head to head · open source
vllm vs Agent_Memory_Techniques
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. Agent_Memory_Techniques has 1,131 stars, 141 forks, 2 open issues and last shipped 2 days ago. vllm leads on adoption by 8,402% (96,158 vs 1,131 stars). vllm is written in Python under Apache-2.0; Agent_Memory_Techniques is written in Jupyter Notebook under Apache-2.0. vllm has attracted 25% as many forks as stars, Agent_Memory_Techniques 12%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (llm), 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
| vllm | Agent_Memory_Techniques | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 1.1K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-11-02 | 2026-11-01 |
| Forks | ⑂ 24K | ⑂ 141 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick vllm if
- You weight community size — 96K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- 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 vllm
Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy to use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State of the art serving throughput Efficient management of…
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 vllm or Agent_Memory_Techniques more popular?
vllm has 96,158 GitHub stars and Agent_Memory_Techniques has 1,131. vllm has the larger community by that measure.
Are vllm and Agent_Memory_Techniques free?
Both are open source. vllm is licensed under Apache-2.0 and Agent_Memory_Techniques under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and Agent_Memory_Techniques?
vllm is written in Python 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, vllm or Agent_Memory_Techniques?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose Agent_Memory_Techniques if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.