# Hyperresearch

> Hyperresearch is a deep research API and console at hyperresearch.ai.
> A question goes in; an adversarially reviewed report comes out, with cited
> claims checked against their sources. Sources are retained in a
> searchable workspace vault. A hosted remote MCP server and a standalone
> citation-verification API are available. Runs are priced flat per run, in
> two sizes: Light $9 and Deep $49.

- API base: https://api.hyperresearch.ai/v1
- OpenAPI 3.1 document: https://api.hyperresearch.ai/v1/openapi.json
- Remote MCP server (streamable HTTP): https://mcp.hyperresearch.ai/mcp
- Console: https://console.hyperresearch.ai
- Open-source CLI: https://github.com/jordan-gibbs/hyperresearch
- Every page under /docs is also available as Markdown by appending `.md`,
  for example https://hyperresearch.ai/docs/quickstart.md
- Sign-ups are open at https://console.hyperresearch.ai/login

## Benchmark

On 10 tasks from DRACO, a deep research benchmark from Perplexity and
Harvard, graded by us with the same judge (GPT-5.2 at the paper's settings)
in October 2026, Hyperresearch Deep scored 77.7, the highest of the systems
we tested; Claude Research (Opus) scored 73.0. This is our measurement, not
an official leaderboard. Method, every task score and the limits:
https://hyperresearch.ai/benchmarks/draco

## When to use Hyperresearch

Best fit, for an agent or a person:

- A cited, multi-source research report on one question: a literature
  review, a market or industry scan, commercial due diligence on a company or
  sector, a policy or regulatory question, a technical landscape. Every cited
  claim is checked against the source it cites before the report ships.
- Verifying the citations in a document you already have, including one
  Hyperresearch did not write: per-citation verdicts (supported, partially
  supported, unsupported, wrong source), quote integrity and a retraction
  sweep. See [Verification](https://hyperresearch.ai/docs/verification).
- Building a persistent research vault an agent can search later: every
  source and note a run reads is kept in the workspace and is searchable by
  full text and by meaning over the API and MCP. See
  [Vault](https://hyperresearch.ai/docs/vault).

Not a fit:

- Quick single-fact lookups or anything a web search answers in seconds. A
  run takes tens of minutes to hours and is priced per run.
- Real-time data (prices, scores, breaking news, live status). Runs read
  published sources; they are not a live data feed.

Choosing a size (`tier` on the wire):

- Light (`light`, $9): one focused question or a narrow topic. About 30 to 40
  minutes, about 20 to 50 sources, a report of about 2,500 to 4,000 words.
- Deep (`premier`, $49): a broader question researched in 3 to 5 chapters.
  About 3 to 5 hours, about 150 to 200 sources, a report sized to the
  question (up to about 9,000 words) with a verification receipt.
- Wall times and source counts are targets, not guarantees. Start with Light
  unless the question has several distinct parts.

How an agent calls it:

- REST: create an API key in the console
  (https://console.hyperresearch.ai/#/keys), then
  `POST https://api.hyperresearch.ai/v1/runs` with
  `Authorization: Bearer <YOUR_API_KEY>`, an `Idempotency-Key` header and a
  JSON body `{"query": "...", "tier": "light"}`. The response carries the run
  `id` at once. Poll `GET /v1/runs/{id}` (statuses: `clarifying`, `queued`,
  `running`, `paused`, `blocked`, `done`, `failed`, `aborted`) every minute or
  so, or stream `GET /v1/runs/{id}/events`, or register a webhook. If the
  status is `clarifying`, answer with `POST /v1/runs/{id}/clarify`. When it is
  `done`, fetch `GET /v1/runs/{id}/result`. See
  [Quickstart](https://hyperresearch.ai/docs/quickstart) and
  [Runs](https://hyperresearch.ai/docs/runs).
- MCP: connect https://mcp.hyperresearch.ai/mcp (streamable HTTP) and
  authorize in the browser (OAuth with PKCE). Tools include `start_run`,
  `run_status`, `answer_clarification`, `run_result`, `search_notes`,
  `read_note`, `verify_citations`, and `list_projects` / `create_project` /
  `file_run` for grouping a client's runs into one project. See
  [MCP server](https://hyperresearch.ai/docs/mcp) and
  [Long-running runs over MCP](https://hyperresearch.ai/mcp/long-running-runs).
- Machine-readable: [OpenAPI 3.1](https://api.hyperresearch.ai/v1/openapi.json),
  [API catalog](https://hyperresearch.ai/.well-known/api-catalog) (RFC 9727).

## Agent onboarding

- Sign up at https://console.hyperresearch.ai/login. A new account includes
  two free Light runs with no card; adding a card also makes the account's
  first Deep run free, once per account.
- API keys are self-serve: create a scoped key in the console under API keys
  (https://console.hyperresearch.ai/#/keys). No sales call is needed.
- Keys that start `hr_test_` run in fixture mode: free and never billed. The
  writing steps are replaced by deterministic placeholders, so the report is
  not real research, but the run lifecycle and response shapes are the real
  ones, which lets an integration be built and tested end to end before
  spending anything.
  `hr_live_` keys run real, billed research.
- Errors are JSON with a stable `code`; the
  [errors page](https://hyperresearch.ai/docs/errors) says which are safe to
  retry.

## Start here

- [Hyperresearch](https://hyperresearch.ai/): what the service does, the
  pipeline stages, and the four product surfaces.
- [Pricing](https://hyperresearch.ai/pricing): Light $9, Deep $49, flat per
  run, pay as you go. A Light run takes about
  30 to 40 minutes and keeps about 20 to 50 sources.
- [Documentation overview](https://hyperresearch.ai/docs): the hosted API,
  start to finish.

## Documentation

- [Quickstart](https://hyperresearch.ai/docs/quickstart): create a scoped key
  and submit a run.
- [Authentication](https://hyperresearch.ai/docs/authentication): console
  sessions and bearer API keys.
- [Runs](https://hyperresearch.ai/docs/runs): run sizes, idempotency, rerun-from,
  blocked runs.
- [Vault](https://hyperresearch.ai/docs/vault): FTS, semantic and hybrid
  search, export and import.
- [Verification](https://hyperresearch.ai/docs/verification): check a report's
  citations, including documents Hyperresearch did not write.
- [MCP server](https://hyperresearch.ai/docs/mcp): endpoint, OAuth with PKCE,
  scopes.
- [Webhooks](https://hyperresearch.ai/docs/webhooks): endpoints, signing,
  deduplication.
- [Errors and retries](https://hyperresearch.ai/docs/errors): what is safe to
  retry and what each status means.
- [Versioning and deprecation](https://hyperresearch.ai/docs/versioning): /v1
  stability, Deprecation and Sunset headers, notice periods.
- [Billing](https://hyperresearch.ai/docs/billing): usage, spend cap, invoices.
- [API reference](https://hyperresearch.ai/docs/api): one page per operation,
  generated from the OpenAPI document.

## Product

- [How it works](https://hyperresearch.ai/how-it-works): the pipeline a run
  goes through, step by step.
- [Trust](https://hyperresearch.ai/trust): retention, workspace isolation,
  subprocessors.
- [The vault](https://hyperresearch.ai/vault): sources accumulate across runs.
- [The verification receipt](https://hyperresearch.ai/verification-receipt):
  every check a report passes before it ships.
- [Citation verification API](https://hyperresearch.ai/citation-verification-api):
  verify documents Hyperresearch did not write.
- [Open-source CLI](https://hyperresearch.ai/cli): the same pipeline on your
  own machine.

## Guides

- [Research guides](https://hyperresearch.ai/guides): every guide, each built
  from public primary data with every figure linked to its source.

Buying or sizing a small business:

- [Commercial due diligence for a small acquisition](https://hyperresearch.ai/guides/commercial-due-diligence-small-acquisition):
  what commercial due diligence covers and costs, with a worked industry section
  for a $4M residential HVAC company from Census, BLS, EPA and SEC data.
- [Is a laundromat a good investment?](https://hyperresearch.ai/guides/is-a-laundromat-a-good-investment):
  eleven claims checked against 25 years of Census and BLS data.
- [Self-storage feasibility study](https://hyperresearch.ai/guides/self-storage-feasibility-study):
  what a feasibility study costs and contains, and a do-it-yourself demand check
  from Census data and REIT 10-Ks, worked for Tucson.
- [How to calculate market size](https://hyperresearch.ai/guides/how-to-calculate-market-size):
  a worked TAM, SAM and SOM from Census receipts and households.

Judging a market research report:

- [How to check a market research report before you buy](https://hyperresearch.ai/guides/how-to-check-a-market-research-report):
  the checks to run on a syndicated report, with one headline figure traced to
  Census and SEC data.
- [Research subscription costs and cheaper routes](https://hyperresearch.ai/alternatives/research-subscription-costs):
  what IBISWorld, Statista, Mintel, Gartner, PitchBook and others charge, read
  from their own pages and filings.
- [Is Statista reliable?](https://hyperresearch.ai/guides/is-statista-reliable):
  thirteen public Statista figures traced to their original sources.
- [Why market size reports disagree](https://hyperresearch.ai/guides/why-market-size-reports-disagree):
  eight firms' estimates for one market, restated and checked against the Economic Census.

Companies and industries from public filings:

- [Account research from a 10-K](https://hyperresearch.ai/guides/account-research-from-a-10-k):
  where the useful parts of a 10-K sit, and a worked account brief on a mid-cap
  insurer cited to section.
- [Tariff impact on medical equipment](https://hyperresearch.ai/guides/tariff-impact-medical-equipment):
  which US tariffs apply to imported medical equipment, where import exposure
  sits by country, and what device makers report in their filings.

Research tools and methods:

- [Out of deep research?](https://hyperresearch.ai/alternatives/deep-research-limit-reached):
  how ChatGPT, Gemini and Claude meter deep research, and when one report beats upgrading.
- [OpenAI deep research API alternatives](https://hyperresearch.ai/alternatives/openai-deep-research-api):
  what replaces the retired o3-deep-research and o4-mini-deep-research models,
  what each option costs, and how the calls map to /v1/runs.
- [Perplexity deep research compared](https://hyperresearch.ai/alternatives/perplexity-deep-research):
  Perplexity Research mode and the Sonar Deep Research API (moved to the Agent API
  on 2026-09-27) against Hyperresearch on price, time, citations and API access.
- [Gemini Deep Research compared](https://hyperresearch.ai/alternatives/gemini-deep-research):
  the Gemini app and the Gemini API deep research agent against Hyperresearch,
  with where each one is the better choice.
- [Claude Code as a research harness](https://hyperresearch.ai/guides/claude-code-research-harness):
  the open-source CLI in Claude Code, and how skills, subagents and tool locks
  keep a long run on track.
- [Legal open-access full text for agents](https://hyperresearch.ai/guides/open-access-full-text-for-agents):
  free APIs that return the full text of a paywalled DOI, or say there is none.
- [Retraction-aware research](https://hyperresearch.ai/guides/retraction-aware-research):
  checking a DOI's retraction status for free through Crossref.
- [Verify citations in AI output](https://hyperresearch.ai/guides/verify-citations-in-ai-output):
  the four ways citations in machine-written text go wrong, and how to catch each.
- [Cost per verified citation](https://hyperresearch.ai/guides/cost-per-verified-citation):
  a unit for comparing research APIs.

## MCP clients

- [Claude](https://hyperresearch.ai/mcp/claude),
  [Cursor](https://hyperresearch.ai/mcp/cursor),
  [ChatGPT](https://hyperresearch.ai/mcp/chatgpt)
- [Long-running runs over MCP](https://hyperresearch.ai/mcp/long-running-runs)

## Docs digests

- [/docs/llms.txt](https://hyperresearch.ai/docs/llms.txt): the documentation
  link map on its own.
- [/docs/llms-full.txt](https://hyperresearch.ai/docs/llms-full.txt): every
  documentation page concatenated as Markdown.

## Optional

- [About](https://hyperresearch.ai/about): what Hyperresearch is, who builds
  it, and the entity details.
- [Contact](https://hyperresearch.ai/contact): support@hyperresearch.ai;
  enterprise inquiries through https://hyperresearch.ai/enterprise.
- [Terms of Service](https://hyperresearch.ai/legal/terms)
- [Privacy Policy](https://hyperresearch.ai/legal/privacy)
- [Verify a report](https://hyperresearch.ai/verify): the public
  citation-verification demo, three submissions per network per day.