head to head · open source
Agent-Reach vs graphrag
Agent-Reach has 108,014 GitHub stars, 9,640 forks, 137 open issues and last shipped 2 days ago. graphrag has 36,531 stars, 3,867 forks, 47 open issues and last shipped yesterday. Agent-Reach leads on adoption by 196% (108,014 vs 36,531 stars). Agent-Reach is written in Python under MIT; graphrag is written in Python under MIT. Agent-Reach has attracted 9% as many forks as stars, graphrag 11%. graphrag 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 | graphrag | |
|---|---|---|
| GitHub stars | ★ 108K | ★ 37K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-11-01 | 2026-11-02 |
| Forks | ⑂ 9.6K | ⑂ 3.9K |
| 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 graphrag if
- You want the graphrag feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and graphrag 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 graphrag
GraphRAG [!WARNING] GraphRAG is a research project that explores the functional use of graphs to form a targeted context for question answering. Since our first release in July 2024 the capabilities of frontier models have changed dramatically, and our portfolio of research projects has diversified to match. This project is largely in maintenance mode, and won't be accepting new PRs or implementing new features. We'll perform bug fixes and dependency updates as appropriate, particularly to address CVEs as they arise. 👉 Microsoft Research Blog Post 👉 Read the docs 👉 GraphRAG Arxiv Overview The GraphRAG project…
read the full graphrag 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 graphrag more popular?
Agent-Reach has 108,014 GitHub stars and graphrag has 36,531. Agent-Reach has the larger community by that measure.
Are Agent-Reach and graphrag free?
Both are open source. Agent-Reach is licensed under MIT and graphrag under MIT. Neither carries a licence fee.
What is the difference between Agent-Reach and graphrag?
Agent-Reach is written in Python and graphrag in Python. 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 graphrag?
Choose Agent-Reach if you want the larger community (108,014 stars) or its MIT licence terms. Choose graphrag if its feature set, stack or MIT licence fits better. Both are self-hostable.