head to head · open source
PageIndex vs deep-searcher
PageIndex has 35,765 GitHub stars, 3,152 forks, 103 open issues and last shipped yesterday. deep-searcher has 8,273 stars, 804 forks, 55 open issues and last shipped 10 months ago. PageIndex leads on adoption by 332% (35,765 vs 8,273 stars). PageIndex is written in Python under MIT; deep-searcher is written in Python under Apache-2.0. PageIndex has attracted 9% as many forks as stars, deep-searcher 10%. PageIndex 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.
← all 20902 open source comparisons
Side by side
| PageIndex | deep-searcher | |
|---|---|---|
| GitHub stars | ★ 36K | ★ 8.3K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-09-19 | 2025-11-19 |
| Forks | ⑂ 3.2K | ⑂ 804 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick PageIndex if
- You weight community size — 36K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick deep-searcher if
- You want the deep-searcher 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 deep-searcher fits your workflow better
About PageIndex
PageIndex is a vectorless, reasoning based retrieval augmented generation engine written in Python and released under the MIT license. It lives in the AI and machine learning infrastructure ecosystem, with topics spanning RAG, information retrieval, LLM reasoning, context engineering, and agentic AI. Instead of building a vector index, PageIndex generates a hierarchical tree index for each document and then lets a large language model reason its way through that tree, in the same way a human expert turns to the right section of a long report. The project ships as a Python SDK, a hosted cloud service, and a docume…
read the full PageIndex overview →
About deep-searcher
DeepSearcher is an open source deep research tool that combines large language models with vector databases to search, evaluate, and reason over private data, producing accurate answers and comprehensive reports. Written in Python and released under the Apache 2.0 license, it lives in the AI and machine learning ecosystem as a machine learning infrastructure project, built around agentic retrieval augmented generation and the Zilliz/Milvus vector search stack. The project has been on GitHub for two years, carries 8265 stars and 803 forks, and had its most recent push on 19 November 2025.
read the full deep-searcher 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 PageIndex or deep-searcher more popular?
PageIndex has 35,765 GitHub stars and deep-searcher has 8,273. PageIndex has the larger community by that measure.
Are PageIndex and deep-searcher free?
Both are open source. PageIndex is licensed under MIT and deep-searcher under Apache-2.0. Neither carries a licence fee.
What is the difference between PageIndex and deep-searcher?
PageIndex is written in Python and deep-searcher 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, PageIndex or deep-searcher?
Choose PageIndex if you want the larger community (35,765 stars) or its MIT licence terms. Choose deep-searcher if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.