head to head · open source
Activeloop vs learn-agentic-ai
Activeloop has 9,241 GitHub stars, 722 forks, 66 open issues and last shipped 4 months ago. learn-agentic-ai has 4,375 stars, 1,010 forks, 59 open issues and last shipped 11 months ago. Activeloop leads on adoption by 111% (9,241 vs 4,375 stars). Activeloop is written in C++ under Apache-2.0; learn-agentic-ai is written in Jupyter Notebook under MIT. Activeloop has attracted 8% as many forks as stars, learn-agentic-ai 23%. Activeloop was the more recently maintained of the two, and both are self-hostable with no licence fee.
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
| Activeloop | learn-agentic-ai | |
|---|---|---|
| GitHub stars | ★ 9.2K | ★ 4.4K |
| License | Apache-2.0 | MIT |
| Written in | C++ | Jupyter Notebook |
| Last push | 2026-05-21 | 2025-10-26 |
| Forks | ⑂ 722 | ⑂ 1.0K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Activeloop if
- You weight community size — 9.2K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches C++
- You value the larger contributor base for long-term maintenance
pick learn-agentic-ai if
- You want the learn-agentic-ai feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Jupyter Notebook
- You evaluated both and learn-agentic-ai fits your workflow better
About Activeloop
Activeloop Deep Lake is an open source database for AI data, written in C++ under Apache 2.0. It presents itself as a tensor database and AI data runtime for agents, combining serverless Postgres with a multimodal datalake for scalable retrieval and training. The project lives in the machine learning infrastructure ecosystem.
read the full Activeloop overview →
About learn-agentic-ai
learn agentic ai is an MIT licensed Jupyter Notebook learning repository from Panaversity that teaches developers how to design and scale agentic AI systems using the Dapr Agentic Cloud Ascent (DACA) design pattern and agent native cloud technologies.
read the full learn-agentic-ai overview →
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Frequently asked questions
Is Activeloop or learn-agentic-ai more popular?
Activeloop has 9,241 GitHub stars and learn-agentic-ai has 4,375. Activeloop has the larger community by that measure.
Are Activeloop and learn-agentic-ai free?
Both are open source. Activeloop is licensed under Apache-2.0 and learn-agentic-ai under MIT. Neither carries a licence fee.
What is the difference between Activeloop and learn-agentic-ai?
Activeloop is written in C++ and learn-agentic-ai 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, Activeloop or learn-agentic-ai?
Choose Activeloop if you want the larger community (9,241 stars) or its Apache-2.0 licence terms. Choose learn-agentic-ai if its feature set, stack or MIT licence fits better. Both are self-hostable.