head to head · open source
llama_index vs SeaGOAT
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. SeaGOAT has 1,316 stars, 95 forks, 44 open issues and last shipped yesterday. llama_index leads on adoption by 3,911% (52,779 vs 1,316 stars). llama_index is written in Python under MIT; SeaGOAT is written in Python under MIT. llama_index has attracted 16% as many forks as stars, SeaGOAT 7%. SeaGOAT was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (llm, 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 | SeaGOAT | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 1.3K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 95 |
| 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 SeaGOAT if
- You want the SeaGOAT feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and SeaGOAT 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 SeaGOAT
[!TIP] Check out zeitgrep, another search tool I am working on! SeaGOAT A code search engine for the AI age. SeaGOAT is a local search tool that leverages vector embeddings to enable you to search your codebase semantically. Getting started Install SeaGOAT In order to install SeaGOAT, you need to have the following dependencies already installed on your computer: Python 3.11 or newer ripgrep bat ( optional , highly recommended) When bat is installed, it is used to display results as long as color is enabled. When SeaGOAT is used as part of a pipeline, a grep line output format is used. When color is enabled, but…
read the full SeaGOAT overview →
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Frequently asked questions
Is llama_index or SeaGOAT more popular?
llama_index has 52,779 GitHub stars and SeaGOAT has 1,316. llama_index has the larger community by that measure.
Are llama_index and SeaGOAT free?
Both are open source. llama_index is licensed under MIT and SeaGOAT under MIT. Neither carries a licence fee.
What is the difference between llama_index and SeaGOAT?
llama_index is written in Python and SeaGOAT 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 SeaGOAT?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose SeaGOAT if its feature set, stack or MIT licence fits better. Both are self-hostable.