head to head · open source
llama_index vs next-plaid
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. next-plaid has 608 stars, 67 forks, 29 open issues and last shipped 24 days ago. llama_index leads on adoption by 8,581% (52,779 vs 608 stars). llama_index is written in Python under MIT; next-plaid is written in Rust under Apache-2.0. llama_index has attracted 16% as many forks as stars, next-plaid 11%. 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 (vector-database), 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 | next-plaid | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 608 |
| License | MIT | Apache-2.0 |
| Written in | Python | Rust |
| Last push | 2026-10-26 | 2026-10-10 |
| Forks | ⑂ 8.5K | ⑂ 67 |
| 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 next-plaid if
- You want the next-plaid 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 next-plaid 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 next-plaid
NextPlaid & ColGREP NextPlaid is a multi vector search engine. ColGREP is semantic code search, built on it. ColGREP · NextPlaid · Models ColGREP Semantic code search for your terminal and your coding agents. Searches combine regex filtering with semantic ranking. All local, your code never leaves your machine. Quick start Install: Build the index: Search: That's it. No server, no API, no dependencies. ColGREP is a single Rust binary with everything baked in. colgrep init builds the index for the first time. After that, every search detects file changes and updates the index automatically before returning…
read the full next-plaid overview →
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Frequently asked questions
Is llama_index or next-plaid more popular?
llama_index has 52,779 GitHub stars and next-plaid has 608. llama_index has the larger community by that measure.
Are llama_index and next-plaid free?
Both are open source. llama_index is licensed under MIT and next-plaid under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and next-plaid?
llama_index is written in Python and next-plaid 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 next-plaid?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose next-plaid if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.