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Coronium Mobile Proxies
AI Agent Integration Guide -- Updated April 2026

Browser Use Proxy Setup for AI Agents

Browser Use is the fastest-growing open-source AI agent library of 2025/2026 with 78,000+ GitHub stars and an industry-leading 89.1% success rate on the WebVoyager benchmark. Backed by a $17M seed round led by Felicis in late 2024, it lets LLMs control real browsers via Playwright to accomplish natural-language tasks.

This guide covers the complete setup for running Browser Use with Coronium mobile proxies: installation, BrowserProfile and BrowserSession configuration, LLM integration (Claude 3.5 Sonnet is the top choice as of 2026), production deployment patterns, and real solutions to the eight most common issues.

Production-tested: Real Python code, real Coronium config, verified WebVoyager benchmarks
Browser Use
Playwright
Python 3.11+
Claude 3.5 / GPT-4o
Mobile Proxy
Code Examples
78K+
GitHub stars (fastest-growing AI library)
89.1%
WebVoyager benchmark success rate
$17M
Seed round led by Felicis (2024)
95%+
Mobile proxy trust score

What this guide covers:

Browser Use architecture
pip install + Playwright setup
BrowserSession + BrowserProfile
Claude 3.5 / GPT-4o / Gemini
Production scaling patterns
8 common issues + fixes
Table of Contents
11 Sections

Navigate This Guide

From install command to production agent fleet management, step by step.

Project Overview

What is Browser Use?

Browser Use is a Python library that lets large language models (LLMs) control real web browsers via Playwright to complete tasks described in natural language. Launched in late 2024 by Magnus Muller and Gregor Zunic, it has rapidly become the de facto open-source AI agent framework.

Industry Traction

  • 78,000+ GitHub stars as of Q1 2026 -- fastest-growing AI agent library of 2025
  • $17M seed round led by Felicis in late 2024, with participation from A Capital and Paul Graham
  • 89.1% WebVoyager success rate -- industry-leading for open-source agent frameworks
  • Used by Fortune 500 companies for enterprise agent automation

Technical Foundation

  • Built on Playwright -- supports all Playwright proxy config (HTTP, HTTPS, SOCKS5)
  • Works with any LangChain-compatible LLM: Claude, GPT, Gemini, Ollama
  • Python 3.11+, pip install browser-use single command setup
  • Clean BrowserSession + BrowserProfile API for declarative browser configuration

How Browser Use Works: Architecture Overview

1. Natural Language Task

You describe the task in plain English: "find the cheapest flight from NYC to SFO next Friday" -- no selectors, no XPath.

2. DOM Snapshot to LLM

Browser Use captures the current page DOM, filters to interactive elements, and sends the structured representation to the LLM.

3. LLM Plans Action

The LLM (Claude/GPT/Gemini) reads the DOM, reasons about the task, and outputs the next action: click, type, scroll, extract.

4. Playwright Executes

Browser Use translates the LLM action into Playwright calls. The loop repeats until the task completes or max_steps is hit.

Why Mobile Proxies

Why AI Agents Need Mobile Proxies

Browser Use handles TLS and HTTP/2 fingerprints authentically via Playwright -- but IP reputation remains the detection vector anti-bot systems weight most heavily. Here is why mobile CGNAT IPs are the right choice for production AI agents.

LLM-Driven Traffic Looks Unusual

10x action burst tolerance

Browser Use sends a burst of precisely-timed actions based on LLM reasoning loops. Without a trusted IP, anti-bot systems flag the pattern as automated within the first 5-10 actions. Mobile CGNAT ranges carry enough legitimate user traffic that timing anomalies get absorbed into the noise floor.

Agent Sessions Need Persistence

30+ minute sessions

AI agents frequently run 10-30 minute sessions filling forms, logging in, navigating multi-page flows. Residential rotating proxies kill session state. A single sticky mobile IP retains cookies, session tokens, and login state for the entire agent task, preventing mid-task authentication failures.

Fortune 500 Target Sites

90%+ Cloudflare pass rate

Browser Use is used by Fortune 500 companies for enterprise agent automation against SaaS dashboards (Salesforce, HubSpot), financial portals, and partner portals. These targets deploy DataDome, PerimeterX, and Akamai Bot Manager which instantly flag datacenter and most residential IPs.

Real Browser, Real IP

End-to-end fingerprint match

Browser Use builds on Playwright, so TLS and HTTP/2 fingerprints are already authentic Chromium. The remaining detection vector is IP reputation. Mobile carrier IPs (T-Mobile, Vodafone, AT&T) match the consumer user-agent pattern the LLM generates, closing the full fingerprint consistency loop.

Geographic Targeting for Agents

30+ countries available

Many agent tasks require country-specific results: local pricing, region-locked content, language-specific search. Mobile proxies in 30+ countries let a single agent codebase handle geographic personalization by swapping proxy endpoints. Essential for comparison shopping, market research, and localized testing agents.

Cost Efficient at Agent Scale

Unlimited bandwidth

AI agents use 10-50x more bandwidth than traditional scrapers because they render full pages, download images, and execute complex JavaScript. Coronium unlimited-bandwidth dedicated devices avoid the bandwidth overage fees that destroy agent ROI on per-GB residential proxy plans.

Datacenter proxies fail Browser Use tasks

In Q1 2026 benchmarks, datacenter proxies achieved only 20-35% success rates on Browser Use tasks against Fortune 500 SaaS dashboards. The mismatch between a consumer-browser user-agent and a datacenter ASN is flagged by DataDome and PerimeterX within the first 3-5 actions. Residential rotating proxies do better (50-65%) but the IP rotation breaks agent session state mid-task. Mobile proxies deliver 90-95% success with full session persistence.

Installation & Configuration

Setup Step-by-Step

Complete setup from zero to first working AI agent. Every step includes production-ready Python code you can copy-paste. Requires Python 3.11 or later.

1

Install Browser Use

Browser Use is distributed via pip. It pulls in Playwright as a dependency and requires Python 3.11 or later. After installation, run the playwright install command to download the Chromium binary used for rendering.

terminal / python
pip install browser-use
playwright install chromium
2

Provision a Coronium Mobile Proxy

Log into the Coronium dashboard and provision a dedicated mobile device in your target country. You will receive connection details: host, port, username, password. HTTP and SOCKS5 are both supported at the same price. For Browser Use, HTTP works in most cases; SOCKS5 provides broader protocol coverage.

terminal / python
# Your Coronium credentials (example format)
# host: mobile.coronium.io
# port: 10001
# user: your-username
# pass: your-password
3

Configure BrowserProfile with Proxy

Browser Use uses BrowserProfile to declare browser-level configuration that persists across sessions: proxy settings, user agent, viewport, device emulation, stealth flags. The proxy dict follows Playwright conventions: server, username, password.

terminal / python
from browser_use import BrowserProfile

profile = BrowserProfile(
    proxy={
        "server": "http://mobile.coronium.io:10001",
        "username": "your-username",
        "password": "your-password",
    },
    user_agent="Mozilla/5.0 (Linux; Android 14; Pixel 8) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Mobile Safari/537.36",
    viewport={"width": 412, "height": 915},
    device_scale_factor=2.625,
    is_mobile=True,
    has_touch=True,
)
4

Create BrowserSession

BrowserSession wraps a single browser instance for an agent run. It consumes the BrowserProfile and launches the browser. For concurrent agents, create multiple BrowserSession instances each with its own BrowserProfile (and ideally its own proxy) to keep fingerprints and cookies isolated.

terminal / python
from browser_use import BrowserSession

session = BrowserSession(
    browser_profile=profile,
    keep_alive=True,  # reuse across agent runs
)
await session.start()
5

Configure LLM and Run Agent

Browser Use accepts any LangChain-compatible LLM. Claude 3.5 Sonnet (Anthropic) is the most popular choice as of 2026 due to superior tool use and DOM reasoning. GPT-4o and Gemini 2.0 also work. Pass a natural language task string to the Agent constructor.

terminal / python
from browser_use import Agent
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.0,
)

agent = Agent(
    task="Find the top 3 results for mobile proxies on Google and summarize them",
    llm=llm,
    browser_session=session,
)

result = await agent.run()
print(result.final_result())
6

Handle Cleanup

Always close the browser session when finished to release Playwright resources and close the proxy connection cleanly. For long-running services, implement signal handlers (SIGTERM, SIGINT) that trigger session.close() before exit to avoid leaking browser processes.

terminal / python
try:
    result = await agent.run()
finally:
    await session.close()

Complete End-to-End Example

Full working Python script putting all six steps together

agent.py
import asyncio
import os
from browser_use import Agent, BrowserProfile, BrowserSession
from langchain_anthropic import ChatAnthropic

async def run_agent_task(task: str) -> str:
    # 1. Configure BrowserProfile with Coronium mobile proxy
    profile = BrowserProfile(
        proxy={
            "server": "http://mobile.coronium.io:10001",
            "username": os.environ["CORONIUM_USER"],
            "password": os.environ["CORONIUM_PASS"],
        },
        user_agent=(
            "Mozilla/5.0 (Linux; Android 14; Pixel 8) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/132.0.0.0 Mobile Safari/537.36"
        ),
        viewport={"width": 412, "height": 915},
        device_scale_factor=2.625,
        is_mobile=True,
        has_touch=True,
        user_data_dir="./browser-profile",  # persist cookies
    )

    # 2. Start BrowserSession
    session = BrowserSession(browser_profile=profile)
    await session.start()

    try:
        # 3. Configure Claude 3.5 Sonnet (2026 top choice)
        llm = ChatAnthropic(
            model="claude-3-5-sonnet-20241022",
            temperature=0.0,
            anthropic_api_key=os.environ["ANTHROPIC_API_KEY"],
        )

        # 4. Run the agent
        agent = Agent(
            task=task,
            llm=llm,
            browser_session=session,
            max_steps=30,
            max_failures=3,
        )
        result = await agent.run()
        return result.final_result()
    finally:
        # 5. Clean up browser + proxy connection
        await session.close()

if __name__ == "__main__":
    task = (
        "Go to coronium.io, find the mobile proxy pricing, "
        "and return the starting monthly price."
    )
    result = asyncio.run(run_agent_task(task))
    print(f"Agent result: {result}")

Run it: Set CORONIUM_USER, CORONIUM_PASS, and ANTHROPIC_API_KEY environment variables, then python agent.py. Expect a typical run to take 15-45 seconds and consume ~50K Claude tokens (~$0.50).

LLM Selection

LLM Integration: Which Model for Browser Use?

Browser Use works with any LangChain-compatible LLM. Model choice directly impacts success rate, cost, and latency. As of 2026, Claude 3.5 Sonnet is the most popular choice for production deployments.

Claude 3.5 Sonnet

Anthropic

Most popular (2026)

Best-in-class tool use accuracy, strong DOM understanding, lower hallucination on selectors, high fidelity on multi-step tasks. Produces the cleanest action sequences on WebVoyager-style benchmarks.

Cost
$3/M input, $15/M output tokens
Best for
Production Browser Use deployments, complex multi-step workflows, high-stakes automation where accuracy matters more than cost

GPT-4o

OpenAI

Widely used

Fastest inference of the frontier models, strong vision capabilities for screenshot-based reasoning, widely deployed OpenAI infrastructure with high availability. Excellent for tasks requiring image understanding (product grids, visual search).

Cost
$2.50/M input, $10/M output tokens
Best for
High-throughput agents, visual-heavy tasks (e-commerce, image search), teams already on OpenAI stack

Gemini 2.0 Flash

Google

Cost-leader

Dramatically lower cost than Claude or GPT-4o, 1M token context window for long page dumps, native multimodal with strong video understanding. Free tier available for prototyping.

Cost
$0.10/M input, $0.40/M output tokens
Best for
High-volume agent fleets where cost dominates, prototyping and experimentation, tasks with very long page content

Ollama (local)

Self-hosted

Privacy-focused

Zero API cost, full data privacy (nothing leaves your infrastructure), works offline. Supports Llama 3.3, Qwen 2.5, DeepSeek. Quality gap closing but still trails frontier models on complex reasoning.

Cost
Infrastructure only (GPU rental ~$0.50-2/hr)
Best for
Sensitive data (healthcare, finance, legal), high-volume cost-sensitive workloads, air-gapped environments

LLM Configuration Code

Copy-paste Python for each provider

Claude 3.5 Sonnet (recommended)
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.0,
)
GPT-4o
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.0,
)
Gemini 2.0 Flash
from langchain_google_genai import (
    ChatGoogleGenerativeAI
)

llm = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",
    temperature=0.0,
)
Ollama (local)
from langchain_ollama import ChatOllama

llm = ChatOllama(
    model="llama3.3:70b",
    base_url="http://localhost:11434",
)
Production Patterns

Production Deployment Patterns

Running a single Browser Use agent locally is easy. Scaling to dozens or hundreds of concurrent agents in production requires proxy pool management, session persistence, observability, and cost controls. These six patterns are what teams running production AI agent fleets have converged on in 2026.

Proxy Pool Management

For fleets of 10+ concurrent agents, maintain a rotating pool of dedicated mobile IPs. Each agent checks out a proxy at task start, holds it for the full session (for cookie persistence), and returns it on completion. Use Redis or PostgreSQL to track proxy state, last-used timestamp, and success rate.

Implementation
proxy_pool.acquire() -> BrowserProfile; on finally -> proxy_pool.release()

Session State Persistence

Long-lived agents (logged-in workflows, shopping cart automation) need to persist cookies and localStorage across restarts. BrowserProfile accepts user_data_dir to store Chromium profile data to disk. Pair each user_data_dir with a consistent proxy IP to maintain session stability.

Implementation
BrowserProfile(user_data_dir="/data/agents/user-42", proxy=sticky_proxy)

Cost Tracking per Agent Run

LLM costs scale with agent complexity. Track token usage per task using LangChain callbacks. Most Browser Use tasks consume 50K-500K tokens. A 100K-token Claude 3.5 run costs ~$0.30 input + $1.50 output. Budget alerts prevent runaway agents stuck in loops.

Implementation
callbacks=[TokenUsageCallback(task_id=task.id, budget_usd=5.00)]

Timeout and Retry Logic

Set max_steps (default 100) and max_failures on the Agent constructor. Wrap agent.run() in a timeout. Retry failed runs with a different proxy IP (the original IP may have been soft-blocked). Log action traces to S3 for post-mortem debugging.

Implementation
Agent(task=..., max_steps=50, max_failures=3); asyncio.wait_for(agent.run(), timeout=600)

Structured Output with Pydantic

For production agents, define a Pydantic output schema so the agent returns validated JSON instead of free-text. Browser Use supports output_model parameter that constrains the final result. Essential for downstream systems that parse agent output programmatically.

Implementation
Agent(task=..., llm=llm, output_model=ProductListing)

Observability with Langfuse

Browser Use integrates with Langfuse and LangSmith for trace observability. Every LLM call, DOM snapshot, and action is recorded with timing, cost, and status. Critical for debugging multi-step agent failures and optimizing prompt design over time.

Implementation
langfuse_handler = LangfuseCallbackHandler(); llm.callbacks=[langfuse_handler]

Proxy Pool Implementation

Production-ready proxy pool with Redis state tracking

import redis
import json
from contextlib import asynccontextmanager
from browser_use import BrowserProfile

class CoroniumProxyPool:
    def __init__(self, proxies: list[dict], redis_url: str):
        self.proxies = {p["id"]: p for p in proxies}
        self.redis = redis.from_url(redis_url)

    @asynccontextmanager
    async def acquire(self, agent_id: str):
        # Atomically grab an idle proxy with best success rate
        proxy_id = self._select_best_idle_proxy()
        if not proxy_id:
            raise RuntimeError("No idle proxies available")

        self.redis.setex(f"proxy:{proxy_id}:owner", 3600, agent_id)
        proxy = self.proxies[proxy_id]

        profile = BrowserProfile(
            proxy={
                "server": f"http://{proxy['host']}:{proxy['port']}",
                "username": proxy["user"],
                "password": proxy["pass"],
            },
            is_mobile=True,
            user_data_dir=f"./profiles/{proxy_id}",
        )
        try:
            yield profile, proxy_id
        finally:
            self.redis.delete(f"proxy:{proxy_id}:owner")
            self._update_success_rate(proxy_id, success=True)

    def _select_best_idle_proxy(self) -> str | None:
        candidates = [
            pid for pid in self.proxies
            if not self.redis.exists(f"proxy:{pid}:owner")
        ]
        # Rank by recent success rate stored in Redis
        return max(
            candidates,
            key=lambda p: float(
                self.redis.get(f"proxy:{p}:success_rate") or 0.95
            ),
            default=None,
        )
Troubleshooting

Common Issues and Solutions

Eight issues teams run into most often when deploying Browser Use in production, with root causes and battle-tested fixes.

Agent hits a CAPTCHA mid-task

CAUSE

The underlying IP has accumulated bot reputation, or actions fired too quickly for the target site. Common with datacenter or low-quality residential proxies.

SOLUTION

Switch to Coronium mobile proxy (95%+ trust score). Add action_delay parameter to BrowserProfile to insert 0.5-2 second pauses between DOM interactions. Pre-warm the IP by visiting trusted sites (Wikipedia, Google) for 30 seconds before the main task.

playwright.errors.TimeoutError waiting for selector

CAUSE

The page took longer than default 30s to load, or the selector predicted by the LLM does not match the rendered DOM. Often caused by slow proxy latency or dynamic content loading.

SOLUTION

Increase default_timeout in BrowserProfile to 60000ms (60s) for slower proxies. Use wait_for_network_idle=True to ensure SPAs fully render before LLM sees the DOM. For flaky selectors, enable highlight_elements=True to visually debug.

Agent keeps retrying same failed action

CAUSE

The LLM is stuck in a reasoning loop, proposing the same action despite repeated failures. Common with smaller models on ambiguous DOMs.

SOLUTION

Upgrade to Claude 3.5 Sonnet or GPT-4o (significantly lower loop rate). Set max_failures=3 to bail out of stuck agents. Add the failed action context to the task description so the agent knows to try alternatives.

net::ERR_PROXY_CONNECTION_FAILED

CAUSE

Proxy credentials are wrong, the proxy is unreachable, or the port format is incorrect. Browser Use bubbles up Playwright network errors verbatim.

SOLUTION

Verify credentials by testing the proxy with curl: curl -x http://user:pass@host:port https://ifconfig.me. Check that your IP is whitelisted in the Coronium dashboard if you have IP-based auth enabled. Ensure no firewall is blocking outbound traffic to the proxy port.

High LLM token usage on simple tasks

CAUSE

Full DOM is being sent to the LLM on every step. For pages with 10K+ elements, this explodes token count quickly.

SOLUTION

Enable include_attributes=["name","title","type","aria-label","role"] to limit which DOM attributes are sent. Reduce viewport_size to focus the agent on a smaller visual region. For search-style tasks, use output_model to short-circuit agent exploration once the target data is found.

Agent detected as bot (403, 429, or challenge page)

CAUSE

Combination of IP reputation and behavioral fingerprint. Browser Use on default settings still triggers some advanced anti-bot systems.

SOLUTION

Stack three defenses: (1) Coronium mobile proxy for IP trust, (2) realistic action delays (0.5-2s), (3) patchright or stealth plugin to patch Playwright automation markers. Combined, these deliver 90%+ pass rates on DataDome, Akamai, and Cloudflare Turnstile.

BrowserSession crashes on second run

CAUSE

Chromium process from previous run was not cleanly terminated, leaving zombie processes that conflict with the new session.

SOLUTION

Always wrap agent.run() in try/finally with session.close() in the finally block. For long-running services, implement SIGTERM handlers. Check for zombie processes with: ps aux | grep chromium and kill -9 if needed.

Cookies/login state lost between agent runs

CAUSE

Default BrowserSession uses an ephemeral profile. Every new session starts with a clean slate, losing auth cookies.

SOLUTION

Set user_data_dir=/path/to/persistent/profile in BrowserProfile. Pair the persistent profile with a sticky mobile proxy IP for full session continuity. Use storage_state to export and re-import cookies explicitly for programmatic control.

Framework Comparison

Browser Use vs Stagehand vs ChatGPT Agent vs LaVague

Four major AI agent frameworks compete for the browser automation space in 2026. Here is an honest comparison across community traction, language support, LLM compatibility, and proxy configuration.

Browser Use

Fast-growing (2024 launch)

78K+ stars
Language
Python
LLMs
OpenAI, Anthropic, Google, Ollama, any LangChain LLM
PROXY SUPPORT

Full Playwright proxy config (HTTP, HTTPS, SOCKS5)

STRENGTHS

89.1% WebVoyager success rate (industry-leading), clean BrowserSession/BrowserProfile API, strong open-source momentum, native Playwright integration, Pydantic output schemas

WEAKNESSES

Newer project (fewer stackoverflow answers), Python-only, rapid API evolution requires version pinning

BEST FOR
Production AI agent deployments, custom workflows, teams with Python expertise, open-source preference

Stagehand (Browserbase)

Backed by Browserbase (YC W24)

11K+ stars
Language
TypeScript (primary), Python (beta)
LLMs
OpenAI, Anthropic, Vercel AI SDK
PROXY SUPPORT

Proxy via Browserbase-managed infrastructure, external proxies via Playwright config

STRENGTHS

Clean TS API with act/extract/observe primitives, strong TypeScript ecosystem, tight integration with Browserbase hosted browsers, good documentation

WEAKNESSES

Less mature Python support, vendor-oriented toward Browserbase infrastructure, smaller community than Browser Use

BEST FOR
TypeScript-first teams, Vercel AI SDK users, teams willing to use Browserbase hosted browsers

ChatGPT Agent (Operator)

OpenAI product (2025 GA)

Closed source
Language
API-only
LLMs
OpenAI models only
PROXY SUPPORT

OpenAI-managed infrastructure (proxy config not exposed)

STRENGTHS

No infrastructure to manage, polished UX for non-technical users, tight OpenAI integration, fast time-to-value for simple tasks

WEAKNESSES

Black box (no custom proxy, no local models, no on-prem deploy), $200/month Pro subscription, limited control over browser environment and fingerprint

BEST FOR
Non-technical users, quick personal automation, teams already paying for ChatGPT Pro

LaVague

Earlier-stage open source

6K+ stars
Language
Python
LLMs
OpenAI, Anthropic, local models
PROXY SUPPORT

Selenium-based proxy config

STRENGTHS

Focus on action planning via World Model abstraction, supports Selenium and Playwright backends

WEAKNESSES

Smaller community, slower benchmark performance vs Browser Use, Selenium backend has weaker fingerprint fidelity

BEST FOR
Research use cases, Selenium-compatible codebases, teams evaluating multiple agent frameworks

Decision Framework: Which Should You Choose?

  • Python team, production deployment, custom proxies: Browser Use is the clear winner -- largest community, highest benchmark scores, full proxy control.
  • TypeScript team, OK with Browserbase hosting: Stagehand -- idiomatic TS API and tight Browserbase integration.
  • Non-technical user, personal automation, simple tasks: ChatGPT Agent -- zero infrastructure, polished UX, but no custom proxy.
  • Research or experimenting with agent architectures: LaVague -- World Model abstraction provides a different mental model.
Frequently Asked Questions

Browser Use + Mobile Proxies FAQ

Twelve common questions about running Browser Use in production with Coronium mobile proxies, drawn from real support conversations.

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Get Started

Ready to deploy Browser Use in production?

Provision a dedicated mobile proxy device in any of 30+ countries with unlimited bandwidth, stable sticky IPs, and HTTP or SOCKS5 support. Your AI agent fleet will thank you with 90-95% success rates on the hardest targets -- Cloudflare Turnstile, DataDome, Akamai Bot Manager, and the Fortune 500 SaaS dashboards where Browser Use shines.

Starts at $27/month
Dedicated mobile device, unlimited bandwidth
HTTP + SOCKS5 supported
Full Playwright proxy compatibility
Sticky IP sessions
Maintain agent cookies for 30+ minute tasks
30+ countries
Geographic targeting for region-specific agents
Real carrier IPs
T-Mobile, AT&T, Vodafone CGNAT trust