Hyperresearch turns Claude Code into a deep research agent, and it runs in OpenAI Codex too. Its hosted version, Hyperresearch Deep, is #1 on the DRACO deep research benchmark at 77.7 (10 tasks), ahead of Claude Research, ChatGPT, Gemini and Perplexity. A tier-adaptive 16-step pipeline takes one prompt and produces an adversarially-audited report with full source provenance. Every source it reads lands in a persistent, searchable vault, so each session starts smarter than the last.
Don't want to run it locally? Hyperresearch is the hosted version: the same pipeline, with no Claude Code or Codex install needed, plus a REST API, a remote MCP server and a citation-verification API.
[!NOTE]
New: hyperresearch runs on OpenAI Codex. One command installs the full pipeline for Codex: entry skill, step procedures, the subagent roster as Codex custom agents, and a Stop hook that keeps Codex from skipping steps. Same vault, same ship gate as on Claude Code.
pip install hyperresearch && hyperresearch install . --target codex
codex --sandbox workspace-write -c sandbox_workspace_write.network_access=true # then: $hyperresearch <question>
Codex support ships in the next PyPI release; until then install from GitHub with pip install git+https://github.com/jordan-gibbs/hyperresearch. Details and what differs on Codex: Codex.
See a sample report
Read a full report before you install anything. These are unedited Deep runs from the hosted version, and you don't need to log in:
These are the same kind of hosted Deep runs that put Hyperresearch at #1 on DRACO, at 77.7 (10 tasks). Our Finance, Academic and Law reports were produced by our current Deep writer from the research each task's run collected. Method and per-task scores are on the benchmark page.
Why it wins
- 250+ sources in a single run. The
premier scale profile targets 100–130 in the width sweep alone; citation chasing and gap-fill fetches more than double what actually lands in the corpus.
- Every citation is verified before the report ships. A skeptical cite-checker audits whether each cited source actually supports its sentence. Hallucinated quotes and unacknowledged retractions are hard blocks at the gate.
- Syndication doesn't count as consensus. An independence audit clusters derivative copies, so five reprints of one press release argue with the weight of one source.
- Adversarial by construction. Four critics attack every draft in parallel, and a tool-locked patcher can only apply surgical edits. It physically cannot rewrite the report.
- Eight scholarly sources, one query.
hpr scholar search hits OpenAlex, Crossref, CORE, DOAB, ClinicalTrials.gov, SEC EDGAR and FRED through one client layer and returns a single list deduplicated by DOI and title. Books, trials and filings come back alongside papers, each tagged so the pipeline knows which is which. The humanities and social sciences are covered on purpose, not as an afterthought.
- Paywalled papers get read, not skimmed. A closed paper normally enters a vault as a 1,500-character abstract that the report then cites as though it had been read. Hyperresearch asks Unpaywall, Europe PMC and CORE for a legal open-access copy and stores the full text instead, even when the publisher blocks the fetch outright. Every substitution is disclosed in the note, the frontmatter, and the CLI output.
- Nothing is thrown away. Every source lands in a searchable markdown-plus-SQLite vault that your next session reuses before it fetches anything new.
- Crashed runs resume. Each run keeps a manifest;
run resume picks up at the exact step where it died.
- Scales from 30 minutes to a dissertation. Bounded queries auto-route to a 5-step fast path. Opt-in dissertation runs (experimental) write 25K–80K words across chapters, from 300–450 sources.
Browse the vault by DOI, venue, citation count, retraction status, or retrieved
manuscript version with scholarly metadata filters.
Install
Works in Claude Code and OpenAI Codex: same pipeline, same vault.
cd your-project
pip install hyperresearch
hyperresearch install # Claude Code, then: /hyperresearch <anything>
hyperresearch install . --target codex # OpenAI Codex, then: $hyperresearch <anything>
--target all installs both side by side. Codex sessions need write access and network, see below.
Prefer a plugin or a single skill? Each route below installs one bootstrap skill, deep-research, that sets the pipeline up in the current project on first use. It still needs pip install hyperresearch.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
