head to head · open source
llama_index vs vearch
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. vearch has 2,333 stars, 366 forks, 170 open issues and last shipped 3 months ago. llama_index leads on adoption by 2,162% (52,779 vs 2,333 stars). llama_index is written in Python under MIT; vearch is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, vearch 16%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (rag, 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 | vearch | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 2.3K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-07-27 |
| Forks | ⑂ 8.5K | ⑂ 366 |
| 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 vearch if
- You want the vearch 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 vearch 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 vearch
简体中文 English Overview Vearch is a cloud native distributed vector database for efficient similarity search of embedding vectors in your AI applications. Key features Hybrid search : Both vector search and scalar filtering. Performance : Fast vector retrieval search from millions of objects in milliseconds. Scalability & Reliability : Replication and elastic scaling out. Document Restful APIs Tutorial 参考文档 OpenAPIs API Documentation SDK Usage Cases Use Vearch as a Memory Backend Vearch integrates with popular AI frameworks: Real world Demos VisualSearch : Vearch can be leveraged to build a complete visual search…
read the full vearch overview →
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Frequently asked questions
Is llama_index or vearch more popular?
llama_index has 52,779 GitHub stars and vearch has 2,333. llama_index has the larger community by that measure.
Are llama_index and vearch free?
Both are open source. llama_index is licensed under MIT and vearch under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and vearch?
llama_index is written in Python and vearch 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 vearch?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose vearch if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.