head to head · open source
Agent-Reach vs ruby_llm
Agent-Reach has 108,502 GitHub stars, 9,679 forks, 137 open issues and last shipped 4 days ago. ruby_llm has 4,493 stars, 530 forks, 7 open issues and last shipped 2 days ago. Agent-Reach leads on adoption by 2,315% (108,502 vs 4,493 stars). Agent-Reach is written in Python under MIT; ruby_llm is written in Ruby under MIT. Agent-Reach has attracted 9% as many forks as stars, ruby_llm 12%. ruby_llm 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
| Agent-Reach | ruby_llm | |
|---|---|---|
| GitHub stars | ★ 109K | ★ 4.5K |
| License | MIT | MIT |
| Written in | Python | Ruby |
| Last push | 2026-11-01 | 2026-11-03 |
| Forks | ⑂ 9.7K | ⑂ 530 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Agent-Reach if
- You weight community size — 109K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick ruby_llm if
- You want the ruby_llm feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Ruby
- You evaluated both and ruby_llm fits your workflow better
About Agent-Reach
👁️ Agent Reach 给你的 AI Agent 一键装上互联网能力 当下最稳的接入方式，替你选好、装好、体检好——接入方式会换代，你不用操心 快速开始 · English · 日本語 · 한국어 · 支持平台 · 设计理念 ❤️赞助商 想出现在这里？ 点击折叠 BrowserAct 支持从 Amazon、LinkedIn、X、Google Maps 等复杂网站提取你需要的任意数据。你只需用自然语言描述抓取需求，Agent 就会基于真实浏览器自动探索并测试页面流程，生成可靠、可复用的数据采集 Bot，并返回结构化结果。无需手动构建爬虫，无需编写代码。BrowserAct 内置隐身浏览、验证码处理和高质量住宅代理，帮助你更稳定地完成复杂网页数据采集。新用户注册即送 1000 积分， 立即免费试用 。 在腾讯云 Lighthouse 秒级部署 OpenClaw 全能助手，可通过对话丝滑接入 Agent Reach，给你的 OpenClaw 一键装上互联网能力。 CoreClaw 网页抓取平台与现成数据采集工具，CoreClaw 提供 100+ 现成数据采集工具，支持 Amazon、TikTok、Google Maps、Instagram、Facebook、YouTube 等平台，无需代码，支持 JSON/CSV 导出，仅对成功结果计费。 免费$3测试！ 优刻得星图astraflow大模型…
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About ruby_llm
Build AI features the Ruby way RubyLLM is the Ruby native AI framework. Work with models, tools, and agents through one consistent API, in plain Ruby or Rails. Website · Getting Started · What's New in 2.0 Battle tested at Fully private work AI [!NOTE] Using RubyLLM? Share your story! Takes 5 minutes. 17 providers. One Ruby API. Build with the models you want. Move between hosted and local providers without rewriting your application, or connect an OpenAI compatible endpoint. Browse models and pricing · Connect a provider Start with one line. Add files, tools, and agents These examples use 2.0.0 . Follow Getting…
read the full ruby_llm overview →
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Frequently asked questions
Is Agent-Reach or ruby_llm more popular?
Agent-Reach has 108,502 GitHub stars and ruby_llm has 4,493. Agent-Reach has the larger community by that measure.
Are Agent-Reach and ruby_llm free?
Both are open source. Agent-Reach is licensed under MIT and ruby_llm under MIT. Neither carries a licence fee.
What is the difference between Agent-Reach and ruby_llm?
Agent-Reach is written in Python and ruby_llm in Ruby. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Agent-Reach or ruby_llm?
Choose Agent-Reach if you want the larger community (108,502 stars) or its MIT licence terms. Choose ruby_llm if its feature set, stack or MIT licence fits better. Both are self-hostable.