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

⭐ 8971 · C++ · Last activity on GitHub: 2026-01-06

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

Why it matters for engineering teams

Deeplake addresses the challenge of managing complex AI datasets by providing a unified database designed to store and query vectors, images, text, and video data efficiently. It is particularly suited for machine learning and AI engineering teams who need a production ready solution to handle large-scale, multi-modal data with version control and real-time streaming to frameworks like PyTorch and TensorFlow. The project is mature and reliable enough for production use, offering a self hosted option that integrates well with modern AI workflows. However, it may not be the best choice for teams seeking a lightweight or purely cloud-native vector database, as its focus on comprehensive data types and versioning can introduce additional complexity and resource requirements.

When to use this project

Deeplake is a strong choice when your project requires managing diverse AI data types with versioning and real-time access for training or inference. Teams should consider alternatives if they only need a simple vector search or if minimal infrastructure overhead is a priority.

Team fit and typical use cases

Machine learning engineers and AI researchers benefit most from this open source tool for engineering teams, using it to organise, version, and stream large datasets directly into model training pipelines. It commonly appears in products involving computer vision, natural language processing, and multi-modal AI applications where managing complex, evolving datasets is critical.

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: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.