head to head · open source

llama.cpp vs deep-searcher

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. deep-searcher has 8,269 stars, 803 forks, 55 open issues and last shipped 10 months ago. llama.cpp leads on adoption by 1,455% (128,581 vs 8,269 stars). llama.cpp is written in C++ under MIT; deep-searcher is written in Python under Apache-2.0. llama.cpp has attracted 18% 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.

llama.cpp ★ 129K deep-searcher ★ 8.3K category AI & Machine Learning

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Side by side

	llama.cpp	deep-searcher
GitHub stars	★ 129K	★ 8.3K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2025-11-19
Forks	⑂ 23K	⑂ 803
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 129K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

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

full deep-searcher profile →

About llama.cpp

llama.cpp is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.

read the full llama.cpp 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.cpp or deep-searcher more popular?

llama.cpp has 128,581 GitHub stars and deep-searcher has 8,269. 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 (128,581 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.