jackswl

jackswl / deep-researcher

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Deep Research based on Claude Code's agentic framework 🦉⚡ export const SPINNER_VERBS = [ 'Accomplishing', 'Actioning', 'Actualizing', 'Architecting', 'Baking', 'Beaming', "Beboppin'", 'Befuddling', 'Billowing', 'Blanching', 'Bloviating', 'Boogieing', 'Boondoggling', ...]

83
23
100% credibility
Found Apr 01, 2026 at 83 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Deep Researcher is an open-source tool that uses AI to search multiple academic databases, collect and deduplicate papers, follow citation chains, and generate structured literature reviews with BibTeX exports.

How It Works

1
🔍 Discover Deep Researcher

You find this helpful research buddy while looking for a way to quickly gather and organize academic papers on your topic.

2
💻 Set it up on your computer

Download and install it with a simple command, so it's ready to use right away.

3
Pick your thinking helper
🏠
Local free brain

Download a free model to think privately on your own computer.

☁️
Online service

Link a fast online helper with your account for powerful results.

4
📖 Unlock more paper sources

Sign up for free passes to special libraries like Scopus to find even more papers from everywhere.

5
❓ Ask your research question

Type in your topic like 'AI in medicine' and watch it spring into action.

6
🔄 It gathers and sorts papers

Your buddy searches libraries, follows important connections, and builds a full collection just for you.

📄 Enjoy your ready review

Get a neat report with categories, key insights, gaps to explore, and copy-paste citations for your work.

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Star Growth

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AI-Generated Review

What is deep-researcher?

Deep Researcher is a Python CLI tool that automates academic literature reviews using AI agents. Feed it a query like "transformer models in structural health monitoring," and it searches eight real databases—including arXiv, PubMed, Scopus, and IEEE Xplore—follows citation chains, builds a deduplicated paper corpus, then synthesizes a categorized report with gaps analysis. Outputs include Markdown reports, BibTeX for LaTeX, and JSON/CSV for spreadsheets, all checkpointed for interrupted runs.

Why is it gaining traction?

Unlike deep research ChatGPT or Gemini tools that scrape the web and hallucinate sources, this hits paywalled abstracts via free APIs, supports local LLMs like Ollama (qwen3.5:9b), and needs just three dependencies—no LangChain bloat. Adjustable breadth/depth controls and interactive query refinement deliver comprehensive, verifiable results fast, with open-access detection as a bonus.

Who should use this?

Grad students drafting thesis lit reviews, researchers scoping new projects in CS/engineering/biomed, or engineers surveying prior art before prototyping. Ideal for anyone tired of manual database hopping or Perplexity's shallow web hits.

Verdict

Promising early prototype (79 stars, 1.0% credibility) for deep research AI—excellent docs and MIT license make it forkable, but low adoption means watch for bugs in edge cases. Try it locally if you need a lean github deep researcher alternative to bloated alternatives.

(198 words)

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