head to head · open source
llama_index vs kvcached
llama_index has 52,875 GitHub stars, 8,541 forks, 770 open issues and last shipped yesterday. kvcached has 2,038 stars, 239 forks, 105 open issues and last shipped yesterday. llama_index leads on adoption by 2,494% (52,875 vs 2,038 stars). llama_index is written in Python under MIT; kvcached is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, kvcached 12%. 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 | kvcached | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 2.0K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-11 | 2026-11-11 |
| Forks | ⑂ 8.5K | ⑂ 239 |
| 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 kvcached if
- You want the kvcached 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 kvcached 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 kvcached
Make GPU Sharing Flexible and Easy kvcached (KV cache daemon) is a KV cache library for LLM serving/training on shared GPUs . By bringing OS style virtual memory abstraction to LLM systems, it enables elastic and demand driven KV cache allocation, improving GPU utilization under dynamic workloads. kvcached achieves this by decoupling GPU virtual addressing from physical memory allocation for KV caches. It allows serving engines to initially reserve virtual memory only and later back it with physical GPU memory when the cache is actively used. This decoupling enables on demand allocation and flexible sharing…
read the full kvcached overview →
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Frequently asked questions
Is llama_index or kvcached more popular?
llama_index has 52,875 GitHub stars and kvcached has 2,038. llama_index has the larger community by that measure.
Are llama_index and kvcached free?
Both are open source. llama_index is licensed under MIT and kvcached under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and kvcached?
llama_index is written in Python and kvcached 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 kvcached?
Choose llama_index if you want the larger community (52,875 stars) or its MIT licence terms. Choose kvcached if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.