Hyperresearch

The Most Powerful Deep Research Harness: a deep research skill for Claude Code and OpenAI Codex

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Hyperresearch turns Claude Code into a deep research agent, and it's #1 on the DRACO deep research benchmark, ahead of Claude Research, ChatGPT, Gemini and Perplexity. It runs in OpenAI Codex too. 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.

[!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:

• Toast vs Square vs Clover: a restaurant POS competitor analysis, built mostly from SEC filings (241 sources)
• Buying a $2M HVAC business: multiples, SBA loans and diligence (213 sources)
• Who is winning AI coding agents, October 2026 (259 sources)

These are the same kind of Deep runs that put Hyperresearch at #1 on DRACO. Method and per-task scores are on the benchmark page.

Why it wins