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What is hyperresearch?

jordan-gibbs/hyperresearch — explained in plain English

Analysis updated 2026-05-18

1,133PythonAudience · researcherComplexity · 3/5LicenseSetup · easy

In one sentence

Turns Claude Code into a deep research agent that searches the web, verifies its own citations, and builds a reusable knowledge base.

Mindmap

mindmap
  root((Hyperresearch))
    What it does
      Deep web research
      Cited reports
      Persistent knowledge base
    Tech stack
      Python
      Claude Code
      SQLite vault
    Use cases
      In depth research
      Verified citations
      Dissertation length reports
    Audience
      Researchers
      Analysts
    Setup
      pip install
      Requires Python 3.11 to 3.13
      Run inside Claude Code

Code map

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What do people build with it?

USE CASE 1

Research a topic in depth and get a written report with verified citations.

USE CASE 2

Build a persistent, searchable knowledge base of sources across multiple research sessions.

USE CASE 3

Generate a long, chaptered research document like a dissertation from hundreds of sources.

USE CASE 4

Resume a long-running research task that was interrupted partway through.

What is it built with?

PythonClaude CodeSQLite

How does it compare?

jordan-gibbs/hyperresearchopenmoss/moss-transcribe-diarizelyra81604/zhengxi-views
Stars1,1331,1391,151
LanguagePythonPythonPython
Last pushed2026-06-30
MaintenanceActive
Setup difficultyeasyhardmoderate
Complexity3/54/53/5
Audienceresearcherdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

Requires Python 3.11-3.13 and Claude Code, runs itself take 30 minutes to several hours depending on tier.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

So what is it?

Hyperresearch turns Claude Code into a deep research assistant. You give it one question or topic, and it runs a long, structured pipeline of up to sixteen steps that searches the web, reads many sources, checks its own work, and writes a full report with citations. Every source it reads gets saved into a persistent, searchable knowledge base stored as markdown files plus a small database, so if you research a related topic later, it can reuse what it already found instead of starting from zero. What makes this different from a simple search-and-summarize tool is the amount of checking built in. After a draft report is written, several separate reviewer roles read it looking for problems, then a dedicated fixer role is only allowed to make small, surgical edits rather than rewrite the whole thing from scratch. A separate citation checker verifies that each fact cited in the report is actually backed up by the source it points to, and catches cases where many copies of the same press release are being treated as several independent sources instead of just one. The pipeline adjusts its depth automatically based on the question. Simple factual questions take a short five-step path and finish in around 30 to 40 minutes. More complex questions needing deeper argument and review use the full sixteen-step path and take one and a half to two and a half hours. There is also an opt-in dissertation mode for very large research projects, which can take four to eight hours and produce a much longer chaptered report using hundreds of sources. If a run is interrupted partway through, it can resume from where it left off rather than starting over. To use it, you install it as a Python package inside a project that uses Claude Code, then run a single command inside Claude Code to start a research task. It requires Python version 3.11 through 3.13. The project is released under the MIT license.

Copy-paste prompts

Prompt 1
Set up a multi-step research pipeline in Claude Code that decomposes a query, searches the web, and drafts a report.
Prompt 2
Write a citation checker that verifies each cited sentence is actually supported by its source.
Prompt 3
Design a patcher agent that is only allowed to make small surgical edits to a draft, never rewrite it.
Prompt 4
Build a resumable pipeline that saves a manifest so a crashed run can continue from the last completed step.
Prompt 5
Create an independence audit that detects when multiple sources are just copies of the same original.

Frequently asked questions

What is hyperresearch?

Turns Claude Code into a deep research agent that searches the web, verifies its own citations, and builds a reusable knowledge base.

What language is hyperresearch written in?

Mainly Python. The stack also includes Python, Claude Code, SQLite.

What license does hyperresearch use?

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

How hard is hyperresearch to set up?

Setup difficulty is rated easy, with roughly 30min to a first successful run.

Who is hyperresearch for?

Mainly researcher.

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