Open Networking Datasets & Machine-Readable APIs
Direct open-access database downloads for network administrators, developers, IT support engineers, and automated diagnostic tools. Verified router gateway IPs, default credentials, and CIDR subnet mappings.
All RouterIPHub datasets are maintained under open licenses with zero paywalls. You can query endpoints directly over HTTP or download raw database dumps in JSON, CSV, or ANSI SQL format for local offline integration in network scanners, inventory software, and diagnostic scripts.
Complete JSON Dataset
.JSONStandard JSON array containing 62+ router models, manufacturer names, default gateway IPs, local web portals, default usernames, passwords, and technical notes.
Tabular CSV Dataset
.CSVRFC 4180 compliant comma-separated values file formatted for direct import into Microsoft Excel, Google Sheets, LibreOffice Calc, or pandas dataframes.
SQL Schema & Dump
.SQLFull database table creation schema (`CREATE TABLE router_credentials`) with batched INSERT statements compatible with PostgreSQL, MySQL, SQLite, and MariaDB.
Gateway Subnets JSON
.JSONComplete CIDR subnet specifications for major gateways (192.168.1.1, 192.168.0.1, 10.0.0.1) including network ID, broadcast IP, wildcard masks, and usable host counts.
Quick Integration Snippets
Incorporate RouterIPHub's live datasets directly into your network engineering scripts:
import urllib.request
import json
url = 'https://routeriphub.com/data/routers.json'
with urllib.request.urlopen(url) as res:
routers = json.loads(res.read().decode())
# Filter for ASUS models
asus_routers = [r for r in routers if 'asus' in r['brand'].lower()]
for r in asus_routers:
print(f"{r['model']} -> IP: {r['ip']} | Default Pass: {r['password']}")
# Fetch all Netgear credentials via terminal:
curl -s https://routeriphub.com/data/routers.json | jq '.[] | select(.brand == "Netgear") | {model, ip, password}'
Machine-Readable AI Context Files (llms.txt)
RouterIPHub implements the standardized /llms.txt and /llms-full.txt specifications, providing plain-text markdown datasets optimized for retrieval-augmented generation (RAG), LLM embeddings, and autonomous agent queries: