WikiTimeline - Interactive AI Timelines from Wikipedia

TL;DR

TL;DR: WikiTimeline transforms any Wikipedia article into an interactive AI-generated timeline — search a topic, generate a chronological view, and compare multiple timelines side by side.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: wiki-timeline.com
• Source repository: github.com/wenzhenl/wikitimeline
• License: MIT (verified via GitHub API)
• HN launch thread: not available (repo pushed March 2026)

What Is WikiTimeline?

WikiTimeline is an open-source web app that takes any Wikipedia article and converts its content into a structured, interactive timeline using Google Gemini AI. You search for a topic, the tool extracts chronological events from the article, and renders them as a zoomable, scrollable timeline. Multiple timelines can be opened side by side for comparison.

The project is built with Next.js, uses Vercel KV for storage, and relies on Google Gemini for the AI extraction layer. It was created by a solo developer and released in early 2026.

Key capabilities from the README:

• Instant conversion of any Wikipedia article into a timeline
• AI-powered extraction via Google Gemini
• Multi-timeline side-by-side comparison
• Dark mode and responsive design
• Zoom, scroll, and interactive event exploration

Setup Workflow

Prerequisites

• Node.js 18+
• npm or yarn
• Google Gemini API key (from Google AI Studio)
• Vercel CLI (for KV storage)

Step 1: Clone the Repository

git clone https://github.com/wenzhenl/wikitimeline.git
cd wikitimeline

Step 2: Install Dependencies

npm install

Step 3: Configure Environment Variables

cp .env.example .env.development.local

Open .env.development.local and set:

GEMINI_API_KEY=your_gemini_api_key_here

Set up Vercel KV storage:

npm i -g vercel
vercel link
# Create a KV storage in your Vercel Dashboard
vercel env pull .env.development.local

Step 4: Run the Development Server

npm run dev

Visit http://localhost:3000, search for a Wikipedia topic, and click Generate Timeline.

Deeper Analysis

How the AI Extraction Works

WikiTimeline does not use a dedicated Wikipedia parsing library. Instead, it feeds the raw article text to Google Gemini with a prompt that instructs the model to extract chronological events and their dates. The structured output is then rendered as a timeline. This means quality depends heavily on the Wikipedia article’s structure — well-structured articles with clear date annotations produce better timelines.

Deployment

The README documents deployment to Vercel. A vercel deploy command handles the build, and all environment variables (Gemini key + KV store) are configured through the Vercel dashboard.

The architecture is documented on DeepWiki for anyone wanting to understand the internals.

Limitations

• Requires a Gemini API key (not free for heavy usage)
• Wikipedia-only as an input source — no support for arbitrary URLs or documents
• Timelines are as good as the underlying Wikipedia article’s date annotations
• No offline mode; everything runs through the web UI

Practical Evaluation Checklist

• Does it convert a Wikipedia article to a timeline in under 30 seconds?
• Are the extracted dates accurate for a well-structured article?
• Does multi-timeline comparison work without page reload?
• Does dark mode toggle function correctly?
• Is the responsive design usable on mobile?

Security Notes

• Gemini API key is stored server-side in Vercel environment variables — never exposed to the client
• Vercel KV stores user session data; no sensitive PII is handled by the app itself
• Wikipedia content is fetched at request time; no persistent cache of article content outside Vercel KV

FAQ

Q: Does it work with any Wikipedia language edition? A: The README demonstrates English Wikipedia usage. The underlying Gemini extraction should work with any Wikipedia article regardless of language, but this has not been tested.

Q: Is there a hosted version I can try without deploying? A: Yes — visit wiki-timeline.com directly and use the live demo without any setup.

Q: What happens if the Wikipedia article has no dates? A: Gemini will return an empty or minimal timeline. The quality is entirely dependent on the source article having extractable chronological information.

Q: Can I use a different LLM instead of Gemini? A: Not without modifying the source code. The prompt and extraction logic are hardcoded for Google’s Gemini API.

Conclusion

WikiTimeline solves a specific and genuinely useful problem: making the linear, prose-heavy narrative of Wikipedia articles spatial and chronological. The side-by-side comparison feature is its strongest differentiator — comparing the timelines of two historical figures or events surfaces connections that reading two articles linearly would not.

If you frequently work with historical data from Wikipedia, it is worth bookmarking the live demo at wiki-timeline.com. For developers, the self-host option gives you full control over the Gemini API key and data handling.