head to head · open source

llama.cpp vs OpenLIT

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. OpenLIT has 2,899 stars, 455 forks, 60 open issues and last shipped yesterday. llama.cpp leads on adoption by 4,494% (133,181 vs 2,899 stars). llama.cpp is written in C++ under MIT; OpenLIT is written in TypeScript under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, OpenLIT 16%. 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 OpenLIT ★ 2.9K category AI & Machine Learning

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Side by side

	llama.cpp	OpenLIT
GitHub stars	★ 133K	★ 2.9K
License	MIT	Apache-2.0
Written in	C++	TypeScript
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 455
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 OpenLIT if

You want the OpenLIT 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 OpenLIT fits your workflow better

full OpenLIT 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 OpenLIT

Open source observability & evaluation for AI agents Trace, evaluate, debug, and optimize AI applications and coding agents with OpenTelemetry. ⭐ Star 🚀 Quickstart 📚 Docs Documentation · Quickstart · Examples · Join Slack See what your AI agents are actually doing AI applications are no longer just LLM calls. A production agent can involve: OpenLIT gives you visibility across the entire workflow. Trace every LLM call, tool invocation, prompt, agent step, token, cost, error, and evaluation — using OpenTelemetry. ⚡ Get started in 5 minutes 1. Start OpenLIT Open: 2. Install the SDK Python: TypeScript: 3…

read the full OpenLIT overview →

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Frequently asked questions

Is llama.cpp or OpenLIT more popular?

llama.cpp has 133,181 GitHub stars and OpenLIT has 2,899. llama.cpp has the larger community by that measure.

Are llama.cpp and OpenLIT free?

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

What is the difference between llama.cpp and OpenLIT ?

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

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose OpenLIT if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.