head to head · open source
Agent-Reach vs GenerativeAIExamples
Agent-Reach has 108,014 GitHub stars, 9,640 forks, 137 open issues and last shipped 2 days ago. GenerativeAIExamples has 4,220 stars, 1,102 forks, 84 open issues and last shipped 2 months ago. Agent-Reach leads on adoption by 2,460% (108,014 vs 4,220 stars). Agent-Reach is written in Python under MIT; GenerativeAIExamples is written in Jupyter Notebook under Apache-2.0. Agent-Reach has attracted 9% as many forks as stars, GenerativeAIExamples 26%. Agent-Reach 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 | GenerativeAIExamples | |
|---|---|---|
| GitHub stars | ★ 108K | ★ 4.2K |
| License | MIT | Apache-2.0 |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-11-01 | 2026-09-09 |
| Forks | ⑂ 9.6K | ⑂ 1.1K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Agent-Reach if
- You weight community size — 108K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick GenerativeAIExamples if
- You want the GenerativeAIExamples feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Jupyter Notebook
- You evaluated both and GenerativeAIExamples 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…
read the full Agent-Reach overview →
About GenerativeAIExamples
NVIDIA Generative AI Examples This repository is a starting point for developers looking to integrate with the NVIDIA software ecosystem to speed up their generative AI systems. Whether you are building RAG pipelines, agentic workflows, or fine tuning models, this repository will help you integrate NVIDIA, seamlessly and natively, with your development stack. Table of Contents What's New? Data Flywheel Safer Agentic AI Knowledge Graph RAG Agentic Workflows with Llama 3.1 RAG with Local NIM Deployment and LangChain Vision NIM Workflows Try it Now! Data Flywheel Tool Calling Notebooks RAG RAG Notebooks RAG…
read the full GenerativeAIExamples overview →
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Frequently asked questions
Is Agent-Reach or GenerativeAIExamples more popular?
Agent-Reach has 108,014 GitHub stars and GenerativeAIExamples has 4,220. Agent-Reach has the larger community by that measure.
Are Agent-Reach and GenerativeAIExamples free?
Both are open source. Agent-Reach is licensed under MIT and GenerativeAIExamples under Apache-2.0. Neither carries a licence fee.
What is the difference between Agent-Reach and GenerativeAIExamples?
Agent-Reach is written in Python and GenerativeAIExamples 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, Agent-Reach or GenerativeAIExamples?
Choose Agent-Reach if you want the larger community (108,014 stars) or its MIT licence terms. Choose GenerativeAIExamples if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.