If you’re already crawling with Python, you know the drill: rotating proxies, spoofed headers… still getting blocked.

In 2025, anti-bot systems are tougher. They look past IPs and spot mismatched fingerprints, reused sessions, and anything that doesn’t behave like a real browser.

This article skips the basics and gets straight to what matters: why your crawler’s getting flagged — and how tools like Multilogin can help you scale without getting blocked.

Let’s go.

The case for Smarter identity management

User agent spoofing alone is no longer effective.

Modern websites analyze a wide range of signals beyond simple headers — including screen resolution, WebGL data, system fonts, Canvas fingerprinting, and even mouse movements. Any inconsistencies can quickly lead to detection.

This is where browser fingerprinting becomes a critical factor. It’s a layer often overlooked in standard Python crawling setups, yet it’s frequently the reason requests are blocked despite using new proxies.

To improve reliability and avoid detection, it’s essential to manage identity at the browser level rather than relying solely on network-level changes.

Perfect combo for crawling 

Python gives you full control over crawling logic — requests, parsing, scheduling. But when it comes to acting like a real browser, it falls short.

Most Python scripts don’t handle persistent sessions, real-time browser behavior, or fingerprint-level identity. Tools like requests, aiohttp, and even headless browsers still leave traces that modern detection systems pick up.

That’s where Multilogin comes in. It creates separate, isolated browser environments with unique fingerprints — just like real users. Each profile holds its own cookies, local storage, and settings. You can spin up hundreds of them, all behaving differently, all running through your script.

Other key benefits include:

• Built-in residential proxy network with 30M+ IPs in 150+ countries
• Persistent sessions that survive across restarts
• Team collaboration with shared profile access and permissions
• API access for full automation and profile control
• Daily testing on 50+ major websites to stay undetectable
• Support for popular automation tools like Playwright, Puppeteer, and Selenium

It’s not a replacement for Python — it’s the missing piece.

Building a hybrid crawling stack

If you’re serious about avoiding blocks, you need more than just Python and proxies. The modern stack looks more like this:

• Python – Handles crawling logic, parsing, task scheduling, retries.
• Optional: Headless browsers or cloud runners – For rendering-heavy pages or parallel execution.
• Multilogin – Manages browser fingerprints, session isolation, and persistent environments. Also providers built-in residential proxies.

Each layer solves a different problem. Python fetches and extracts, Multilogin keeps you undetected, and proxies handle geo and IP-based restrictions.

Scaling crawls without triggering alarms

Running a few crawlers is easy. Scaling to hundreds without getting flagged? That’s the hard part.

With Multilogin, each browser profile is fully isolated — different fingerprints, cookies, local storage, and proxy settings. You can launch multiple profiles in parallel, each acting like a separate user.

This means your crawlers don’t just hit endpoints — they move through websites like real humans. No overlaps, no shared sessions, no red flags.

That’s what keeps you under the radar when volume goes up.

Handling logins, cookies & dynamic content more reliably

Most websites don’t just check what you access — they care how you access it.

If your crawler logs in, scrapes, then disappears without a trace, it raises suspicion. Without persistent cookies or session storage, you’re basically logging in from scratch every time.

Multilogin keeps each session alive inside its own browser profile. That means:

• Logins stay logged in
• Cookies and local storage persist between runs
• Dynamic content loads like it would in a real browser

This is especially useful on platforms that use multi-step authentication or rely heavily on JavaScript.

Step-by-step: Crawling the Web With Python + Multilogin (With JavaScript Support)

If your target websites rely on JavaScript, traditional HTTP requests won’t be enough. Here’s how to build a more reliable, undetectable crawling setup using Python and Multilogin:

1. Set up your tools

Make sure you have Python ready with libraries that support web automation, like Playwright or Selenium. These allow your crawler to behave like a real browser and interact with dynamic content. You’ll also need access to Multilogin’s API to control browser profiles remotely.

2. Create browser profiles in Multilogin

In Multilogin, create separate browser profiles for each identity you plan to use. You can randomize their fingerprints or configure specific settings like operating system, screen resolution, and timezone. Assign each profile a unique proxy for geo-targeting and IP rotation — either your own or Multilogin built-in residential proxies, which give you access to 30M+ IPs across 150+ countries.

3. Launch profiles using Multilogin

Start the profiles through Multilogin — either manually or using the API. Once launched, each profile acts like a real user, complete with its own cookies, local storage, and system fingerprint.

4. Connect Python to the browser

Instead of scraping websites directly with Python, use your script to control the launched browser profiles. This lets your crawler navigate pages naturally, trigger JavaScript, scroll, click, and extract content without raising flags.

5. Handle dynamic content and login sessions

With Multilogin, session data is persistent. That means when your crawler logs into a site, the profile remembers that login across sessions — no need to start over every time. This is essential for platforms with multi-step logins or heavy use of local storage.

6. Rotate profiles and distribute the load

To scale up, run multiple profiles in parallel — each with its own fingerprint, proxy, and browsing behavior. You can assign specific tasks to each profile and run them simultaneously, avoiding overlapping patterns or shared sessions.

7. Manage errors and adapt

Anti-bot systems change constantly. Monitor which profiles or sites are triggering blocks, and be ready to swap profiles, adjust fingerprints, or rotate proxies. Automation tools and job schedulers can help you manage retries and downtime efficiently.

Team-based crawling & coordination

When you’re managing large-scale scraping projects, coordination is key. Whether you’re working with a small team or need to scale across multiple workers, having a central system for managing browser profiles makes all the difference.

Multilogin allows you to:

• Organize profiles by project — Group them, assign roles, and manage access.
• Collaborate efficiently — Share profiles within teams, with control over who can edit or launch them.
• Launch profiles in the cloud — Whether you’re on your laptop or managing from the office, profiles are accessible anywhere.

This structure makes it easy for teams to scale crawlers, track progress, and avoid overlapping work.

Common pitfalls (and how to avoid them)

Even with the best tools, some issues still slip through the cracks. Here’s how to avoid the most common mistakes:

• Reused fingerprints – If you’re using the same profiles for multiple accounts, it’s a red flag. Keep profiles isolated to prevent detection.
• Inconsistent proxy + profile combos – Ensure each profile uses its own dedicated proxy. Mixing proxies between profiles can cause patterns that get flagged.
• Misconfigured headers vs. browser identity – Always match your headers to the fingerprint. Any mismatch between headers, user agent, and actual browser behavior can trigger bot detection.
• Overlapping crawl patterns across profiles – Keep your crawlers’ behaviors unique. If multiple profiles hit the same pages at the same times, websites will notice.

From POCs to production: Real-world workflows

Moving from a proof of concept (POC) to full-scale production requires automation, stability, and consistency. Here’s how to integrate Python crawlers with Multilogin in a production environment:

• CI/CD pipelines – Automate the deployment and execution of crawlers. Use version control to track changes and ensure stable builds.
• High-volume scraping schedules – Use cloud-based Multilogin profiles that can be launched and managed remotely to handle large batches of crawls without downtime.
• Data validation & error handling – Set up automated checks for successful data extraction. If an account gets blocked, a new profile can be launched to continue crawling seamlessly.

This approach gives you a scalable, flexible, and reliable setup that can grow with your needs. Whether you’re crawling for data on thousands of products or running a multi-account operation, integrating Python with Multilogin ensures consistency and avoids downtime.