head to head · open source

llama.cpp vs Laminar

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. Laminar has 3,471 stars, 257 forks, 116 open issues and last shipped yesterday. llama.cpp leads on adoption by 3,737% (133,181 vs 3,471 stars). llama.cpp is written in C++ under MIT; Laminar is written in TypeScript under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, Laminar 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 Laminar ★ 3.5K category AI & Machine Learning

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Side by side

	llama.cpp	Laminar
GitHub stars	★ 133K	★ 3.5K
License	MIT	Apache-2.0
Written in	C++	TypeScript
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 257
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 Laminar if

You want the Laminar 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 Laminar fits your workflow better

full Laminar 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 Laminar

Laminar Laminar is an open source observability platform purpose built for AI agents. [x] Tracing. Docs [x] OpenTelemetry native powerful tracing SDK 1 line of code to automatically trace Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, Gemini, and more . [x] Signals. Docs [x] Describe any behavior of your agent that you want to track in plain English (e.g. "agent is stuck in a loop") [x] Laminar reads every agent run and pings you in Slack when it happens. [x] Evals. Docs [x] Unopinionated, extensible SDK and CLI for running evals locally or in CI/CD pipeline. [x] UI for visualizing evals…

read the full Laminar overview →

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Frequently asked questions

Is llama.cpp or Laminar more popular?

llama.cpp has 133,181 GitHub stars and Laminar has 3,471. llama.cpp has the larger community by that measure.

Are llama.cpp and Laminar free?

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

What is the difference between llama.cpp and Laminar?

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

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