Citations wired to the sentence, not the page
Inline markers map to the exact source that produced each claim. Hover one and the source card surfaces — title, domain, favicon, snippet — so verifying an answer takes a second, not a new tab.
Fireplexity is an open-source AI answer engine. It searches the live web, scrapes what it finds with Firecrawl, and streams back a cited answer — alongside news, images, and ticker charts.
Sources · 4
Answer
Firecrawl renders each page in a real headless browser before extraction, so client-side routes and SPA content resolve the same way they would for a visitor1. The rendered DOM is then converted to clean markdown, stripping nav and boilerplate so only the substance reaches the model24
Images
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What it does
Every response is assembled from live sources, not a training snapshot — and every claim carries a link you can open.
Inline markers map to the exact source that produced each claim. Hover one and the source card surfaces — title, domain, favicon, snippet — so verifying an answer takes a second, not a new tab.
Firecrawl returns ranked results and their full markdown together, so the model reads whole pages instead of guessing from search snippets.
Time-sensitive questions route through a news pass that keeps outlet and publish date visible, so you can judge how stale an answer is.
Visual queries return a thumbnail grid with a full-size lightbox and the page each image came from.
Mention a company and Fireplexity spots the symbol, then drops a live TradingView chart in beside the answer.
Each answer ends with three generated next questions, drawn from what the sources actually covered.
How it works
No vector store, no nightly crawl, no index to keep warm. The pipeline runs per query, start to finish, in one request.
01
The query fans out to Firecrawl's web, news, and image endpoints at once, so a single ask covers all three surfaces.
02
Each result is rendered in a real browser, then stripped of nav, ads, and boilerplate — leaving LLM-ready markdown instead of raw HTML.
03
Kimi K2 on Groq reads the markdown and streams an answer token by token through the AI SDK, tagging claims with the source they came from.
Why run your own
Fireplexity is the whole thing — prompt, pipeline, and UI — in a repo you control.
The system prompt, the citation format, the follow-up logic — all of it is a file in app/api. Nothing about how answers are shaped is hidden from you.
Deploy it inside your own infrastructure and sensitive searches never pass through a vendor's logs or analytics.
The model, the search provider, the renderer, the theme. Each sits behind its own boundary, so replacing one doesn't mean rewriting the rest.
Two API keys and a Vercel deploy. No per-user pricing, no plan tier gating the features you already have the code for.
The stack
Everything here is something you already know how to debug. No bespoke framework, no custom runtime.
Self-host
$ git clone https://github.com/firecrawl/fireplexity.git $ cd fireplexity $ npm install # two keys, both free to start $ cp .env.example .env.local $ echo "FIRECRAWL_API_KEY=fc-..." >> .env.local $ echo "GROQ_API_KEY=gsk_..." >> .env.local $ npm run dev → ready on http://localhost:3000
Fireplexity is MIT licensed and the entire pipeline is a few hundred lines. Read it, fork it, point it at your own sources.