Qwen Agent Deployment Made Easy: Avoid IP Bans with IPFLY’s No-Client Proxy

In the era of AI-driven automation, agents built on large language models (LLMs) have become powerful tools for handling repetitive and complex tasks—from e-commerce price monitoring and competitor data collection to automated testing and web research. Among the many LLM options, Alibaba’s Qwen (Tongyi Qianwen) stands out with its open-source agent framework, strong tool integration capabilities, and efficient task execution. Learning to “use Qwen to run agent” opens the door to building custom automation solutions tailored to your needs.

However, many developers encounter a critical roadblock when running Qwen agents: IP restrictions and bans. When agents perform tasks like large-scale web scraping or multi-region data collection, frequent requests from a single IP easily trigger anti-bot systems (e.g., Cloudflare) or result in IP blocks from target platforms. This not only interrupts task execution but also wastes valuable development time.

The key to solving this problem lies in pairing Qwen Agent with a reliable proxy service. A high-quality proxy masks your real IP, distributes requests, and ensures stable access. Among proxy providers, IPFLY’s no-client residential proxy emerges as the ideal partner for Qwen Agent. Its 99.9% uptime, pure residential IP pool, and seamless integration with Qwen’s workflow eliminate IP-related disruptions. In this guide, we’ll walk you through the entire process of using Qwen to run agent—from environment setup and basic implementation to proxy integration with IPFLY. We’ll also compare IPFLY with top competitors and share practical optimization tips to help you build stable, efficient automation agents.

What Is Qwen Agent? Core Advantages for Automation Tasks

Qwen Agent is an open-source framework built on Qwen LLMs, designed to simplify the development and operation of AI agents. It provides out-of-the-box components for tool integration, task planning, and memory management, enabling developers to build agents without starting from scratch. Here are its core advantages for automation scenarios:

Powerful Tool Integration Capabilities

Qwen Agent supports seamless integration with over 20 common tools, including web browsers (via Chrome DevTools MCP), code interpreters, file operators, and APIs. This means your agent can directly interact with web pages, execute code, process files, and call external services—critical for tasks like web scraping and data analysis.

Flexible Deployment Options

You can run Qwen agents using Qwen’s cloud service (via DashScope) or deploy open-source Qwen models locally (compatible with GPU/CPU). This flexibility allows you to choose the deployment method based on your budget, data privacy requirements, and task complexity.

Intuitive Task Planning & Memory

Qwen Agent can break down complex user requests into subtasks, plan execution sequences, and retain task context (memory) across multiple interactions. For example, it can automatically schedule hourly price checks, store historical data, and trigger alerts when thresholds are met—no manual intervention required.

Easy-to-Use Development Framework

With a simple API and rich documentation, Qwen Agent lowers the barrier to entry for agent development. Even developers with limited LLM experience can quickly build custom agents by registering tools and configuring prompts.

Why Qwen Agents Need Proxy Services: Key Scenarios & Pain Points

While Qwen Agent excels at task automation, its performance in web-related tasks is heavily dependent on stable network access. Here are the top 3 scenarios where a proxy is essential, and the pain points of using low-quality proxies:

Web Scraping & Data Collection

When an agent scrapes product prices, customer reviews, or market data from e-commerce platforms (e.g., Taobao, JD) or search engines, high-frequency requests from a single IP will trigger anti-bot systems. Low-quality data center proxies are easily detected and banned, leading to incomplete data collection.

Multi-Region Task Execution

Agents tasked with accessing region-restricted content (e.g., regional e-commerce platforms, localized APIs) need IPs from specific geographic locations. Proxies with limited region coverage can’t meet this demand, restricting the agent’s operational scope.

Continuous Operation & High Availability

Long-term automated tasks (e.g., 24/7 price monitoring) require stable proxy connections. Unstable free proxies or proxies with low uptime cause frequent disconnections, interrupting task execution and requiring manual reconfiguration.

To address these pain points, you need a proxy service that offers pure residential IPs (low detection risk), global region coverage, 99.9%+ uptime, and seamless integration with Qwen Agent. This is where IPFLY’s no-client proxy shines.

Proxy Comparison for Qwen Agent: IPFLY vs. Bright Data vs. Oxylabs

We tested three leading proxy providers against the key criteria that matter most for Qwen Agent users: IP type (residential purity), uptime, integration ease (no-client design), region coverage, and cost. The results show IPFLY is the optimal choice for most developers and small teams, thanks to its no-client advantage, high uptime, and cost-effectiveness. Bright Data and Oxylabs are better suited for enterprise-scale deployments but come with higher costs and complexity.

Detailed Comparison Table

Why IPFLY Is the Best Proxy for Qwen Agent

No-Client Design: Effortless Integration with Qwen’s Python Workflow

Unlike Bright Data and Oxylabs, which force you to install cumbersome client software, IPFLY has no client application. This is a game-changer for Qwen Agent developers: you can integrate IPFLY’s proxy directly into your Qwen Agent code by adding a few lines of parameter configuration—no extra software installation or complex API calls required. This keeps your development environment clean and avoids compatibility issues between proxy clients and Qwen’s framework.

99.9% High Uptime: Uninterrupted Qwen Agent Operations

IPFLY’s self-built global residential IP network and BGP multi-line redundancy ensure 99.9% uptime. For Qwen Agents running 24/7 tasks (e.g., price monitoring, real-time data collection), this means no unexpected disconnections. By contrast, Bright Data’s basic package (99.7% uptime) can lead to hours of downtime, disrupting your agent’s workflow and causing data loss.

Pure Residential IPs: Avoid Detection & IP Bans

IPFLY’s 99.9% pure residential IPs mimic real user devices, making them nearly undetectable by anti-bot systems. When paired with Qwen Agent for web scraping, this eliminates the risk of IP bans and ensures complete data collection. Data center proxies (used by many low-cost providers) are easily flagged, turning your Qwen Agent into an ineffective tool.

Cost-Effective: Scale Without Breaking the Bank

IPFLY’s pay-as-you-go pricing ($0.8/GB) is a fraction of Bright Data ($2.94/GB) and Oxylabs ($8/GB). For a developer running a Qwen Agent that uses 40GB of traffic monthly for web scraping, IPFLY costs only $32—compared to $117.60 for Bright Data and $320 for Oxylabs. This affordability allows you to scale your agent operations (e.g., add more IPs for multi-region tasks) without exceeding your budget.

Step-by-Step Guide: Use Qwen to Run Agent with IPFLY Proxy

We’ll walk you through the entire process of setting up a Qwen Agent for e-commerce price monitoring, including environment configuration, agent implementation, and IPFLY proxy integration. This example uses Qwen’s cloud service (DashScope) for easy access, and the agent will scrape product prices from a target e-commerce site using IPFLY’s SOCKS5 proxy.

Prerequisites

• Python 3.8–3.11 (compatible with Qwen Agent)
• Qwen Agent installation: pip install -u "qwen-agent[code_interpreter, mcp]"
• DashScope API Key (for accessing Qwen’s cloud model; get from Alibaba Cloud’s DashScope console)
• IPFLY account: Sign up, generate a SOCKS5 proxy, and record parameters (Proxy IP, Port, Username, Password)

Step 1: Configure Environment Variables

Create a .env file to store your DashScope API Key and IPFLY proxy parameters:

# .env file
DASHSCOPE_API_KEY=your_dashscope_api_key
IPFLY_PROXY_IP=your_ipfly_proxy_ip
IPFLY_PROXY_PORT=your_ipfly_proxy_port
IPFLY_USERNAME=your_ipfly_username
IPFLY_PASSWORD=your_ipfly_password

Step 2: Implement Qwen Agent with IPFLY Proxy

This code creates a Qwen Agent that scrapes product prices from a target e-commerce URL, using IPFLY’s proxy to avoid IP bans. The agent will run hourly and save data to a JSON file.

import os
import time
import json
from dotenv import load_dotenv
from qwen_agent.agents import assistant
from qwen_agent.tools.base import BaseTool, register_tool
import requests

# Load environment variables
load_dotenv()

# Configure IPFLY proxy
IPFLY_PROXY = {
    "http": f"socks5://{os.getenv('IPFLY_USERNAME')}:{os.getenv('IPFLY_PASSWORD')}@{os.getenv('IPFLY_PROXY_IP')}:{os.getenv('IPFLY_PROXY_PORT')}",
    "https": f"socks5://{os.getenv('IPFLY_USERNAME')}:{os.getenv('IPFLY_PASSWORD')}@{os.getenv('IPFLY_PROXY_IP')}:{os.getenv('IPFLY_PROXY_PORT')}"
}

# Register a custom tool for price scraping (integrated with IPFLY proxy)
@register_tool('ecommerce_price_scraper')
class EcommercePriceScraper(BaseTool):
    description = 'Scrapes product name and price from e-commerce URLs using a proxy to avoid IP bans.'
    parameters = {
        'name': 'url',
        'type': 'string',
        'description': 'Target e-commerce product URL',
        'required': True
    }

    def call(self, params: str, **kwargs) -> str:
        try:
            url = json.loads(params)['url']
            # Send request with IPFLY proxy
            headers = {
                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
            }
            response = requests.get(url, proxies=IPFLY_PROXY, headers=headers, timeout=15)
            response.raise_for_status()  # Raise error for HTTP status codes >=400

            # Extract product name and price (adjust selectors based on target site)
            from bs4 import BeautifulSoup
            soup = BeautifulSoup(response.text, 'html.parser')
            product_name = soup.find('h1', class_='product-title').text.strip() if soup.find('h1', class_='product-title') else 'Unknown Product'
            product_price = soup.find('span', class_='price').text.strip() if soup.find('span', class_='price') else 'Unknown Price'

            result = {
                'product_name': product_name,
                'price': product_price,
                'scrape_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())
            }
            return json.dumps(result, ensure_ascii=False)
        except Exception as e:
            return f"Scraping failed: {str(e)}"

# Configure Qwen Agent
llm_cfg = {
    'model': 'qwen-turbo',  # Lightweight Qwen model (replace with qwen-max for complex tasks)
    'model_server': 'dashscope',
    'api_key': os.getenv('DASHSCOPE_API_KEY')
}

# System prompt: Define agent behavior
system_prompt = """
You are a price monitoring agent. Your tasks:
1. Use the 'ecommerce_price_scraper' tool to scrape prices from the given URL.
2. Save the scraping result to 'price_history.json'.
3. Run hourly and append new data to the file.
4. If the price is lower than 200 yuan, output an alert.
"""

# Initialize agent with the custom tool
bot = assistant(
    llm=llm_cfg,
    system_message=system_prompt,
    function_list=['ecommerce_price_scraper']
)

# Run the agent (hourly loop)
def run_agent_hourly(target_url):
    while True:
        messages = [{'role': 'user', 'content': f'Start price monitoring for {target_url}'}]
        # Execute agent task
        for response in bot.run(messages=messages):
            print(f"Agent Response: {response[0]['content']}")
            # Save result to JSON
            if 'scraping succeeded' in response[0]['content']:
                result = json.loads([r for r in response if 'function_call' in r][0]['function_call']['result'])
                with open('price_history.json', 'a', encoding='utf-8') as f:
                    json.dump(result, f, ensure_ascii=False)
                    f.write('\n')
        # Wait for 1 hour (3600 seconds)
        time.sleep(3600)

# Start the agent with your target e-commerce URL
if __name__ == "__main__":
    target_url = "https://detail.tmall.com/item.htm?id=692996234101"  # Replace with your target URL
    run_agent_hourly(target_url)

Step 3: Test & Verify the Agent

1.Replace the placeholders in the .env file with your actual DashScope API Key and IPFLY parameters.

2.Adjust the HTML selectors (product-title, price) in theEcommercePriceScraper tool to match the target e-commerce site’s structure.

3.Run the script: python qwen_agent_price_monitor.py.

4.Verify: Check the price_history.json file for scraped data. If the price is below 200 yuan, the agent will output an alert.

Want to access blocked overseas academic databases, geo-restricted streaming platforms, or cross-border platform backends? Don’t let geo-barriers hold you back! Visit IPFLY.net now for region-specific proxies (190+ countries), then join the IPFLY Telegram community—get “step-by-step guides to unlock Netflix US/BBC UK” and “cross-border academic resource access tips”. Bypass restrictions easily and access global resources freely!

Optimization Tips for Running Qwen Agent with IPFLY

To maximize the stability and efficiency of your Qwen Agent, follow these optimization tips when using IPFLY:

Rotate Proxies for High-Frequency Tasks

For agents performing high-frequency scraping (e.g., 10+ requests per minute), rotate IPFLY proxies every 5–10 requests. Use IPFLY’s dashboard to generate multiple proxies and add a rotation logic in your agent code to further reduce detection risk.

Match Proxy Region with Target Site

If your agent accesses a region-specific site (e.g., US Amazon, Japanese Rakuten), select an IPFLY proxy from the corresponding region. This ensures your request headers (e.g., Accept-Language) match the IP’s location, avoiding anti-bot suspicion.

Enable Proxy Connection Testing

Add a pre-execution check in your agent code to verify the IPFLY proxy is working. This prevents task failures due to proxy issues:

def check_ipfly_proxy():
    try:
        response = requests.get('https://api.ipify.org', proxies=IPFLY_PROXY, timeout=5)
        if response.status_code == 200:
            print(f"IPFLY proxy working: {response.text}")
            return True
        else:
            print("IPFLY proxy not working")
            return False
    except Exception as e:
        print(f"IPFLY proxy check failed: {str(e)}")
        return False

# Call before running the agent
if not check_ipfly_proxy():
    exit("Exiting: Proxy not available")

Use Qwen’s Code Interpreter for Data Processing

Leverage Qwen Agent’s built-in code interpreter tool to process scraped data (e.g., generate price trend charts, calculate averages) directly within the agent, eliminating the need for separate data processing scripts.

Unlock Qwen Agent’s Full Potential with IPFLY

Using Qwen to run agent opens up endless possibilities for automation, but IP restrictions and bans often hold back its full potential. A high-quality proxy isn’t a luxury—it’s a necessity for stable, efficient agent operations. IPFLY’s no-client design, 99.9% pure residential IPs, 99.9% uptime, and cost-effectiveness make it the perfect partner for Qwen Agent.

Unlike enterprise-focused proxies like Bright Data and Oxylabs, IPFLY is built for developers and small teams. It integrates seamlessly with Qwen’s Python workflow, avoids detection by anti-bot systems, and keeps your agent running 24/7 without interruptions. Whether you’re building a price monitoring agent, web scraping tool, or multi-region automation solution, IPFLY ensures your Qwen Agent performs at its best.

Stop letting IP bans and unstable connections disrupt your automation tasks. Follow this guide to use Qwen to run agent with IPFLY proxy, and unlock the full power of AI-driven automation today.