head to head · open source
llama_index vs lightly-studio
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. lightly-studio has 902 stars, 42 forks, 53 open issues and last shipped yesterday. llama_index leads on adoption by 5,751% (52,779 vs 902 stars). llama_index is written in Python under MIT; lightly-studio is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, lightly-studio 5%. lightly-studio 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
| llama_index | lightly-studio | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 902 |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 42 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick lightly-studio if
- You want the lightly-studio feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and lightly-studio fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About lightly-studio
Curate, Annotate, and Manage Your Data in LightlyStudio. The embedding plot shows how images relate to each other, with a preview on hover. A lasso selection filters the grid to one cluster. A search for "coffee" finds a match, and the annotation editor opens to label it. ⚡ Works smoothly with 2M+ images, embeddings included, on a single MacBook (M1, 16GB RAM). 🚀 Try it in 60 seconds LightlyStudio runs on your computer and opens in your browser. The following command downloads an example dataset with images, annotations, and evaluation results — no account needed. Local only: your images and datasets never…
read the full lightly-studio 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 llama_index or lightly-studio more popular?
llama_index has 52,779 GitHub stars and lightly-studio has 902. llama_index has the larger community by that measure.
Are llama_index and lightly-studio free?
Both are open source. llama_index is licensed under MIT and lightly-studio under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and lightly-studio?
llama_index is written in Python and lightly-studio 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, llama_index or lightly-studio?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose lightly-studio if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.