head to head · open source
llama_index vs kividb
llama_index has 52,641 GitHub stars, 8,420 forks, 770 open issues and last shipped 2 days ago. kividb has 543 stars, 15 forks, 9 open issues and last shipped yesterday. llama_index leads on adoption by 9,594% (52,641 vs 543 stars). llama_index is written in Python under MIT; kividb is written in Rust under AGPL-3.0. llama_index has attracted 16% as many forks as stars, kividb 3%. kividb 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 | kividb | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 543 |
| License | MIT | AGPL-3.0 |
| Written in | Python | Rust |
| Last push | 2026-10-23 | 2026-10-24 |
| Forks | ⑂ 8.4K | ⑂ 15 |
| 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 kividb if
- You want the kividb feature set and don't need the biggest community
- You prefer the AGPL-3.0 license terms
- Your stack matches Rust
- You evaluated both and kividb 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 kividb
KiviDB KiviDB is a fast, multi threaded alternative to Redis® and Valkey, written in Rust. It speaks the Redis protocol (RESP2 and RESP3), so existing Redis clients, redis cli and libraries work unchanged, and it has full text and vector search built in. It runs every core on a sharded in memory key value design, and persists to snapshots and an optional append only file. On the same 32 cores, KiviDB serves 21 million pipelined GETs per second: about 9× Redis 8 and 2.3× Dragonfly , with under half Dragonfly's p99 latency (benchmarks). KiviDB listens on 6380 , not Redis's 6379, so the two can run side by side…
read the full kividb overview →
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Frequently asked questions
Is llama_index or kividb more popular?
llama_index has 52,641 GitHub stars and kividb has 543. llama_index has the larger community by that measure.
Are llama_index and kividb free?
Both are open source. llama_index is licensed under MIT and kividb under AGPL-3.0. Neither carries a licence fee.
What is the difference between llama_index and kividb?
llama_index is written in Python and kividb 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 kividb?
Choose llama_index if you want the larger community (52,641 stars) or its MIT licence terms. Choose kividb if its feature set, stack or AGPL-3.0 licence fits better. Both are self-hostable.