head to head · open source

llama.cpp vs Agenta

llama.cpp has 128,880 GitHub stars, 23,468 forks, 2,464 open issues and last shipped today. Agenta has 4,766 stars, 674 forks, 309 open issues and last shipped today. llama.cpp leads on adoption by 2,604% (128,880 vs 4,766 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 18% as many forks as stars, Agenta 14%. 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 Agenta ★ 4.8K category AI & Machine Learning

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Side by side

	llama.cpp	Agenta
GitHub stars	★ 129K	★ 4.8K
License	MIT	Custom / other
Written in	C++	TypeScript
Last push	2026-09-20	2026-09-20
Forks	⑂ 23K	⑂ 674
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 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 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 Agenta

Agenta is an open source workspace where a team builds, runs, and improves AI agents by chatting with them, intended for individuals and teams that want agent automation without moving every task onto metered API billing.

read the full Agenta overview →

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Frequently asked questions

Is llama.cpp or Agenta more popular?

llama.cpp has 128,880 GitHub stars and Agenta has 4,766. 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 (128,880 stars) or its MIT licence terms. Choose Agenta if its feature set, stack or Custom / other licence fits better. Both are self-hostable.