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KResearch Review: How This AI Assistant Writes 10-Page Reports in Minutes

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How to Let AI Write a 10-Page Research Report in the Time It Takes to Sip a Coffee

An end-to-end, plain-English guide to KResearch, the open-source deep-research assistant

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Table of Contents

Why You Need a Second Brain

Writing a term paper, a competitive-analysis memo, or an investment brief usually follows the same exhausting pattern:

  1. Spend 80 % of the time hunting for sources.
  2. Spend the remaining 20 % trying to make sense of them.
  3. Realize the deadline is in six hours.

KResearch (KR) flips that ratio. It hands the “hunt and sort” work to a team of AI agents, leaving you free to think, verify, and polish.

What KResearch Actually Is

In one sentence: KR is an open-source, browser-based research assistant that plans, searches, reads, and writes an entire report for you.

Picture a miniature research lab inside your laptop:

  • Alpha, the strategist, draws the high-level plan.
  • Beta, the tactician, decides which paragraphs to skim and which to ignore.

Together they run several iterative cycles: plan → search → read → refine → repeat. The final product is a structured Markdown report, a knowledge graph, and a list of live web citations.

Core Capabilities at a Glance

Capability Plain-English Explanation Immediate Benefit
Conversational AI agents Two agents chat with each other to improve the plan Fewer blind spots
Iterative research cycles Up to five passes of search-and-refine Deeper coverage
Real-time progress log Watch every step like a timeline Full transparency
Four research modes Balanced / Deep Dive / Fast / Ultra Fast Match your time budget
Markdown report Clean, copy-paste-ready text No re-formatting
Knowledge graph Auto-generated Mermaid diagram See relationships at a glance
Sourced citations Every claim links to a live webpage One-click fact-check
Responsive UI Works on phone, tablet, or desktop Research on the go

How the Workflow Feels in Real Time

flow
  1. Type your question: “What are the barriers to solid-state battery commercialization?”
  2. Pick a mode: Deep Dive (≈15 min).
  3. Watch the log:
    • Alpha creates an outline: tech bottlenecks → supply chain → policy.
    • Beta fetches 12 authoritative pages and extracts 38 key points.
    • Alpha notices the policy angle is thin and launches one more search round.
  4. Collect results: a 2 400-word report, a knowledge graph, and a bibliography ready for download.

Install and Run in Three Steps

The entire setup takes about five minutes and requires zero configuration beyond pasting an API key.

Step 1: Prerequisites

  • Google Gemini API key (free tier available at Google AI Studio)
  • Node.js LTS (v20 or newer)
  • npm, pnpm, or yarn

Step 2: Clone and Install

git clone https://github.com/KuekHaoYang/KResearch.git
cd KResearch
npm install        # or pnpm install

Step 3: Add Your Key and Launch

Create a file called .env in the project root:

API_KEY="your_gemini_api_key_here"

Start the dev server:

npm run dev

Your terminal will display a local address—usually http://localhost:5173. Open it in any browser.

If you prefer not to touch dot-files, paste the key directly into the in-app Settings modal. KR stores it in your browser’s local storage; nothing ever leaves your machine.

Tour the Interface

ui
  • Top-left: Research-mode selector (Balanced / Deep Dive / Fast / Ultra Fast).
  • Center: Large query box that accepts Chinese, English, or mixed-language questions.
  • Right panel: Live timeline showing every agent decision.
  • Bottom toolbar: Theme toggle (light / dark) and Settings button.

Choosing the Right Research Mode

Mode Search Rounds Pages per Round Typical Use Case Estimated Time
Balanced 3 6 Weekly briefing, class assignment 5–8 min
Deep Dive 5 10 Thesis intro, white paper 12–20 min
Fast 2 4 Pre-meeting prep, quick scan 2–3 min
Ultra Fast 1 3 Just the gist < 60 s

Pro tip: Run Ultra Fast first to test your question’s scope; then escalate to Deep Dive once you’re sure the topic is worth the quota.

Understanding the Deliverables

1. Markdown Report

  • Auto-structured with four levels of headings: Background → Current Status → Challenges → Outlook.
  • Each bullet ends with an inline citation [1], [2], etc.
  • Copy directly into Word, Notion, or LaTeX without re-formatting.

2. Knowledge Graph

  • Generated in Mermaid syntax; paste into Mermaid Live Editor for tweaks.
  • Color-coded nodes: green for technologies, blue for companies, orange for regulations.
  • Hover tooltips display short summaries.

3. Citation List

  • Tabular view with columns: Title | Domain | Access Time.
  • One-click “Copy All Links” button for quick sharing.
  • Optional BibTeX export for reference managers.

A Real Case Study

Topic: “Quantum Computing Applications in Drug Discovery, Technical Bottlenecks, and Commercial Prospects”
Mode: Deep Dive (≈15 min)

Log Excerpt (condensed)

[00:00] Alpha: Outline draft – Algorithms, Hardware, Case Studies, Regulation.  
[01:15] Beta: Extract 3 key metrics from IBM 2023 white paper.  
[02:30] Alpha: Regulation angle sparse; new keyword “FDA + quantum”.  
[04:05] Beta: Locate Nature Reviews Drug Discovery 2024 review; append 7 new citations.  
[06:10] Final deliverable ready – 2 400 words, one graph, 21 sources.

TL;DR from KR

Quantum drug discovery is still at the proof-of-concept stage. The chief bottlenecks are qubit count and error rates. Commercial deployment before 2028 is unlikely except through joint R&D programs.

Frequently Asked Questions

Question Answer
Is it free? The code is MIT-licensed. Gemini API usage is billable, but Google gives new accounts $60 in credits.
Does it support Chinese queries? Yes; it returns Chinese-language reports if the query is in Chinese.
Where is my data stored? Nowhere. All processing happens in your browser and only communicates with Google.
Can I run it offline? Not yet; the Gemini API call requires an internet connection.
Can I swap in OpenAI? You’d need to fork the repo and replace the genai SDK with the OpenAI SDK.

Contribute to the Project

  1. Open an Issue describing the bug or feature.
  2. Fork the repository, create a branch feature/your-name.
  3. Follow the existing TypeScript conventions (ESLint and Prettier are pre-configured).
  4. Submit a pull request; maintainers typically respond within 48 hours.

Final Thoughts on Human-AI Collaboration

end

KResearch does not replace thinking—it replaces the grunt work.

  • Researchers can use the time saved to design sharper experiments.
  • Students can let KR run the first pass, then dive into the primary sources it surfaces.
  • Product managers can run Ultra Fast to spot a trend and Deep Dive to size the market.

When AI handles the breadth, humans can focus on the depth. That is where the real leverage lies.

Project Link

https://github.com/KuekHaoYang/KResearch

If you publish anything interesting with KR, drop a link in the Issues tab—let the community learn from your findings.

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