DEV Community: Scraper0024

Scrapeless and Nstbrowser Jointly Establish “Browser Labs”

Scraper0024 — Wed, 15 Oct 2025 12:39:25 +0000

Today, we’re excited to share some remarkable progress from our team:

Scrapeless has entered into a strategic partnership with Nstbrowser. Together, we will integrate our product lines, upgrade our cloud browser services, and establish a new joint R&D center — “Browser Labs.”

Over the past few years, Nstbrowser has built strong expertise in fingerprint browser and anti-detection technologies, while Scrapeless has been advancing in web automation and AI agent infrastructure.

As AI and autonomous agent technologies evolve rapidly, agents are demanding higher standards of browser authenticity, isolation, and large-scale concurrency.

Both parties agreed that only by integrating the “authentic interaction and isolation capabilities of physical browser” with the “high-concurrency adaptation capabilities of cloud browsers” can long-term competitiveness be established for enterprises and automated scenarios.

Welcome to click to watch the video and learn more!

Future Dual-Brand Strategy

Scrapeless will focus on cloud browser technology, providing enterprise clients with scalable, high-performance infrastructure for data extraction, automation, and AI Agents. Leveraging its robust cloud capabilities, Scrapeless will deliver customized, scenario-driven solutions across industries such as finance, retail, e-commerce, SEO, and marketing.
Nstbrowser will concentrate on fingerprint browser clients, Browser kernel customization, and anti-detection technologies, serving users in affiliate marketing, cross-border e-commerce, social media management, and ad verification. It will offer authentic browser fingerprints and stable isolated environments to support multi-account operations and environment segregation.

Cloud Browser Service Upgrade

To provide you with a higher-quality cloud browser service, we plan to migrate the original Nstbrowser Browserless service to the Scrapeless platform. After the migration, you will enjoy the following upgraded core capabilities on Scrapeless:

Out-of-the-Box Ready: Natively compatible with Puppeteer and Playwright, supporting CDP connections. Migrate your projects with just one line of code.
Global IP Resources: Covers residential IPs, static ISP IPs, and unlimited IPs across 195 countries. Transparent costs ($0.6–$1.8/GB, far lower than Browserbase) with support for custom browser proxies.
Bulk Isolated Environment Creation: Each profile corresponds to an exclusive browser environment, enabling persistent login and identity isolation.
Unlimited Concurrent Scaling: A single task supports second-level launch of 50 to 1000+ browser instances. Auto-scaling is available with no server resource limits.
Edge Node Service (ENS): Multiple nodes worldwide, offering 2–3× faster launch speed and higher stability than other cloud browsers.
Intelligent Anti-Detection: Built-in real-time solutions for major protections like reCAPTCHA, Cloudflare Turnstile/Challenge, and AWS WAF.
Flexible Fingerprint Customization: Generate random fingerprints or customize fingerprint parameters as needed.
Visual Debugging: Achieve human-machine interactive debugging and real-time proxy traffic monitoring via Live View. Replay sessions page-by-page through Session Recordings to quickly identify issues and optimize operations.
Enterprise Customization: Undertake customization of enterprise-level automation projects and AI Agent customization.

Migration Schedule

This collaboration plans to fully migrate the original Nstbrowser Browserless service to the Scrapeless platform. The following is the specific service migration timeline.

Date	Description
October 15, 2025	Official shutdown of the Nstbrowser Browserless Service panel and documentation
October 15 – November 15, 2025	Transition period: Existing users may continue accessing the service via the original API
November 15, 2025 00:00 (UTC+2)	Nstbrowser Browserless Service will be officially discontinued

User Support

Existing users of the Nstbrowser Browserless Service can request assistance via our Ticket System, including:

Exclusive migration package
One-on-one technical support

Contact Us

For migration support or further information, please reach out via:

Browser Labs will continue to invest in R&D, delivering more professional, efficient, and reliable cloud browser services through the Scrapeless platform.

We sincerely appreciate your continued trust and support.

Browser Labs

How to Scrape Data on Make Automatically?

Scraper0024 — Tue, 01 Jul 2025 10:42:54 +0000

We've recently launched an official integration on Make, now available as a public app. This tutorial will show you how to create a powerful automated workflow that combines our Google Search API with Web Unlocker to extract data from search results, process it with Claude AI, and send it to a webhook.

What We'll Build

In this tutorial, we'll create a workflow that:

Triggers automatically every day using integrated scheduling
Searches Google for specific queries using Scrapeless Google Search API
Processes each URL individually with Iterator
Scrapes each URL with Scrapeless WebUnlocker to extract content
Analyzes content with Anthropic Claude AI
Sends processed data to a webhook (Discord, Slack, database, etc.)

Prerequisites

A Make.com account
A Scrapeless API key (get one at scrapeless.com)

An Anthropic Claude API key
A webhook endpoint (Discord webhook, Zapier, database endpoint, etc.)
Basic understanding of Make.com workflows

Complete Workflow Overview

Your final workflow will look like this:

Scrapeless Google Search (with integrated scheduling) → Iterator → Scrapeless WebUnlocker → Anthropic Claude → HTTP Webhook

Step 1: Adding Scrapeless Google Search with Integrated Scheduling

We'll start by adding the Scrapeless Google Search module with built-in scheduling.

Create a new scenario in Make.com
Click the "+" button to add the first module
Search for "Scrapeless" in the module library
Select Scrapeless and choose Search Google action

Configuring Google Search with Scheduling

Connection Setup:

Create a connection by entering your Scrapeless API key
Click "Add" and follow the connection setup

Search Parameters:

Search Query: Enter your target query (e.g., "artificial intelligence news")
Language: en (English)
Country: US (United States)

Scheduling Setup:

Click the clock icon on the module to open scheduling
Run scenario: Select "At regular intervals"
Minutes: Set to 1440 (for daily execution) or your preferred interval
Advanced scheduling: Use "Add item" to set specific times/days if needed

Step 2: Processing Results with Iterator

The Google Search returns multiple URLs in an array. We'll use Iterator to process each result individually.

Add an Iterator module after Google Search
Configure the Array field to process search results

Iterator Configuration:

Array: {{1.result.organic_results}}

This will create a loop that processes each search result separately, allowing better error handling and individual processing.

Step 3: Adding Scrapeless WebUnlocker

Now we'll add the WebUnlocker module to scrape content from each URL.

Add another Scrapeless module
Select Scrape URL (WebUnlocker) action
Use the same Scrapeless connection

WebUnlocker Configuration:

Connection: Use your existing Scrapeless connection
Target URL: {{2.link}} (mapped from Iterator output)
Js Render: Yes
Headless: Yes
Country: World Wide
Js Instructions: [{"wait":1000}] (wait for page load)
Block: Configure to block unnecessary resources for faster scraping

Step 4: AI Processing with Anthropic Claude

Add Claude AI to analyze and summarize the scraped content.

Add an Anthropic Claude module
Select Make an API Call action
Create a new connection with your Claude API key

Claude Configuration:

Connection: Create connection with your Anthropic API key
Prompt: Configure to analyze the scraped content
Model: claude-3-sonnet-20240229 / claude-3-opus-20240229 or your preferred model
Max Tokens: 1000-4000 depending on your needs

URL

/v1/messages

Header 1

Key : Content-Type
Value : application/json

Header 2

Key : anthropic-version
Value : 2023-06-01

Example Prompt copy paste in body:

{
  "model": "claude-3-sonnet-20240229",
  "max_tokens": 1000,
  "messages": [
    {
      "role": "user",
      "content": "Analyze this web content and provide a summary in English with key points:\n\nTitle: {{14.title}}\nURL: {{14.link}}\nDescription: {{14.snippet}}\nContent: {{13.content}}\n\nSearch Query: {{1.result.search_information.query_displayed}}"
    }
  ]
}

Don't forget to change number `14 by your module number.

Step 5: Webhook Integration

Finally, send the processed data to your webhook endpoint.

Add an HTTP module
Configure it to send a POST request to your webhook

HTTP Configuration:

URL: Your webhook endpoint (Discord, Slack, database, etc.)
Method: POST
Headers: Content-Type: application/json
Body Type: Raw (JSON)

Example Webhook Payload:



{
  "embeds": [
    {
      "title": "{{14.title}}",
      "description": "*{{15.body.content[0].text}}*",
      "url": "{{14.link}}",
      "color": 3447003,
      "footer": {
        "text": "Analysis complete"
      }
    }
  ]
}

Running Results

Module Reference and Data Flow

Data Flow Through Modules:

Module 1 (Scrapeless Google Search): Returns result.organic_results[]
Module 14 (Iterator): Processes each result, outputs individual items
Module 13 (WebUnlocker): Scrapes {{14.link}}, returns content
Module 15 (Claude AI): Analyzes {{13.content}}, returns summary
Module 16 (HTTP Webhook): Sends final structured data

Key Mappings:

Iterator Array: {{1.result.organic_results}}
WebUnlocker URL: {{14.link}}
Claude Content: {{13.content}}
Webhook Data: Combination of all previous modules

Testing Your Workflow

Run once to test the complete scenario
Check each module:
Google Search returns organic results
Iterator processes each result individually
WebUnlocker successfully scrapes content
Claude provides meaningful analysis
Webhook receives structured data
Verify data quality in your webhook destination
Check scheduling - ensure it runs at your preferred intervals

Advanced Configuration Tips

Error Handling

Add Error Handler routes after each module
Use Filters to skip invalid URLs or empty content
Set Retry logic for temporary failures

Benefits of This Workflow

Fully Automated: Runs daily without manual intervention
AI-Enhanced: Content is analyzed and summarized automatically
Flexible Output: Webhook can integrate with any system
Scalable: Processes multiple URLs efficiently
Quality Controlled: Multiple filtering and validation steps
Real-time Notifications: Immediate delivery to your preferred platform

Use Cases

Perfect for:

Content Monitoring: Track mentions of your brand or competitors
News Aggregation: Automated news summaries on specific topics
Market Research: Monitor industry trends and developments
Lead Generation: Find and analyze potential business opportunities
SEO Monitoring: Track search result changes for target keywords
Research Automation: Gather and summarize academic or industry content

Conclusion

This automated workflow combines the power of Scrapeless's Google Search and WebUnlocker with Claude AI's analysis capabilities, all orchestrated through Make's visual interface. The result is an intelligent content discovery system that runs automatically and delivers enriched, analyzed data directly to your preferred platform via webhook.

The workflow will run on your schedule, automatically discovering, scraping, analyzing, and delivering relevant content insights without any manual intervention.

Time to build your first AI Agent on Make using Scrapeless!

Session URL: How to Ensure User Privacy During Human-Computer Interaction?

Scraper0024 — Tue, 27 May 2025 06:31:44 +0000

Scrapeless Scraping Browser now fully supports automation tasks through Session-based workflows. Whether initiated via the Playground or API, all program executions can be synchronously tracked in the Dashboard.

Open Live View to monitor runtime status in real time.
Share the Live URL for remote user interaction—such as login pages, form filling, or payment completion.
Review the entire execution process with Session Replay.

But you might wonder:

What exactly are these Session features? How do they benefit me? And how do I use them?

In this blog, we’ll explore Scrapeless Scraping Browser’s Session in depth, covering:

The concept and purpose of Live View
What the Live URL is
How to use Live URL for direct user interaction
Why Session Replay is essential

Live View: Real-Time Program Monitoring

The Live View feature in Scrapeless Scraping Browser allows you to track and control browser sessions in real time. Specifically, it enables you to observe clicks, inputs, and all browser actions, monitor automation workflows, debug scripts manually, and take direct control of the session if needed.

Creating a Browser Session

First, you need to create a session. There are two ways to do this:

Method 1: Create a session via Playground

Method 2: Create a session via API

You can also create a session using our API. Please refer to the API documentation: Scraping Browser API Documentation. Our session feature will help you manage your session, including real-time viewing capabilities.

const puppeteer =require('puppeteer-core');

const token = 'API Key'

// custom fingerprint
const fingerprint = {
    platform: 'Windows',
}

const query = new URLSearchParams({
    session_ttl: 180,
    session_name: 'test_scraping', // session name
    proxy_country: 'ANY',
    token: token,
    fingerprint: encodeURIComponent(JSON.stringify(fingerprint)),
});

const connectionURL = `wss://browser.scrapeless.com/browser?${query.toString()}`;

(async () => {
    const browser = await puppeteer.connect({browserWSEndpoint: connectionURL});
    const page = await browser.newPage();

    await page.goto('https://www.scrapeless.com');
    await new Promise(res => setTimeout(res, 3000));

    await page.goto('https://www.google.com');
    await new Promise(res => setTimeout(res, 3000));

    await page.goto('https://www.youtube.com');
    await new Promise(res => setTimeout(res, 3000));

    await browser.close();
})();

View Live Sessions

In the Scrapeless session management interface, you can easily view live sessions:

Method 1: View Live Sessions Directly in the Dashboard

After creating a session in the Playground, you’ll see the live running session on the right side.

Or, you can check session status on the Live Sessions page.

Method 2: View Session via Live URL

A Live URL is generated for a running session, allowing you to watch the process live in a browser.

Live URLs are useful for:

Debugging & Monitoring: Watch everything in real time or share it with teammates.
Human Interaction: Control or input directly—let the user enter sensitive info like passwords securely.

You can copy the Live URL by clicking the "🔗" icon on the Live Sessions page. Both Playground and API-created sessions support Live URL.

Get Live URL from the Dashboard. See our tutorial on document.

Get Live URL via API

You can also retrieve the Live URL through API calls. The sample code below fetches all running sessions via the session API, then uses the Live URL API to retrieve the live view for a specific session:

import requests

API_CONFIG = {
    "host": "https://api.scrapeless.com",
    "headers": {
        "x-api-token": "API Key",
        "Content-Type": "application/json"
    }
}


async def fetch_live_url(task_id):
    try:
        live_response = requests.get(f"{API_CONFIG['host']}/browser/{task_id}/live", headers=API_CONFIG["headers"])

        if not live_response.ok:
            raise Exception(f"failed to fetch live url: {live_response.status_code} {live_response.reason}")

        live_result = live_response.json()
        if live_result and live_result.get("data"):
            print(f"taskId: {task_id}")
            print(f"liveUrl: {live_result['data']}")
        else:
            print("no live url data available for this task")
    except Exception as error:
        print(f"error fetching live url for task {task_id}: {str(error)}")


async def fetch_browser_sessions():
    try:
        session_response = requests.get(f"{API_CONFIG['host']}/browser/running", headers=API_CONFIG["headers"])

        if not session_response.ok:
            raise Exception(f"failed to fetch sessions: {session_response.status_code} {session_response.reason}")

        session_result = session_response.json()

        sessions = session_result.get("data")
        if not sessions or not isinstance(sessions, list) or len(sessions) == 0:
            print("no active browser sessions found")
            return

        task_id = sessions[0].get("taskId")
        if not task_id:
            print("task id not found in the session data")
            return

        await fetch_live_url(task_id)
    except Exception as error:
        print(f"error fetching browser sessions: {str(error)}")


import asyncio

asyncio.run(fetch_browser_sessions())

Get Live URL via CDP command. To obtain the Live URL while the code is running, use the CDP command Agent.liveURL:

import asyncio

from pyppeteer import launcher


async def main():
    try:
        browser = await launcher.connect(
            browserWSEndpoint="wss://browser.scrapeless.com/browser?token=APIKey&session_ttl=180&proxy_country=ANY"
        )
        page = await browser.newPage()
        await page.goto('https://www.scrapeless.com')

        client = await page.target.createCDPSession()
        result = await client.send('Agent.liveURL')
        print(result)
    except Exception as e:
        print(e)


asyncio.run(main())

A Highlight Worth Mentioning:

Live URL not only allows real-time monitoring, but also human-machine interaction.

For example: You need the user to enter their login password.

“Oh no! Are you trying to steal my private info? No way!”

Actually, the user can input the data themselves on the screen — and everything remains 100% private. This direct yet secure method is what Live URL enables — remote interaction.

Live URL: How It Enables Collaboration and User Interaction

Let’s take registering and logging into Scrapeless as an example and walk through how to interact directly with users.

Here’s the code you'll need:

const puppeteer = require("puppeteer-core");

(async () => {
    const fingerprint = {
        // custom screen fingerprint
        screen: {
            width: 1920,
            height: 1080,
        },
        args: {
            // set windows size with same value to screen fingerprint
            "--window-size": "1920,1080",
        },
    };

    const query = new URLSearchParams({
        token: "APIKey",
        session_ttl: 600,
        proxy_country: "ANY",
        fingerprint: encodeURIComponent(JSON.stringify(fingerprint)),
    });
    const browserWsEndpoint = `wss://browser.scrapeless.com/browser?${query.toString()}`;

    try {
        const browser = await puppeteer.connect({
            browserWSEndpoint: browserWsEndpoint,
        });

        const page = await browser.newPage();
        await page.setViewport(null);

        await page.goto(`https://app.scrapeless.com/passport/register`, {
            timeout: 120000,
            waitUntil: "domcontentloaded",
        });

        const client = await page.createCDPSession();
        const result = await client.send("Agent.liveURL");

        // you can share the live url to any user
        console.log(`${result.liveURL}`);

        // wait for 5 minutes for user registration
        await page.waitForSelector("#none-existing-selector", {timeout: 300_000});
    } catch (e) {
        console.log(e);
    }
})()

Run the above and share the Live URL with the user, such as: Scrapeless Registration URL.

Every steps before like:

Navigating to the website
Visiting Scrapeless homepage
Clicking login and entering the registration page

All of these can be done directly by creating a session using the above code. The most critical step is that the user needs to enter their email and password to complete the registration.

After you share the Live URL with the user, you can remotely track the program execution process. The program will automatically run and jump until the page that requires user interaction. The password entered by the other party will be completely hidden, and the user does not need to worry about password leakage.

In order to more intuitively reflect the user operation process, please refer to the following interaction steps:

The following interactive process is completely executed in the Live URL

Session Replay: Replay Program Execution to Debug Everything

Session Replay is a video-like recreation of a user session built using the Recording Library. Replays are created based on snapshots of the web application DOM state (the HTML representation in the browser's memory). When you replay each snapshot, you'll see a record of the actions taken during the entire session: including all page loads, refreshes, and navigations that occurred during your visit to the website.

Session Replay can help you troubleshoot all aspects of your program's operation. All page operations will be recorded and saved as a video. If you find any problems during the session, you can troubleshoot and adjust them through replay.

Go to Sessions
Click Session History
Locate the session

In the session details, click the Play button to watch and review execution:

The Bottom Lines

Scrapeless Scraping Browser lets you monitor in real-time, interact remotely, and replay every steps.

Live View: Watch browser activity like a live stream. See every click and input!
Live URL: Generate a shareable link where users can input their data directly. Fully private, completely secure.
Session Replay: Debug like a pro by replaying exactly what happened — no need to rerun the program.

Whether you’re a developer debugging, a PM demoing, or customer support guiding a user — Scrapeless Sessions have your back.

It’s time to make automation smart and human-friendly.

Start your free trial now!

AI Agent vs. Agentic AI: Data-driven Intelligent Evolution and Scrapeless Solutions

Scraper0024 — Tue, 20 May 2025 07:36:38 +0000

In the rapidly developing field of artificial intelligence, two concepts are gaining increasing attention: AI agents and Agentic AI. While both are dedicated to automating tasks and enhancing decision-making capabilities, they differ in architecture, functionality, and application scenarios.

In this blog post, we will take a deep dive into the meaning of AI agents and Agentic AI, their differences, applicable scenarios, and advantages. In addition, we will explore how Scraping Browser can be an effective solution for both.

What is the AI Agent?

AI Agents are single - entity systems. They are augmented with Large Language Models (LLMs) and external tools. These agents are designed for task - specific autonomy, meaning they can perform a particular set of tasks with a high degree of independence. They are modular and reactive, and typically operate within narrow domains such as email triage, scheduling, and basic customer support.

What is the Agentic AI?

Agentic AI represents a paradigm shift. It involves multiple collaborating agents working together. These agents have features like dynamic task decomposition, persistent memory, and orchestration layers. Agentic AI is built to handle complex, multi - step workflows, such as coordinated research assistance, ICU decision - making support, and robotic orchard harvesting.

AI Agent vs. Agentic AI: Key Differences

Comparison Dimension	AI Agents	Agentic AI
Definition & Core Concept	Single-entity systems based on LLMs and external tools, focused on task-specific autonomy (e.g., email classification, scheduling).	Multi-agent collaborative systems with dynamic task decomposition, persistent memory, and orchestration layers, designed for complex multi-step workflows.
Autonomy	High autonomy within specific tasks.	Greater autonomy across multi-step, cross-domain tasks.
Task Complexity	Single, narrow tasks (e.g., report summarization).	Complex, interdependent tasks (e.g., ICU decision support, robotic orchard harvesting).
Collaboration Capability	Operate independently without collaboration.	Multi-agent collaboration and information sharing.
Adaptability	Learn within a fixed domain.	Learn and adapt across environments.
Memory Mechanism	Session-level or limited memory.	Persistent and shared memory system.
Typical Use Cases	Email filtering, customer support chatbots, content recommendation.	Collaborative research assistant, adaptive game AI, coordinated robot control.
Core Challenges	Fragile long-term planning, hallucinations, limited causal reasoning.	Error cascades between agents, emerging instabilities, low communication transparency, ethical governance issues.
Future Directions	Proactive intelligence, continual learning, enhanced trust and security.	Multi-agent expansion, simulation-based planning, ethical frameworks, domain-specific optimization.

Scraping Browser: Effective Solution for AI Agent and Agentic AI

The AI landscape is shifting—Agentic AI pushes autonomy and adaptability, while AI Agents stick to structured execution. As mentioned above, both AI Agent and Agentic AI have their own inherent challenges. How to ensure high accuracy and success rate during execution? Scrapeless Scraping Browser can be your best choice.

What is the Scraping Browser?

Scrapeless Scraping Browser is a high-concurrency automation solution for AI. A high-concurrency, low-cost, and anti-ban browser platform designed for large-scale data crawling, highly anthropomorphic.
Scraping Browser is a browser automation tool based on a cloud-based serverless architecture, designed to solve the three core problems of high-concurrency bottlenecks, anti-bot avoidance, and cost control in dynamic web crawling.

Core functions:

Dynamic Stealth Mode support: Customize fingerprint parameters such as User-Agent, device information, locale, operating system, screen size, language, etc. to simulate real user devices; integrated CAPTCHA solver; support SDKs API, Node.js, Python SDK, etc., and implement advanced incognito mode through Scrapeless Chromium kernel.
Global proxy and IP management
- 70 million+ residential IPs: covering 195 countries, automatic IP rotation, support routing by target website geographic location, and manual selection of target country/region.
- Transparent proxy cost: 1.26−1.80/GB (compared to competitors' $9.5+/GB), support for self-provided proxy.
Automatic verification code cracking
- Built-in solutions: real-time processing of reCAPTCHA, Cloudflare Turnstile/Challange, AWS WAF, DataDome, etc.
Session replay
- Human-computer interactive debugging, intuitive testing of failures and errors, analysis of user behavior and interaction through Live URL, and proxy traffic monitoring and real-time optimization of proxy traffic.
- Replay the session through Session Replay to fully check the executed operations and network requests.

Why Scrapeless Scraping Browser works well for AI Agent and Agentic AI?

Through a deeply customized Chromium kernel and a global proxy network, Scraping Browser can simulate highly anthropomorphic user behaviors in headless mode and run hundreds or even thousands of browser instances in batches. Whether it is an AI agent that performs a single task or an Agentic AI system that coordinates multi-task processes, Scrapeless can achieve more stable, efficient, and scalable data interaction.

The tool supports one-click access to mainstream automation frameworks such as Puppeteer and Playwright, and is easy to use out of the box. Through built-in verification code cracking, dynamic fingerprint control, and session replay mechanisms, it not only helps AI systems improve task success rates, but also provides key support for debugging and behavior analysis. For building intelligent systems with real Web perception capabilities, Scraping Browser is a key bridge connecting AI and the real network world.

Scrapeless Scraping Browser provides powerful data crawling and anti-blocking functions to help AI Agents complete complex browser automation tasks. It supports multi-task parallel processing and is an ideal tool for building intelligent agent systems and AI-driven applications. Users do not need to build automation infrastructure from scratch, just focus on AI applications, and Scrapeless can easily handle all complex problems.

In addition, in order to make Scraping Browser better adapt to AI tools and agent services, Scrapeless integrates cloud-hosted AI Agent solutions such as browser use and computer use; and integrates AI frameworks such as LangChain to achieve highly autonomous operation processes.

The Bottom Lines

AI agents and Agentic AI are two completely different but powerful concepts in the field of artificial intelligence. They each have unique characteristics, applicable scenarios and advantages, but also face challenges in data access and browser automation.

Scrapeless Scraping Browser has become an ideal solution for AI agents and Agentic AI with its high concurrency, anti-blocking and cost-effective features, as well as seamless integration with AI frameworks. As the field of AI continues to develop, Scraping browsers will play a key role in helping these AI systems get the data they need to achieve optimal performance.

Don't miss our free trial!

Browser Use & Scraping Browser: Achieving Maximum Effectiveness of AI Agent

Scraper0024 — Mon, 12 May 2025 09:58:28 +0000

Scraping Browser has become the go-to tool for daily data extraction and automation tasks. By integrating Browser-Use with Scrapeless Scraping Browser, you can overcome browser automation limitations and avoid blocks.

In this article, we’ll build an automated AI Agent tool using Browser-Use and Scrapeless Scraping Browser to perform automated data scraping. You’ll see how it saves you time and effort, making automation tasks a breeze!

You will learn:

What is Browser-Use, and how does it help build AI agents?
Why can Scraping Browser effectively overcome the limitations of Browser-Use?
How to build a block-free AI agent using Browser-Use and Scraping Browser?

What Is Browser-Use?

Browser-Use is a Python-based AI browser automation library designed to empower AI agents with advanced browser automation capabilities. It can recognize all interactive elements on a webpage and allows agents to interact with the page programmatically—performing common tasks like search, clicking, form-filling, and data scraping. At its core, Browser-Use converts websites into structured text and supports browser frameworks like Playwright, greatly simplifying web interactions.

Unlike traditional automation tools, Browser-Use combines visual understanding with HTML structure parsing, allowing AI agents to control the browser using natural language instructions. This makes the AI more intelligent in perceiving page content and efficiently executing tasks. Additionally, it supports multi-tab management, element interaction tracking, custom action handling, and built-in error recovery mechanisms to ensure the stability and consistency of automation workflows.

More importantly, Browser-Use is compatible with all major large language models (such as GPT-4, Claude 3, Llama 2). With LangChain integration, users can simply describe tasks in natural language, and the AI agent will complete complex web operations. For users seeking AI-driven web interaction automation, this is a powerful and promising tool.

Limitations of Browser-Use in AI Agent Development

As mentioned above, Browser-Use doesn’t work like a magic wand from Harry Potter. Instead, it combines visual input with AI control to automate browsers using Playwright.

Browser-Use inevitably comes with some drawbacks, but these limitations do not stem from the automation framework itself. Rather, they arise from the browsers it controls. Tools like Playwright launch browsers with specific configurations and tools for automation, which can also be exposed to anti-bot detection systems.

As a result, your AI agent may frequently encounter CAPTCHA challenges or blocked pages such as “Sorry, something went wrong on our end.” To unlock the full potential of Browser-Use, thoughtful adjustments are required. The ultimate goal is to avoid triggering anti-bot systems to ensure your AI automation runs smoothly.

After extensive testing, we can confidently say: Scraping Browser is the most effective solution.

What Is Scrapeless Scraping Browser?

Scraping Browser is a cloud-based, serverless browser automation tool designed to solve three core problems in dynamic web scraping: high concurrency bottlenecks, anti-bot evasion, and cost control.

It consistently provides a high-concurrency, anti-blocking headless browser environment to help developers easily scrape dynamic content.
It comes with a global proxy IP pool and fingerprinting technology, capable of automatically solving CAPTCHA and bypassing blocking mechanisms.

Built specifically for AI developers, Scrapeless Scraping Browser features a deeply customized Chromium core and a globally distributed proxy network. Users can seamlessly run and manage multiple headless browser instances to build AI applications and agents that interact with the web. It eliminates the constraints of local infrastructure and performance bottlenecks, allowing you to fully focus on building your solutions.

How Do Browser-Use and Scraping Browser Work Together?

When combined, developers can use Browser-Use to orchestrate browser operations while relying on Scrapeless’s stable cloud service and powerful anti-blocking capabilities to reliably acquire web data.

Browser-Use offers simple APIs that allow AI agents to “understand” and interact with web content. For example, it can use LLMs like OpenAI or Anthropic to interpret task instructions and complete actions such as searches or link clicks in the browser via Playwright.

Scrapeless’s Scraping Browser complements this setup by addressing its weaknesses. When dealing with large websites with strict anti-bot measures, its high-concurrency proxy support, CAPTCHA solving, and browser emulation mechanisms ensure stable scraping.

In summary, Browser-Use handles intelligence and task orchestration, while Scrapeless provides a robust scraping foundation, making automated browser tasks more efficient and reliable.

How to Integrate a Scraping Browser with Browser-Use?

Step 1. Get Scrapeless API Key

Register and log in to the Scrapeless Dashboard.
Navigate to "Settings".
Click "API Key Management".

Then copy and set the SCRAPELESS_API_KEY environment variables in your .env file.

To enable AI features in Browser-Use, you need a valid API key from an external AI provider. In this example, we will use OpenAI. If you haven't generated an API key yet, follow OpenAI's official guide to create one.

The OPENAI_API_KEYenvironment variables in your .env file are required too.

Disclaimer: The following steps focus on how to integrate OpenAI, but you can adapt the following to your needs, just make sure to use any other AI tool supported by Browser-Use.

OPENAI_API_KEY=your-openai-api-key
SCRAPELESS_API_KEY=your-scrapeless-api-key

💡Remember to replace the sample API key with your actual API key

Next, import ChatOpenAI in your program: langchain_openaiagent.py

from langchain_openai import ChatOpenAI

Note that Browser-Use relies on LangChain to handle AI integration. Therefore, even if you haven't explicitly installed langchain_openai in your project, it is already available for use.

gpt-4o sets up the OpenAI integration with the following model:

llm = ChatOpenAI(model="gpt-4o")

No additional configuration is required. This is because langchain_openai automatically reads the API key from the environment variable OPENAI_API_KEY.

For integration with other AI models or providers, see the official Browser-Use documentation.

Step 2. Install Browser Use

With pip (Python at least v.3.11):

pip install browser-use

For memory functionality (requires Python<3.13 due to PyTorch compatibility):

pip install "browser-use[memory]"

Step 3. Set up Browser and Agent Configuration

Here’s how to configure the browser and create an automation agent:

from dotenv import load_dotenv
import os
import asyncio
from urllib.parse import urlencode
from langchain_openai import ChatOpenAI
from browser_use import Agent, Browser, BrowserConfig
from pydantic import SecretStr

task = "Go to Google, search for 'Scrapeless', click on the first post and return to the title"
SCRAPELESS_API_KEY = os.environ.get("SCRAPELESS_API_KEY")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

async def setup_browser() -> Browser:
    scrapeless_base_url = "wss://browser.scrapeless.com/browser"
    query_params = {
        "token": SCRAPELESS_API_KEY,
        "session_ttl": 1800,
        "proxy_country": "ANY"
    }
    browser_ws_endpoint = f"{scrapeless_base_url}?{urlencode(query_params)}"
    config = BrowserConfig(cdp_url=browser_ws_endpoint)
    browser = Browser(config)
    return browser

async def setup_agent(browser: Browser) -> Agent:
    llm = ChatOpenAI(
        model="gpt-4o", # Or choose the model you want to use
        api_key=SecretStr(OPENAI_API_KEY),
    )

    return Agent(
        task=task,
        llm=llm,
        browser=browser,
    )

Step 4. Create the Main Function

Here’s the main function that puts everything together:

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    await browser.close()

asyncio.run(main())

Step 5. Run your script

Run your script:

python run main.py

You should see your Scrapeless session start in the Scrapeless Dashboard.

In addition, Scrapeless supports session replay, which enables program visualization. Before running the program, make sure you have enabled the Web Recording function. When the session is completed, you can see the record directly on the Dashboard to help you quickly troubleshoot problems.

Full Code

from dotenv import load_dotenv
import os
import asyncio
from urllib.parse import urlencode
from langchain_openai import ChatOpenAI
from browser_use import Agent, Browser, BrowserConfig
from pydantic import SecretStr

task = "Go to Google, search for 'Scrapeless', click on the first post and return to the title"
SCRAPELESS_API_KEY = os.environ.get("SCRAPELESS_API_KEY")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

async def setup_browser() -> Browser:
    scrapeless_base_url = "wss://browser.scrapeless.com/browser"
    query_params = {
        "token": SCRAPELESS_API_KEY,
        "session_ttl": 1800,
        "proxy_country": "ANY"
    }
    browser_ws_endpoint = f"{scrapeless_base_url}?{urlencode(query_params)}"
    config = BrowserConfig(cdp_url=browser_ws_endpoint)
    browser = Browser(config)
    return browser

async def setup_agent(browser: Browser) -> Agent:
    llm = ChatOpenAI(
        model="gpt-4o", # Or choose the model you want to use
        api_key=SecretStr(OPENAI_API_KEY),
    )

    return Agent(
        task=task,
        llm=llm,
        browser=browser,
    )

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    await browser.close()

asyncio.run(main())

💡Browser Use currently only supports Python.

💡You can copy the URL in live session to watch the session's progress in real-time, and you can also watch a replay of the session in session history.

Step 6. Running Results

{
    "done": {
        "text": "The title of the first search result clicked is: 'Effortless Web Scraping Toolkit - Scrapeless'.",
        "success": True,
    }
}

Then, the Browser Use Agent will automatically open the URL and print the page title: “Scrapeless: Effortless Web Scraping Toolkit” (this is an example of the title on Scrapeless’s official homepage).

The entire execution process can be viewed in the Scrapeless console under the "Dashboard" → "Session" → "Session History" page, where you’ll see the details of the recently executed session.

Step 7. Exporting the Results

For team sharing and archiving purposes, we can save the scraped information into a JSON or CSV file. For example, the following code snippet shows how to write the title results into a file:

import json
from pathlib import Path

def save_to_json(obj, filename):
    path = Path(filename)
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open('w', encoding='utf-8') as f:
        json.dump(obj, f, ensure_ascii=False, indent=4)

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    save_to_json(result.model_dump(), "scrapeless_update_report.json")
    await browser.close()

asyncio.run(main())

The code above demonstrates how to open a file and write content in JSON format, including the search keywords, links, and page titles. The generated scrapeless_update_report.json file can be shared internally through a company knowledge base or collaboration platform, making it easy for team members to view the scraping results. For plain text format, you can simply change the extension to .txt and use basic text output methods instead.

Wrap Up

By using Scrapeless’s Scraping Browser service in combination with the Browser Use AI agent, we can easily build an automated system for information retrieval and reporting.

Scrapeless provides a stable and efficient cloud-based scraping solution that can handle complex anti-scraping mechanisms.
Browser Use allows the AI agent to intelligently control the browser to perform tasks such as search, click, and extract.

This integration enables developers to offload tedious web data collection tasks to automated agents, significantly improving research efficiency while ensuring accuracy and real-time results.

Scrapeless’s Scraping Browser helps AI avoid network blocks while retrieving real-time search data and ensures operational stability. Combined with Browser Use’s flexible strategy engine, we’re able to build a more powerful AI automation research tool that offers strong support for smart business decision-making. This toolset enables AI agents to "query" web content as if they were interacting with a database, greatly reducing the cost of manual competitor monitoring and improving the efficiency of R&D and marketing teams.

Scrapeless Session Replay - Visualize Your Programs!

Scraper0024 — Mon, 12 May 2025 03:31:45 +0000

Session Replay is now live on Scrapeless! This groundbreaking feature unifies Live View, and Session Recording into one seamless experience—offering a cutting-edge visual analysis capability for your browser automation workflows.

What Is Session Replay?

Session Replay is a video-like playback of user sessions, constructed using smart DOM snapshot technology rather than traditional screen recording. It captures your session at the DOM level—based on the browser's internal HTML structure—so you can accurately revisit every interaction, including page loads, refreshes, and navigations.

Unlike conventional video recordings, Scrapeless leverages intelligent DOM snapshots to capture:

Every page load, refresh, and navigation
User interactions such as clicks, scrolls, and inputs
Dynamic DOM changes in real-time
Full request/response network traffic

This innovative recording method allows you to travel back in time and see the exact context of what happened before, during, and after an issue. It eliminates guesswork and helps you reproduce, debug, and solve problems with complete clarity—just like having DevTools running during every session.

Why Choose Scrapeless Session Replay?

During data extraction and automation testing, developers often face the "black box dilemma"—not knowing what truly happened inside the browser. Scrapeless Session Replay changes that by providing a visual session management system that requires no additional code and works directly from a user-friendly dashboard.

Key Benefits:

✅ Real-Time Recording – Automatically logs all network requests during script execution

✅ Frame-by-Frame Playback – Rewind browser actions with precision

✅ Team Collaboration – Share session recordings easily for group debugging

✅ Millisecond Accuracy – Inspect event-level timestamps for enhanced script tuning

✅ Secure Isolation – Session data is encrypted and protected with fine-grained access control

✅ Lightweight Format – Thanks to rrweb-powered DOM diffing, recording files are 90% smaller than video files

Features Breakdown

🔍 Live Session Management

👍 Interactive monitoring dashboard. Instantly view any running session, including:

Locate the target session from the session list
Session information – ID, uptime and resource usage
Live preview – live browser view
Instantly stop the session with the stop button
Adjust the content of the session in the playground

📼 Historical Session Replay

Precision Search. Search sessions by Session ID or Session Name to group and locate sessions easily. > Session ID is unique, and Session Name can be customized as needed, which is equivalent to Session grouping.
Session Recording. Automatically logs every browser action during a session and stores it as a playable recording.
Video Playback
- Enable Web Record to activate replay
- Click any session log to enter the detail view
- Watch auto-generated recordings
- Support full screen playback
- Use the playback controller to:
- Play / Pause
- Scrub timeline
- Adjust speed (1x, 2x, 4x, 8x) > ⚠️ Session history is stored for 15 days. Older records will be auto-deleted.
Smart Status Indicators
- ✅ Successful sessions: Green label + “Completed”. You can play the recording in session history.
- ❌ Failed sessions: Red label + “Error” status
Common Session Failure Reasons
- Browser launch failure (e.g., invalid parameters or environment issues)
- Scheduling timeout
- Session exceeded maximum allowed time and didn’t complete

Experience the Power of Session Replay Today

Scrapeless Session Replay transforms the abstract script execution process into a visual process, helping developers achieve the following in complex scenarios:

Rapidly reproduce problems: such as dynamic content loading failure, anti-crawling mechanism triggering, etc.
Accurate optimization strategy: Targetedly adjust request frequency, interaction logic or fingerprint parameters based on video feedback

From eCommerce scraping to social media monitoring and search engine data extraction, this tool is indispensable for debugging and analysis in high-stakes environments.

👉 Visit our Documentation Center to explore advanced usage, or contact our technical consultants for more usage details.

How to Integrate Browser Use into Scrapless Scraping Browser?

Scraper0024 — Thu, 08 May 2025 15:29:56 +0000

Browser Use is a browser automation SDK that uses screenshots to capture the state of the browser and actions to simulate user interactions. This chapter will introduce how you can easily use browser-use to execute agent tasks on the Web with simple calls

Get Scrapeless API Key

Go over the Dashboard’s Settings tab:

Then copy and set the SCRAPELESS_API_KEY environment variables in your .env file.

The OPENAI_API_KEY environment variables in your .env file are required too.

OPENAI_API_KEY=your-openai-api-key
SCRAPELESS_API_KEY=your-scrapeless-api-key

💡 Remember to replace the sample API key with your actual API key

Install Browser Use

With pip (Python>=3.11):

pip install browser-use

For memory functionality (requires Python<3.13 due to PyTorch compatibility):

pip install "browser-use[memory]"

Set up Browser and Agent Configuration

Here’s how to configure the browser and create an automation agent:

from dotenv import load_dotenv
import os
import asyncio
from urllib.parse import urlencode
from langchain_openai import ChatOpenAI
from browser_use import Agent, Browser, BrowserConfig
from pydantic import SecretStr

task = "Go to Google, search for 'Scrapeless', click on the first post and return to the title"

async def setup_browser() -> Browser:
    scrapeless_base_url = "wss://browser.scrapeless.com/browser"
    query_params = {
        "token": os.environ.get("SCRAPELESS_API_KEY"),
        "session_ttl": 180,
        "proxy_country": "ANY"
    }
    browser_ws_endpoint = f"{scrapeless_base_url}?{urlencode(query_params)}"
    config = BrowserConfig(cdp_url=browser_ws_endpoint)
    browser = Browser(config)
    return browser

async def setup_agent(browser: Browser) -> Agent:
    llm = ChatOpenAI(
        model="gpt-4o", # Or choose the model you want to use
        api_key=SecretStr(os.environ.get("OPENAI_API_KEY")),
    )

    return Agent(
        task=task,
        llm=llm,
        browser=browser,
    )

Create the Main Function

Here’s the main function that puts everything together:

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    await browser.close()

asyncio.run(main())

Run your script

Run your script:

python run main.py

You should see your Scrapeless session start in the Scrapeless Dashboard.

Full Code

from dotenv import load_dotenv
import os
import asyncio
from urllib.parse import urlencode
from langchain_openai import ChatOpenAI
from browser_use import Agent, Browser, BrowserConfig
from pydantic import SecretStr

task = "Go to Google, search for 'Scrapeless', click on the first post and return to the title"

async def setup_browser() -> Browser:
    scrapeless_base_url = "wss://browser.scrapeless.com/browser"
    query_params = {
        "token": os.environ.get("SCRAPELESS_API_KEY"),
        "session_ttl": 180,
        "proxy_country": "ANY"
    }
    browser_ws_endpoint = f"{scrapeless_base_url}?{urlencode(query_params)}"
    config = BrowserConfig(cdp_url=browser_ws_endpoint)
    browser = Browser(config)
    return browser

async def setup_agent(browser: Browser) -> Agent:
    llm = ChatOpenAI(
        model="gpt-4o", # Or choose the model you want to use
        api_key=SecretStr(os.environ.get("OPENAI_API_KEY")),
    )

    return Agent(
        task=task,
        llm=llm,
        browser=browser,
    )

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    await browser.close()

asyncio.run(main())

💡 Browser Use currently only supports Python.

💡 You can copy the URL in live session to watch the session's progress in real-time, and you can also watch a replay of the session in session history.

How to Integrate Browser Use into Scrapless Scraping Browser?

Scraper0024 — Thu, 08 May 2025 15:24:30 +0000

Get Scrapeless API Key

Go over the Dashboard’s Settings tab:

Then copy and set the SCRAPELESS_API_KEY environment variables in your .env file.

The OPENAI_API_KEY environment variables in your .env file are required too.

OPENAI_API_KEY=your-openai-api-key
SCRAPELESS_API_KEY=your-scrapeless-api-key

💡 Remember to replace the sample API key with your actual API key

Install Browser Use

With pip (Python>=3.11):

pip install browser-use

For memory functionality (requires Python<3.13 due to PyTorch compatibility):

pip install "browser-use[memory]"

Set up Browser and Agent Configuration

Here’s how to configure the browser and create an automation agent:

from dotenv import load_dotenv
import os
import asyncio
from urllib.parse import urlencode
from langchain_openai import ChatOpenAI
from browser_use import Agent, Browser, BrowserConfig
from pydantic import SecretStr

task = "Go to Google, search for 'Scrapeless', click on the first post and return to the title"

async def setup_browser() -> Browser:
    scrapeless_base_url = "wss://browser.scrapeless.com/browser"
    query_params = {
        "token": os.environ.get("SCRAPELESS_API_KEY"),
        "session_ttl": 180,
        "proxy_country": "ANY"
    }
    browser_ws_endpoint = f"{scrapeless_base_url}?{urlencode(query_params)}"
    config = BrowserConfig(cdp_url=browser_ws_endpoint)
    browser = Browser(config)
    return browser

async def setup_agent(browser: Browser) -> Agent:
    llm = ChatOpenAI(
        model="gpt-4o", # Or choose the model you want to use
        api_key=SecretStr(os.environ.get("OPENAI_API_KEY")),
    )

    return Agent(
        task=task,
        llm=llm,
        browser=browser,
    )

Create the Main Function

Here’s the main function that puts everything together:

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    await browser.close()

asyncio.run(main())

Run your script

Run your script:

python run main.py

You should see your Scrapeless session start in the Scrapeless Dashboard.

Full Code

from dotenv import load_dotenv
import os
import asyncio
from urllib.parse import urlencode
from langchain_openai import ChatOpenAI
from browser_use import Agent, Browser, BrowserConfig
from pydantic import SecretStr

task = "Go to Google, search for 'Scrapeless', click on the first post and return to the title"

async def setup_browser() -> Browser:
    scrapeless_base_url = "wss://browser.scrapeless.com/browser"
    query_params = {
        "token": os.environ.get("SCRAPELESS_API_KEY"),
        "session_ttl": 180,
        "proxy_country": "ANY"
    }
    browser_ws_endpoint = f"{scrapeless_base_url}?{urlencode(query_params)}"
    config = BrowserConfig(cdp_url=browser_ws_endpoint)
    browser = Browser(config)
    return browser

async def setup_agent(browser: Browser) -> Agent:
    llm = ChatOpenAI(
        model="gpt-4o", # Or choose the model you want to use
        api_key=SecretStr(os.environ.get("OPENAI_API_KEY")),
    )

    return Agent(
        task=task,
        llm=llm,
        browser=browser,
    )

async def main():
    load_dotenv()
    browser = await setup_browser()
    agent = await setup_agent(browser)
    result = await agent.run()
    print(result)
    await browser.close()

asyncio.run(main())

💡 Browser Use currently only supports Python.

💡 You can copy the URL in live session to watch the session's progress in real-time, and you can also watch a replay of the session in session history.

👨‍💻 How to Integrate Naver Smart Store API in 5 Minutes to Automatically Fetch Product Data?

Scraper0024 — Tue, 22 Apr 2025 07:17:12 +0000

With the rapid growth of online shopping, e-commerce now accounts for 24% of all retail sales globally. By 2025, worldwide e-commerce retail sales are expected to hit $7.4 trillion, reflecting its expanding influence on consumer behavior.

At the center of South Korea's digital ecosystem lies Naver, the country's leading search engine and tech powerhouse. As a cornerstone of daily digital life, Naver integrates e-commerce, digital payment solutions, webtoons, blogging platforms, and mobile messaging services. This diverse range of offerings allows Naver to gather user data across more sectors than any other platform in the region, solidifying its role as a key player in shaping the nation's online landscape.

How can you scrape product data and from Naver Shop quickly, at scale, and at minimal cost?

Let's Figure out the details now!

💡 What Product Data Can We Extract from Naver?

A robust Naver scraping tool can extract a wide range of data fields, ensuring comprehensive and up-to-date insights. These include:

Core Product Information:

Product Name
Descriptions
Images
Categories & Subcategories
Brand
Product ID
SKU (Stock Keeping Unit)
Weight/Volume

Pricing and Promotions:

Original Price
Discounted Price
Discount Percentage
Unit Price
Promotions
Bundle Offers

Availability and Logistics:

Stock Status
Delivery Options
Delivery Time
Return Policy
Store Location

Consumer Insights:

Customer Ratings
Reviews
Seller Information
Expiration Date
Ingredients
Nutritional Information

Metadata:

Last Updated
Categories/Subcategories

⚠️ Challenges in Scraping Naver Data

While the benefits are clear, scraping Naver data is not without its hurdles. Here are the six major challenges businesses must navigate:

Lack of Stable Entry Points or Session Control

Naver requires consistent user behavior for session validation. Anonymous scraping often triggers suspicion, leading to blocked access.

JavaScript Rendering Challenges

Critical content is often loaded dynamically via JavaScript. Tools that fail to render JS accurately will miss vital data.

Session Validation, Geo-Locking, and CAPTCHA

Naver employs multiple layers of protection, including CAPTCHA and geo-restrictions. Without robust session simulation and proxy rotation, scraping efforts can quickly fail.

Frequent Layout Changes

Naver frequently updates its interface, altering pagination logic, tag structures, and load sequences. This requires constant adjustments to scraping tools.

Rate Limiting and Blockades

High request volumes can trigger rate limits. Effective scraping requires behavior simulation, diversified access protocols, and careful pacing.

Legal and Regulatory Compliance

South Korea has stringent data privacy laws. Non-compliance can result in legal risks and reputational damage, especially for overseas businesses.

🤔 Why Use Scrapeless for Naver Data Extraction?

Scrapeless offers a cutting-edge solution to overcome these challenges, providing seamless and reliable data extraction tailored to your business needs. Here’s why Scrapeless stands out:

Key Features:

1️⃣ Ultra-Fast and Reliable: Acquire data quickly without compromising stability, even at scale.

2️⃣ Rich Data Fields: Extract detailed information, including product details, seller info, pricing, reviews, and more.

3️⃣ Intelligent Proxy Rotation System: Automatically switch proxy IPs to bypass IP-based restrictions and ensure uninterrupted access.

4️⃣ Advanced Fingerprint Technology: Dynamically simulate browser characteristics and user interactions to bypass anti-scraping mechanisms.

5️⃣ Integrated CAPTCHA Solving: Handle reCAPTCHA and Cloudflare challenges seamlessly, ensuring smooth data collection.

6️⃣ Automation: Fully automated scraping processes adapt to updates in real time, minimizing manual intervention.

Business Benefits:

Market Analysis: Gain deep insights into consumer behavior, emerging trends, and competitor strategies.
Pricing Optimization: Stay competitive by tracking price changes and promotional activities.
Inventory Management: Ensure optimal stock levels and reduce operational inefficiencies.
Customer-Centric Decisions: Use reviews and ratings to refine products and enhance satisfaction.

With Scrapeless’ Naver Scraping API, businesses can effortlessly track market trends, optimize strategies, and maintain a competitive edge in the fast-evolving FMCG industry.

By addressing the complexities of Naver data scraping, Scrapeless empowers businesses to unlock valuable insights and drive growth. Whether you’re a retailer, e-commerce platform, or market analyst, leveraging Naver’s data can transform your decision-making and operational efficiency.

Naver Scraping API: Extract Naver Product Details Easily

How it works

Simply configure the Store ID and Product ID .
The Scrapeless Naver API will extract detailed product data from Naver Shop, including pricing, seller information, reviews, and more.
You can download and analyze the data.

Step 1: Create your API Token

To get started, you’ll need to obtain your API Key:

Log in to the Scrapeless Dashboard.
Navigate to API Key Management.
Click Create to generate your unique API Key.
Once created, you can simply click on the API Key to copy it.

Step 2. Launch the Naver Shop API

Find the Scraping API under the For Data collection.
Simply click on the Naver Shop actor to get ready for scraping products data.

Step 3: Define Your Target

To scrape product data using the Naver Scraping API, you must provide two mandatory parameters: storeId and productId. The channelUid parameter is optional.

You can find the Product ID and Store ID directly in the product URL. Let's take [바르닭] 닭가슴살 143종 크런치 소품닭 닭스테이크 소스큐브 골라담기 [원산지:국산(경기도 포천시) 등] as an example：

Store ID: barudak
Product ID: 4469033180

We firmly protect the privacy of the website. All data in this blog is public and is only used as a demonstration of the crawling process. We do not save any information and data.

Step 4: Start Scraping Naver Product Data

Once you’ve filled in the required parameters, simply click Start Scraping to obtain comprehensive product data.

Here’s an example code snippet for extracting Naver product data. Just replace YOUR_SCRAPELESS_API_TOKEN with your actual API key:

import json

import requests

def send_request():
    host = "api.scrapeless.com"
    url = f"https://{host}/api/v1/scraper/request"
    token = "YOUR_SCRAPELESS_API_TOKEN"

    headers = {
        "x-api-token": token
    }

    json_payload = json.dumps({
        "actor": "scraper.naver.product",
        "input": {
            "storeId": "barudak",
            "productId": "4469033180",
            "channelUid": " " ## Optional
        }
    })

    response = requests.post(url, headers=headers, data=json_payload)

    if response.status_code != 200:
        print("Error:", response.status_code, response.text)
        return

    print("body", response.text)


if __name__ == "__main__":
    send_request()

Ready to take your business to the next level? Trust Scrapeless to deliver actionable insights and streamline your data extraction process.

Key Takeaways

Extracting data from Naver is a strategic move that can yield significant value. However, when relying on programming for scraping, teams must build adaptive systems, manage session behaviors effectively, and comply with platform rules and South Korean data regulations. Navigating Naver’s dynamic infrastructure often involves setting up proxies, solving CAPTCHAs, and mimicking real user interactions—all of which can be complex and time-consuming.

The good news? Maintenance doesn't have to be a burden. By utilizing a reliable tech stack, including browser automation tools and APIs, you can ensure efficient, compliant, and scalable extraction of Naver product data without the fear of being blocked.

Ready to get started? Sign up for a free trial today! With pricing as low as $3 for 1,000 requests, it’s the most affordable solution available online!

Only $3 — Scrape Naver Shop Product Details within 5 Seconds!

Scraper0024 — Mon, 21 Apr 2025 13:02:29 +0000

With the rise of online shopping, 24% of all retail sales now come from e-commerce markets. By 2025, global e-commerce retail sales are projected to reach $7.4 trillion.

Naver, South Korea's largest search engine and tech giant, is the heart of the country's digital life. From e-commerce and digital payments to webtoons, blogs, and mobile messaging, it captures user data across more verticals than any other platform.

Naver’s architecture is designed to break predictable patterns, detect inconsistencies, and adapt faster than most systems. If your scraping strategy relies on static scripts or brute-force proxies, it’s already outdated. Successful Naver Shop data scraping isn’t just about bypassing defenses—it requires coordinating session behavior, timing logic, and aligning with platform expectations.

How can you scrape product data from Naver Shop quickly, at scale, and at minimal cost?

This guide is for business teams, data owners, and leaders facing modern Naver scraping challenges!

💼 Why Scrape Naver Data?

Competitive Pricing Strategies: Use Naver Shopping data scraping to collect competitor pricing, enabling you to stay ahead in the market.
Inventory Optimization: Monitor stock levels in real time to reduce shortages and improve efficiency.
Market Trend Analysis: Identify emerging trends and consumer preferences to tailor your offerings.
Enhanced Product Listings: Extract detailed descriptions, images, and specs to create compelling listings.
Price Monitoring & Adjustments: Track price changes and discounts to optimize promotions.
Competitor Analysis: Analyze rivals’ product offerings, pricing, and promotions to outperform them.
Data-Driven Marketing: Gather consumer behavior insights for targeted campaigns.
Improved Customer Satisfaction: Monitor reviews and ratings to refine products and boost satisfaction.

💡 What Product Data Can We Extract from Naver?

Scraping prices, stock status, descriptions, reviews, and discounts ensures comprehensive, up-to-date data. A robust Naver scraping tool can extract:

Field	Field	Field
✅ Product Name	✅ Customer Ratings	✅ Promotions
✅ Product Features	✅ Descriptions	✅ Images
✅ Reviews	✅ Delivery Options	✅ Categories
✅ Subcategories	✅ Product ID	✅ Brand
✅ Delivery Time	✅ Return Policy	✅ Availability
✅ Price	✅ Seller Information	✅ Expiration Date
✅ Store Location	✅ Ingredients	✅ Discounted Price
✅ Original Price	✅ Bundle Offers	✅ Last Updated
✅ Stock Keeping Unit (SKU)	✅ Weight/Volume	✅ Discount Percentage
✅ Unit Price	✅ Nutritional Information

🤔 Why Use Scrapeless to Extract Naver Product Data?

Scrapeless employs advanced web data scraping technology to ensure high-quality, precise data extraction to meet various business needs—from market analysis and competitive pricing strategies to inventory management and consumer behavior analysis. Our service provides seamless solutions for retailers, e-commerce platforms, and market analysts, helping them gain deep insights into the fast-moving consumer goods (FMCG) market.

With our Naver Scraping API, you can easily track market trends, optimize pricing strategies, and maintain a competitive edge in the rapidly evolving grocery industry. Trust us to provide actionable insights to drive your business growth and innovation.

Key Features

1️⃣ Ultra-Fast and Reliable: Quickly acquire data without compromising stability.
2️⃣ Rich Data Fields: Includes product details, seller information, pricing, ratings, and more.
3️⃣ Intelligent Proxy Rotation System: Automatically switches proxy IPs to effectively bypass IP-based access restrictions.
4️⃣ Advanced Fingerprint Technology: Dynamically simulates browser characteristics and user interaction patterns to bypass sophisticated anti-scraping mechanisms.
5️⃣ Integrated CAPTCHA Solving: Automatically handles reCAPTCHA and Cloudflare challenges, ensuring smooth data collection.
6️⃣ Automation: Fully automated scraping process with rapid response to updates.

⏯️ PLAN-A. Extract Naver product data with API

Simply configure the Store ID and Product ID .
The Scrapeless Naver API will extract detailed product data from Naver Shop, including pricing, seller information, reviews, and more.
You can download and analyze the data.

Step 1: Create your API Token

To get started, you’ll need to obtain your API Key from the Scrapeless Dashboard:

Log in to the Scrapeless Dashboard.
Navigate to API Key Management.
Click Create to generate your unique API Key.
Once created, you can simply click on the API Key to copy it.

Step 2. Launch the Naver Shop API

Find the Scraping API under the For Data collection.
Simply click on the Naver Shop actor to get ready for scraping products data.

Step 3: Define Your Target

To scrape product data using the Naver Scraping API, you must provide two mandatory parameters: storeId and productId . The channelUid parameter is optional.

You can find the Product ID and Store ID directly in the product URL. For example:

Store ID: barudak
Product ID: 4469033180

We firmly protect the privacy of the website. All data in this blog is public and is only used as a demonstration of the crawling process. We do not save any information and data.

Step 4: Start Scraping Naver Product Data

Once you’ve filled in the required parameters, simply click Start Scraping to obtain comprehensive product data.

Here’s an example code snippet for extracting Naver product data. Just replace YOUR_SCRAPELESS_API_TOKEN with your actual API key:

import json

import requests

def send_request():
    host = "api.scrapeless.com"
    url = f"https://{host}/api/v1/scraper/request"
    token = "YOUR_SCRAPELESS_API_TOKEN"

    headers = {
        "x-api-token": token
    }

    json_payload = json.dumps({
        "actor": "scraper.naver.product",
        "input": {
            "storeId": "barudak",
            "productId": "4469033180",
            "channelUid": " " ## Optional
        }
    })

    response = requests.post(url, headers=headers, data=json_payload)

    if response.status_code != 200:
        print("Error:", response.status_code, response.text)
        return

    print("body", response.text)


if __name__ == "__main__":
    send_request()

⏯️ PLAN-B. Extract Naver product data with Scraping Browser

If your team prefers programming, Scrapeless’s Scraping Browser is an excellent choice. It encapsulates all complex operations, simplifying efficient, large-scale data extraction from dynamic websites. It integrates seamlessly with popular tools like Puppeteer and Playwright.

Step 1: Integrate with Scrapeless Scraping Browser

After entering the Scraping Browser, simply fill in the configuration parameters on the left to automatically generate a scraping script.

Here’s an example integration code (JavaScript recommended):

const puppeteer = require('puppeteer-core');
const connectionURL = 'wss://browser.scrapeless.com/browser?token=" YourAPIKey"&session_ttl=180&proxy_country=ANY';

(async () => {
    const browser = await puppeteer.connect({browserWSEndpoint: connectionURL});
    const page = await browser.newPage();
    await page.goto('https://www.scrapeless.com');
    console.log(await page.title());
    await browser.close();
})();

Scrapeless automatically matches proxies for you, so no additional configuration or CAPTCHA handling is required. Combined with proxy rotation, browser fingerprint management, and robust concurrent scraping capabilities, Scrapeless ensures large-scale scraping of Naver product data without detection, efficiently bypassing IP blocks and CAPTCHA challenges.

Step 2: Set Export Format

Now, you need to filter and clean the scraped data. Consider exporting the results in CSV format for easier analysis:

  const csv = parse([productData]);
  fs.writeFileSync('naver_product_data.csv', csv, 'utf-8');
  console.log('CSV file saved: naver_product_data.csv');

  await browser.close();
})();

Further reading: Detailed Guide of Scrapeless Scraping Browser

Here is our scraping script, as a reference:

const puppeteer = require('puppeteer-core');
const fs = require('fs');
const { parse } = require('json2csv');

const connectionURL = 'wss://browser.scrapeless.com/browser?token=YourAPIKey&session_ttl=180&proxy_country=KR';

(async () => {
  const browser = await puppeteer.connect({
    browserWSEndpoint: connectionURL
  });

  const page = await browser.newPage();

// Replace with the URL of the Naver product page you actually want to crawl
  const url = 'https://smartstore.naver.com/barudak/products/4469033180';
  await page.goto(url, { waitUntil: 'networkidle2' });

// Simple example: crawl product title, price, description, etc. (adapt according to the actual page structure)
  const productData = await page.evaluate(() => {
    const title = document.querySelector('h3._2Be85h')?.innerText || '';
    const price = document.querySelector('span._1LY7DqCnwR')?.innerText || '';
    const description = document.querySelector('div._2w4TxKo3Dx')?.innerText || '';
    return {
      title,
      price,
      description
    };
  });

  console.log('Product data:', productData);

// Export to CSV
  const csv = parse([productData]);
  fs.writeFileSync('naver_product_data.csv', csv, 'utf-8');
  console.log('CSV file saved: naver_product_data.csv');

  await browser.close();
})();

Congratulations, you have successfully completed the entire process of crawling Naver product data!

The Bottom Lines

Scraping Naver data is a strategic investment! However, when teams use programming to scrape, they need to implement adaptive systems, coordinate session behaviors, and strictly adhere to platform regulations and South Korean data laws. Competing with Naver’s dynamic architecture means configuring proxies, CAPTCHA solvers, and simulating real user operations—all labor-intensive tasks.

In reality, we don’t need to spend much time on maintenance! To achieve this, simply leverage a robust tech stack, including browser automation tools and APIs, ensuring scalable, compliant Naver product data extraction at any scale without worrying about web blocks.

Start your free trial now! At just $3 for 1,000 requests, it’s the lowest price on the web!

How to Set Up Scrapeless MCP Server on Cline?

Scraper0024 — Thu, 03 Apr 2025 12:52:23 +0000

What is Cline?

Cline is another AI model developed by Anthropic, similar to Claude, but with a focus on a different set of capabilities, particularly for specific use cases in conversational AI. Cline is designed with an emphasis on coherence, efficiency, and responsibility in interactions, tailored to complex and high-stakes environments where precision and ethical behavior are crucial.

The main goal of Cline is to offer robust, safe AI that can handle more intricate tasks with a focus on minimizing errors and maintaining a high level of ethical standards, which makes it especially useful in professional, educational, and enterprise environments. It supports advanced conversational AI use cases, including long-form content generation, decision support, and detailed analysis.

How Does Cline Support MCP (Managed Cognitive Processing)?

Cline, like Claude, supports MCP (Managed Cognitive Processing), which means it is designed to efficiently handle cognitive tasks with a structured and ethical approach. The MCP framework enables Cline to provide higher-quality reasoning, better context management, and adaptability for complex, multi-step operations.

How Cline Supports MCP:

Advanced Reasoning and Multi-step Processing – Cline can handle complicated cognitive processes that require multi-step reasoning. This is ideal for tasks like strategy development, financial forecasting, or intricate problem-solving.
Contextual Awareness and Long-Term Memory – Cline has enhanced capabilities to retain and utilize context over longer periods of time. This makes it particularly useful in situations where multiple exchanges are needed to arrive at a conclusion or where understanding past interactions is critical.
Personalization and Adaptation – Cline can dynamically adjust its responses and interactions based on user behavior, needs, and preferences. It can change its tone, adjust the depth of its responses, and modify its approach to align with a user's specific requirements or industry.
Ethical Decision Support – Cline excels in areas requiring high ethical standards. It uses MCP to support responsible decision-making, ensuring that the information it generates or suggests is ethically sound, reducing the risks associated with bias, misinformation, or harmful suggestions.

What is Scrapeless MCP server?

Scrapeless MCP Server is a server built on the Model Context Protocol (MCP) by Scrapeless. It enables AI models (such as Claude and GPT) to access external information sources during conversations. With advanced search capabilities, Scrapeless MCP Server retrieves real-time data from sources like Google Search, including Google Maps, Google Jobs, Google Hotels, and Google Flights, ensuring accurate and relevant responses.

How to Set Up Scrapeless MCP Server on Claude?

Step 1. Install Node.js and npm

To run Scrapeless MCP Server, you must first install Node.js and npm:

Download the latest stable version of Node.js from the official website.
Install it on your system.
Verify the installation by running the following commands in your terminal:

node -v
npm -v

If installed correctly, you should see output like:

v22.x.x
10.x.x

Step 2. Obtain a Scrapeless API Key

To use Scrapeless MCP Server, you need an API key:

Register and log in to the Scrapeless Dashboard.
Navigate to API Key Management and generate your Scrapeless API Key.

Step 3. Download the Cline

Click Cline to go to the download page. However, ensure your device has VS Code installed. If not, follow the prompts to complete the installation.

After clicking Install , you’ll be redirected to VS Code. Click the Install button again. Once you see the Cline logo on the left, the installation is complete!

Step 4. Configure Scrapeless MCP Server

Click the search bar and type: >MCP Servers
Enter the Installed.
Select Configure MCP Servers

Remember the connection code we used earlier? Paste it into the program, replacing your Scrapeless API Key.

{
  "mcpServers": {
    "scrapelessMcpServer": {
      "command": "npx",
      "args": ["-y", "scrapeless-mcp-server"],
      "env": {
        "SCRAPELESS_KEY": "YOUR_SCRAPELESS_KEY"  // replace with your API key
      }
    }
  }
}

Save after completing the configuration.

Step 5. Run Scrapeless MCP Server

Return to the chat page (click Done on the previous page) and select Use MCP.

Send your query to Cline: For example, "Please check for me the Gold price today", and click Approve to allow Cline to fetch information via MCP.

Wait for Cline to process and retrieve the result.

Benefits of Using Scrapeless MCP Server on Cline

Real-time search: Access the latest data from external sources.
Seamless integration: Works directly within Cline’s AI-driven environment.
Enhanced AI context: Enables AI models to provide more accurate and up-to-date responses.

The Ending

By integrating Scrapeless MCP Server with Cline, you can significantly enhance AI-assisted coding with real-time information retrieval. Follow this guide to set up your environment and unlock the full potential of AI-powered development.

How to Set Up Scrapeless MCP Server on Claude?

Scraper0024 — Thu, 03 Apr 2025 10:14:20 +0000

What is Claude?

Claude is a family of AI chatbots developed by Anthropic, designed to provide safe, efficient, and intelligent conversational AI services. Named after Claude Shannon, the father of information theory, Claude focuses on ethical AI, advanced reasoning, and maintaining a coherent dialogue experience. It competes with models like OpenAI’s ChatGPT and Google’s Gemini.

What is Scrapeless MCP Server?

How Does Claude Support MCP (Managed Cognitive Processing)?

MCP (Managed Cognitive Processing) refers to Claude’s ability to intelligently manage cognitive tasks, ensuring efficient reasoning, contextual understanding, and decision-making. This allows Claude to handle complex interactions with structured thought processes and enhanced problem-solving capabilities.

Key Ways Claude Supports MCP:

Complex Problem Solving – Claude can efficiently process multi-step reasoning tasks, such as logical inference, data analysis, and strategic planning.
Context Retention in Multi-Turn Conversations – Through effective context management, Claude maintains coherence across extended discussions, reducing information loss and improving response accuracy.
Adaptive and Personalized Responses – Claude adjusts its tone, depth of knowledge, and response style based on user needs, making interactions more personalized and relevant.
Decision Support & Advanced Reasoning – By applying structured cognitive processing, Claude assists users in making informed decisions, even in complex or uncertain scenarios.

How to Set Up Scrapeless MCP Server on Claude?

Step 1. Install Node.js and npm

To run Scrapeless MCP Server, you must first install Node.js and npm:

Download the latest stable version of Node.js from the official website.
Install it on your system.
Verify the installation by running the following commands in your terminal:

node -v
npm -v

If installed correctly, you should see output like:

v22.x.x
10.x.x

Step 2. Obtain a Scrapeless API Key

To use Scrapeless MCP Server, you need an API key:

Register and log in to the Scrapeless Dashboard.
Navigate to API Key Management and generate your Scrapeless API Key.
Copy the key for later use.

Step 3. Open your terminal and enter the following command:

vim ~/Library/Application\ Support/Claude/claude_desktop_config.json

After pressing Enter, you should see the following result:

Step 4. Use the following code to connect to Scrapeless MCP:

You can also visit our Scrapeless MCP Server Tutorial Documentation for more details.

{
  "mcpServers": {
    "scrapelessMcpServer": {
      "command": "npx",
      "args": ["-y", "scrapeless-mcp-server"],
      "env": {
        "SCRAPELESS_KEY": "YOUR_SCRAPELESS_KEY" // replace with your API key
      }
    }
  }

Paste the code and save it by typing :, then x, and finally press Enter.

Step 5. Using Claude with Scrapeless MCP server

Now, you can open the Claude. When you see a hammer icon, it means the MCP server is successfully connected! Now Claude can invoke MCP.

Input your query, e.g., "Please check for me the Gold price today."
Allow Claude to invoke the Scrapeless MCP server.

Get the response.

Benefits of Using Scrapeless MCP Server on Cursor

Real-time search: Access the latest data from external sources.
Seamless integration: Works directly within Cursor’s AI-driven environment.
Enhanced AI context: Enables AI models to provide more accurate and up-to-date responses.

The Ending

By integrating Scrapeless MCP Server with Claude, you can significantly enhance AI-assisted coding with real-time information retrieval. Follow this guide to set up your environment and unlock the full potential of AI-powered development.

Get the free trial now and figure out a new possibility!