head to head · open source
serviette vs Agent_Memory_Techniques
serviette has 58,732 GitHub stars, 1,547 forks, 8 open issues and last shipped yesterday. Agent_Memory_Techniques has 1,131 stars, 141 forks, 2 open issues and last shipped 2 days ago. serviette leads on adoption by 5,093% (58,732 vs 1,131 stars). serviette is written in Python under MIT; Agent_Memory_Techniques is written in Jupyter Notebook under Apache-2.0. serviette has attracted 3% as many forks as stars, Agent_Memory_Techniques 12%. serviette 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
| serviette | Agent_Memory_Techniques | |
|---|---|---|
| GitHub stars | ★ 59K | ★ 1.1K |
| License | MIT | Apache-2.0 |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-11-02 | 2026-11-01 |
| Forks | ⑂ 1.5K | ⑂ 141 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick serviette if
- You weight community size — 59K stars and counting
- You want the MIT 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 serviette
serviette Looking for llm app templates? All templates, cookbooks and examples are still available in full in the Pathway repository under examples/llm app . A no code, always up to date RAG server for any vector database — built on the Pathway Live Data Framework. Point serviette at a folder of documents, pick a vector database and an embedder in one YAML file, and run two commands: you get a chat and a REST API that answer from those documents and cite their sources. From then on, any change to the documents — an edit, a new file, a deletion — is reflected in the answers within seconds. serviette runs on…
read the full serviette 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 serviette or Agent_Memory_Techniques more popular?
serviette has 58,732 GitHub stars and Agent_Memory_Techniques has 1,131. serviette has the larger community by that measure.
Are serviette and Agent_Memory_Techniques free?
Both are open source. serviette is licensed under MIT and Agent_Memory_Techniques under Apache-2.0. Neither carries a licence fee.
What is the difference between serviette and Agent_Memory_Techniques?
serviette 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, serviette or Agent_Memory_Techniques?
Choose serviette if you want the larger community (58,732 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.