Devtoolwatch BoardDeep Lake review
Deep Lake is a database for AI that stores multimodal data and vectors for LLM retrieval and deep learning training.
TL;DR
Deep Lake is a database for AI built on a storage format optimized for deep learning. It is for developers building LLM applications or training models on images, video, audio and text. Deep Lake stores raw data alongside embeddings, runs computations client-side, and offers dataset versioning and visualization.
- 9.3kGitHub stars
- +7new stars, week of Oct 25
- –not ranked this week
Deep Lake at a glance
- Category
- RAG, data and memory
- Maintained by
- Activeloop (organization, @activeloopai on GitHub)
- License
- Apache 2.0
- Language
- C++, Python, PLpgSQL
- Repository
- github.com/activeloopai/deeplake
- Website
- deeplake.ai
- PyPI package
- deeplake 4.7.4
- Latest release
- deeplake 4.7.4 on PyPI, Aug 25, 2026
- First published
- Aug 9, 2019
What Deep Lake does
- Deep Lake stores and searches data plus vectors as a vector store for LLM applications.
- Deep Lake stores many data types, including embeddings, audio, text, video, images, DICOM, PDFs and annotations.
- Deep Lake streams datasets to PyTorch and TensorFlow through built-in dataloaders, including dataset shuffling.
- Deep Lake uses one API to upload, download and stream datasets to S3, Azure, GCP, Activeloop cloud, local or in-memory storage.
- Deep Lake provides dataset version control and lineage.
- Deep Lake keeps images, audio and video in native compression with lazy, NumPy-like indexing.
- Deep Lake integrates with LangChain, LlamaIndex, Weights & Biases, MMDetection and MMSegmentation.
How it works
Deep Lake is a Python package installed with pip. It is serverless, and all computations run client-side. Data is stored as chunked, compressed columnar arrays in your own cloud, locally or in memory. Deep Lake connects to S3-compatible storage, Azure and GCP. It plugs into LangChain and LlamaIndex as a vector store. A C++ library has been released since v4.4.3.
How to install and start using Deep Lake
With pip
pip install deeplakeRequirements
- A Deep Lake App account is required to access all features.
- Python, since Deep Lake is installed as a Python package with pip.
First steps
- Install Deep Lake with pip.
- Register in the Deep Lake App to access all features.
- Follow the Vector Store Quickstart or the Deep Learning Quickstart in the docs.
Who Deep Lake is for
- Developers building RAG or LLM apps that need a vector store holding raw multimodal data.
- ML teams streaming large image, video or audio datasets into PyTorch or TensorFlow training.
- Teams that want to keep AI data in their own cloud storage.
- Researchers who need versioned datasets of many files, such as image collections.
Limitations to know
- Deep Lake requires registering in the Deep Lake App to access all features.
- Deep Lake's README states that MosaicML MDS offers more advanced shuffling strategies.
- Deep Lake does not grant licenses for the public datasets it gives access to. Users must check dataset licenses themselves.
Is Deep Lake growing? Usage data
Deep Lake was not ranked on the Devtoolwatch Board in the week of Oct 25–31, 2026: it needs 25 or more new stars or 1,000 or more added downloads in a week to rank.
Show the weekly numbers
| Week of | New stars |
|---|---|
| Aug 9, 2026 | 2 |
| Aug 16, 2026 | 4 |
| Aug 23, 2026 | 4 |
| Aug 30, 2026 | 3 |
| Sep 6, 2026 | 7 |
| Sep 13, 2026 | 7 |
| Sep 20, 2026 | 5 |
| Sep 27, 2026 | 8 |
| Oct 4, 2026 | 5 |
| Oct 11, 2026 | 6 |
| Oct 18, 2026 | 7 |
| Oct 25, 2026 | 7 |
The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.
Deep Lake alternatives
Other RAG, data and memory tools on the Board in the week of Oct 25–31, 2026, by momentum:
| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | XERJXERJ is a local-first search engine and MCP server for AI agents, packaged as one Rust binary with an Elasticsearch-compatible API. | 17.9k | – |
| #2 | MemvidMemvid is a single-file memory layer for AI agents that packages data, embeddings, search indexes, and metadata into one portable .mv2 file. | 17.3k | – |
| #3 | LEANNLEANN is a Python vector database and RAG library that recomputes embeddings on demand to keep indexes small on personal devices. | 13.7k | – |
| #4 | GraphifyGraphify is an AI coding assistant skill that turns a codebase, with its docs, PDFs, images and videos, into a queryable knowledge graph. | 140k | 480.7k |
| #5 | MarkItDownMarkItDown is a lightweight Python utility from Microsoft that converts PDFs, Office documents, images, audio and other files to… | 193.2k | 4.7M |
| #6 | FirecrawlFirecrawl is an API and open source project to search, scrape, crawl, and interact with web pages, returning LLM-ready markdown or JSON… | 197.4k | 2.9M |
Recent Deep Lake releases
Deep Lake FAQ
Is Deep Lake free and open source?
Yes. Deep Lake's source code is on GitHub (activeloopai/deeplake) under the Apache 2.0 license, so you can use it for free. Its website also links to a pricing page (https://deeplake.ai/pricing) for any paid plans.
How do I install Deep Lake?
Run pip install deeplake.
Is Deep Lake still maintained?
Activity has slowed: the activeloopai/deeplake repository was last updated on May 21, 2026, and the latest release, deeplake 4.7.4 on PyPI, came out on Aug 25, 2026.
How popular is Deep Lake?
Deep Lake has 9.3k GitHub stars as of Nov 1, 2026. It gained 7 new stars in the week of Oct 25–31, 2026, against a 12-week average of 5 a week.
What are the best alternatives to Deep Lake?
Other RAG, data and memory tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: XERJ (#1), Memvid (#2), LEANN (#3), Graphify (#4), MarkItDown (#5). The alternatives table above has their numbers.
Does Deep Lake need a server?
No. Deep Lake is serverless. All computations run client-side, and data can live in your own cloud, locally or in memory.
Can Deep Lake be used with LangChain and LlamaIndex?
Yes. Deep Lake integrates with both as a vector store. The LangChain integration uses Deep Lake datasets as the underlying storage and can be deployed locally or in a cloud of your choice.
Which cloud storage does Deep Lake support?
Deep Lake supports S3, GCP, Azure, Activeloop cloud, local storage and in-memory storage. It also works with any S3-compatible storage, such as MinIO.
Can Deep Lake store raw files and not just embeddings?
Yes. Deep Lake's data format stores raw images, videos and text alongside embeddings. Datasets can also be visualized and version controlled.
Sources
Deep Lake README badge
Maintainers can show the tool's weekly Board rank in their README:
[](https://index.devtoolwatch.com/tools/activeloopai-deeplake/)How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].