head to head · open source

llama.cpp vs knowhere

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. knowhere has 3,341 stars, 318 forks, 40 open issues and last shipped yesterday. llama.cpp leads on adoption by 3,749% (128,581 vs 3,341 stars). llama.cpp is written in C++ under MIT; knowhere is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, knowhere 10%. 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 knowhere ★ 3.3K category AI & Machine Learning

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Side by side

	llama.cpp	knowhere
GitHub stars	★ 129K	★ 3.3K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 318
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 knowhere if

You want the knowhere feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches Python
You evaluated both and knowhere fits your workflow better

full knowhere 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 knowhere

Knowhere is an Apache 2.0 Python document parsing and retrieval system that turns complex, dirty files into persistent, navigable memory for AI agents and RAG pipelines, built for developers and teams assembling Agentic RAG, vector based RAG, or other LLM workflows over local and offline document collections.

read the full knowhere overview →

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Frequently asked questions

Is llama.cpp or knowhere more popular?

llama.cpp has 128,581 GitHub stars and knowhere has 3,341. llama.cpp has the larger community by that measure.

Are llama.cpp and knowhere free?

Both are open source. llama.cpp is licensed under MIT and knowhere under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and knowhere?

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

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose knowhere if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.