head to head · open source
llama_index vs vllm-omni
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. vllm-omni has 7,494 stars, 2,037 forks, 1,977 open issues and last shipped yesterday. llama_index leads on adoption by 604% (52,779 vs 7,494 stars). llama_index is written in Python under MIT; vllm-omni is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, vllm-omni 27%. vllm-omni 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 | vllm-omni | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 7.5K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 2.0K |
| 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 vllm-omni if
- You want the vllm-omni 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 vllm-omni 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 vllm-omni
Easy, fast, and cheap omni modality model serving for everyone Latest News 🔥 [2026/08] We released 0.28.0, featuring production ready MiniMax H3 serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full duplex serving for the MiniCPM o series. [2026/08] VeRL Omni v0.2.0 is released: faster diffusion RL powered by vLLM Omni (request level/step wise batching with FA3), rebuilt Qwen3 Omni multimodal training (DPO & GSPO), plus LTX 2.3, Qwen Image Edit support and more. See the release notes. [2026/08] We released 0.26.0 aligned with the vLLM 0.26 release line, featuring MiniMax H3…
read the full vllm-omni overview →
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Frequently asked questions
Is llama_index or vllm-omni more popular?
llama_index has 52,779 GitHub stars and vllm-omni has 7,494. llama_index has the larger community by that measure.
Are llama_index and vllm-omni free?
Both are open source. llama_index is licensed under MIT and vllm-omni under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and vllm-omni?
llama_index is written in Python and vllm-omni 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 vllm-omni?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose vllm-omni if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.