Fireplexity
v2 · shipped

Answers that
show their
receipts.

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.

MIT licensed Next.js 15 Bring your own keys

What it does

One query in.
Six kinds of answer out.

Every response is assembled from live sources, not a training snapshot — and every claim carries a link you can open.

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.

Live web search, scraped in the same pass

Firecrawl returns ranked results and their full markdown together, so the model reads whole pages instead of guessing from search snippets.

News with dates attached

Time-sensitive questions route through a news pass that keeps outlet and publish date visible, so you can judge how stale an answer is.

Image results

Visual queries return a thumbnail grid with a full-size lightbox and the page each image came from.

Ticker detection

Mention a company and Fireplexity spots the symbol, then drops a live TradingView chart in beside the answer.

Follow-ups that go somewhere

Each answer ends with three generated next questions, drawn from what the sources actually covered.

How it works

Three hops from
question to citation.

No vector store, no nightly crawl, no index to keep warm. The pipeline runs per query, start to finish, in one request.

web results news + dates images

01

Search, three ways

The query fans out to Firecrawl's web, news, and image endpoints at once, so a single ask covers all three surfaces.

02

Scrape to markdown

Each result is rendered in a real browser, then stripped of nav, ads, and boilerplate — leaving LLM-ready markdown instead of raw HTML.

answer

03

Synthesize and stream

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

A closed answer engine
is a black box you rent.

Fireplexity is the whole thing — prompt, pipeline, and UI — in a repo you control.

Read the prompt, change the prompt

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.

Your queries stay yours

Deploy it inside your own infrastructure and sensitive searches never pass through a vendor's logs or analytics.

Swap any layer

The model, the search provider, the renderer, the theme. Each sits behind its own boundary, so replacing one doesn't mean rewriting the rest.

Pay for calls, not seats

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

Boring parts,
on purpose.

Everything here is something you already know how to debug. No bespoke framework, no custom runtime.

Next.js 15 app router React 19 AI SDK 5 streaming Firecrawl v3 search + scrape Groq inference Kimi K2 synthesis Tailwind v4 shadcn/ui Streamdown Shiki code blocks TradingView charts TypeScript

Self-host

Running in
about four minutes.

  1. Clone and installNode 18 or newer. No database, no queue, no extra services.
  2. Add two keysGrab them from firecrawl.dev and console.groq.com, then drop them in .env.local.
  3. Start itOpen localhost:3000 and ask something. Or push straight to Vercel — the repo deploys as-is.
bash
$ 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

Stop trusting answers
you can't check.

Fireplexity is MIT licensed and the entire pipeline is a few hundred lines. Read it, fork it, point it at your own sources.