head to head · open source
llama_index vs rag_api
llama_index has 52,206 GitHub stars, 8,165 forks, 770 open issues and last shipped today. rag_api has 901 stars, 403 forks, 45 open issues and last shipped 1 months ago. llama_index leads on adoption by 5,694% (52,206 vs 901 stars). llama_index is written in Python under MIT; rag_api is written in Python under MIT. llama_index has attracted 16% as many forks as stars, rag_api 45%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (rag, vector-database), so they are genuine substitutes rather than adjacent tools.
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
| llama_index | rag_api | |
|---|---|---|
| GitHub stars | ★ 52K | ★ 901 |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-09-18 | 2026-08-15 |
| Forks | ⑂ 8.2K | ⑂ 403 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 52K stars and counting
- You want the MIT license terms
- Your stack matches Python
- 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 llama_index
LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.
read the full llama_index 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 llama_index or rag_api more popular?
llama_index has 52,206 GitHub stars and rag_api has 901. llama_index has the larger community by that measure.
Are llama_index and rag_api free?
Both are open source. llama_index is licensed under MIT and rag_api under MIT. Neither carries a licence fee.
What is the difference between llama_index and rag_api?
llama_index is written in Python 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_index or rag_api?
Choose llama_index if you want the larger community (52,206 stars) or its MIT licence terms. Choose rag_api if its feature set, stack or MIT licence fits better. Both are self-hostable.