head to head · open source

llama.cpp vs autoflow

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. autoflow has 2,973 stars, 193 forks, 74 open issues and last shipped 5 months ago. llama.cpp leads on adoption by 4,226% (128,619 vs 2,973 stars). llama.cpp is written in C++ under MIT; autoflow is written in TypeScript under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, autoflow 6%. 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 autoflow ★ 3.0K category AI & Machine Learning

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Side by side

	llama.cpp	autoflow
GitHub stars	★ 129K	★ 3.0K
License	MIT	Apache-2.0
Written in	C++	TypeScript
Last push	2026-09-18	2026-04-27
Forks	⑂ 23K	⑂ 193
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 autoflow if

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

full autoflow 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 autoflow

AutoFlow [!WARNING] Autoflow is still in the early stages of development. And we are actively working on it, the next move is to make it to a python package and make it a RAG solution e.g. pip install autoflow ai . If you have any questions or suggestions, please feel free to contact us on Discussion. Introduction AutoFlow is an open source graph rag (graphrag: knowledge graph rag) based knowledge base tool built on top of TiDB Vector and LlamaIndex and DSPy. Live Demo : Deployment Docs : Deployment Docs Features 1. Perplexity style Conversational Search page : Our platform features an advanced built in website c…

read the full autoflow overview →

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Frequently asked questions

Is llama.cpp or autoflow more popular?

llama.cpp has 128,619 GitHub stars and autoflow has 2,973. llama.cpp has the larger community by that measure.

Are llama.cpp and autoflow free?

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

What is the difference between llama.cpp and autoflow?

llama.cpp is written in C++ and autoflow in TypeScript. 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 autoflow?

Choose llama.cpp if you want the larger community (128,619 stars) or its MIT licence terms. Choose autoflow if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.