head to head · open source
GPT4All vs rag_api
GPT4All has 77,401 GitHub stars, 8,291 forks, 771 open issues and last shipped 1.3 years ago. rag_api has 901 stars, 403 forks, 45 open issues and last shipped 1 months ago. GPT4All leads on adoption by 8,491% (77,401 vs 901 stars). GPT4All is written in C++ under MIT; rag_api is written in Python under MIT. GPT4All has attracted 11% as many forks as stars, rag_api 45%. rag_api 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.
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Side by side
| GPT4All | rag_api | |
|---|---|---|
| GitHub stars | ★ 77K | ★ 901 |
| License | MIT | MIT |
| Written in | C++ | Python |
| Last push | 2025-05-27 | 2026-08-15 |
| Forks | ⑂ 8.3K | ⑂ 403 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick GPT4All if
- You weight community size — 77K stars and counting
- You want the MIT license terms
- Your stack matches C++
- You value the larger contributor base for long-term maintenance
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
About GPT4All
GPT4All is an MIT licensed C++ chat application and Python client from Nomic that runs large language models privately on everyday desktops and laptops, built for developers and teams who want local inference without API calls, GPUs, or sending data off device.
read the full GPT4All 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 →
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Frequently asked questions
Is GPT4All or rag_api more popular?
GPT4All has 77,401 GitHub stars and rag_api has 901. GPT4All has the larger community by that measure.
Are GPT4All and rag_api free?
Both are open source. GPT4All is licensed under MIT and rag_api under MIT. Neither carries a licence fee.
What is the difference between GPT4All and rag_api?
GPT4All 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, GPT4All or rag_api?
Choose GPT4All if you want the larger community (77,401 stars) or its MIT licence terms. Choose rag_api if its feature set, stack or MIT licence fits better. Both are self-hostable.