deeplake open source analysis

Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

Project overview

⭐ 8913 · Python · Last activity on GitHub: 2025-11-30

GitHub: https://github.com/activeloopai/deeplake

Why it matters for engineering teams

Deeplake addresses the challenge of managing and querying large, complex AI datasets that include vectors, images, text, and videos in a unified way. This open source tool for engineering teams is particularly suited for machine learning and AI engineers who need to version, visualise, and stream data efficiently into frameworks like PyTorch and TensorFlow. Its maturity and reliability make it a production ready solution for teams building multi-modal AI applications and large language model integrations. However, it may not be the best choice for projects with simpler data needs or where lightweight, specialised databases are preferred, as Deeplake’s comprehensive features can introduce overhead in such scenarios.

When to use this project

Deeplake is a strong choice when working with diverse AI datasets that require real-time streaming and version control in production environments. Teams should consider alternatives if their focus is on simpler or purely relational data storage without the need for deep integration with AI frameworks.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from Deeplake, using it to manage complex datasets and streamline workflows involving vector search and multi-modal data. It is commonly employed in products requiring scalable data lakes for AI, such as recommendation systems, computer vision applications, and large language model pipelines. The self hosted option for data management supports teams looking for control over their AI data infrastructure.

Best suited for

Topics and ecosystem

ai computer-vision cv data-science datalake datasets deep-learning image-processing langchain large-language-models llm machine-learning ml mlops multi-modal python pytorch tensorflow vector-database vector-search

Activity and freshness

Latest commit on GitHub: 2025-11-30. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.