head to head · open source
llama_index vs Agent_Memory_Techniques
llama_index has 52,206 GitHub stars, 8,165 forks, 770 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_index leads on adoption by 4,793% (52,206 vs 1,067 stars). llama_index is written in Python under MIT; Agent_Memory_Techniques is written in Jupyter Notebook under Apache-2.0. llama_index has attracted 16% as many forks as stars, Agent_Memory_Techniques 13%. llama_index 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
| llama_index | Agent_Memory_Techniques | |
|---|---|---|
| GitHub stars | ★ 52K | ★ 1.1K |
| License | MIT | Apache-2.0 |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-09-18 | 2026-09-15 |
| Forks | ⑂ 8.2K | ⑂ 137 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 52K 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 llama_index
LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.
read the full llama_index 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_index or Agent_Memory_Techniques more popular?
llama_index has 52,206 GitHub stars and Agent_Memory_Techniques has 1,067. llama_index has the larger community by that measure.
Are llama_index and Agent_Memory_Techniques free?
Both are open source. llama_index is licensed under MIT and Agent_Memory_Techniques under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and Agent_Memory_Techniques?
llama_index 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, llama_index or Agent_Memory_Techniques?
Choose llama_index if you want the larger community (52,206 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.