head to head · open source
txtai vs rag_api
txtai has 13,062 GitHub stars, 962 forks, 9 open issues and last shipped 2 days ago. rag_api has 920 stars, 413 forks, 45 open issues and last shipped 13 days ago. txtai leads on adoption by 1,320% (13,062 vs 920 stars). txtai is written in Python under Apache-2.0; rag_api is written in Python under MIT. txtai has attracted 7% as many forks as stars, rag_api 45%. txtai was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (embeddings, python, rag), 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.
← all 20902 open source comparisons
Side by side
| txtai | rag_api | |
|---|---|---|
| GitHub stars | ★ 13K | ★ 920 |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-11-03 | 2026-10-23 |
| Forks | ⑂ 962 | ⑂ 413 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick txtai if
- You weight community size — 13K stars and counting
- You want the Apache-2.0 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 txtai
All in one AI framework txtai is an all in one AI framework for semantic search, LLM orchestration and language model workflows. The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases. This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications. Build autonomous agents, retrieval augmented generation (RAG) processes, multi model workflows and more. Summary of txtai features: 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and…
read the full txtai 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 →
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 txtai or rag_api more popular?
txtai has 13,062 GitHub stars and rag_api has 920. txtai has the larger community by that measure.
Are txtai and rag_api free?
Both are open source. txtai is licensed under Apache-2.0 and rag_api under MIT. Neither carries a licence fee.
What is the difference between txtai and rag_api?
txtai 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, txtai or rag_api?
Choose txtai if you want the larger community (13,062 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.