DataChain: The Context Layer for Unstructured Data

A Python library that turns files in S3, GCS, and Azure into versioned, typed datasets, queryable at warehouse speed.

• Compute Engine: parallel Python over files, distributed on Studio. Async I/O, checkpoint recovery, incremental updates.
• Dataset DB: Pydantic schemas, versioning, file pointers, automatic lineage. Sub-second filter, join, and group_by over millions of typed records locally, hundreds of millions on Studio. Vector search over the same rows, no separate store.

Optional, for agent workflows:

• Knowledge Base: markdown summaries derived from the Dataset DB and enriched by LLM. Readable by humans and LLMs.
• Agent Harness: a skill that plugs all three into Claude Code, Cursor, Codex, GitHub Copilot, and Pi, so they understand your data. On Studio, agents reach the same datasets over MCP.

Bytes never leave your storage. Every run deposits a typed dataset the next pipeline (or agent) reads instead of recomputing.

1. Install

pip install datachain

To add the agent skill (Knowledge Base + code generation):

datachain skill install --target claude     # also: cursor, codex, copilot, pi

Works with S3, GCS, Azure, and local filesystems.

2. Quickstart: agent-driven pipeline

Task: find dogs in S3 similar to a reference image, filtered by breed, mask availability, and image dimensions.

Grab a reference image and run Claude Code (or other agent):

datachain cp --anon s3://dc-readme/fiona.jpg .

claude

Prompt:

Find dogs in s3://dc-readme/oxford-pets-micro/ similar to ./fiona.jpg:
  - Pull breed metadata and mask files from annotations/
  - Exclude images without mask
  - Exclude Cocker Spaniels
  - Only include images wider than 400px

Result:

  ┌──────┬───────────────────────────────────┬────────────────────────────┬──────────┐
  │ Rank │               Image               │           Breed            │ Distance │
  ├──────┼───────────────────────────────────┼────────────────────────────┼──────────┤
  │    1 │ shiba_inu_52.jpg                  │ shiba_inu                  │    0.244 │
  ├──────┼───────────────────────────────────┼────────────────────────────┼──────────┤
  │    2 │ shiba_inu_53.jpg                  │ shiba_inu                  │    0.323 │
  ├──────┼───────────────────────────────────┼────────────────────────────┼──────────┤
  │    3 │ great_pyrenees_17.jpg             │ great_pyrenees             │    0.325 │
  └──────┴───────────────────────────────────┴────────────────────────────┴──────────┘

  Fiona's closest matches are shiba inus (both top spots), which makes sense given her
  tan coloring and pointed ears.

The agent decomposed the task into steps - embeddings, breed metadata, mask join, quality filter - and saved each as a named, versioned dataset. Next time you ask a related question, it starts from what's already built.

The datasets are registered in a Knowledge Base optimized for both agents and humans:

dc-knowledge
├── buckets
│   └── s3
│       └── dc_readme.md
├── datasets
│   ├── oxford_micro_dog_breeds.md
│   ├── oxford_micro_dog_embeddings.md
│   └── similar_to_fiona.md
└── index.md

Browse it as markdown files, navigate with wikilinks, or open in Obsidian:

3. Data Harness