head to head · open source

llama.cpp vs SeaGOAT

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. SeaGOAT has 1,316 stars, 95 forks, 44 open issues and last shipped yesterday. llama.cpp leads on adoption by 10,020% (133,181 vs 1,316 stars). llama.cpp is written in C++ under MIT; SeaGOAT is written in Python under MIT. llama.cpp has attracted 19% as many forks as stars, SeaGOAT 7%. 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 SeaGOAT ★ 1.3K category AI & Machine Learning

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Side by side

	llama.cpp	SeaGOAT
GitHub stars	★ 133K	★ 1.3K
License	MIT	MIT
Written in	C++	Python
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 95
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 SeaGOAT if

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

full SeaGOAT 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 SeaGOAT

[!TIP] Check out zeitgrep, another search tool I am working on! SeaGOAT A code search engine for the AI age. SeaGOAT is a local search tool that leverages vector embeddings to enable you to search your codebase semantically. Getting started Install SeaGOAT In order to install SeaGOAT, you need to have the following dependencies already installed on your computer: Python 3.11 or newer ripgrep bat ( optional , highly recommended) When bat is installed, it is used to display results as long as color is enabled. When SeaGOAT is used as part of a pipeline, a grep line output format is used. When color is enabled, but…

read the full SeaGOAT overview →

More in AI & Machine Learning

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Frequently asked questions

Is llama.cpp or SeaGOAT more popular?

llama.cpp has 133,181 GitHub stars and SeaGOAT has 1,316. llama.cpp has the larger community by that measure.

Are llama.cpp and SeaGOAT free?

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

What is the difference between llama.cpp and SeaGOAT?

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

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose SeaGOAT if its feature set, stack or MIT licence fits better. Both are self-hostable.