head to head · open source
llama_index vs Flide
llama_index has 52,905 GitHub stars, 8,561 forks, 770 open issues and last shipped yesterday. Flide has 505 stars, 33 forks, 2 open issues and last shipped 3 days ago. llama_index leads on adoption by 10,376% (52,905 vs 505 stars). llama_index is written in Python under MIT; Flide is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, Flide 7%. 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 (llm), 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 | Flide | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 505 |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-14 | 2026-11-12 |
| Forks | ⑂ 8.6K | ⑂ 33 |
| 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 Flide if
- You want the Flide 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 Flide 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 Flide
English · 简体中文 · 日本語 · 한국어 News · Quick start · Models · Options Flide is fast inference infrastructure for Jev style decision models. With exact state forking it is up to 9.5 times faster than vLLM and SGLang, and every answer is the one the model gives the question alone. It serves the Jev /v1/systemone API. 🔥 News 🔥 2026 11 12 We added d1 3B from LiquidAI in Flide 0.1.15, and Gemma 4 now runs in FP8 with quantize fp8 , so Gemma 4 12B fits one 24 GB card. 🔥 2026 11 12 We added GLiDE 9B and GLiDE 27B in Flide 0.1.14, and GLiDE 27B also reads images. 🔥 2026 11 11 We added FlexDM LoRA v2.1, Sieve 31B v3 and…
read the full Flide overview →
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Frequently asked questions
Is llama_index or Flide more popular?
llama_index has 52,905 GitHub stars and Flide has 505. llama_index has the larger community by that measure.
Are llama_index and Flide free?
Both are open source. llama_index is licensed under MIT and Flide under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and Flide?
llama_index is written in Python and Flide 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 Flide?
Choose llama_index if you want the larger community (52,905 stars) or its MIT licence terms. Choose Flide if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.