head to head · open source
serviette vs deep-searcher
serviette has 58,732 GitHub stars, 1,547 forks, 8 open issues and last shipped yesterday. deep-searcher has 8,366 stars, 815 forks, 55 open issues and last shipped 1 months ago. serviette leads on adoption by 602% (58,732 vs 8,366 stars). serviette is written in Python under MIT; deep-searcher is written in Python under Apache-2.0. serviette has attracted 3% as many forks as stars, deep-searcher 10%. serviette was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (llm, milvus), 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
| serviette | deep-searcher | |
|---|---|---|
| GitHub stars | ★ 59K | ★ 8.4K |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-09-22 |
| Forks | ⑂ 1.5K | ⑂ 815 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick serviette if
- You weight community size — 59K 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 serviette
serviette Looking for llm app templates? All templates, cookbooks and examples are still available in full in the Pathway repository under examples/llm app . A no code, always up to date RAG server for any vector database — built on the Pathway Live Data Framework. Point serviette at a folder of documents, pick a vector database and an embedder in one YAML file, and run two commands: you get a chat and a REST API that answer from those documents and cite their sources. From then on, any change to the documents — an edit, a new file, a deletion — is reflected in the answers within seconds. serviette runs on…
read the full serviette 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 serviette or deep-searcher more popular?
serviette has 58,732 GitHub stars and deep-searcher has 8,366. serviette has the larger community by that measure.
Are serviette and deep-searcher free?
Both are open source. serviette is licensed under MIT and deep-searcher under Apache-2.0. Neither carries a licence fee.
What is the difference between serviette and deep-searcher?
serviette 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, serviette or deep-searcher?
Choose serviette if you want the larger community (58,732 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.