GH Repository · activeloopai
deeplake
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
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agent-appC++ragskillmultimodalfilesystemopenclawdeep-learningaillmmlopscomputer-visionlarge-language-modelsmakevector-databasepostgresmemorydatalakeagentic-ragagentpytorchclawbot
Deeplake, by activeloopai, describes itself as an AI Data Runtime for Agents, offering serverless Postgres combined with a multimodal datalake. Its repository topics indicate support for scalable retrieval and training workflows, including vector database, RAG, and memory use cases.
You reach for it when your agent applications need a unified data runtime that combines Postgres-like querying with multimodal storage for retrieval and training.
Use it to
- Store and retrieve multimodal data for agent memory
- Build agentic RAG pipelines with a vector database
- Serve data via a serverless Postgres interface
- Prepare datasets for deep-learning training in PyTorch
- Manage LLM-related datalake workloads
For ML and AI engineers building data-driven agent applications
- Role
- agent-app
- Language
- C++
- Licence
- Apache-2.0
- Forks
- 722
- Open issues
- 53
- Last push
- 2026-05-21
- Latest release
- 1.0.6 · 2020-12-15
topicsaidatalakevector-databasepostgresagentic-ragmemory