Aurora

Name: Aurora
Author: FantasyLab-ai

by FantasyLab-aiversion 0.10.0i★ –iRuns on your computer

Registry nameio.github.FantasyLab-ai/aurora

Install

Overview

Description from the maker (original English)

Glass-box statistical analysis for agents: 19 methods, cited findings, measured error rates.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 36/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "aurora": {
      "command": "uvx",
      "args": [
        "aurora-mcp",
        "--allow-root"
      ]
    }
  }
}

Run this in your terminal

claude mcp add aurora -- uvx aurora-mcp --allow-root

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "aurora": {
      "command": "uvx",
      "args": [
        "aurora-mcp",
        "--allow-root"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "aurora": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "aurora-mcp",
        "--allow-root"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account FantasyLab-ai, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18444 servers in this category
Published to the registry more recently than 21% of the other 18444 servers in this category
Basic checks equal to or better than 100% of the other 18444 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Aurora safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.10.0) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Egovframe Scaffold MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

by formulahendryEarly↓ 66/wk★ 440Updated 14 months ago

Runs on your computer

Install

Remote Desktop Commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

by desktop-commanderGrowing★ 211Updated 3 days ago

Runs in the cloud✓ desktopcommander.app

Install

Egovframe Scaffold MCP

eGovFrame (전자정부 표준프레임워크) projects: templates, components, CRUD, build, 5.x migration, dependencies

by EricSeokgonEarly↓ 1.4K/wkUpdated today

Runs on your computer

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Aurora

Glass-box quantitative intelligence. Local. Open. Cited.

Aurora is the verification cortex for serious quantitative work — for humans analyzing hard data, and for AI systems that can't afford to hallucinate.

Cloud LLMs guess. Aurora computes.

[⬇️ Download the desktop app](#%EF%B8%8F-download-the-desktop-app) · Run from source · Aurora Sentinel demos · See it in action · Aurora Copilot · Aurora Cortex (MCP + SDK) · Roadmap · FantasyLab.ai

⬇️ Download the desktop app

The easiest way to run Aurora — no Python, no terminal, no setup. Download the installer for your OS, double-click, and Aurora opens as a native app with the analysis backend bundled inside it.

→ Download the latest release

Pick the file that matches your OS (filenames carry the version, e.g. 0.2.0):

OS	File to download	Install
Windows 10/11	`Aurora_x.x.x_x64-setup.exe` (recommended)	Run the setup wizard.
Windows 10/11	`Aurora_x.x.x_x64_en-US.msi`	Alternative MSI installer (same app).
macOS (Apple Silicon · M1/M2/M3/M4)	`Aurora_x.x.x_aarch64.dmg`	Open the `.dmg`, drag Aurora to Applications.
Linux (Debian / Ubuntu / Mint)	`Aurora_x.x.x_amd64.deb`	`sudo apt install ./Aurora_x.x.x_amd64.deb`
Linux (Fedora / RHEL / openSUSE)	`Aurora-x.x.x-1.x86_64.rpm`	`sudo dnf install ./Aurora-x.x.x-1.x86_64.rpm`

Aurora_aarch64.app.tar.gz is an auto-updater artifact, not a download —

use the .dmg on macOS. There is currently no Intel-Mac (x86_64) or Linux

AppImage build; Intel-Mac users can run from source.

On first launch Aurora bootstraps a small knowledge-bank seed, then runs fully offline — no API keys, no cloud, no telemetry. Drop a CSV on the window and watch it analyze. Aurora is local-first: your data never leaves your machine unless you explicitly share a single finding — see PRIVACY.md.

Heads up on the "Unknown Publisher" warning. Current releases are not yet

code-signed, so Windows SmartScreen may show *"Windows protected your PC"* and

macOS Gatekeeper may say *"unidentified developer."* This is expected for a

young open-source project. On Windows: More info → Run anyway. On macOS:

right-click the app → Open. Code-signing is on the roadmap.

Desktop app — tips & known quirks

A few things that are expected behavior, not bugs:

First launch shows a welcome screen, not a run. That's intentional — a fresh start is clean. Drop a CSV (or click a demo card) to begin; your past runs are always available under Data → Bundles.
Give the backend a few seconds on first launch. Aurora starts a local analysis server (127.0.0.1:8001) the first time you open it; the status dot reads "connecting…" for ~5–10 s before it goes live. If it lingers, the app is still warming up — it is not frozen.
Big datasets take longer, and results land all at once. A large file can spend a while in *"analyzing…"*. When it finishes you'll see *"analysis complete"* and the views populate. If the Overview looks momentarily empty right after completion, give it a few seconds or click another tab and back — the assembled state is still being fetched, and it will fill in.
Re-running the same file is instant. Identical data + settings reuse a cached analysis; the banner reads *"instant · cached."* That's a speed win, not a skipped run.
Knowledge Bank starts small on a fresh install. The bundled app ships with a seed bank that grows over time; the large multi-thousand-entry bank you may see in screenshots is built up on a machine that's been ingesting data for a while. Citations still work against whatever is local.

Want to build it yourself or run from source? Keep reading.

⚡ Quickstart (60 seconds)

# 1. Clone + create a virtualenv

git clone https://github.com/FantasyLab-ai/aurora.git cd aurora python -m venv .venv

Windows:

.\.venv\Scripts\Activate.ps1

macOS / Linux:

source .venv/bin/activate

2. Install

pip install -r requirements.txt

3. Run the Studio

python studio_api.py

Open http://127.0.0.1:8000. Click ▶ Try a demo for an instant smoke test, or drop your own CSV / Parquet / JSON / XLSX.

First run will download a small knowledge-bank seed (~50 MB). After that, Aurora runs fully offline — no API keys, no cloud, no telemetry.

Optional extras (only if you need them):

pip install cryptography             # Ed25519 bundle signing

pip install mcp # MCP server for LLM agents (Claude Desktop, Cursor, etc.) pip install -r requirements-dev.txt # contributor / test extras

For the optional Vite + TypeScript frontend build (developers only), see § Optional frontend build.

🖥️ The desktop app

A native, installable app — frameless window, sidebar navigation, drag-and-drop any file onto it. Built with Tauri 2, with Aurora's full analysis backend bundled inside the installer, so it runs with zero setup: no Python, no venv, no terminal.

Most people should just [download it](#%EF%B8%8F-download-the-desktop-app).

The rest of this section is for developers who want to build it from

source or hack on the UI.

What it does

Surface	Behavior
Frameless window	Rounded card on transparent OS background; `aurora ◆` titlebar with min/max/close
Sidebar (left)	Workspace · Data · Full Studio sections + live "aurora: online / offline" status dot
Tab strip (top)	Overview / Findings / Data with one shared sliding underline
Drag & drop ANY file	CSV, TSV, JSON, JSONL, Parquet, XLSX — drop anywhere on the window, or click to browse
Overview	Stat cards (Findings · Methods · Anomalies · Regimes) + Aurora's narrative in plain English
Findings	Severity-filtered card grid; click a card for the full evidence panel
Methods	Per-run method tally with share-bars
Datasets	Bundled fixtures + demo datasets — click any card to run it
Bundles	Past runs — every signed `.aurora.json` on disk, status-coded
Aurora Studio sidebar	Full legacy UI in an iframe — every feature still reachable

Build it from source / develop

The desktop app lives in desktop/. To build the installer yourself (PyInstaller backend + Tauri shell) or run the UI in hot-reload dev mode, see [`desktop/README.md`](desktop/README.md) — it covers the prerequisites (Node, Rust), the one-command launcher (launch.ps1), the installer build (build_installer.ps1), and the Windows Smart App Control caveat. A git tag vX.Y.Z push triggers the cross-platform release CI.

🚨 Aurora Sentinel — Decision Contracts in the room

Aurora ships a complete demo rig under demos/ that turns a Decision Contract trip into something tangible — a Discord ping, a Slack alert, an OBS overlay card, a smart-plug flipping in your room. Five "I gave my local AI X and watch what it caught" videos, all built on the same scaffolding.

#	Demo	Hero shot	What it shows
1	Aurora Alarm	A physical light flips when the data breaks	The closed loop is real — software → cited reason → real-world consequence
2	Community Sentinel	A Discord embed lands with method + row +	z	σ	Drop-in for any Discord community; cited anomalies, no cloud
3	The Save	A Slack ping arrives at the regime shift	The "human watching dashboards would have slept through this" demo
4	Verification Cortex	An agent calls Aurora's MCP and acts on a verified number	Sells the agent-builder use case; no more confidently-wrong z-scores
5	Rediscover the Law	Aurora derives `y = ½·a·t²` from a falling-ball video	The flagship hook — a free local AI rediscovers gravity, cited to SINDy

Quickstart for the demos (after the install above):

# Generate the synthetic datasets one time:

python -m demos.datasets.falling_ball.generate python -m demos.datasets.server_metrics.generate

Install the demo contracts into Aurora's contracts dir:

cp demos/contracts/*.json ~/.aurora/decision_contracts/ # macOS / Linux copy demos\contracts\*.json $env:USERPROFILE\.aurora\decision_contracts\ # Windows

Configure your webhooks once (copy the template + paste your URLs):

cp demos/.env.demos.example demos/.env.demos # macOS / Linux copy demos\.env.demos.example demos\.env.demos # Windows

Run the relay (it reads .env.demos for Discord/Slack URLs):

python -m demos.relay.app

Full step-by-step recording walkthrough with OBS setup, contract installation, and a per-demo runbook lives at [`demos/README.md`](demos/README.md).

See Aurora in action

A real run on an environmental air-quality dataset — captured straight from a Studio session.

The run banner. Domain selector across the top (Research / Ops / Industrial / Finance / Medical / Economics / Sports / Logistics / Custom+) sets context. The Aurora Pulse line below states the run's status in plain English. The 0 fabricated chip is the contractual signal that every finding traces to a method.

The Studio. The Overview cube rotates through six analytical lenses (Overview, Anomalies, Regimes, Motifs, Forecast, Physics). Below it, the Intelligence row surfaces the top anomalies (20 critical), the forecast peak prediction, a what-if causal answer, and the discovered physics law — y = a·t² + b·t + c at RMSE 126.720 — all live and grounded in artifacts, not LLM guesses.

The moat: every sentence cites a source

"What This Means" reads like a research paragraph because it is one. Each claim is tagged with a seed:* citation — seed:diurnal_cycle , seed:mutual_information_kg , seed:causal_chain , seed:physics_match , seed:wavelet_morlet , seed:sindy — that links to the exact knowledge-bank entry backing it. The panel below lists all 12 entries Aurora actually retrieved, each with its real source: Newton (1701), French AP (1971), Pierson & Moskowitz (1964), NIST, NOAA NDBC, Torrence & Compo (1998). No invented citations. No invented numbers. No invented papers.

Only the beginning is shown; read the rest in the repo

Aurora

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Egovframe Scaffold MCP

README from the maker

Aurora

Glass-box quantitative intelligence. Local. Open. Cited.

⬇️ Download the desktop app

→ Download the latest release

Desktop app — tips & known quirks

⚡ Quickstart (60 seconds)

Windows:

macOS / Linux:

2. Install

3. Run the Studio

🖥️ The desktop app

What it does

Build it from source / develop

🚨 Aurora Sentinel — Decision Contracts in the room

Install the demo contracts into Aurora's contracts dir:

Configure your webhooks once (copy the template + paste your URLs):

Run the relay (it reads .env.demos for Discord/Slack URLs):

See Aurora in action

The moat: every sentence cites a source