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hyperresearch

Deep-research harness for Claude Code: a 16-step, tier-adaptive pipeline with adversarial critics, cite-checking and 250+ sources per run, every source kept in a persistent markdown+SQLite vault.

3,426 336 Python MITupdated 7 days ago
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Curator's take

The most serious open deep-research pipeline you can run inside Claude Code: width sweep, contradiction graph, parallel depth investigators, four adversarial critics and a tool-locked patcher that can only apply surgical edits. Scholarly search fans out to OpenAlex/Crossref/CORE/EDGAR/FRED and it fetches legal OA full-text for paywalled papers instead of citing abstracts. Use it for reports you would otherwise pay a research analyst for; the vault means the second run on a topic starts warm. Not for quick lookups - even the 5-step fast path is minutes and the full pipeline is 30 min to hours of Claude Code time, plus your Max quota. Locked to Claude Code (uses the Skill tool for step loading); last30days-skill is the fast social-signal alternative, openresearcher is the trained-model route.

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The Most Powerful Deep Research Harness

PyPI version Python 3.11+ License: MIT GitHub stars

Hyperresearch turns Claude Code into a deep research agent: one that currently leads the DeepResearch-Bench RACE leaderboard (benchmarked internally). 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.

DeepResearch-Bench top-5 hyperresearch leads the chart ahead of Grep Deep Research, Cellcog Max, nvidia-aiq, Gemini Deep Research, and OpenAI Deep Research

Forward-looking projection from a stratified pilot against the DeepResearch-Bench leaderboard snapshot (https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard). Third party validation is pending.

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 write 25K–80K words across chapters, from 300–450 sources.

Install

cd your-project
pip install hyperresearch && hyperresearch install

Then /hyperresearch <anything> in Claude Code.

Python 3.11–3.13. (3.14 not yet supported. Use pyenv install 3.13, uv venv -p 3.13, or `py -3.