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

⭐ 8896 · Python · Last activity on GitHub: 2025-11-05

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 video. It provides a production ready solution that supports versioning, real-time streaming and integration with popular frameworks like PyTorch and TensorFlow, making it suitable for machine learning and AI engineering teams focused on deep learning and multi-modal applications. Its maturity and reliability have been proven in production environments where consistent data handling and efficient querying are critical. However, it may not be the right choice for projects that require a lightweight or purely relational database, or where a fully managed cloud service is preferred over a self hosted option for AI data lakes.

When to use this project

Deeplake is a strong choice when your team needs to store and query large-scale, multi-modal AI datasets with tight integration to ML frameworks. Teams should consider alternatives if the use case demands simpler data storage or if a fully managed cloud database with less setup is preferred.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from this open source tool for engineering teams, using it to manage datasets that power deep learning models and large language models. It is commonly employed in products involving computer vision, natural language processing and vector search, where efficient data versioning and streaming to training pipelines are essential.

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: 2025-11-05. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.