head to head · open source
llama.cpp vs Agent_Memory_Techniques
llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. Agent_Memory_Techniques has 1,067 stars, 137 forks, 2 open issues and last shipped 3 days ago. llama.cpp leads on adoption by 11,954% (128,619 vs 1,067 stars). llama.cpp is written in C++ under MIT; Agent_Memory_Techniques is written in Jupyter Notebook under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, Agent_Memory_Techniques 13%. 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 | Agent_Memory_Techniques | |
|---|---|---|
| GitHub stars | ★ 129K | ★ 1.1K |
| License | MIT | Apache-2.0 |
| Written in | C++ | Jupyter Notebook |
| Last push | 2026-09-18 | 2026-09-15 |
| Forks | ⑂ 23K | ⑂ 137 |
| 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
pick Agent_Memory_Techniques if
- You want the Agent_Memory_Techniques feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Jupyter Notebook
- You evaluated both and Agent_Memory_Techniques fits your workflow better
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 Agent_Memory_Techniques
🧠 Agent Memory Techniques Learn every agent memory technique for LLM agents. ⭐ If you find this useful, please star the repo so more learners can discover it. 🧭 New here? Start with 01 Conversation Buffer Memory or pick a Learning Path. Prefer a visual? See the Decision Tree below. 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, working memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production memory patterns. 🎓 From memory demos to production agents Prompt to Production my full course on building software with AI t…
read the full Agent_Memory_Techniques overview →
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Frequently asked questions
Is llama.cpp or Agent_Memory_Techniques more popular?
llama.cpp has 128,619 GitHub stars and Agent_Memory_Techniques has 1,067. llama.cpp has the larger community by that measure.
Are llama.cpp and Agent_Memory_Techniques free?
Both are open source. llama.cpp is licensed under MIT and Agent_Memory_Techniques under Apache-2.0. Neither carries a licence fee.
What is the difference between llama.cpp and Agent_Memory_Techniques?
llama.cpp is written in C++ and Agent_Memory_Techniques in Jupyter Notebook. 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 Agent_Memory_Techniques?
Choose llama.cpp if you want the larger community (128,619 stars) or its MIT licence terms. Choose Agent_Memory_Techniques if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.