Stop coding scrapers that break, train agents instead. Browse AI turns any website into a clean, schema-stable JSON feed your model can consume for RAG, fine-tuning, agents, and evals.
[
{ "title": "Sony WH-1000XM5",
"price": 329.99,
"rating": 4.7, "reviews": 28104 },
{ "title": "Bose QC Ultra",
"price": 379.00 }Clean, structured web data is the foundation of every RAG system, agent, and fine-tuning run. Most teams spend more engineering time maintaining that foundation than building on top of it.
Proxy rotation, captcha solving, retry logic, selector maintenance, schema drift. Every sprint your engineers spend on scraping infrastructure is a sprint not spent on your model, your agent, or your product.
Prices shift hourly. Listings appear and vanish. Documentation gets rewritten. If your RAG store refreshes quarterly, your model is confidently returning yesterday's answers.
80 KB of nav, ads, tracking pixels, and schema markup per page. That's context window spent on noise, and extraction quality degrades with every kilobyte of irrelevant markup your model has to parse.
You define the schema by pointing and clicking. Browse AI owns everything underneath: proxies, rendering, anti-bot, retries, monitoring, and delivery.
Open any URL, click the fields you want. The robot learns the extraction pattern in minutes. No selectors, no code, no parsing logic to maintain.
Paginate, deep-scrape, chain across sub-pages. Browse AI handles proxies, captchas, JS rendering, and rate limiting. Up to 500,000 pages per task.
Check for changes on a custom schedule (hourly, daily, weekly). Detect changes, dedupe, and alert. When sites change layout, the robot adapts automatically.
Webhook, REST API, Google Sheets, Airtable, AWS S3, or 7,000+ apps via Zapier and Make. Structured JSON, ready for LangChain, LlamaIndex, or your vector DB.
Browse AI does not just extract web data, it stores, transforms, and delivers your data on autopilot.
Every robot writes to a managed table with filtering, historical snapshots, and structured exports (CSV, JSON, S3). Query and slice your dataset before it ever hits your pipeline.
Connect robots in workflows: one crawls a sitemap, the next deep-scrapes each page, a third extracts details. Output from one becomes input for the next, fully automated.
Transform, clean, and enrich extracted data with spreadsheet-style formulas or plain-language AI prompts. New columns calculate automatically on every run with no post-processing scripts required.
Make any robot reusable by turning URLs, search terms, and form fields into variables. Feed a CSV of 500,000 inputs and let Browse AI run them all.
Push data to AWS S3 on a recurring schedule (hourly, daily, weekly, or custom). Drop straight into your Airflow, database, or training pipeline.
Run robots from specific countries to capture region-specific pricing, content, and listings. Built-in proxy routing with no configuration needed.
A REST endpoint and clean JSON. Drop it into LangChain, LlamaIndex, or call it directly from your agent loop.
Same shape on every run. No HTML cleanup, no custom parsers.
Push fresh rows the moment a site changes. Your RAG never goes stale.
List robots, trigger tasks, pull results, manage bulk runs. API access included on every plan.
from langchain.tools import Tool
from browseai import Robot
# a robot you trained in the UI, no scraping code
robot = Robot("price-tracker-prod")
def live_prices(query: str):
rows = robot.run(input={"search": query})
return rows # clean JSON, schema-stable
prices_tool = Tool(
name="live_prices",
func=live_prices,
description="Look up real-time prices from the web",
)
agent.add_tool(prices_tool)From RAG to agents to fine-tuning datasets, the same robot powers them all.
Keep your vector store current. Monitor sources on any cadence, push only changed rows, and skip full re-indexes. Chain workflows to crawl an entire docs site end-to-end.
RAG / vector-dbGive your agent a function that returns structured JSON from any URL. No headless browser, no parser, no infra to manage, just clean data in your tool loop.
agents / function-callingBuild domain-specific corpora at scale. Up to 500k pages per task, with calculated columns to clean and label before export. Re-pull on a schedule to keep golden datasets current.
fine-tune / evals / datasetsPricing, inventory, listings, regulatory changes. Monitor at any cadence, detect changes, and stream into the model that briefs your team or feeds your agent's context window.
monitoring / enrichmentA side-by-side of what your team usually owns vs. what Browse AI handles for you.
Turn any URL into clean text plus a screenshot. Built to feed Claude, ChatGPT, or Gemini directly.
Pull every URL from XML sitemaps. Crawl entire docs sites for RAG without writing a crawler.
Live SERPs, ads, and knowledge panels. Plug straight into your agent loop.
Discussions, sentiment, and the richest unfiltered text on the web. RAG and training-ready.
Train your first robot in 2 minutes. No credit card. Free credits every month.
No credit card required · 50 free credits monthly · Up and running in minutes
