head to head · open source
llama_index vs dingo
llama_index has 52,206 GitHub stars, 8,165 forks, 770 open issues and last shipped today. dingo has 1,704 stars, 265 forks, 8 open issues and last shipped 2 months ago. llama_index leads on adoption by 2,964% (52,206 vs 1,704 stars). llama_index is written in Python under MIT; dingo is written in Java under Apache-2.0. llama_index has attracted 16% as many forks as stars, dingo 16%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (vector-database), 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.
← all 8884 open source comparisons
Side by side
| llama_index | dingo | |
|---|---|---|
| GitHub stars | ★ 52K | ★ 1.7K |
| License | MIT | Apache-2.0 |
| Written in | Python | Java |
| Last push | 2026-09-18 | 2026-07-10 |
| Forks | ⑂ 8.2K | ⑂ 265 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 52K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick dingo if
- You want the dingo feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Java
- You evaluated both and dingo fits your workflow better
About llama_index
LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.
read the full llama_index overview →
About dingo
DingoDB DingoDB is an open source distributed multi modal vector database independently designed and developed by DataCanvas, which integrates real time strong consistency, relational semantics, and vector semantics into a unified platform, DingoDB positioning itself as a distinctive multi modal database solution. With exceptional horizontal scalability and elastic scaling capabilities, it effortlessly meets enterprise grade high availability requirements. Furthermore, DingoDB offers extensive multi language interfaces and seamless compatibility with the MySQL protocol, delivering unparalleled flexibility and con…
read the full dingo 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 dingo more popular?
llama_index has 52,206 GitHub stars and dingo has 1,704. llama_index has the larger community by that measure.
Are llama_index and dingo free?
Both are open source. llama_index is licensed under MIT and dingo under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and dingo?
llama_index is written in Python and dingo in Java. 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 dingo?
Choose llama_index if you want the larger community (52,206 stars) or its MIT licence terms. Choose dingo if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.