Understand Main Markdown, State JSON, Appendix, Iris, Spatial Markdown, snapshots, and evidence.
Fetch renders the page, reads the visible content and structured state, then returns the useful working context first. When the task needs more, the output keeps stable references back to State JSON, Appendix, Highlights, citations, and replay.
Useful first, complete when needed.Most extraction tools force a bad choice: return everything and waste context, or prune aggressively and lose evidence. Fetch takes a different path. It gives the agent a useful working document first, then keeps the rest addressable through stable references.
Complete, but expensive. Raw payloads, navigation, footers, duplicated UI, and low-signal text crowd out the answer.
Concise, but lossy. Tables, icons, app state, links, and source evidence disappear when the agent needs the next step.
Useful first, complete when needed. Main Markdown and compact State JSON carry the common case; references preserve depth.
markdownjsonappendixmetaUseful page context includes the visible article or product copy, but it also includes layout, tables, icons, app state, secondary regions, links, and source evidence. Plain HTML-to-Markdown converters usually flatten that into one stream. Fetch turns the page into layers an agent can reason about.
Main Markdown is the first thing the agent should read, not the only thing Fetch knows.
Main Markdown is the readable working document. It is designed to answer the common case with fewer low-signal tokens, while preserving page structure that matters for reasoning.
State JSON is structured page and network state. It can answer questions Markdown alone cannot, especially when the page is backed by frontend data or API responses. It is part of the useful working context when it fits; when it is too large or better retrieved later, Fetch keeps references so the agent can ask for more.
Spatial Markdown keeps layout-sensitive information readable. Tables, pricing grids, comparison rows, icons, and visual affordances should not collapse into loose paragraphs when they carry meaning.
Frontmatter and metadata give the agent request and page context such as the source URL, title, capture metadata, snapshotId, and links to inspect deeper evidence. Exact fields belong in Output Model; this page is the mental model.
Progressive disclosure is how Fetch stays compact without becoming lossy.
Less context does not mean less evidence. The default output gives the agent the likely answer path first, then leaves handles for the rest:
snapshotId.This is why Fetch can help agents answer more questions with fewer tokens: it improves signal density instead of merely making pages shorter.
Iris is the part of Fetch that decides which page signals should become working context, structured state, spatial Markdown, or referenced evidence.
Iris is not an API field and it is not a separate product you configure. It is the product mechanism behind three jobs:
These are product layers, not exact response field names. For fields and schemas, use Output Model.
Read top to bottom, the output moves from the working document the agent reads first to the evidence it can reach for later. None of these layers is discarded; the lower ones stay addressable by reference.
Frontmatter, Main Markdown, and Spatial Markdown form the working document. State JSON and Appendix add structured state and secondary regions when they carry signal. Evidence handles, snapshotId, and Playground / Replay keep every claim traceable back to what Fetch saw.
Product terms map to current technical names in a few places:
markdown field.json in current API fields and include options./v1/fetch/search, fetch_search, and expandai search where exact names are required.snapshotId is the public handle used for replay, evidence, and follow-up retrieval.Fetch does not win by making pages shorter at any cost. It wins by improving signal density: the agent starts with the context most likely to answer the question, while deeper evidence stays recoverable.
That matters when an agent needs to: