head to head · open source
llama.cpp vs Agent_Memory_Techniques
llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. Agent_Memory_Techniques has 1,131 stars, 141 forks, 2 open issues and last shipped 2 days ago. llama.cpp leads on adoption by 11,676% (133,181 vs 1,131 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 19% as many forks as stars, Agent_Memory_Techniques 12%. 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 | ★ 133K | ★ 1.1K |
| License | MIT | Apache-2.0 |
| Written in | C++ | Jupyter Notebook |
| Last push | 2026-11-02 | 2026-11-01 |
| Forks | ⑂ 25K | ⑂ 141 |
| 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 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 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 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…
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 133,181 GitHub stars and Agent_Memory_Techniques has 1,131. 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 (133,181 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.