head to head · open source

llama.cpp vs Agenta

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. Agenta has 4,881 stars, 722 forks, 309 open issues and last shipped yesterday. llama.cpp leads on adoption by 2,629% (133,181 vs 4,881 stars). llama.cpp is written in C++ under MIT; Agenta is written in TypeScript under a custom or non-standard licence. llama.cpp has attracted 19% as many forks as stars, Agenta 15%. Agenta 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 Agenta ★ 4.9K category AI & Machine Learning

← all 20902 open source comparisons

Side by side

	llama.cpp	Agenta
GitHub stars	★ 133K	★ 4.9K
License	MIT	Custom / other
Written in	C++	TypeScript
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 722
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 Agenta if

You want the Agenta feature set and don't need the biggest community
You prefer the Custom / other license terms
Your stack matches TypeScript
You evaluated both and Agenta fits your workflow better

full Agenta 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 Agenta

The open source workspace for building and running agents Build agents that automate your work by chatting with them. Share them with your team , connect them to the apps you use, and run them in the background . Documentation • Website • Agenta Cloud What is Agenta? Agenta is an open source workspace where you build specialized agents that automate and augment your work. You build agents by chatting with them. You describe the work, connect the apps they need, and improve them through feedback. You can work with your agents directly in chat and share them with your team. For recurring work, you can…

read the full Agenta overview →

More in AI & Machine Learning

Related comparisons

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

llama.cpp vs Ollama llama.cpp vs vllm llama.cpp vs GPT4All llama.cpp vs serviette llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs faiss llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs xerj

Frequently asked questions

Is llama.cpp or Agenta more popular?

llama.cpp has 133,181 GitHub stars and Agenta has 4,881. llama.cpp has the larger community by that measure.

Are llama.cpp and Agenta free?

Both are open source. llama.cpp is licensed under MIT, and Agenta has no licence declared in this registry. Both are free to self-host.

What is the difference between llama.cpp and Agenta?

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

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