head to head · open source

llama.cpp vs llama_index

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. llama_index has 52,202 stars, 8,162 forks, 770 open issues and last shipped yesterday. llama.cpp leads on adoption by 146% (128,581 vs 52,202 stars). llama.cpp is written in C++ under MIT; llama_index is written in Python under MIT. llama.cpp has attracted 18% as many forks as stars, llama_index 16%. llama_index 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 llama_index ★ 52K category AI & Machine Learning

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Side by side

	llama.cpp	llama_index
GitHub stars	★ 129K	★ 52K
License	MIT	MIT
Written in	C++	Python
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 8.2K
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 llama_index if

You want the llama_index feature set and don't need the biggest community
You prefer the MIT license terms
Your stack matches Python
You evaluated both and llama_index fits your workflow better

full llama_index 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 llama_index

LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.

read the full llama_index overview →

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Frequently asked questions

Is llama.cpp or llama_index more popular?

llama.cpp has 128,581 GitHub stars and llama_index has 52,202. llama.cpp has the larger community by that measure.

Are llama.cpp and llama_index free?

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

What is the difference between llama.cpp and llama_index?

llama.cpp is written in C++ and llama_index 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 llama_index?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose llama_index if its feature set, stack or MIT licence fits better. Both are self-hostable.