head to head · open source
llama_index vs deep-searcher
llama_index has 52,202 GitHub stars, 8,162 forks, 770 open issues and last shipped yesterday. deep-searcher has 8,269 stars, 803 forks, 55 open issues and last shipped 10 months ago. llama_index leads on adoption by 531% (52,202 vs 8,269 stars). llama_index is written in Python under MIT; deep-searcher is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, deep-searcher 10%. 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 (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.
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Side by side
| llama_index | deep-searcher | |
|---|---|---|
| GitHub stars | ★ 52K | ★ 8.3K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-09-17 | 2025-11-19 |
| Forks | ⑂ 8.2K | ⑂ 803 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 52K 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 llama_index
LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.
read the full llama_index 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 →
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Frequently asked questions
Is llama_index or deep-searcher more popular?
llama_index has 52,202 GitHub stars and deep-searcher has 8,269. llama_index has the larger community by that measure.
Are llama_index and deep-searcher free?
Both are open source. llama_index is licensed under MIT and deep-searcher under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and deep-searcher?
llama_index 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, llama_index or deep-searcher?
Choose llama_index if you want the larger community (52,202 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.