head to head · open source
ProxyPool vs learn-agentic-ai
ProxyPool has 6,262 GitHub stars, 2,229 forks, 2 open issues and last shipped 4 months ago. learn-agentic-ai has 4,396 stars, 1,006 forks, 59 open issues and last shipped 1.0 years ago. ProxyPool leads on adoption by 42% (6,262 vs 4,396 stars). ProxyPool is written in Python under MIT; learn-agentic-ai is written in Jupyter Notebook under MIT. ProxyPool has attracted 36% as many forks as stars, learn-agentic-ai 23%. ProxyPool was the more recently maintained of the two, and both are self-hostable with no licence fee.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| ProxyPool | learn-agentic-ai | |
|---|---|---|
| GitHub stars | ★ 6.3K | ★ 4.4K |
| License | MIT | MIT |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-07-04 | 2025-10-26 |
| Forks | ⑂ 2.2K | ⑂ 1.0K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick ProxyPool if
- You weight community size — 6.3K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick learn-agentic-ai if
- You want the learn-agentic-ai feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Jupyter Notebook
- You evaluated both and learn-agentic-ai fits your workflow better
About ProxyPool
ProxyPool 简易高效的代理池，提供如下功能： 定时抓取免费代理网站，简易可扩展。 使用 Redis 对代理进行存储并对代理可用性进行排序。 定时测试和筛选，剔除不可用代理，留下可用代理。 提供代理 API，随机取用测试通过的可用代理。 代理池原理解析可见「如何搭建一个高效的代理池」，建议使用之前阅读。 使用前注意 本代理池是基于市面上各种公开代理源搭建的，所以可用性并不高，很可能上百上千个代理中才能找到一两个可用代理，不适合直接用于爬虫爬取任务。 如果您的目的是为了尽快使用代理完成爬取任务，建议您对接一些付费代理或者直接使用已有代理资源；如果您的目的是为了学习如何搭建一个代理池，您可以参考本项目继续完成后续步骤。 付费代理推荐： ADSL 拨号代理：海量拨号（中国境内）高质量代理 海外/全球代理：中国境外高质量代理 蜂窝 4G/5G 代理：极高质量（中国境内）防风控代理 使用准备 首先当然是克隆代码并进入 ProxyPool 文件夹： 然后选用下面 Docker 和常规方式任意一个执行即可。 使用要求 可以通过两种方式来运行代理池，一种方式是使用 Docker（推荐），另一种方式是常规方式运行，要求如下： Docker 如果使用 Docker，则需要安装如下环境： Docker Docker Compose 安装方法自行搜索即可。 官方 Docker Hub 镜像：germey/proxypool 常规方式…
read the full ProxyPool overview →
About learn-agentic-ai
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern: From Start to Scale This repo is part of the Panaversity Certified Agentic & Robotic AI Engineer program. You can also review the certification and course details in the program guide. This repo provides learning material for Agentic AI and Cloud courses. Here’s a polished, professional rewrite you can use as a one pager or slide—tight on wording, clear on stakes, and just a touch playful so it doesn’t read like it was written by a committee (no offense to committees 😄). Our Agentic Strategy for Pakistan: Four Working Hypotheses Pakistan…
read the full learn-agentic-ai overview →
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Frequently asked questions
Is ProxyPool or learn-agentic-ai more popular?
ProxyPool has 6,262 GitHub stars and learn-agentic-ai has 4,396. ProxyPool has the larger community by that measure.
Are ProxyPool and learn-agentic-ai free?
Both are open source. ProxyPool is licensed under MIT and learn-agentic-ai under MIT. Neither carries a licence fee.
What is the difference between ProxyPool and learn-agentic-ai?
ProxyPool is written in Python and learn-agentic-ai in Jupyter Notebook. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, ProxyPool or learn-agentic-ai?
Choose ProxyPool if you want the larger community (6,262 stars) or its MIT licence terms. Choose learn-agentic-ai if its feature set, stack or MIT licence fits better. Both are self-hostable.