head to head · open source
txtai vs RAGLight
txtai has 13,062 GitHub stars, 962 forks, 9 open issues and last shipped 2 days ago. RAGLight has 672 stars, 102 forks, 22 open issues and last shipped 20 days ago. txtai leads on adoption by 1,844% (13,062 vs 672 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.
← all 20902 open source comparisons
Side by side
| txtai | RAGLight | |
|---|---|---|
| GitHub stars | ★ 13K | ★ 672 |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-11-03 | 2026-10-16 |
| Forks | ⑂ 962 | ⑂ 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
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 RAGLight
RAGLight RAGLight is a lightweight and modular Python library for implementing Retrieval Augmented Generation (RAG) . It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference. Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context aware AI solutions. 📚 Table of Contents Requirements Features Import library Chat with Your Documents Instantly With CLI Ignore Folders Feature Ignore Folders in Configuration Classes…
read the full RAGLight 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 RAGLight more popular?
txtai has 13,062 GitHub stars and RAGLight has 672. 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 (13,062 stars) or its Apache-2.0 licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.