The API

There is a REST API — this is its whole surface, one link per component

Yes, there's an API. DeepSieve is a REST API first; the MCP server, SDK, and CLI are all conveniences layered on top of it. Everything you can do in the app — define a schema, start research, read a cited dataset, get notified when it changes — you can do over plain HTTP.

If you'd rather not read this page, point your coding agent at https://deepsieve.ai/setup.md and it wires the whole thing up. If you're choosing how to connect, start at Choosing an integration. This page is the map of what there is to connect to.

The shape of it

You work with a handful of components, each with its own page below. The usual arc:

1. Authenticate — mint a key or log in through the browser.
2. Define a Blueprint (your schema) once.
3. Start research runs from natural-language queries.
4. Read the dataset back — cited, and re-verified while monitoring is on.
5. Have DeepSieve push changes to you (webhooks) instead of polling, and ask the research agent about any value.

# The one call that proves you're connected:
curl -s https://deepsieve.ai/v1/me -H "Authorization: Bearer $DEEPSIEVE_API_KEY"

API components

Each of these is a first-class part of the API. Follow the link for the endpoints, request/response shapes, and the honest caveats.

• Blueprints & your schema — the schema that defines your entities, columns, and how the data reads back. You may create a new Blueprint over the API; the active one is edited only by a human.
• Research runs — start a run from a query, poll it to completion, and understand what a run costs. Test free with dry_run first.
• Dataset sync — read the cited dataset, page through it, and read a citation's verdict honestly before repeating a value as fact.
• Webhooks — get pushed an event when a run finishes or a cell changes, instead of polling. Signed and replayable.
• Global Reports — the saved, shareable views over your dataset.
• Chat with the research agent — ask about a value, or get a specific cell re-checked, and read the answer back programmatically.

Reference

• Authentication & API keys — browser login vs. ds_live_... keys, scopes, and the Agent preset.
• Errors — every failure is {error: {code, message, retriable, request_id}}; retriable=false means do not retry.
• Rate limits & budgets — the limits, and how prepaid credits gate real runs.
• Versioning & stability — what /v1 promises now (announced breaking changes) and at GA (additive-only).

Machine-readable

Everything above is also available in forms an agent can consume directly:

• /openapi.json — the full machine-readable contract; the Python SDK is generated from it.
• /llms.txt · /llms-full.txt — the docs, indexed and concatenated for an agent.
• Any page here is markdown if you append .md to its URL.