https://github.com/datachain-ai/datachain
Data Memory: the operational data context layer for AI agents - typed, versioned datasets over images, video, docs and tables
https://github.com/datachain-ai/datachain
ai-agents claude-code codex data-context-layer data-memory data-processing harness-engineering knowledge-base mlops multimodal pydantic unstructured-data
Last synced: 21 days ago
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Data Memory: the operational data context layer for AI agents - typed, versioned datasets over images, video, docs and tables
- Host: GitHub
- URL: https://github.com/datachain-ai/datachain
- Owner: datachain-ai
- License: apache-2.0
- Created: 2024-06-25T22:29:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2026-04-28T18:24:05.000Z (23 days ago)
- Last Synced: 2026-04-28T19:32:32.194Z (23 days ago)
- Topics: ai-agents, claude-code, codex, data-context-layer, data-memory, data-processing, harness-engineering, knowledge-base, mlops, multimodal, pydantic, unstructured-data
- Language: Python
- Homepage: https://docs.datachain.ai
- Size: 15.8 MB
- Stars: 2,737
- Watchers: 17
- Forks: 139
- Open Issues: 76
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.rst
Awesome Lists containing this project
README
#  DataChain - Data Memory for AI Agents
[](https://pypi.org/project/datachain/)
[](https://pypi.org/project/datachain)
[](https://codecov.io/gh/datachain-ai/datachain)
[](https://github.com/datachain-ai/datachain/actions/workflows/tests.yml)
[](https://deepwiki.com/datachain-ai/datachain)
**The model floor is the same for everyone. The context ceiling is yours.**
Your data lives in object storage (millions of images, hours of video, documents) and databases (structured tables). Every chain a teammate or agent runs deposits a typed, versioned dataset into **Data Memory**: embeddings, classifications, joins, scores. At scale, those datasets are too expensive to recompute and too scattered to find on demand.
DataChain is the Python library that runs your code over heavy files and tables in parallel and queries Data Memory at warehouse speed. Read from S3, GCS, or Azure, run your code, save as a Pydantic-typed dataset; the next pipeline or agent picks up from there.
## 1. Why Data Memory
Claude Code, Cursor, and Codex made AI good at code by giving it the repo context. Agents over your data need the same: a **data context layer** with schemas, lineage, and prior conclusions. **That layer is captured during production, not curated after.** Every DataChain pipeline run deposits a typed, versioned dataset into Data Memory; the Knowledge Base compiles those datasets into what agents read. Without production through DataChain, the layer has nothing structured to describe.
## 2. Install
```bash
pip install datachain
```
To add the agent skill (Knowledge Base + code generation):
```bash
datachain skill install --target claude # also: --target cursor, --target codex
```
Works with S3, GCS, Azure, and local filesystems.
## 3. 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):
```bash
datachain cp --anon s3://dc-readme/fiona.jpg .
claude
```
Prompt:
```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:
```bash
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](https://obsidian.md/):
## 4. Data Harness
Code harnesses (Claude Code, Cursor, Codex) give agents repo context, dedicated tools, and memory across sessions. DataChain adds the same for data: typed datasets the agent reads, chain operations the agent calls (`read_storage`, `map`, `save`), Data Memory where its results persist.
A **dataset** is the unit of work - a named, versioned result of a pipeline step like `pets_embeddings@1.0.0`. Every `.save()` registers one.
For the data-flow architecture (Python Data Engine, Data Memory, Query Engine, Knowledge Base) and how the components connect, see [Architecture](https://docs.datachain.ai/architecture/).
## 5. Core concepts
### 5.1. Dataset
A dataset is a versioned data reasoning step - what was computed, from what input, producing what schema. DataChain indexes your storage into one: no data copied, just typed metadata and file pointers. Re-runs only process new or changed files.
Create a dataset manually `create_dataset.py`:
```python
from PIL import Image
import io
from pydantic import BaseModel
import datachain as dc
class ImageInfo(BaseModel):
width: int
height: int
def get_info(file: dc.File) -> ImageInfo:
img = Image.open(io.BytesIO(file.read()))
return ImageInfo(width=img.width, height=img.height)
ds = (
dc.read_storage(
"s3://dc-readme/oxford-pets-micro/images/**/*.jpg",
anon=True,
update=True,
delta=True, # re-runs skip unchanged files
)
.settings(prefetch=64)
.map(info=get_info)
.save("pets_images")
)
ds.show(5)
```
`pets_images@1.0.0` is now the shared reference to this data - schema, version, lineage, and metadata.
Every `.save()` registers the dataset in **Data Memory**, DataChain's persistent store for schemas, versions, lineage, and processing state, kept locally in SQLite DB `.datachain/db`. Pipelines reference datasets by name, not paths. When the code or input data changes, the next run bumps dataset version.
This is what makes a **dataset a management unit:** owned, versioned, and queryable by everyone on the team.
### 5.2. Schemas and types
DataChain uses Pydantic to define the shape of every column. The return type of your UDF becomes the dataset schema - each field a queryable column in Data Memory.
`show()` in the previous script renders nested fields as dotted columns:
```bash
file file info info
path size width height
0 oxford-pets-micro/images/Abyssinian_141.jpg 111270 461 500
1 oxford-pets-micro/images/Abyssinian_157.jpg 139948 500 375
2 oxford-pets-micro/images/Abyssinian_175.jpg 31265 600 234
3 oxford-pets-micro/images/Abyssinian_220.jpg 10687 300 225
4 oxford-pets-micro/images/Abyssinian_3.jpg 61533 600 869
[Limited by 5 rows]
```
`.print_schema()` renders it's schema:
```bash
file: File@v1
source: str
path: str
size: int
version: str
etag: str
is_latest: bool
last_modified: datetime
location: Union[dict, list[dict], NoneType]
info: ImageInfo
width: int
height: int
```
Models can be arbitrarily nested - a `BBox` inside an `Annotation`, a `List[Citation]` inside an LLM Response - every leaf field stays queryable the same way. The schema lives in Data Memory and is enforced at dataset creation time.
Data Memory handles datasets of any size - 100 millions of files, hundreds of metadata rows - without loading anything into memory. **Pandas is limited by RAM; DataChain is not.** Export to pandas when you need it, on a filtered subset:
```python
import datachain as dc
df = dc.read_dataset("pets_images").filter(dc.C("info.width") > 500).to_pandas()
print(df)
```
### 5.3. Fast queries
Filters, aggregations, and joins run as vectorized operations directly against Data Memory - metadata never leaves your machine, no files downloaded.
```python
import datachain as dc
cnt = (
dc.read_dataset("pets_images")
.filter(
(dc.C("info.width") > 400) &
~dc.C("file.path").ilike("%cocker_spaniel%") # case-insensitive
)
.count()
)
print(f"Large images with Cocker Spaniel: {cnt}")
```
Milliseconds, even at 100M-file scale.
```
Large images with Cocker Spaniel: 6
```
## 6. Resilient Pipelines
When computation is expensive, bugs and new data are both inevitable. DataChain tracks processing state in Data Memory - so crashes and new data are handled automatically, without changing how you write pipelines.
### 6.1. Data checkpoints
Save to `embed.py`:
```python
import open_clip, torch, io
from PIL import Image
import datachain as dc
model, _, preprocess = open_clip.create_model_and_transforms("ViT-B-32", "laion2b_s34b_b79k")
model.eval()
counter = 0
def encode(file: dc.File, model, preprocess) -> list[float]:
global counter
counter += 1
if counter > 236: # ← bug: remove these two lines
raise Exception("some bug") # ←
img = Image.open(io.BytesIO(file.read())).convert("RGB")
with torch.no_grad():
return model.encode_image(preprocess(img).unsqueeze(0))[0].tolist()
(
dc.read_dataset("pets_images")
.settings(batch_size=100)
.setup(model=lambda: model, preprocess=lambda: preprocess)
.map(emb=encode)
.save("pets_embeddings")
)
```
It fails due to a bug in the code:
```
Exception: some bug
```
Remove the two marked lines and re-run - DataChain resumes from image 201 (two 100 size batches are completed), the start of the last uncommitted batch:
```
$ python embed.py
UDF 'encode': Continuing from checkpoint
```
### 6.2. Similarity search
The vectors live in Data Memory alongside all the metadata - `list[float]` type in pydentic schemas. Querying them is instant - no files re-read and can be combined with not vector filters like `info.width`:
Prepare data:
```bash
datachain cp s3://dc-readme/fiona.jpg .
```
`similar.py`:
```python
import open_clip, torch, io
from PIL import Image
import datachain as dc
model, _, preprocess = open_clip.create_model_and_transforms("ViT-B-32", "laion2b_s34b_b79k")
model.eval()
ref_emb = model.encode_image(
preprocess(Image.open("fiona.jpg")).unsqueeze(0)
)[0].tolist()
(
dc.read_dataset("pets_embeddings")
.filter(dc.C("info.width") > 500) # from pets_images - no re-read
.mutate(dist=dc.func.cosine_distance(dc.C("emb"), ref_emb))
.order_by("dist")
.limit(3)
.show()
)
```
Under a second - everything runs against Data Memory.
### 6.3. Incremental updates
The bucket in this walkthrough is static, so there's nothing new to process. But in production - when new images land in your bucket - re-run the same scripts unchanged. `delta=True` in the original dataset ensures only new files are processed end to end while the whole dataset will be updated to `pets_images@1.0.1`:
```python
$ python create_dataset.py # 500 new images arrived
Skipping 10,000 unchanged · indexing 500 new
Saved pets_images@1.0.1 (+500 records)
# Next day:
$ python create_dataset.py
Skipping 10,000 unchanged · processing 500 new
Saved pets_images@1.0.2 (+500 records)
```
## 7. Knowledge Base
DataChain maintains two layers. **Data Memory** is the ground truth: schemas, processing state, lineage, the vectors themselves. **The Knowledge Base** is derived from it: structured markdown for humans and agents to read. Because it's derived, it's always accurate. The Knowledge Base is stored in `dc-knowledge/`.
Ask the agent to build it (from Calude Code, Codex or Cursor):
```bash
claude
```
Prompt:
```prompt
Build a Knowledge Base for my current datasets
```
The skill generates `dc-knowledge/` directory from Data Memory - one file per dataset and bucket:
## 8. AI-Generated Pipelines
The skill gives the agent data awareness: it reads `dc-knowledge/` to understand what datasets exist, their schemas, which fields can be joined - and the meaning of columns inferred from the code that produced them.
See section `1. See it in action`. All the steps that were manually created could be just generated.
## 9. Team and cloud: Studio
Data context built locally stays local. DataChain Studio makes it shared.
```bash
datachain auth login
datachain job run --workers 20 --cluster gpu-pool caption.py
# ✓ Job submitted → studio.datachain.ai/jobs/1042
# Resuming from checkpoint (4,218 already done)...
# Saved oxford-pets-caps@0.0.1 (3,182 processed)
```
Studio adds: shared dataset registry, access control, UI for video/DICOM/NIfTI/point clouds, lineage graphs, reproducible runs.
Bring Your Own Cloud - all data and compute stay in your infrastructure. AWS, GCP, Azure, on-prem Kubernetes.
→ [studio.datachain.ai](https://studio.datachain.ai)
## 10. Contributing
Contributions are very welcome. To learn more, see the [Contributor Guide](https://docs.datachain.ai/contributing).
## 11. Community and Support
- [Report an issue](https://github.com/datachain-ai/datachain/issues) if you encounter any problems
- [Docs](https://docs.datachain.ai/)
- [Email](mailto:support@datachain.ai)
- [Twitter](https://twitter.com/datachain_ai)