Data Analysis & Investment Research · Public

deeplake

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

Best fitUsers who want source-backed project understanding before installing it.

Check whether this project matches your task before installing it.

What it can doPortable AI capability asset

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot9.2k stars

717 forks · 141 contributors

Doramagic.ai Last verification date: 2026-07-21 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-07-21

What is deeplake?

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
Best fit: Users who want source-backed project understanding before installing it.
Not for: Not for users who want to skip sandbox verification or cannot accept configuration, permission, or maintenance overhead.
Capability added to an AI workflow: Portable AI capability asset
First safe verification step: Verify the smallest path in an isolated environment and keep a rollback path.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/activeloopai/deeplake, https://github.com/activeloopai/deeplake#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forUsers who want source-backed project understanding before installing it.

Match the project to your task before installing it.

CapabilityPortable AI capability asset

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

Repositoryactiveloopai/deeplake

9.2k stars · 717 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Project Overview and Architecture

Related topics: Core Type System and N-Dimensional Array Engine, Python API and Integrations

Source: https://github.com/activeloopai/deeplake / Human Manual

Core Type System and N-Dimensional Array Engine

Related topics: Storage and Persistence Layer, Tensor Query Language (TQL) Engine, Lazy View System (Heimdall)

Source: https://github.com/activeloopai/deeplake / Human Manual

Tensor Query Language (TQL) Engine

Related topics: Core Type System and N-Dimensional Array Engine, Known Issues, Releases, and Version Compatibility

Source: https://github.com/activeloopai/deeplake / Human Manual

Storage and Persistence Layer

Related topics: Lazy View System (Heimdall), Core Type System and N-Dimensional Array Engine

Source: https://github.com/activeloopai/deeplake / Human Manual

Lazy View System (Heimdall)

Related topics: Core Type System and N-Dimensional Array Engine, Tensor Query Language (TQL) Engine

Source: https://github.com/activeloopai/deeplake / Human Manual

Sources: https://github.com/activeloopai/deeplake, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars9.2k stars

Forks717 forks

Contributors141 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using deeplake with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
deeplake 3.9.52 incompatible with NumPy 2.x (NEP 50): TypeError in get_i
github / github_issue
02
Partnership inquiry from MyClaw.ai
github / github_issue
03
My DeepLake account is corrupted and I need a full account deletion
github / github_issue
04
[BUG] deeplake 4.5.10 raises Dtype is unknown error for int * JSON, but
github / github_issue
05
[BUG] deeplake v4.5.8 returns empty set for the same query executed afte
github / github_issue
06
[BUG] deeplake v4.5.6 raises 'deeplake._deeplake.InvalidType: Dtype is u
github / github_issue
07
[BUG] deeplake v4.5.6 returns inconsistent query results after delete()
github / github_issue
08
[BUG] deeplake v4.5.6 produces a segmentation fault with WHERE (NOT ((f9
github / github_issue
09
v4.5.2
github / github_release
10
v4.5.1
github / github_release
11
v4.4.5
github / github_release
12
v4.4.4
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install deeplake

Official start command · https://github.com/activeloopai/deeplake#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

deeplake Manual

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

Open the full manual

https://github.com/activeloopai/deeplake Project Manual
Table of Contents
Project Overview and Architecture
Related Pages
Goals and Scope
Repository Layout
Layered Architecture
Python Layer

Project Overview and Architecture

Related topics: Core Type System and N-Dimensional Array Engine, Python API and Integrations

Source: https://github.com/activeloopai/deeplake / Human Manual

Core Type System and N-Dimensional Array Engine

Related topics: Storage and Persistence Layer, Tensor Query Language (TQL) Engine, Lazy View System (Heimdall)

Source: https://github.com/activeloopai/deeplake / Human Manual

Tensor Query Language (TQL) Engine

Related topics: Core Type System and N-Dimensional Array Engine, Known Issues, Releases, and Version Compatibility

Source: https://github.com/activeloopai/deeplake / Human Manual

Storage and Persistence Layer

Related topics: Lazy View System (Heimdall), Core Type System and N-Dimensional Array Engine

Source: https://github.com/activeloopai/deeplake / Human Manual

Lazy View System (Heimdall)

Related topics: Core Type System and N-Dimensional Array Engine, Tensor Query Language (TQL) Engine

Source: https://github.com/activeloopai/deeplake / Human Manual

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Capability evidence risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Runtime risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Maintenance risk requires verification

May increase setup, validation, or first-run risk for the user.