head to head · open source

llama.cpp vs rag_api

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. rag_api has 920 stars, 413 forks, 45 open issues and last shipped 11 days ago. llama.cpp leads on adoption by 14,376% (133,181 vs 920 stars). llama.cpp is written in C++ under MIT; rag_api is written in Python under MIT. llama.cpp has attracted 19% as many forks as stars, rag_api 45%. 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 rag_api ★ 920 category AI & Machine Learning

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Side by side

	llama.cpp	rag_api
GitHub stars	★ 133K	★ 920
License	MIT	MIT
Written in	C++	Python
Last push	2026-11-02	2026-10-23
Forks	⑂ 25K	⑂ 413
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 rag_api if

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

full rag_api 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 rag_api

ID based RAG FastAPI Overview This project integrates Langchain with FastAPI in an Asynchronous, Scalable manner, providing a framework for document indexing and retrieval, using PostgreSQL/pgvector. Files are organized into embeddings by file id . The primary use case is for integration with LibreChat, but this simple API can be used for any ID based use case. The main reason to use the ID approach is to work with embeddings on a file level. This makes for targeted queries when combined with file metadata stored in a database, such as is done by LibreChat. The API will evolve over time to employ different…

read the full rag_api overview →

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Frequently asked questions

Is llama.cpp or rag_api more popular?

llama.cpp has 133,181 GitHub stars and rag_api has 920. llama.cpp has the larger community by that measure.

Are llama.cpp and rag_api free?

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

What is the difference between llama.cpp and rag_api?

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

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