head to head · open source
txtai vs rag_api
txtai has 12,964 GitHub stars, 891 forks, 9 open issues and last shipped 5 days ago. rag_api has 901 stars, 404 forks, 45 open issues and last shipped 1 months ago. txtai leads on adoption by 1,339% (12,964 vs 901 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.
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Side by side
| txtai | rag_api | |
|---|---|---|
| GitHub stars | ★ 13K | ★ 901 |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-09-15 | 2026-08-15 |
| Forks | ⑂ 891 | ⑂ 404 |
| 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
txtai is an all in one AI framework for semantic search, LLM orchestration and language model workflows, written in Python and released under the Apache 2.0 license. It lives in the Python machine learning ecosystem and is built on Hugging Face Transformers, Sentence Transformers and FastAPI. The core component is an embeddings database, which is a union of vector indexes (both sparse and dense), graph networks and relational databases. That foundation enables vector search and also serves as a knowledge source for large language model applications.
read the full txtai overview →
About rag_api
rag api is an MIT licensed Python FastAPI service that indexes documents into embeddings by file id and serves ID based retrieval augmented generation over Langchain and PostgreSQL/pgvector, built primarily for LibreChat deployments but usable by any application that stores embeddings at the file level.
read the full rag_api overview →
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Frequently asked questions
Is txtai or rag_api more popular?
txtai has 12,964 GitHub stars and rag_api has 901. 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 (12,964 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.