head to head · open source
llama.cpp vs deep-searcher
llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. deep-searcher has 8,366 stars, 815 forks, 55 open issues and last shipped 1 months ago. llama.cpp leads on adoption by 1,492% (133,181 vs 8,366 stars). llama.cpp is written in C++ under MIT; deep-searcher is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, deep-searcher 10%. llama.cpp was the more recently maintained of the two, and both are self-hostable with no licence fee.
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.cpp | deep-searcher | |
|---|---|---|
| GitHub stars | ★ 133K | ★ 8.4K |
| License | MIT | Apache-2.0 |
| Written in | C++ | Python |
| Last push | 2026-11-02 | 2026-09-22 |
| Forks | ⑂ 25K | ⑂ 815 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama.cpp if
- You weight community size — 133K stars and counting
- You want the MIT license terms
- Your stack matches C++
- 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.cpp
llama.cpp LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…
read the full llama.cpp 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.cpp or deep-searcher more popular?
llama.cpp has 133,181 GitHub stars and deep-searcher has 8,366. llama.cpp has the larger community by that measure.
Are llama.cpp and deep-searcher free?
Both are open source. llama.cpp is licensed under MIT and deep-searcher under Apache-2.0. Neither carries a licence fee.
What is the difference between llama.cpp and deep-searcher?
llama.cpp is written in C++ 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.cpp or deep-searcher?
Choose llama.cpp if you want the larger community (133,181 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.