head to head · open source
llama_index vs RAGLight
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 18 days ago. llama_index leads on adoption by 7,742% (52,779 vs 673 stars). llama_index is written in Python under MIT; RAGLight is written in Python under MIT. llama_index has attracted 16% as many forks as stars, RAGLight 15%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (framework), 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
| llama_index | RAGLight | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 673 |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-10-16 |
| Forks | ⑂ 8.5K | ⑂ 102 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick RAGLight if
- You want the RAGLight feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and RAGLight fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About RAGLight
RAGLight RAGLight is a lightweight and modular Python library for implementing Retrieval Augmented Generation (RAG) . It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference. Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context aware AI solutions. 📚 Table of Contents Requirements Features Import library Chat with Your Documents Instantly With CLI Ignore Folders Feature Ignore Folders in Configuration Classes…
read the full RAGLight overview →
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Frequently asked questions
Is llama_index or RAGLight more popular?
llama_index has 52,779 GitHub stars and RAGLight has 673. llama_index has the larger community by that measure.
Are llama_index and RAGLight free?
Both are open source. llama_index is licensed under MIT and RAGLight under MIT. Neither carries a licence fee.
What is the difference between llama_index and RAGLight?
llama_index is written in Python and RAGLight 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, llama_index or RAGLight?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.