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After Meta's Acquisition of MultiOn: Earning 20k RMB/Month via Autonomous Cross-Border E-Commerce Price Comparison Powered by Agent Q

Workflow: Deploy MultiOn Agent Q instances on the cloud via API calls. After inputting competitor product links and price ranges,

AGENT

Key Fields

FIELD STAMPS

IndustryE-commerce / Retail

RegionGlobal

ScaleSME

ChannelOnline

🔧 Workflow

Deploy MultiOn Agent Q instances on the cloud via API calls. After inputting competitor product links and price ranges, the agent automatically opens target websites, simulates multi-step operations (searching, adding to cart, checkout simulation) to scrape prices and inventory, and outputs structured comparison reports automatically pushed to Lark or email. Conduct a weekly review with clients to adjust monitoring frequencies and site lists based on metrics, ensuring coverage of newly listed products and limited-time promotional changes. The entire process runs fully automated from daily triggering to report delivery, with human intervention limited to occasional events such as handling exception login CAPTCHAs and site structure redesigns.

🛠 Setup Requirements

Requires Python fundamentals and REST API invocation experience, along with the ability to deploy agent instances on cloud servers using Docker. Lightweight servers on AWS or Alibaba Cloud are sufficient, with a setup time of about one week. Can be initially packaged as a vertical SaaS subscription service without self-developed models, focusing on agent behavior orchestration and result data cleaning, packaging MultiOn's capabilities into client-understandable price reports and alert notifications.

🧰 Toolchain

🔧 MultiOn API
🔧 Agent Q
🔧 n8n
🔧 Lark
🔧 Playwright

💰 Revenue

Calculated at a monthly fee of 3,000 RMB per account, acquiring 20 enterprise clients yields 60,000 RMB per month; if priced at 500 RMB per single competitor price comparison report, securing 3 stable major clients yields 9,000 USD per month. In practical cases, individual developers using MultiOn to replace manual operations can reduce single-client service costs from 3,000 RMB to 500 RMB per month, with the price difference forming the gross profit; upon scaling, a monthly income of 20,000 to 60,000 RMB is a reasonable range.

💸 Cost

Cloud server costs about 500 RMB/month, agent API calls are billed per use at approximately 0.1 USD per call, and 50 tasks/day cost around 150 USD/month, keeping overall operating costs under 5,000 RMB. If using Agent Q's reasoning enhancement mode, an extra token budget should be reserved, and setting a daily upper limit is recommended to prevent runaway costs.

⏱ Time Investment

Initial setup requires a concentrated 1-week investment, followed by 2 hours of daily monitoring of results and exception handling. After the initial delivery, the system runs mostly automatically, requiring only a weekly review of new client requirements, representing a classic low-time-investment, high-compound-return business model.

🚀 Getting Started

The first step is to run through a real price comparison scenario (such as scraping Amazon product prices) using a MultiOn developer account to familiarize yourself with its event-driven interface and error handling logic. The second step is to find open-source Agent Q demo code on GitHub, modify it into a price comparison tool, and once functional, serve cross-border e-commerce friends around you to gain seed users, charge based on custom requirements to accumulate case studies, and then gradually standardize it into a subscription product.

🔑 Keys to Success

✅ Precise scenarios rather than generalized tasks (price comparison and inventory monitoring outperform general browser operations)
✅ Result delivery speed (compressed from minutes to seconds, resulting in high client retention rates)
✅ Locking in continuous demand via a subscription model to avoid revenue fluctuations from one-off project contracts
✅ Establishing a proxy pool and anti-detection strategies to ensure stable multi-platform scraping without account bans

⚠️ 风险

⚠️ Uncontrollable Agent Q reasoning invocation costs; a daily token budget upper limit must be set to prevent losses
⚠️ Blockade of automated access by multiple mainstream e-commerce platforms, requiring configured proxy pools and simulated human operation frequencies to avoid service interruptions that affect client trust
⚠️ Uncertainty in Meta's integration direction for the MultiOn product line, with potential major adjustments to API pricing or features, necessitating advance design of alternative solutions (such as self-built Playwright) to avoid vendor lock-in

📌 Real Cases

📌 36Kr reported that Manus-like agents successfully executed real business scenarios in 2025, validating the deployment capabilities of browser agents and providing a reference for the commercialization of similar tools
📌 InfoQ/Senzhiyuan practically tested Agent Q on the AgentBench benchmark, driving Llama 3's success rate up threefold from 19.1% to 34.6%, proving a substantial enhancement in the usability of post-trained agents on real web tasks
📌 AI Box Navigation lists MultiOn as a web-operation AI agent that helps users automatically complete multi-step tasks in browsers, with typical use cases including e-commerce price comparison, form filling, and competitor monitoring, and developer communities are already sharing practical cases of earning thousands of dollars per month

Sources