A beginner-friendly guide. No programming knowledge needed - just follow each step.

Instagram blocks normal HTTP requests, so we use a real browser controlled through Chrome DevTools Protocol (CDP). The browser opens, loads the page, waits for it to render, and grabs the HTML. After that first fetch, everything runs offline against the saved file.

sudo apt update
sudo apt install python3.12 python3.12-venv


Open a terminal (Command Prompt on Windows, Terminal on Mac/Linux) and type:

 installed. Either works - the program controls it through a debugging port.

 If you install Brave via the terminal, note where it ends up. The default is usually 

. If it's somewhere else, you'll update one line of code later.

 is a fast tool that installs Python libraries. We use it instead of 

powershell -c "irm <https://astral.sh/uv/install.ps1> | iex"


curl -LsSf <https://astral.sh/uv/install.sh> | sh


Step 4: Create the Project Folder and Files

Open your terminal and run these commands 

# Create the main folder
mkdir instagram-scraper
cd instagram-scraper

# Create subfolders
mkdir browser
mkdir models
mkdir data


[project]
name = "instagram-com"
version = "0.1.0"
description = "Instagram profile and post scraper using Brave CDP browser"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    "beautifulsoup4>=4.15.0",
    "httpx>=0.28.1",
    "lxml>=6.1.1",
    "psutil>=7.0.0",
    "pydantic>=2.13.4",
    "websocket-client>=1.9.0",
]

[tool.ruff.lint]
fixable = ["ALL"]
select = ["I", "B", "E"]

[tool.pyright]
typeCheckingMode = "strict"
venvPath = "."
venv = ".venv"


from pathlib import Path

# Paths
HOME = Path.home()
BASE_DIR = Path(__file__).parent

# Browser location - CHANGE THIS if your browser is elsewhere
DEBUG_BROWSER_PATH = "/usr/bin/brave-browser"

# Profile folder (cookies and settings are saved here automatically)
USER_PROFILE_DIR = BASE_DIR / "browser" / "browser_profile"

# Browser window size
WIN_W = 720
WIN_H = 760

# Instagram
INSTAGRAM_HOME = "<https://www.instagram.com/>"
SAMPLE_HTML_PATH = BASE_DIR / "sample.html"


Change the browser path to something like:

DEBUG_BROWSER_PATH = "C:\\Program Files\\BraveSoftware\\Brave-Browser\\Application\\brave.exe"


DEBUG_BROWSER_PATH = "/Applications/Brave Browser.app/Contents/MacOS/Brave Browser"


from subprocess import Popen

import websocket
from pydantic import BaseModel, Field

class DebuggerTab(BaseModel):
    id: str = ""
    url: str = ""
    type: str = ""
    title: str = ""
    description: str = ""
    debug_url: str = Field(alias="webSocketDebuggerUrl")
    devtools_frontend_url: str = Field(alias="devtoolsFrontendUrl")

class BrowserDebuggerInfo(BaseModel):
    tabs: list[DebuggerTab] = []
    last_updates: float = -1

class Browser:
    def __init__(self) -> None:
        self.ws: websocket.WebSocket = websocket.WebSocket()
        self.ws_url: str = ""
        self.__msg_id: int = -1
        self.debugger_info: BrowserDebuggerInfo = BrowserDebuggerInfo()
        self.authorization_token: str = ""
        self.process: Popen[bytes] | None = None
        self.process_id: int = -1

    def connect(self) -> None:
        if not self.ws_url:
            self.ws_url = self.debugger_info.tabs[0].debug_url
        self.ws.connect(self.ws_url)

    def next_id(self) -> int:
        self.__msg_id += 1
        return self.__msg_id

    @property
    def msg_id(self):
        return self.__msg_id


from pydantic import BaseModel, Field

class BioLink(BaseModel):
    title: str = ""
    url: str = ""
    link_type: str = ""

class ProfilePost(BaseModel):
    shortcode: str
    post_url: str
    display_uri: str | None = None
    media_type: int = 1  # 1=photo, 2=video, 8=carousel
    is_video: bool = False
    is_carousel: bool = False
    carousel_media_count: int | None = None
    caption: str | None = None
    accessibility_caption: str | None = None
    owner_username: str | None = None

class InstagramProfileData(BaseModel):
    pk: str | None = None
    username: str | None = None
    full_name: str | None = None
    biography: str | None = None
    profile_pic_url: str | None = None
    is_private: bool | None = None
    is_verified: bool | None = None
    follower_count: int | None = None
    following_count: int | None = None
    bio_links: list[BioLink] = Field(default_factory=list)
    posts: list[ProfilePost] = Field(default_factory=list)


These files let the program open and control the browser

import os
import subprocess
import time

import httpx

from constants import (
    DEBUG_BROWSER_PATH,
    USER_PROFILE_DIR,
    WIN_H,
    WIN_W,
)
from models.browser import Browser, DebuggerTab

def spawn_debug_browser(debug_port: int, headless: bool = False):
    user_data_dir = USER_PROFILE_DIR
    if not user_data_dir.exists():
        os.makedirs(user_data_dir, exist_ok=True)

    cmd = [
        DEBUG_BROWSER_PATH,
        "--no-sandbox",
        f"--remote-debugging-port={debug_port}",
        "--remote-allow-origins=*",
        "--no-first-run",
        "--no-default-browser-check",
        f"--window-size={WIN_W},{WIN_H}",
        "--window-position=0,0",
        "--disable-popup-blocking",
        f"--user-data-dir={user_data_dir}",
        "--disable-component-update",
        "--metrics-recording-only",
        "--disable-gpu",
        "--disable-dev-shm-usage",
    ]
    if headless:
        cmd += ["--headless"]
    process = subprocess.Popen(
        cmd,
        stdout=subprocess.DEVNULL,
        stderr=subprocess.DEVNULL,
    )

    return process.pid, process

def attach_debugger(browser: Browser, debug_port: int):
    while True:
        try:
            response = httpx.get(f"<http://127.0.0.1>:{debug_port}/json")
            response.raise_for_status()
            browser.debugger_info.tabs = [DebuggerTab(**i) for i in response.json()]
            browser.debugger_info.last_updates = time.time()
            break
        except httpx.ConnectError:
            print("Waiting for browser to be ready ...")
            time.sleep(2)


import json
import time
from typing import Any

from models.browser import Browser

def wait_for_page_load(browser: Browser, timeout: int = 30) -> None:
    start_time = time.time()
    while time.time() - start_time < timeout:
        try:
            response = json.loads(browser.ws.recv())
            if (
                "method" in response
                and response["method"] == "Page.frameStoppedLoading"
            ):
                print("Frame stopped loading")
                return
        except Exception as e:
            print(f"Error waiting for load: {e}")
            break
    print(f"Timeout after {timeout}s waiting for page load")

def browser_send_command(
    browser: Browser,
    method: str,
    params: dict[str, str | int | float | list[Any]] | None = None,
    wait_for_load_event: bool = False,
):
    message = {"id": browser.next_id(), "method": method, "params": params or {}}
    browser.ws.send(json.dumps(message))

    while True:
        response = json.loads(browser.ws.recv())
        if response.get("id") == browser.msg_id:
            break

    if "error" in response:
        raise ValueError(
            f"CDP command failed: {response['error']['message']} "
            f"(code {response['error']['code']})"
        )

    result = response.get("result")

    if wait_for_load_event and method == "Page.navigate":
        wait_for_page_load(browser, timeout=30)

    return result

def browser_init_domains(
    browser: Browser,
    domains: list[str] = [
        "Page.enable",
        "Runtime.enable",
        "DOM.enable",
        "Network.enable",
        "Log.enable",
    ],
):
    for domain in domains:
        browser_send_command(browser, domain, {})

def browser_open_url(
    browser: Browser,
    url: str,
    wait_for_load_event: bool = False,
) -> None:
    browser.ws_url = url
    browser_send_command(browser, "Page.enable", {})
    browser_send_command(
        browser, "Page.navigate", {"url": url},
        wait_for_load_event=wait_for_load_event,
    )

def dom_js_execute(
    browser: Browser,
    js: str,
    options: dict[str, str | bool | int] = {},
):
    result = browser_send_command(
        browser, "Runtime.evaluate", {"expression": js, **options}
    )
    return result.get("result", {})

def dom_element_html(
    browser: Browser,
    selector: str,
    *,
    outer_html: bool = False,
) -> str:
    script = f'document.querySelector("{selector}").innerHTML'
    if outer_html:
        script = f'document.querySelector("{selector}").outerHTML'
    res = dom_js_execute(browser, script)
    return res.get("value", "")


import atexit
import time

import psutil

from browser.actions import (
    browser_init_domains,
    browser_open_url,
    dom_element_html,
)
from browser.utils import attach_debugger, spawn_debug_browser
from models.browser import Browser

def debug_setup_phase(browser: Browser):
    if not browser.ws.connected:
        browser.connect()
        return

def initialize_cpd_domains(browser: Browser):
    br = browser
    browser_init_domains(br)

def get_browser_instance(debug_port: int = 9999, headless: bool = False):
    browser = Browser()
    debug_port = 9999
    browser.process_id, browser.process = spawn_debug_browser(debug_port, headless)
    attach_debugger(browser, debug_port)
    cdp_initialized = False
    while browser.process and browser.process.poll() is None:
        if not browser.ws.connected:
            debug_setup_phase(browser)
            continue

        if not cdp_initialized:
            initialize_cpd_domains(browser)
            cdp_initialized = True
            continue

        if browser.debugger_info.last_updates == -1:
            attach_debugger(browser, debug_port)
            cdp_initialized = False
            continue
        break
    atexit.register(lambda: kill_browser(browser))
    return browser

def kill_browser(browser: Browser):
    process = browser.process
    if process and process.poll() is None:
        try:
            parent = psutil.Process(process.pid)
            children = parent.children(recursive=True)
            for child in children:
                child.terminate()
            _, alive = psutil.wait_procs(children, timeout=3)
            for child in alive:
                child.kill()
            process.terminate()
            process.wait(timeout=3)
        except psutil.NoSuchProcess:
            pass
        except Exception as e:
            print(f"Error killing process: {e}")
            process.terminate()
            time.sleep(2)
            if process.poll() is None:
                process.kill()

def render_html(
    browser: Browser,
    visit_url: str,
    timeout_for_page_load: int = 5,
) -> str:
    browser_open_url(browser, visit_url)
    time.sleep(timeout_for_page_load)
    return dom_element_html(browser, "body", outer_html=True)


These files handle fetching and parsing Instagram data

import json

from bs4 import BeautifulSoup

from models.profile import BioLink, InstagramProfileData, ProfilePost

INSTAGRAM_BASE = "<https://www.instagram.com>"

MEDIA_VIDEO = 2
MEDIA_CAROUSEL = 8

def _extract_xig_user(html: str, *, field_marker: str) -> dict | None:
    soup = BeautifulSoup(html, "lxml")
    for script in soup.find_all("script", type="application/json"):
        txt = script.string or ""
        if "xig_user_by_username" not in txt or field_marker not in txt:
            continue
        try:
            data = json.loads(txt)
            node = (
                data["require"][0][3][0]
                ["__bbox"]["require"][0][3][1]
                ["__bbox"]["result"]["data"]
                ["xig_user_by_username"]
            )
            if field_marker in node:
                return node
        except (KeyError, IndexError, json.JSONDecodeError, TypeError):
            continue
    return None

def _parse_bio_links(raw: list) -> list[BioLink]:
    out = []
    for item in raw or []:
        out.append(BioLink(
            title=item.get("title") or "",
            url=item.get("url") or "",
            link_type=item.get("link_type") or "",
        ))
    return out

def _parse_posts(posts_user: dict) -> list[ProfilePost]:
    connection = posts_user.get("polaris_ordered_timeline_connection") or {}
    edges = connection.get("edges") or []
    out = []
    for edge in edges:
        node = edge.get("node") or {}
        code = node.get("code") or ""
        if not code:
            continue
        media_type = node.get("media_type") or 1
        caption_obj = node.get("caption") or {}
        caption_text = (
            caption_obj.get("text")
            if isinstance(caption_obj, dict) else None
        )
        owner = node.get("user") or {}
        out.append(ProfilePost(
            shortcode=code,
            post_url=f"{INSTAGRAM_BASE}/p/{code}/",
            display_uri=node.get("display_uri"),
            media_type=media_type,
            is_video=media_type == MEDIA_VIDEO,
            is_carousel=media_type == MEDIA_CAROUSEL,
            carousel_media_count=node.get("carousel_media_count"),
            caption=caption_text,
            accessibility_caption=node.get("accessibility_caption"),
            owner_username=owner.get("username"),
        ))
    return out

def parse_profile_page(html: str) -> InstagramProfileData:
    profile_user = _extract_xig_user(html, field_marker="follower_count")
    posts_user = _extract_xig_user(
        html, field_marker="polaris_ordered_timeline_connection"
    )

    if profile_user is None and posts_user is None:
        return InstagramProfileData()

    u = profile_user or {}
    posts = _parse_posts(posts_user) if posts_user else []

    return InstagramProfileData(
        pk=str(u.get("pk") or "") or None,
        username=u.get("username"),
        full_name=u.get("full_name"),
        biography=u.get("biography"),
        profile_pic_url=u.get("profile_pic_url"),
        is_private=u.get("is_private"),
        is_verified=u.get("is_verified"),
        follower_count=u.get("follower_count"),
        following_count=u.get("following_count"),
        bio_links=_parse_bio_links(u.get("bio_links") or []),
        posts=posts,
    )


from pathlib import Path

from browser.browser import get_browser_instance, kill_browser, render_html
from instagram_parse import parse_profile_page
from models.profile import InstagramProfileData

INSTAGRAM_BASE = "<https://www.instagram.com>"
RENDER_TIMEOUT = 15

class InstagramClient:
    def __init__(self) -> None:
        self._browser = None

    def _ensure_browser(self) -> None:
        if self._browser is None:
            print("Spawning Brave browser ...")
            self._browser = get_browser_instance(9999)

    def fetch_profile_html(self, username: str) -> str:
        url = f"{INSTAGRAM_BASE}/{username}/"
        self._ensure_browser()
        assert self._browser is not None
        print(f"Fetching {url} ...")
        return render_html(self._browser, url, timeout_for_page_load=RENDER_TIMEOUT)

    def scrape_profile(
        self,
        username: str,
        *,
        html_path: Path | None = None,
    ) -> InstagramProfileData:
        if html_path and html_path.exists():
            html = html_path.read_text(encoding="utf-8")
        else:
            html = self.fetch_profile_html(username)
            if html_path:
                html_path.write_text(html, encoding="utf-8")
                print(f"Saved {len(html)} bytes to {html_path}")

        return parse_profile_page(html)

    def close(self) -> None:
        if self._browser:
            kill_browser(self._browser)
            self._browser = None

    def __enter__(self) -> "InstagramClient":
        return self

    def __exit__(self, exc_type, exc, tb) -> None:
        self.close()


import argparse

from api import InstagramClient
from constants import SAMPLE_HTML_PATH

DEFAULT_USERNAME = "cristiano"

def print_separator(char: str = "=", length: int = 50) -> None:
    print(char * length)

def main() -> None:
    parser = argparse.ArgumentParser(description="Instagram profile scraper")
    parser.add_argument(
        "--username",
        default=DEFAULT_USERNAME,
        help=f"Instagram username to scrape (default: {DEFAULT_USERNAME})",
    )
    parser.add_argument(
        "--fetch",
        action="store_true",
        help="Force a live browser fetch even if sample.html exists",
    )
    args = parser.parse_args()

    html_path = SAMPLE_HTML_PATH

    if args.fetch and html_path.exists():
        print(f"Removing cached {html_path} for fresh fetch ...")
        html_path.unlink()

    if html_path.exists():
        print(f"Using cached {html_path} ({html_path.stat().st_size} bytes)")
    else:
        print("sample.html not found - will fetch from browser.")

    with InstagramClient() as client:
        data = client.scrape_profile(args.username, html_path=html_path)

    print_separator()
    print("PROFILE")
    print_separator()
    print(f"  username    : {data.username}")
    print(f"  full name   : {data.full_name}")
    print(f"  bio         : {data.biography}")
    print(f"  private     : {data.is_private}")
    print(f"  verified    : {data.is_verified}")
    print(f"  followers   : {data.follower_count}")
    print(f"  following   : {data.following_count}")
    print(f"  profile pic : {(data.profile_pic_url or '')[:70]}...")
    if data.bio_links:
        print("  bio links   :")
        for lnk in data.bio_links:
            print(f"    [{lnk.title}] {lnk.url}")

    print()
    print_separator()
    print(f"POSTS ({len(data.posts)})")
    print_separator()
    for i, post in enumerate(data.posts, 1):
        kind = "carousel" if post.is_carousel else (
            "video" if post.is_video else "photo"
        )
        print(f"\n  {i}. [{kind}] {post.post_url}")
        if post.caption:
            print(f"     caption  : {post.caption[:80]}")
        if post.accessibility_caption:
            print(f"     alt text : {post.accessibility_caption}")
        if post.carousel_media_count:
            print(f"     slides   : {post.carousel_media_count}")

    print()
    print_separator()
    print("Done.")

if __name__ == "__main__":
    main()


from constants import SAMPLE_HTML_PATH
from browser.browser import get_browser_instance, kill_browser, render_html

PROFILE_URL = "<https://www.instagram.com/cristiano/>"
RENDER_TIMEOUT = 15

def main():
    if SAMPLE_HTML_PATH.exists():
        size = SAMPLE_HTML_PATH.stat().st_size
        print(f"sample.html already exists ({size} bytes). Delete it to re-fetch.")
        return

    print("Spawning Brave browser ...")
    browser = get_browser_instance(9999)

    print(f"Navigating to {PROFILE_URL} ...")
    html = render_html(browser, PROFILE_URL, timeout_for_page_load=RENDER_TIMEOUT)

    print(f"Got {len(html)} bytes of HTML")
    SAMPLE_HTML_PATH.write_text(html, encoding="utf-8")
    print(f"Saved to {SAMPLE_HTML_PATH}")

    kill_browser(browser)
    print("Browser closed.")

if __name__ == "__main__":
    main()


How to scrape Instagram (profile & posts) step-by-step Guide

Scrape Instagram Profiles

Businesses rely on web scraping to monitor competitors, track prices, generate leads, conduct market research, and collect public data at scale. While developers often build custom scraping scripts, many teams prefer no-code solutions that make data extraction accessible to anyone.

One of the most popular tools in this category is 

Whether you're gathering eCommerce prices, collecting contact information, or monitoring marketplaces, Octoparse lets you scrape websites without writing code. However, scraping at scale requires more than selecting data fields—you also need the right proxy infrastructure to avoid 

, IP blocks, and rate limits. If you're unfamiliar with CAPTCHA systems, check out our guide on 

Octoparse is a no-code web scraping platform that allows users to extract structured data from websites using a visual interface instead of programming.

Instead of writing Python or JavaScript, users simply click on the information they want to collect, and Octoparse automatically builds a scraping workflow.

Its cloud-based scraping option also lets users run jobs continuously without leaving their own computer online.

Many websites detect repeated requests from the same IP address. Once they identify unusual traffic patterns, they may:

Using rotating proxies distributes requests across multiple IP addresses, making your scraping activity appear more like normal user traffic.

For large scraping projects, proxies are essential for maintaining reliability and reducing interruptions.

 are particularly effective because requests originate from real residential IP addresses, making them more difficult to detect than traditional datacenter IPs.

Not every project requires the same type of proxy.

If you're scraping websites with sophisticated anti-bot systems, rotating residential proxies generally provide the highest success rates.

For simpler websites where speed matters most, datacenter proxies can be a cost-effective alternative.

Setting up proxies in Octoparse is straightforward.

If you're using rotating proxies, each request can automatically use a different IP address, helping reduce the likelihood of detection.

Both Octoparse and ParseHub are popular no-code scraping tools, but they have different strengths.

If your priority is ease of use, Octoparse is often the better choice. ParseHub may appeal to users who need greater customization for complex scraping workflows.

 are developer-focused browser automation frameworks.

If you don't have programming experience, Octoparse is usually the fastest way to build scraping workflows.

Developers, however, often prefer Playwright or Puppeteer for projects requiring complete control over browser behavior. If you're deciding between the two, read our in-depth comparison 

Browser Tools That Work Well with Octoparse

For projects involving account management or browser fingerprinting, dedicated browser tools can complement your scraping workflow.

These browsers help isolate browsing sessions and reduce fingerprint-based detection when managing multiple accounts.

Octoparse is also one of today's most accessible 

Once collected, this data can be analyzed using spreadsheets, databases, or business intelligence platforms.

Usually caused by sending too many requests from the same IP address.

To better understand why websites present CAPTCHA challenges and how they detect automated traffic, read 

 Use rotating residential proxies and reduce your request rate.

Websites may temporarily block your IP after detecting automated traffic.

 Rotate IP addresses regularly and respect crawl delays.

Some websites load data with JavaScript after the page opens.

 Enable browser rendering or wait for elements to load before extracting data.

For more advanced JavaScript scraping techniques, check out our guide to 

Following these practices helps improve data quality while minimizing interruptions.

Yes. Octoparse offers a free plan with limited features, along with paid plans that provide cloud scraping, scheduling, and higher usage limits.

No. Octoparse is designed as a no-code platform, allowing users to create scraping workflows through a visual interface.

Rotating residential proxies generally offer the highest success rate for scraping websites with anti-bot protection. ISP proxies provide stable long sessions, while datacenter proxies are suitable for less restrictive websites.

Both tools are excellent options. Octoparse is often easier for beginners, while ParseHub provides additional flexibility for advanced scraping projects.

Octoparse has become one of the most popular no-code web scraping tools thanks to its intuitive interface, cloud automation, and support for complex websites.

However, even the best scraper can struggle if requests originate from a single IP address. Combining Octoparse with reliable rotating proxies helps reduce blocks, improve scraping success rates, and collect data more consistently.

Whether you're conducting market research, monitoring competitors, generating leads, or building data-driven applications, pairing Octoparse with high-quality residential, ISP, or datacenter proxies provides a more reliable and scalable scraping workflow.

Octoparse Guide 2026

Setup, Proxies & Best Alternatives

Octoparse Guide 2026: Setup, Proxies & Best Alternatives

The web is constantly evolving, and so is Proxyrack.

As websites introduce new anti-bot technologies, businesses scale their data collection efforts, and developers demand more reliable infrastructure, maintaining a high-quality proxy platform requires continuous improvement—not just occasional feature releases.

During the first half of 2026, our team has focused on strengthening every part of the Proxyrack platform, from our proxy network and Scraping API to the dashboard experience, documentation, and overall platform reliability.

Here's a look at what we've been working on.

Reliable proxies are the foundation of every successful data collection project.

, consistent performance depends on constant monitoring and optimization behind the scenes.

Throughout 2026, we've continued investing in:

While these improvements aren't always visible from the dashboard, they help reduce interruptions and improve the overall experience for customers running web scraping, automation, market research, SEO monitoring, ad verification, AI data collection, and other data-driven workloads.

Continuous Improvements to Our Scraping API

 continues to evolve alongside the modern web.

As websites introduce new protections and change their infrastructure, we've been continuously refining our backend to improve compatibility, performance, and overall reliability.

Rather than treating the Scraping API as a finished product, we see it as a continuously evolving service that adapts alongside the websites our customers rely on.

A powerful platform should also be easy to use.

Throughout 2026 we've continued refining the 

 to make managing services faster and more intuitive.

Our work has included improvements to onboarding, account management, authentication, and overall usability, helping both new and existing customers get started more quickly and manage their services more efficiently.

Reducing friction remains one of our ongoing priorities.

A great developer experience goes beyond the product itself.

 and publishing new educational resources covering proxy technologies, web scraping, APIs, CAPTCHA handling, and implementation best practices.

Our goal is to make it easier for developers of all experience levels to successfully integrate Proxyrack into their applications while staying up to date with changes across the web scraping ecosystem.

Maintaining a healthy proxy network requires continuous investment in platform integrity.

Throughout 2026 we've strengthened our internal systems to better protect customer accounts, reduce abuse, and maintain the quality of our network.

Although many of these improvements happen behind the scenes, they help create a more stable and reliable experience for every legitimate customer using Proxyrack.

Many of the improvements we've made this year began with customer conversations.

 from developers, businesses, and long-term customers continues to influence how we prioritize improvements, refine existing workflows, and shape future development.

Our goal isn't simply to add more features—it's to build features that solve real problems.

The first half of 2026 has been focused on strengthening the foundations of Proxyrack.

As we move into the second half of the year, we'll continue investing in the areas that matter most:

We're committed to making Proxyrack more reliable, more powerful, and easier to use with every update.

Whether you're using Proxyrack for web scraping, price monitoring, SEO, market research, brand protection, cybersecurity, AI data collection, automation, or another use case, we appreciate the trust you place in our platform.

Every improvement we make is driven by one objective: providing reliable proxy infrastructure that helps our customers build with confidence.

Thank you for being part of the Proxyrack community. We look forward to sharing more updates as 2026 continues.

Proxyrack Product Update 2026

Network Improvements, Scraping API Enhancements & Platform Updates

Proxyrack Product Update 2026: Network Improvements, Scraping API Enhancements & Platform Updates

A step-by-step guide to scraping eBay search results into structured item data.

We'll create a small program that includes:

 Fetch HTML once and save it. Build and test the parser against that saved file - do not re-fetch while iterating on selectors.

curl -LsSf <https://astral.sh/uv/install.sh> | sh
source $HOME/.local/bin/env


pyright ships with a bundled Node binary that requires

, which is absent on minimal Ubuntu/Debian installs. If

libatomic.so.1: cannot open shared object file


Open your terminal, navigate to the project folder, then run:

uv run pytest          # all tests must pass
uv run ruff check .    # lint
uv run pyright         # strict type check


The parser works directly on HTML. You only need a search-results HTML file (like 

). No live request is required for this step.

uv run python main.py --html sample.html

# Show more rows
uv run python main.py --html sample.html --limit 10

# Optional label stored on the output object
uv run python main.py --html sample.html --keyword "shoes men" --limit 10


Parsed from local file: sample.html
============================================================
Keyword: None
Source: .../sample.html
Items found: 60
------------------------------------------------------------
- 168520328526 | $999.99 | Off White Virgil Abloh Air Jordan 1 High VAA ...
  <https://www.ebay.com/itm/168520328526>
  Pre-Owned | Buy It Now | +$396.57 delivery | United States
------------------------------------------------------------


Probe the saved HTML before changing selectors

uv run python -c "
from bs4 import BeautifulSoup
html = open('sample.html').read()
soup = BeautifulSoup(html, 'lxml')
cards = soup.select('ul.srp-results > li.s-card')
print('cards:', len(cards))
title = cards[0].select_one('.s-card__title .su-styled-text.primary')
print('title:', title.get_text(strip=True)[:80])
print('price:', cards[0].select_one('.s-card__price').get_text(strip=True))
"


uv run python main.py --search "shoes men"

uv run python main.py --search "shoes men" --fresh


keyword → home GET → wait >= 4s → search GET → parse_ebay_search_page() → EbaySearchData

 max 1 search per process run, minimum 4s between requests, stop on captcha/challenge markers, no pagination.

uv run python -m scripts.fetch_fixture "shoes men"
# writes tests/fixtures/shoes_men_search.html and updates session store


How to scrape eBay

CAPTCHAs are one of the biggest obstacles when collecting data from websites. Whether you're building an AI agent, monitoring prices, or performing large-scale data scraping, you'll eventually encounter a CAPTCHA challenge.

, why do websites use it, and how can legitimate businesses handle these challenges responsibly?

This guide explains everything you need to know.

A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) challenge response is the data submitted after solving a CAPTCHA.

Instead of simply clicking a checkbox or selecting traffic lights, your browser generates a response token proving that the challenge was successfully completed.

The website verifies this response before allowing the request to continue.

Without a valid challenge response, many websites will block access to:

CAPTCHAs exist to reduce automated abuse such as:

Unfortunately, they also affect legitimate automation projects like:

Most modern CAPTCHA systems generate temporary encrypted tokens that expire quickly.

Websites evaluate hundreds of signals before deciding whether to display a challenge.

The more automated your traffic appears, the more likely you'll receive a challenge.

One major factor websites evaluate is your IP address.

A proxy server acts as an intermediary between your application and the destination website.

Using quality residential proxies helps distribute requests across multiple real-user IPs instead of sending thousands of requests from one address.

It's important to note that proxies do not "solve" CAPTCHAs—they simply help reduce the likelihood of triggering them when used responsibly.

Why Data Scraping Often Encounters CAPTCHA

Modern websites actively defend against automated traffic.

If you're performing data scraping at scale, you'll often encounter CAPTCHAs after:

Large scraping projects therefore combine:

An unblocker is a service that automatically manages common anti-bot protections.

the unblocker performs these tasks behind the scenes and returns the requested webpage.

This greatly simplifies large-scale scraping projects.

Why JSON Files Are Common in CAPTCHA Workflows

Many scraping APIs return responses as a JSON file or JSON object.

{
  "status":"success",
  "captcha_detected":false,
  "data": {
      ...
  }
}


JSON is widely used because it is lightweight, easy to parse, and supported by nearly every programming language.

Many developers execute requests concurrently instead of one after another.

However, increasing concurrency also increases the likelihood of triggering anti-bot systems.

Sending thousands of simultaneous requests from one IP almost guarantees CAPTCHA challenges.

 is one of the core communication protocols that allows computers to exchange data reliably over the internet. Every time you load a website, send an email, or make an API request, TCP is typically responsible for ensuring that the data reaches its destination correctly and in the right order.

When your browser or application connects to a website, it establishes a TCP connection before any data is transferred. This connection includes details about how your device communicates with the server.

Many modern anti-bot systems analyse TCP-level characteristics, such as:

These signals can help websites distinguish between real users and automated traffic. For example, if thousands of requests come from connections that all behave identically or don't resemble those of a typical web browser, the traffic may be flagged as suspicious.

This is one reason why modern scraping solutions often use real browser automation instead of simple HTTP clients—they more closely replicate the network behaviour of genuine users, reducing the likelihood of triggering anti-bot systems.

 refers to artificial intelligence systems that can independently perform tasks to achieve a goal, rather than simply responding to individual prompts. Unlike traditional AI chatbots that wait for instructions, agentic AI can plan actions, make decisions, use tools, and adapt its approach based on the information it gathers.

Because these AI agents interact with websites much like a human user would, they often encounter the same anti-bot protections, including CAPTCHA challenges. If an agent sends too many requests, accesses websites too quickly, or exhibits behaviour that appears automated, it may be asked to complete a CAPTCHA before continuing.

To improve reliability, many agentic AI applications combine browser automation, residential proxies, intelligent rate limiting, and session management to reduce the likelihood of triggering anti-bot systems while interacting with websites responsibly.

Several companies offer tools designed to simplify web data collection.

Some well-known providers, such as Oxylabs, Bright Data, and Proxyrack, offer solutions aimed at improving scraping reliability through residential proxy networks and anti-blocking technologies.

The best choice depends on your project's scale, compliance requirements, and budget.

Best Practices for Avoiding CAPTCHA Challenges

Rather than trying to bypass protections aggressively, follow best practices:

These practices improve both reliability and the overall health of your scraping infrastructure.

A CAPTCHA challenge response is a verification token that proves a CAPTCHA has been successfully completed. While it plays an essential role in protecting websites from abuse, it can also create friction for legitimate automation tasks such as data scraping, aggregation, and AI-powered workflows.

Understanding how CAPTCHA systems work—and how factors like proxy servers, concurrent requests, TCP behaviour, and unblocker services influence them—can help you build more reliable, responsible automation systems while reducing unnecessary interruptions.

Is a CAPTCHA challenge response the same as a CAPTCHA?

No. The CAPTCHA is the challenge itself, while the challenge response is the verification token generated after it is solved.

No. A proxy server can reduce the chances of triggering a CAPTCHA, but it does not solve the challenge.

Websites often use anti-bot measures to protect resources, prevent abuse, and manage server load. Legitimate scraping projects should respect applicable terms and legal requirements.

An unblocker is a service that combines proxy management, browser automation, and anti-bot handling to improve access to websites that employ protection mechanisms.

Yes. Agentic AI systems that browse websites automatically can trigger CAPTCHA challenges if their behaviour resembles automated traffic.

What Is a CAPTCHA Challenge Response? A Complete Guide (2026)

How to Find the Best Value

SERP APIs make it possible to collect search engine results at scale without building and maintaining your own scraping infrastructure.

But comparing SERP API pricing isn't always straightforward.

One provider might charge per API request. Another might use a credit system where a single request consumes multiple credits depending on location, result type, or complexity. Some charge only for successful requests, while others count every request against your allowance.

That means the cheapest advertised plan isn't necessarily the cheapest option for your project.

In this guide, we'll break down how SERP API pricing works in 2026, what affects the real cost of collecting search data, and what to consider when comparing providers.

SERP stands for Search Engine Results Page.

A SERP API allows developers to programmatically retrieve search engine results and receive them as structured data, typically JSON.

Instead of manually loading search pages or maintaining a custom scraper, your application sends a request to an API. The provider handles the underlying data collection and returns the results.

Depending on the provider, a SERP API may return:

SERP APIs are commonly used for SEO monitoring, rank tracking, competitor research, market intelligence, lead generation, data analysis, and AI applications.

SERP API pricing varies significantly between providers.

Rather than comparing only the monthly subscription price, it's important to calculate how much usable data you're actually receiving.

Each model has advantages and disadvantages.

A $100 plan offering 100,000 requests may initially look cheaper than a $100 plan offering 50,000 requests. But if the first provider has a lower success rate or charges additional credits for features you need, the second option may actually deliver better value.

SERP API Pricing: What Should You Compare?

When comparing SERP API providers, the headline monthly price tells only part of the story.

What Are Mobile Proxies?

Transform Your Online Experience with Proxyrack's Mobile Proxies

Unlock the Power of Proxyrack's Mobile Proxies

Common Use Cases for Mobile Proxies

Web Scraping

Ad Verification

Market Research

Social Media Management

Price Monitoring

SEO Testing

Bypassing Restrictions

E-commerce Management

Frequently Asked Questions

Have a feature idea for our mobile proxies?