Deep Lake

Health v2 · maintenance only

Adoption not part of the score

9249 stars · 726 forks observed · 2026-10-06

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Deep Lake is an open-source database for AI that stores multimodal data (images, video, audio, text, embeddings, annotations) in a format optimized for deep learning and LLM applications. It provides vector search, data streaming for training at scale, dataset versioning, and a serverless Postgres-compatible interface for building agentic RAG systems.

Use cases

• store and search embeddings for building LLM applications
• manage large multimodal datasets while training deep learning models
• stream training data from S3 to PyTorch or TensorFlow at scale
• build agentic RAG pipelines with long-term memory for agents
• version and track lineage of AI datasets
• run vector search over images, video, audio, and PDFs

When to choose

• you need a multimodal datalake plus vector search in one system
• you train deep learning models on large datasets stored in the cloud
• you are building LLM or agent applications that need retrieval over diverse data types
• you want dataset versioning and lineage for ML workflows

When to avoid

• you only need a general-purpose relational database for transactional workloads
• you need a lightweight single-purpose vector store with minimal dependencies
• your data is small and fits comfortably in a traditional SQL database

Facets

library · maturity active

vector-database database rag machine-learning deep-learning data-science etl databases machine-learning deep-learning large-language-models computer-vision python cross-platform cloud datalake multimodal vector-search mlops pytorch data-versioning serverless embeddings llm-memory agentic-rag retrieval-augmented-generation data-engineering ai-agents gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="activeloopai/deeplake")