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Table of Contents

• 🤖 Browser4
  • 🌟 Introduction
    • ✨ Key Capabilities
  • 🎥 Demo Videos
  • 🚀 Quick Start
  • 💡 Usage Examples
    • Browser Agents
    • Workflow Automation
    • LLM + X-SQL
    • High-Speed Parallel Processing
    • Auto Extraction
  • 📦 Modules Overview
  • 📜 Documentation
  • 🔧 Proxies - Unblock Websites
  • ✨ Features
  • 🤝 Support & Community

💖 Browser4: a lightning-fast, coroutine-safe browser engine for your AI 💖

• 👽 Browser Agents — Autonomous agents that reason, plan, and act within the browser.
• 🤖 Browser Automation — High-performance automation for workflows, navigation, and data extraction.
• ⚙️ Machine Learning Agent - Learns field structures across complex pages without consuming tokens.
• ⚡ Extreme Performance — Fully coroutine-safe; supports 100k ~ 200k page visits per machine per day.

// Give your Agent a mission, not just a script.
val agent = AgenticContexts.getOrCreateAgent()

// The Agent plans, navigates, and executes using Browser4 as its hands and eyes.
val result = agent.run("""
    1. Go to amazon.com
    2. Search for '4k monitors'
    3. Analyze the top 5 results for price/performance ratio
    4. Return the best option as JSON
""")

🎬 YouTube:

📺 Bilibili: https://www.bilibili.com/video/BV1fXUzBFE4L

Prerequisites: Java 17+ and Maven 3.6+

1. Clone the repository

   git clone https://github.com/platonai/browser4.git
   cd browser4

2. Configure your LLM API key

   Edit application.properties and add your API key.

3. Build the project

   ./mvnw -DskipTests

4. Run examples

   ./mvnw -pl pulsar-examples exec:java -D"exec.mainClass=ai.platon.pulsar.examples.agent.Browser4AgentKt"

   If you have encoding problem on Windows:

   ./bin/run-examples.ps1

   Explore and run examples in the pulsar-examples module to see Browser4 in action.

For Docker deployment, see our Docker Hub repository.

Autonomous agents that understand natural language instructions and execute complex browser workflows.

val agent = AgenticContexts.getOrCreateAgent()

val task = """
    1. go to amazon.com
    2. search for pens to draw on whiteboards
    3. compare the first 4 ones
    4. write the result to a markdown file
    """

agent.run(task)

Low-level browser automation & data extraction with fine-grained control.

Features:

• Direct and full Chrome DevTools Protocol (CDP) control, coroutine safe
• Precise element interactions (click, scroll, input)
• Fast data extraction using CSS selectors/XPath

val session = AgenticContexts.getOrCreateSession()
val agent = session.companionAgent
val driver = session.getOrCreateBoundDriver()

// Open and parse a page
var page = session.open(url)
var document = session.parse(page)
var fields = session.extract(document, mapOf("title" to "#title"))

// Interact with the page
var result = agent.act("scroll to the comment section")
var content = driver.selectFirstTextOrNull("#comments")

// Complex agent tasks
var history = agent.run("Search for 'smart phone', read the first four products, and give me a comparison.")

// Capture and extract from current state
page = session.capture(driver)
document = session.parse(page)
fields = session.extract(document, mapOf("ratings" to "#ratings"))

Ideal for high-complexity data-extraction pipelines with multiple-dozen entities and several hundred fields per entity.

Benefits:

• Extract 10x more entities and 100x more fields compared to traditional methods
• Combine LLM intelligence with precise CSS selectors/XPath
• SQL-like syntax for familiar data queries

val context = AgenticContexts.create()
val sql = """
select
  llm_extract(dom, 'product name, price, ratings') as llm_extracted_data,
  dom_first_text(dom, '#productTitle') as title,
  dom_first_text(dom, '#bylineInfo') as brand,
  dom_first_text(dom, '#price tr td:matches(^Price) ~ td, #corePrice_desktop tr td:matches(^Price) ~ td') as price,
  dom_first_text(dom, '#acrCustomerReviewText') as ratings,
  str_first_float(dom_first_text(dom, '#reviewsMedley .AverageCustomerReviews span:contains(out of)'), 0.0) as score
from load_and_select('https://www.amazon.com/dp/B08PP5MSVB -i 1s -njr 3', 'body');
"""
val rs = context.executeQuery(sql)
println(ResultSetFormatter(rs, withHeader = true))

Example code:

• X-SQL to scrape 100+ fields from an Amazon's product page
• X-SQLs to crawl all types of Amazon webpages

Achieve extreme throughput with parallel browser control and smart resource optimization.

Performance:

• 100,000+ page visits per machine per day
• Concurrent session management
• Resource blocking for faster page loads

val args = "-refresh -dropContent -interactLevel fastest"
val blockingUrls = listOf("*.png", "*.jpg")
val links = LinkExtractors.fromResource("urls.txt")
    .map { ListenableHyperlink(it, "", args = args) }
    .onEach {
        it.eventHandlers.browseEventHandlers.onWillNavigate.addLast { page, driver ->
            driver.addBlockedURLs(blockingUrls)
        }
    }

session.submitAll(links)

🎬 YouTube:

📺 Bilibili: https://www.bilibili.com/video/BV1kM2rYrEFC

Automatic, large-scale, high-precision field discovery and extraction powered by self-/unsupervised machine learning — no LLM API calls, no tokens, deterministic and fast.

What it does:

• Learns every extractable field on item/detail pages (often dozens to hundreds) with high precision.
• Open source when browser4 has 10K stars on GitHub.

Why not just LLMs?

• LLM extraction adds latency, cost, and token limits.
• ML-based auto extraction is local, reproducible, and scalable to 100k+ ~ 200k pages/day.
• You can still combine both: use Auto Extraction for structured baseline + LLM for semantic enrichment.

Quick Commands (PulsarRPAPro):

curl -L -o PulsarRPAPro.jar https://github.com/platonai/PulsarRPAPro/releases/download/v3.0.0/PulsarRPAPro.jar

Integration Status:

• Available today via the companion project PulsarRPAPro.
• Native Browser4 API exposure is planned; follow releases for updates.

Key Advantages:

• High precision: >95% fields discovered; majority with >99% accuracy (indicative on tested domains).
• Resilient to selector churn & HTML noise.
• Zero external dependency (no API key) → cost-efficient at scale.
• Explainable: generated selectors & SQL are transparent and auditable.

👽 Extract data with machine learning agents:

(Coming soon: richer in-repo examples and direct API hooks.)

Python/Node.js SDKs are on the way.

• 🛠️ Configuration Guide
• 📚 Build from Source
• 🧠 Expert Guide

export PROXY_ROTATION_URL=https://your-proxy-provider.com/rotation-endpoint

• Problem-solving autonomous browser agents
• Parallel agent sessions
• LLM-assisted page understanding & extraction

• Workflow-based browser actions
• Precise coroutine-safe control (scroll, click, extract)
• Flexible event handlers & lifecycle management

• One-line data extraction commands
• X-SQL extended query language for DOM/content
• Structured + unstructured hybrid extraction (LLM & ML & selectors)

• High-efficiency parallel page rendering
• Block-resistant design & smart retries
• 100,000+ pages/day on modest hardware (indicative)

• Advanced anti-bot techniques
• IP & profile rotation
• Resilient scheduling & quality assurance

• Simple API integration (REST, native, text commands)
• Rich configuration layering
• Clear structured logging & metrics

• Local FS & MongoDB support (extensible)
• Comprehensive logs & transparency
• Detailed metrics & lifecycle visibility

• 💬 WeChat: galaxyeye
• 🌐 Weibo: galaxyeye
• 📧 Email: galaxyeye@live.cn, ivincent.zhang@gmail.com
• 🐦 Twitter: galaxyeye8
• 🌍 Website: platon.ai

For Chinese documentation, refer to 简体中文 README.