Home Projects datachain
datachain
Python

datachain

The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure

by datachain-ai · GitHub
Stars
Forks
Trending
License
Created
Last commit
Category
Language
ai-agentsclaude-codecodexApache-2.0Python
View on GitHub
In plain words

Transform files in cloud storage into organized, searchable datasets with version control (Claude Code required)

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
datachain — GitHub preview card
📈 Star history
2.81k2.79k
2026-07-072026-08-31
📈 Track datachain

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure

datachain has 2.8k stars on GitHub. It has been forked 156 times. datachain is written mainly in Python. It has been in active development since 2024. datachain is available under the Apache-2.0 license. Its main topics are ai-agents, claude-code, codex, data-context-layer.

Frequently asked questions

What is datachain?

The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure

Is datachain open source?

datachain is an open-source project. It is released under the Apache-2.0 license.

Is datachain free?

Yes. datachain is free and open source — you can use, modify and self-host it.

What license does datachain use?

datachain is available under the Apache-2.0 license.

What language is datachain written in?

datachain is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — datachain

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=datachain-ai-datachain)](https://olud.ai/project/datachain-ai-datachain.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.