head to head · open source
Activeloop vs learn-agentic-ai
Activeloop has 9,265 GitHub stars, 725 forks, 66 open issues and last shipped 6 months ago. learn-agentic-ai has 4,396 stars, 1,006 forks, 59 open issues and last shipped 1.0 years ago. Activeloop leads on adoption by 111% (9,265 vs 4,396 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.
← all 20902 open source comparisons
Side by side
| Activeloop | learn-agentic-ai | |
|---|---|---|
| GitHub stars | ★ 9.3K | ★ 4.4K |
| License | Apache-2.0 | MIT |
| Written in | C++ | Jupyter Notebook |
| Last push | 2026-05-21 | 2025-10-26 |
| Forks | ⑂ 725 | ⑂ 1.0K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Activeloop if
- You weight community size — 9.3K 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
Deep Lake: Database for AI Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter What is Deep Lake? Deep Lake is a Database for AI powered by a storage format optimized for deep learning applications. Deep Lake can be used for: 1. Storing and searching data plus vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise grade LLM based products by offering storage for all data types (embeddings, audio, text, videos, images, dicom, pdfs…
read the full Activeloop overview →
About learn-agentic-ai
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern: From Start to Scale This repo is part of the Panaversity Certified Agentic & Robotic AI Engineer program. You can also review the certification and course details in the program guide. This repo provides learning material for Agentic AI and Cloud courses. Here’s a polished, professional rewrite you can use as a one pager or slide—tight on wording, clear on stakes, and just a touch playful so it doesn’t read like it was written by a committee (no offense to committees 😄). Our Agentic Strategy for Pakistan: Four Working Hypotheses Pakistan…
read the full learn-agentic-ai overview →
More in Infrastructure & Operations
Related comparisons
More Databases projects
Compare either of these against the rest of the Databases field.
Frequently asked questions
Is Activeloop or learn-agentic-ai more popular?
Activeloop has 9,265 GitHub stars and learn-agentic-ai has 4,396. 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 (9,265 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.