head to head · open source

llama.cpp vs rag_api

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. rag_api has 901 stars, 403 forks, 45 open issues and last shipped 1 months ago. llama.cpp leads on adoption by 14,175% (128,619 vs 901 stars). llama.cpp is written in C++ under MIT; rag_api is written in Python under MIT. llama.cpp has attracted 18% 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 ★ 129K rag_api ★ 901 category AI & Machine Learning

← all 8884 open source comparisons

Side by side

	llama.cpp	rag_api
GitHub stars	★ 129K	★ 901
License	MIT	MIT
Written in	C++	Python
Last push	2026-09-18	2026-08-15
Forks	⑂ 23K	⑂ 403
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 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 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 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 queryi…

read the full rag_api 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 llama_index llama.cpp vs LocalAI llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs dagster llama.cpp vs zvec llama.cpp vs langchain4j

Frequently asked questions

Is llama.cpp or rag_api more popular?

llama.cpp has 128,619 GitHub stars and rag_api has 901. 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 (128,619 stars) or its MIT licence terms. Choose rag_api if its feature set, stack or MIT licence fits better. Both are self-hostable.