head to head · open source
llama_index vs Agent_Memory_Techniques
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. Agent_Memory_Techniques has 1,131 stars, 141 forks, 2 open issues and last shipped 2 days ago. llama_index leads on adoption by 4,567% (52,779 vs 1,131 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 12%. Agent_Memory_Techniques 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.
← all 20902 open source comparisons
Side by side
| llama_index | Agent_Memory_Techniques | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 1.1K |
| License | MIT | Apache-2.0 |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-10-26 | 2026-11-01 |
| Forks | ⑂ 8.5K | ⑂ 141 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K 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 (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
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…
read the full Agent_Memory_Techniques overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is llama_index or Agent_Memory_Techniques more popular?
llama_index has 52,779 GitHub stars and Agent_Memory_Techniques has 1,131. 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 (52,779 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.