh in the background after ttlMs (default one hour), or when you call agent.knowledge.refresh().
• A source that fails to load is skipped with a warning, and shown in doctor().
• The default store is in memory, so every start embeds all sources again. Pass a persistent vectorStore for large knowledge.