{"id":"activeloopai-deeplake","name":"deeplake","af_score":37.2,"security_score":42.8,"reliability_score":26.2,"what_it_does":"Deep Lake is an AI data runtime/database implemented as a Python package and storage format for deep-learning workloads. It stores multimodal data (text, images, audio, video, etc.) alongside vectors/embeddings, supports streaming to ML frameworks (e.g., PyTorch/TensorFlow), and provides dataset versioning/lineage with integrations to common AI tooling (e.g., LangChain, LlamaIndex, W&B).","best_when":"You want to manage and stream large multimodal ML datasets (and embeddings) with dataset versioning, and you can integrate via Python and existing ML framework tooling.","avoid_when":"You need a simple hosted HTTP API with documented error codes, rate-limit headers, and OAuth scopes as the primary integration surface.","last_evaluated":"2026-03-29T14:55:47.751811+00:00","has_mcp":false,"has_api":false,"auth_methods":["Register in Deep Lake App (per README)"],"has_free_tier":true,"known_gotchas":["Primary integration surface appears to be a Python library/data format rather than a documented HTTP API, which can complicate agent orchestration and standardized error handling.","Authentication and operational behavior (rate limiting, retry semantics, idempotency) are not documented in the provided README content."],"error_quality":0.0}