head to head · open source
xerj vs rag_api
xerj has 18,817 GitHub stars, 1,435 forks, 16 open issues and last shipped yesterday. rag_api has 920 stars, 413 forks, 45 open issues and last shipped 11 days ago. xerj leads on adoption by 1,945% (18,817 vs 920 stars). xerj is written in Rust under Apache-2.0; rag_api is written in Python under MIT. xerj has attracted 8% as many forks as stars, rag_api 45%. xerj 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.
← all 20902 open source comparisons
Side by side
| xerj | rag_api | |
|---|---|---|
| GitHub stars | ★ 19K | ★ 920 |
| License | Apache-2.0 | MIT |
| Written in | Rust | Python |
| Last push | 2026-11-02 | 2026-10-23 |
| Forks | ⑂ 1.4K | ⑂ 413 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick xerj if
- You weight community size — 19K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Rust
- 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 xerj
XERJ XERJ is a community trusted local AI search that indexes any folder automatically, so your coding agent stops burning tokens reading files one by one and pulls the exact code it needs instead. Reference coding is its main use case and the clearest win: point an agent at a task and it downloads the open source repos closest to it, indexes them, and reuses how they solved the problem before writing its own code. In a controlled study that cut a coding agent's output tokens by 2.7x at the same 16/16 solve rate (case study), and people report roughly 5x in everyday work (field reports). It is enough for a…
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 →
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.
Frequently asked questions
Is xerj or rag_api more popular?
xerj has 18,817 GitHub stars and rag_api has 920. xerj has the larger community by that measure.
Are xerj and rag_api free?
Both are open source. xerj is licensed under Apache-2.0 and rag_api under MIT. Neither carries a licence fee.
What is the difference between xerj and rag_api?
xerj is written in Rust 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, xerj or rag_api?
Choose xerj if you want the larger community (18,817 stars) or its Apache-2.0 licence terms. Choose rag_api if its feature set, stack or MIT licence fits better. Both are self-hostable.