head to head · open source
Ollama vs RAGLight
Ollama has 181,184 GitHub stars, 17,921 forks, 3,957 open issues and last shipped yesterday. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 16 days ago. Ollama leads on adoption by 26,822% (181,184 vs 673 stars). Ollama is written in Go under MIT; RAGLight is written in Python under MIT. Ollama has attracted 10% as many forks as stars, RAGLight 15%. Ollama 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 13541 open source comparisons
Side by side
| Ollama | RAGLight | |
|---|---|---|
| GitHub stars | ★ 181K | ★ 673 |
| License | MIT | MIT |
| Written in | Go | Python |
| Last push | 2026-09-17 | 2026-09-02 |
| Forks | ⑂ 18K | ⑂ 102 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Ollama if
- You weight community size — 181K stars and counting
- You want the MIT license terms
- Your stack matches Go
- 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 Ollama
Ollama is a Go based, MIT licensed runtime that downloads and runs open source large language models such as DeepSeek, Qwen, Gemma, GLM, MiniMax and gpt oss locally on a user's own machine, and it is aimed at developers and teams that want model inference without routing prompts through a hosted API.
read the full Ollama 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 Dep…
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 Ollama or RAGLight more popular?
Ollama has 181,184 GitHub stars and RAGLight has 673. Ollama has the larger community by that measure.
Are Ollama and RAGLight free?
Both are open source. Ollama is licensed under MIT and RAGLight under MIT. Neither carries a licence fee.
What is the difference between Ollama and RAGLight?
Ollama is written in Go 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, Ollama or RAGLight?
Choose Ollama if you want the larger community (181,184 stars) or its MIT licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.