head to head · open source
llama_index vs examples
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. examples has 3,051 stars, 1,062 forks, 64 open issues and last shipped 11 days ago. llama_index leads on adoption by 1,630% (52,779 vs 3,051 stars). llama_index is written in Python under MIT; examples is written in Jupyter Notebook under MIT. llama_index has attracted 16% as many forks as stars, examples 35%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (llm, rag, 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.
← all 20902 open source comparisons
Side by side
| llama_index | examples | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 3.1K |
| License | MIT | MIT |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-10-26 | 2026-10-23 |
| Forks | ⑂ 8.5K | ⑂ 1.1K |
| 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 examples if
- You want the examples feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Jupyter Notebook
- You evaluated both and examples 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 examples
Pinecone Examples This repository is a collection of sample applications and Jupyter Notebooks that you can run, download, study and modify in order to get hands on with Pinecone vector databases and common AI patterns, tools and algorithms. Two kinds of examples This repo contains: 1. Production ready examples in ./docs that receive regular review and support from the Pinecone engineering team 2. Examples optimized for learning and exploration of AI techniques in ./learn and patterns for building different kinds of applications, created and maintained by the Pinecone Developer Advocacy team. We appreciate your…
read the full examples overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is llama_index or examples more popular?
llama_index has 52,779 GitHub stars and examples has 3,051. llama_index has the larger community by that measure.
Are llama_index and examples free?
Both are open source. llama_index is licensed under MIT and examples under MIT. Neither carries a licence fee.
What is the difference between llama_index and examples?
llama_index is written in Python and examples in Jupyter Notebook. 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 examples?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose examples if its feature set, stack or MIT licence fits better. Both are self-hostable.