head to head · open source
Ollama vs Agent_Memory_Techniques
Ollama has 181,184 GitHub stars, 17,921 forks, 3,957 open issues and last shipped yesterday. Agent_Memory_Techniques has 1,067 stars, 137 forks, 2 open issues and last shipped 3 days ago. Ollama leads on adoption by 16,881% (181,184 vs 1,067 stars). Ollama is written in Go under MIT; Agent_Memory_Techniques is written in Jupyter Notebook under Apache-2.0. Ollama has attracted 10% as many forks as stars, Agent_Memory_Techniques 13%. Ollama 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
| Ollama | Agent_Memory_Techniques | |
|---|---|---|
| GitHub stars | ★ 181K | ★ 1.1K |
| License | MIT | Apache-2.0 |
| Written in | Go | Jupyter Notebook |
| Last push | 2026-09-17 | 2026-09-15 |
| Forks | ⑂ 18K | ⑂ 137 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Ollama if
- You weight community size — 181K stars and counting
- You want the MIT license terms
- Your stack matches Go
- 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 Ollama
Ollama is a Go based, MIT licensed runtime that downloads and runs open source large language models such as DeepSeek, Qwen, Gemma, GLM, MiniMax and gpt oss locally on a user's own machine, and it is aimed at developers and teams that want model inference without routing prompts through a hosted API.
read the full Ollama 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 Ollama or Agent_Memory_Techniques more popular?
Ollama has 181,184 GitHub stars and Agent_Memory_Techniques has 1,067. Ollama has the larger community by that measure.
Are Ollama and Agent_Memory_Techniques free?
Both are open source. Ollama is licensed under MIT and Agent_Memory_Techniques under Apache-2.0. Neither carries a licence fee.
What is the difference between Ollama and Agent_Memory_Techniques?
Ollama is written in Go 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, Ollama or Agent_Memory_Techniques?
Choose Ollama if you want the larger community (181,184 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.