head to head · open source

ProxyPool vs learn-agentic-ai

ProxyPool has 6,249 GitHub stars, 2,228 forks, 2 open issues and last shipped 3 months ago. learn-agentic-ai has 4,375 stars, 1,010 forks, 59 open issues and last shipped 11 months ago. ProxyPool leads on adoption by 43% (6,249 vs 4,375 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.

ProxyPool ★ 6.2K learn-agentic-ai ★ 4.4K category Infrastructure & Operations

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Side by side

ProxyPool learn-agentic-ai
GitHub stars ★ 6.2K ★ 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 — 6.2K stars and counting
  • You want the MIT license terms
  • Your stack matches Python
  • You value the larger contributor base for long-term maintenance

full ProxyPool profile →

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

full learn-agentic-ai profile →

About ProxyPool

ProxyPool is an MIT licensed Python proxy pool that continuously scrapes free public proxy sources, validates them through scheduled testing, stores them in Redis, and serves working proxies over a random selection HTTP API for web scraping and spider projects.

read the full ProxyPool overview →

About learn-agentic-ai

learn agentic ai is an MIT licensed Jupyter Notebook learning repository from Panaversity that teaches developers how to design and scale agentic AI systems using the Dapr Agentic Cloud Ascent (DACA) design pattern and agent native cloud technologies.

read the full learn-agentic-ai overview →

More in Infrastructure & Operations

kubernetes ★ 128K Immich ★ 115K frp ★ 110K Uptime Kuma ★ 92K worldmonitor ★ 87K mall ★ 85K

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ProxyPool vs mall ProxyPool vs Prometheus ProxyPool vs NocoDB ProxyPool vs etcd ProxyPool vs DBeaver ProxyPool vs orm ProxyPool vs Milvus ProxyPool vs TiDB ProxyPool vs drawDB ProxyPool vs typeorm ProxyPool vs AnotherRedisDesktopManager ProxyPool vs Qdrant

Frequently asked questions

Is ProxyPool or learn-agentic-ai more popular?

ProxyPool has 6,249 GitHub stars and learn-agentic-ai has 4,375. 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 (6,249 stars) or its MIT licence terms. Choose learn-agentic-ai if its feature set, stack or MIT licence fits better. Both are self-hostable.