head to head · open source

llama.cpp vs RAGLight

llama.cpp has 133,101 GitHub stars, 25,042 forks, 2,464 open issues and last shipped yesterday. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 18 days ago. llama.cpp leads on adoption by 19,677% (133,101 vs 673 stars). llama.cpp is written in C++ under MIT; RAGLight is written in Python under MIT. llama.cpp has attracted 19% as many forks as stars, RAGLight 15%. llama.cpp 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.

llama.cpp ★ 133K RAGLight ★ 673 category AI & Machine Learning

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Side by side

	llama.cpp	RAGLight
GitHub stars	★ 133K	★ 673
License	MIT	MIT
Written in	C++	Python
Last push	2026-11-02	2026-10-16
Forks	⑂ 25K	⑂ 102
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

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

full RAGLight profile →

About llama.cpp

llama.cpp LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

read the full llama.cpp 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.cpp or RAGLight more popular?

llama.cpp has 133,101 GitHub stars and RAGLight has 673. llama.cpp has the larger community by that measure.

Are llama.cpp and RAGLight free?

Both are open source. llama.cpp is licensed under MIT and RAGLight under MIT. Neither carries a licence fee.

What is the difference between llama.cpp and RAGLight?

llama.cpp is written in C++ 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.cpp or RAGLight?

Choose llama.cpp if you want the larger community (133,101 stars) or its MIT licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.