head to head · open source
txtai vs RAGLight
txtai has 12,964 GitHub stars, 891 forks, 9 open issues and last shipped 5 days ago. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 18 days ago. txtai leads on adoption by 1,826% (12,964 vs 673 stars). txtai is written in Python under Apache-2.0; RAGLight is written in Python under MIT. txtai has attracted 7% as many forks as stars, RAGLight 15%. txtai 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
| txtai | RAGLight | |
|---|---|---|
| GitHub stars | ★ 13K | ★ 673 |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-09-15 | 2026-09-02 |
| Forks | ⑂ 891 | ⑂ 102 |
| 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 RAGLight if
- You want the RAGLight feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and RAGLight 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 RAGLight
RAGLight is a lightweight, modular Python library for building Retrieval Augmented Generation pipelines, aimed at developers who want to swap LLMs, embedding models, and vector stores without rewriting application code.
read the full RAGLight overview →
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Frequently asked questions
Is txtai or RAGLight more popular?
txtai has 12,964 GitHub stars and RAGLight has 673. txtai has the larger community by that measure.
Are txtai and RAGLight free?
Both are open source. txtai is licensed under Apache-2.0 and RAGLight under MIT. Neither carries a licence fee.
What is the difference between txtai and RAGLight?
txtai is written in Python and RAGLight 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 RAGLight?
Choose txtai if you want the larger community (12,964 stars) or its Apache-2.0 licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.