head to head · open source
llama_index vs deep-searcher
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. deep-searcher has 8,366 stars, 815 forks, 55 open issues and last shipped 1 months ago. llama_index leads on adoption by 531% (52,779 vs 8,366 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 | ★ 53K | ★ 8.4K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-09-22 |
| Forks | ⑂ 8.5K | ⑂ 815 |
| 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 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 (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 deep-searcher
DeepSearcher combines cutting edge LLMs (OpenAI o3, Qwen3, DeepSeek, Grok 4, Claude 4 Sonnet, Llama 4, QwQ, etc.) and Vector Databases (Milvus, Zilliz Cloud etc.) to perform search, evaluation, and reasoning based on private data, providing highly accurate answer and comprehensive report. This project is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. 🚀 Features Private Data Search : Maximizes the utilization of enterprise internal data while ensuring data security. When necessary, it can integrate online content for more accurate answers. Vector…
read the full deep-searcher overview →
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Frequently asked questions
Is llama_index or deep-searcher more popular?
llama_index has 52,779 GitHub stars and deep-searcher has 8,366. 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 (52,779 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.