head to head · open source
LightRAG vs R2R
LightRAG has 40,529 GitHub stars, 5,760 forks, 222 open issues and last shipped 2 days ago. R2R has 8,058 stars, 652 forks, 126 open issues and last shipped 12 months ago. LightRAG leads on adoption by 403% (40,529 vs 8,058 stars). LightRAG is written in Python under MIT; R2R is written in Python under MIT. LightRAG has attracted 14% as many forks as stars, R2R 8%. LightRAG was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (large-language-models, rag, retrieval-augmented-generation), so they are genuine substitutes rather than adjacent tools.
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
| LightRAG | R2R | |
|---|---|---|
| GitHub stars | ★ 41K | ★ 8.1K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-11-03 | 2025-11-07 |
| Forks | ⑂ 5.8K | ⑂ 652 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick LightRAG if
- You weight community size — 41K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick R2R if
- You want the R2R feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and R2R fits your workflow better
About LightRAG
🚀 LightRAG: Simple and Fast Retrieval Augmented Generation 🎉 News [2026.07]🎯[New Feature]: Add Smart Heading recognition feature for word documents. [2026.05]🎯[New Feature]: Merge RagAnything into LightRAG 🎉. Multimodal content parsing and extraction via MinerU / Docling services. [2026.05]🎯[New Feature]: Introducing four selectable text chunking strategies: Fix , Recursive , Vector , and Paragraph . [2026.05]🎯[New Feature]: Role specific LLM configuration support, 4 distinct roles: EXTRACT, QUERY, KEYWORDS, and VLM, with independent LLM settings. [2026.03]🎯[New Feature]: Integrated OpenSearch as a…
read the full LightRAG overview →
About R2R
The most advanced AI retrieval system. Agentic Retrieval Augmented Generation (RAG) with a RESTful API. Docs · Report Bug · Feature Request · Discord About R2R is an advanced AI retrieval system supporting Retrieval Augmented Generation (RAG) with production ready features. Built around a RESTful API, R2R offers multimodal content ingestion, hybrid search, knowledge graphs, and comprehensive document management. R2R also includes a Deep Research API , a multi step reasoning system that fetches relevant data from your knowledgebase and/or the internet to deliver richer, context aware answers for complex queries…
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Frequently asked questions
Is LightRAG or R2R more popular?
LightRAG has 40,529 GitHub stars and R2R has 8,058. LightRAG has the larger community by that measure.
Are LightRAG and R2R free?
Both are open source. LightRAG is licensed under MIT and R2R under MIT. Neither carries a licence fee.
What is the difference between LightRAG and R2R?
LightRAG is written in Python and R2R 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, LightRAG or R2R?
Choose LightRAG if you want the larger community (40,529 stars) or its MIT licence terms. Choose R2R if its feature set, stack or MIT licence fits better. Both are self-hostable.