head to head · open source
Agent-Reach vs ragflow
Agent-Reach has 108,014 GitHub stars, 9,640 forks, 137 open issues and last shipped 2 days ago. ragflow has 93,365 stars, 11,207 forks, 1,525 open issues and last shipped yesterday. Agent-Reach leads on adoption by 16% (108,014 vs 93,365 stars). Agent-Reach is written in Python under MIT; ragflow is written in Go under Apache-2.0. Agent-Reach has attracted 9% as many forks as stars, ragflow 12%. ragflow 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.
← all 20902 open source comparisons
Side by side
| Agent-Reach | ragflow | |
|---|---|---|
| GitHub stars | ★ 108K | ★ 93K |
| License | MIT | Apache-2.0 |
| Written in | Python | Go |
| Last push | 2026-11-01 | 2026-11-02 |
| Forks | ⑂ 9.6K | ⑂ 11K |
| 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 ragflow if
- You want the ragflow feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Go
- You evaluated both and ragflow 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 ragflow
Cloud Documentation Roadmap Discord 📕 Table of Contents 💡 What is RAGFlow? 🎮 Get Started 🔥 Latest Updates 🌟 Key Features 🔎 System Architecture 🎬 Self Hosting 🔧 Configurations 🔧 Build a Docker Image 🔨 Launch Service from Source for Development 📚 Documentation 📜 Roadmap 🏄 Community 🙌 Contributing 💡 What is RAGFlow? RAGFlow is a leading open source Retrieval Augmented Generation (RAG) engine that fuses cutting edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine…
read the full ragflow overview →
More in AI & Machine Learning
Related comparisons
More AI Development Platforms projects
Compare either of these against the rest of the AI Development Platforms field.
Frequently asked questions
Is Agent-Reach or ragflow more popular?
Agent-Reach has 108,014 GitHub stars and ragflow has 93,365. Agent-Reach has the larger community by that measure.
Are Agent-Reach and ragflow free?
Both are open source. Agent-Reach is licensed under MIT and ragflow under Apache-2.0. Neither carries a licence fee.
What is the difference between Agent-Reach and ragflow?
Agent-Reach is written in Python and ragflow in Go. 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 ragflow?
Choose Agent-Reach if you want the larger community (108,014 stars) or its MIT licence terms. Choose ragflow if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.