head to head · open source
llama_index vs dingo
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. dingo has 1,705 stars, 263 forks, 8 open issues and last shipped 4 days ago. llama_index leads on adoption by 2,996% (52,779 vs 1,705 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 15%. dingo 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.
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Side by side
| llama_index | dingo | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 1.7K |
| License | MIT | Apache-2.0 |
| Written in | Python | Java |
| Last push | 2026-10-26 | 2026-10-30 |
| Forks | ⑂ 8.5K | ⑂ 263 |
| 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 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 (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 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…
read the full dingo overview →
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Frequently asked questions
Is llama_index or dingo more popular?
llama_index has 52,779 GitHub stars and dingo has 1,705. 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 (52,779 stars) or its MIT licence terms. Choose dingo if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.