head to head · open source

llama.cpp vs RAGLight

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 16 days ago. llama.cpp leads on adoption by 19,011% (128,619 vs 673 stars). llama.cpp is written in C++ under MIT; RAGLight is written in Python under MIT. llama.cpp has attracted 18% 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 ★ 129K RAGLight ★ 673 category AI & Machine Learning

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Side by side

	llama.cpp	RAGLight
GitHub stars	★ 129K	★ 673
License	MIT	MIT
Written in	C++	Python
Last push	2026-09-18	2026-09-02
Forks	⑂ 23K	⑂ 102
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 129K 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 is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.

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 Dep…

read the full RAGLight overview →

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Frequently asked questions

Is llama.cpp or RAGLight more popular?

llama.cpp has 128,619 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 (128,619 stars) or its MIT licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.