@Smartpigai: Over the past 30 days, I've been researching a question: Can ordinary people make money from open-source projects on GitHub? The answer is: yes. But the key isn't finding the hottest project or the one with the most stars—it's finding projects that can be packaged as “concrete business outcomes.” I've compiled a list of 10 high-quality projects suitable for making money.

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Summary

A guide exploring how ordinary people can monetize GitHub open-source projects, listing 10 viable projects and analyzing their business models.

Over the past 30 days, I've been researching a question: Can ordinary people make money from open-source projects on GitHub? The answer is: yes. But the key isn't finding the hottest project or the one with the most stars—it's finding projects that can be packaged as “concrete business outcomes.” I've compiled a list of 10 high-quality GitHub projects suitable for making money. These aren't projects that “look cool but you don't know how to monetize.” Instead, you can build SaaS, automation services, templates, deployment services, consulting, data products, and vertical industry tools around them. The real opportunity isn't on GitHub itself—it's whether you can package an open-source project into a concrete business outcome. 1. browser-use GitHub: https://github.com/browser-use/browser-use… This project lets AI operate browsers like a human—opening web pages, clicking buttons, filling forms, extracting information, and executing tasks. Many companies deal with repetitive web operations daily: finding customers, filling CRMs, checking competitors, submitting forms, processing backend orders, and collecting web data. You can automate these processes and charge a monthly fee. AI's true value isn't in chatting—it's in getting work done. 2. Crawl4AI GitHub: https://github.com/unclecode/crawl4ai… It's suitable for AI data business. It converts web content into Markdown or structured data suitable for LLMs. Many AI applications today need clean, stable, usable data more than they need models. You can build industry knowledge bases, competitor monitoring, RAG data cleaning, content collection systems, and vertical search tools around it. What customers are truly willing to pay for isn't a crawler—it's ready-to-use data. 3. Firecrawl GitHub: https://github.com/mendableai/firecrawl… It's a web scraping and parsing tool for AI applications, converting website content into Markdown, JSON, or structured data. The easiest product to build around it is: input a website, output a report. For example: competitor analysis reports, potential customer lists, product price monitoring, SEO content analysis, industry news summaries. B2B customers are willing to pay for time savings, especially sales, marketing, and research teams. 4. Activepieces GitHub: https://github.com/activepieces/activepieces… Think of it as an open-source version of Zapier—connect different tools and build automation workflows. Many small businesses know they need automation, but they don't know how to set it up. You can help them configure processes like automatically entering CRM after form submission, sending emails after customer payment, notifying sales of new leads, and syncing Notion/Slack/Gmail/Airtable. This kind of service is perfect for setup-and-maintain: a one-time configuration fee plus monthly maintenance. 5. Dub GitHub: https://github.com/dubinc/dub It's an open-source link attribution and short link platform. It's not just short links; it also does conversion tracking, link analysis, and affiliate tracking. Many creators, independent websites, newsletters, and affiliate marketers share a common problem: they don't know which link actually brings in money. You can use Dub to build creator link tracking systems, affiliate marketing management dashboards, independent site promotion analytics, and ad conversion tracking panels. The closer you are to money, the easier it is to charge. 6. Twenty GitHub: https://github.com/twentyhq/twenty It's an open-source CRM, essentially an open-source alternative to Salesforce. The best opportunity isn't building a general-purpose CRM, but a vertical industry CRM—like real estate agent CRM, recruitment company CRM, consulting firm CRM, B2B sales CRM, or study abroad agency CRM. Many teams don't want complex systems; they just want a CRM that fits their business processes perfectly. That's the opportunity. 7. Plane GitHub: https://github.com/makeplane/plane It's an open-source project management tool, an alternative to Jira, Linear, and ClickUp. Many teams want self-hosted project management systems, especially outsourcing teams, software companies, remote teams, and companies sensitive to data privacy. You can offer self-hosted deployment, workflow configuration, team training, and private maintenance around it. These projects aren't necessarily sexy, but it's easy to charge service fees. 8. Medusa GitHub: https://github.com/medusajs/medusa It's an open-source e-commerce infrastructure suitable for headless commerce, B2B marketplaces, marketplaces, and subscription stores. The opportunity here isn't building the next Shopify—it's building a custom e-commerce system for a niche industry. For example: B2B wholesale marketplace, subscription product store, multi-vendor marketplace, brand independent store backend, online-offline integrated order system. Vertical is easier to profit from than general. 9. OpenBB GitHub: https://github.com/OpenBB-finance/OpenBB… It's an open-source financial data platform that connects financial data to Python, Excel, APIs, and AI agents. The finance industry is willing to pay for “faster insights.” You can build stock research dashboards, financial analysis tools, industry data reports, investment memo generators, and AI financial research assistants around it. A note: don't sell “predicting ups and downs”; sell “saving research time” and “organizing information.” 10. Dify GitHub: https://github.com/langgenius/dify It's an open-source LLM application development platform for building AI customer service, AI knowledge bases, sales assistants, enterprise internal agents, and RAG applications. The best way to make money isn't selling an “AI tool”—it's selling a clear outcome to a specific industry: reducing customer service headcount, automating sales replies, helping teams quickly query internal information. The most realistic monetization model is deployment fee plus monthly maintenance. To summarize: the opportunity behind these 10 GitHub projects isn't purely about making money from code. It's about finding a specific industry, using an open-source project to build a solution that helps customers save time, reduce manpower, or increase conversion rates, then charging deployment fees, maintenance fees, template fees, or subscription fees. If I had to prioritize five to study first, I'd look at browser-use, Crawl4AI, Firecrawl, Dub, and Dify. Because they are closest to AI, data, sales, and conversion—and it's easiest to explain “how to make money” with them. I recommend bookmarking all of them.
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Cached at: 06/10/26, 09:57 PM

Over the past 30 days, I’ve been studying one question: can an ordinary person make money from open‑source projects on GitHub? The answer is yes. But the key isn’t finding the hottest project or the one with the most stars – it’s finding projects that can be packaged as “concrete business outcomes.” I’ve curated 10 high‑quality GitHub projects that are suitable for making money. They aren’t the kind that “look cool but you don’t know how to monetize”; rather, you can build SaaS, automation services, templates, deployment services, consulting, data products, or vertical‑industry tools around them.

The real opportunity isn’t in GitHub itself – it’s in whether you can package an open‑source project into a concrete business result.

  1. browser-use
    GitHub: https://github.com/browser-use/browser-use
    This project lets AI operate a browser like a human – opening pages, clicking buttons, filling forms, extracting data, and executing tasks. Many companies have tons of repetitive browser tasks every day: finding leads, filling CRMs, checking competitors, submitting forms, handling backend orders, and scraping web data. You can automate these workflows and charge a monthly fee.
    The real value of AI isn’t chatting – it’s getting work done.

  2. Crawl4AI
    GitHub: https://github.com/unclecode/crawl4ai
    It’s great for AI data businesses – it converts web content into Markdown or structured data suitable for LLMs. What many AI applications lack isn’t models, but clean, stable, usable data. You can build industry knowledge bases, competitor monitoring, RAG data cleaning, content scraping systems, and vertical search tools around it.
    Customers will pay for usable data, not for a scraper.

  3. Firecrawl
    GitHub: https://github.com/mendableai/firecrawl
    A web scraping and parsing tool for AI applications, turning website content into Markdown, JSON, or structured data. The easiest product to build around it: input a website, output a report. For example, competitor analysis reports, lead lists, product price monitoring, SEO content analysis, or industry news summaries.
    B2B customers will pay to save time, especially sales, marketing, and research teams.

  4. Activepieces
    GitHub: https://github.com/activepieces/activepieces
    It’s an open‑source Zapier – connecting different tools to build automation workflows. Many small businesses know they need automation but don’t know how to set it up. You can help them configure: auto‑enter leads into CRM after form submission, auto‑send email after customer payment, auto‑notify sales of new leads, and auto‑sync Notion/Slack/Gmail/Airtable.
    This kind of service is perfect for “deployment‑as‑a‑service”: one‑time setup fee plus monthly maintenance.

  5. Dub
    GitHub: https://github.com/dubinc/dub
    An open‑source link attribution and short‑link platform – not just short links, but also conversion tracking, link analytics, and affiliate tracking. Many creators, independent sites, newsletters, and affiliate marketers share one problem: they don’t know which link actually brings in money.
    You can use Dub to build a creator link tracking system, affiliate marketing dashboard, independent site promotion analytics, or ad conversion tracking panel.
    The closer you are to money, the easier it is to charge.

  6. Twenty
    GitHub: https://github.com/twentyhq/twenty
    An open‑source CRM – think of it as an open‑source Salesforce alternative. Its best opportunity isn’t as a generic CRM, but as a vertical‑industry CRM: real estate agent CRM, recruitment CRM, consulting company CRM, B2B sales CRM, study‑abroad agency CRM. Many teams don’t want a complex system – they just want a CRM that fits their specific workflow. That’s the opportunity.

  7. Plane
    GitHub: https://github.com/makeplane/plane
    An open‑source project management tool – an alternative to Jira, Linear, ClickUp. Many teams want a self‑hosted project management system, especially outsourcing teams, software companies, remote teams, and companies sensitive about data privacy. You can offer self‑hosted deployment, workflow configuration, team training, and private maintenance.
    Projects like this aren’t necessarily sexy, but they make it easy to collect service fees.

  8. Medusa
    GitHub: https://github.com/medusajs/medusa
    An open‑source e‑commerce infrastructure, suitable for headless commerce, B2B marketplaces, multi‑vendor marketplaces, and subscription stores. The opportunity here isn’t to build the next Shopify – it’s to build a custom e‑commerce system for a niche industry, e.g., B2B wholesale marketplace, subscription product store, multi‑vendor marketplace, brand DTC backend, or online‑offline integrated order system.
    Vertical is easier to monetize than generic.

  9. OpenBB
    GitHub: https://github.com/OpenBB-finance/OpenBB
    An open‑source financial data platform that brings financial data into Python, Excel, API, and AI agents. The finance industry is very willing to pay for “faster insights.” You can build stock research dashboards, financial report analysis tools, industry data reports, investment memo generators, and AI financial research assistants.
    Important: don’t sell “predicting ups and downs” – sell “saving research time” and “organizing information.”

  10. Dify
    GitHub: https://github.com/langgenius/dify
    An open‑source LLM application development platform for building AI customer service, AI knowledge bases, sales assistants, enterprise internal agents, and RAG apps. The best way to make money is not selling an “AI tool” but selling a clear outcome to a specific industry – e.g., reducing customer service headcount, letting sales auto‑reply to customers, or helping teams quickly search internal documents.
    The most realistic monetization: deployment fee plus monthly maintenance.

To sum up, the opportunity behind these 10 GitHub projects is not simply making money from code, but finding a specific industry, using an open‑source project to build a solution that saves clients time, saves manpower, or increases conversion – then charging deployment fees, maintenance fees, template fees, or subscription fees.

If I had to focus on just 5 first, I’d pick browser-use, Crawl4AI, Firecrawl, Dub, and Dify – because they are closest to AI, data, sales, and conversion, and it’s easiest to explain “how to make money.”

Recommend bookmarking them all.

browser-use/browser-use

Source: https://github.com/browser-use/browser-use

🌤️ Want to skip the setup? Use our cloud (https://cloud.browser-use.com?utm_source=github&utm_medium=readme-skip-setup) for faster, scalable, stealth-enabled browser automation!

🤖 LLM Quickstart

  1. Direct your favorite coding agent (Cursor, Claude Code, etc) to Agents.md (https://docs.browser-use.com/llms-full.txt)
  2. Prompt away!

👋 Human Quickstart

Browser Use 0.13 introduces a new beta agent powered by a Rust core and a browser harness built for current frontier models. It gives the model a real browser/computer action space, persistent tools, and recovery loops inspired by coding agents.

text Python API -> Rust core -> Browser harness -> Web task done

1. Install Browser Use with the native core runtime (Python>=3.11):

bash uv add "browser-use[core]" # or: pip install "browser-use[core]"

The [core] extra installs the native Browser Use runtime for your platform.

2. [Optional] Get your API key from Browser Use Cloud (https://cloud.browser-use.com/new-api-key?utm_source=github&utm_medium=readme-quickstart-api-key):

``

.env

BROWSER_USE_API_KEY=your-key

GOOGLE_API_KEY=your-key

ANTHROPIC_API_KEY=your-key

``

3. Run your first agent:

``python from browser_use.beta import Agent, BrowserProfile, ChatBrowserUse

from browser_use.beta import ChatOpenAI # ChatOpenAI(model=‘gpt-5.5’)

from browser_use.beta import ChatAnthropic # ChatAnthropic(model=‘claude-opus-4-8’)

import asyncio

async def main(): agent = Agent( task=“Find the number of stars of the browser-use repo”, llm=ChatBrowserUse(model=‘bu-3-max’), # llm=ChatOpenAI(model=‘gpt-5.5’), # llm=ChatAnthropic(model=‘claude-opus-4-8’), # Sonnet also works well. browser_profile=BrowserProfile( headless=False, allowed_domains=[“*.github.com”], ), ) history = await agent.run() print(history.final_result())

if name == “main”: asyncio.run(main()) ``

Existing Python agent users can keep using from browser_use import Agent. The new Rust-powered beta agent is from browser_use.beta import Agent. Check out the library docs (https://docs.browser-use.com/open-source/introduction) and the cloud docs (https://docs.cloud.browser-use.com?utm_source=github&utm_medium=readme-cloud-docs) for more!

Open Source vs Cloud

We benchmark Browser Use across 100 real-world browser tasks. Full benchmark is open source: browser-use/benchmark (https://github.com/browser-use/benchmark).

Use the Open-Source Agent

  • You need custom tools (https://docs.browser-use.com/customize/tools/basics) or deep code-level integration
  • We recommend pairing with our cloud browsers (https://docs.browser-use.com/open-source/customize/browser/remote) for leading stealth, proxy rotation, and scaling
  • Or self-host the open-source agent fully on your own machines

Use the Fully-Hosted Cloud Agent (https://cloud.browser-use.com?utm_source=github&utm_medium=readme-hosted-agent) (recommended)

  • Much more powerful agent for complex tasks (see plot above)
  • Easiest way to start and scale
  • Best stealth with proxy rotation and captcha solving
  • 1000+ integrations (Gmail, Slack, Notion, and more)
  • Persistent filesystem and memory

Demos

📋 Form-Filling

Task = “Fill in this job application with my resume and information.”

Job Application Demo Example code ↗ (https://github.com/browser-use/browser-use/blob/main/examples/use-cases/apply_to_job.py)

🍎 Grocery-Shopping

Task = “Put this list of items into my instacart.”

https://github.com/user-attachments/assets/a6813fa7-4a7c-40a6-b4aa-382bf88b1850 Example code ↗ (https://github.com/browser-use/browser-use/blob/main/examples/use-cases/buy_groceries.py)

💻 Personal-Assistant.

Task = “Help me find parts for a custom PC.”

https://github.com/user-attachments/assets/ac34f75c-057a-43ef-ad06-5b2c9d42bf06 Example code ↗ (https://github.com/browser-use/browser-use/blob/main/examples/use-cases/pcpartpicker.py)

💡See more examples here ↗ (https://docs.browser-use.com/examples) and give us a star!

🚀 Template Quickstart

Want to get started even faster? Generate a ready-to-run template:

bash uvx browser-use init --template default

This creates a browser_use_default.py file with a working example.

Available templates:

  • default - Minimal setup to get started quickly
  • advanced - All configuration options with detailed comments
  • tools - Examples of custom tools and extending the agent

You can also specify a custom output path:

bash uvx browser-use init --template default --output my_agent.py

💻 CLI

Fast, persistent browser automation from the command line:

bash browser-use open https://example.com # Navigate to URL browser-use state # See clickable elements browser-use click 5 # Click element by index browser-use type "Hello" # Type text browser-use screenshot page.png # Take screenshot browser-use close # Close browser

The CLI keeps the browser running between commands for fast iteration. See CLI docs for all commands.

Claude Code Skill

For Claude Code (https://claude.ai/code), install the skill to enable AI-assisted browser automation:

bash mkdir -p ~/.claude/skills/browser-use curl -o ~/.claude/skills/browser-use/SKILL.md \ https://raw.githubusercontent.com/browser-use/browser-use/main/skills/browser-use/SKILL.md

Integrations, hosting, custom tools, MCP, and more on our Docs ↗ (https://docs.browser-use.com)

FAQ

What’s the best model to use? We optimized ChatBrowserUse() specifically for browser automation tasks. On avg it completes tasks 3-5x faster than other models with SOTA accuracy. bu-3 pricing (per 1M tokens):

  • Input tokens: $2.00
  • Cached input tokens: $0.20
  • Output tokens: $11.00 bu-3-max pricing (per 1M tokens):
  • Input tokens: $2.50
  • Cached input tokens: $0.25
  • Output tokens: $50.00 For other LLM providers, see our supported models documentation (https://docs.browser-use.com/supported-models).

Should I use the Browser Use system prompt with the open-source preview model? Yes. If you use ChatBrowserUse(model='browser-use/bu-30b-a3b-preview') with a normal Agent(...), Browser Use still sends its default agent system prompt for you. You do not need to add a separate custom “Browser Use system message” just because you switched to the open-source preview model. Only use extend_system_message or override_system_message when you intentionally want to customize the default behavior for your task. If you want the best default speed/accuracy, we still recommend the newer hosted bu-* models. If you want the open-source preview model, the setup stays the same apart from the model= value.

Can I use custom tools with the agent? Yes! You can add custom tools to extend the agent’s capabilities:

``python from browser_use import Tools tools = Tools()

@tools.action(description=‘Description of what this tool does.’) def custom_tool(param: str) -> str: return f“Result: {param}“

agent = Agent( task=“Your task”, llm=llm, browser=browser, tools=tools, ) ``

Can I use this for free? Yes! Browser-Use is open source and free to use. You only need to choose an LLM provider (like OpenAI, Google, ChatBrowserUse, or run local models with Ollama).

Terms of Service This open-source library is licensed under the MIT License. For Browser Use services & data policy, see our Terms of Service (https://browser-use.com/legal/terms-of-service) and Privacy Policy (https://browser-use.com/privacy/).

How do I handle authentication? Check out our authentication examples:

  • Using real browser profiles (https://github.com/browser-use/browser-use/blob/main/examples/browser/real_browser.py) - Reuse your existing Chrome profile with saved logins
  • If you want to use temporary accounts with inbox, choose AgentMail
  • To sync your auth profile with the remote browser, run curl -fsSL https://browser-use.com/profile.sh | BROWSER_USE_API_KEY=XXXX sh (replace XXXX with your API key) These examples show how to maintain sessions and handle authentication seamlessly.

How do I solve CAPTCHAs? For CAPTCHA handling, you need better browser fingerprinting and proxies. Use Browser Use Cloud (https://cloud.browser-use.com?utm_source=github&utm_medium=readme-faq-captcha) which provides stealth browsers designed to avoid detection and CAPTCHA challenges.

How do I go into production? Chrome can consume a lot of memory, and running many agents in parallel can be tricky to manage. For production use cases, use our Browser Use Cloud API (https://cloud.browser-use.com?utm_source=github&utm_medium=readme-faq-production) which handles:

  • Scalable browser infrastructure
  • Memory management
  • Proxy rotation
  • Stealth browser fingerprinting
  • High-performance parallel execution

Tell your computer what to do, and it gets it done.

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