head to head · open source
llama_index vs xerj
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. xerj has 18,817 stars, 1,435 forks, 16 open issues and last shipped yesterday. llama_index leads on adoption by 180% (52,779 vs 18,817 stars). llama_index is written in Python under MIT; xerj is written in Rust under Apache-2.0. llama_index has attracted 16% as many forks as stars, xerj 8%. xerj 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.
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Side by side
| llama_index | xerj | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 19K |
| License | MIT | Apache-2.0 |
| Written in | Python | Rust |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 1.4K |
| 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 xerj if
- You want the xerj feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Rust
- You evaluated both and xerj 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 xerj
XERJ XERJ is a community trusted local AI search that indexes any folder automatically, so your coding agent stops burning tokens reading files one by one and pulls the exact code it needs instead. Reference coding is its main use case and the clearest win: point an agent at a task and it downloads the open source repos closest to it, indexes them, and reuses how they solved the problem before writing its own code. In a controlled study that cut a coding agent's output tokens by 2.7x at the same 16/16 solve rate (case study), and people report roughly 5x in everyday work (field reports). It is enough for a…
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Frequently asked questions
Is llama_index or xerj more popular?
llama_index has 52,779 GitHub stars and xerj has 18,817. llama_index has the larger community by that measure.
Are llama_index and xerj free?
Both are open source. llama_index is licensed under MIT and xerj under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and xerj?
llama_index is written in Python and xerj in Rust. 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 xerj?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose xerj if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.