---
title: "pixeltable"
type: "tool"
slug: "pixeltable-pixeltable"
canonical_url: "https://www.graphcanon.com/tools/pixeltable-pixeltable"
github_url: "https://github.com/pixeltable/pixeltable"
homepage_url: "https://docs.pixeltable.com"
stars: 1594
forks: 216
primary_language: "Python"
license: "Apache-2.0"
categories: ["model-training", "inference-serving", "vector-databases"]
tags: ["data-science", "ai", "artificial-intelligence", "feature-store", "feature-engineering", "database", "chatbot", "computer-vision"]
updated_at: "2026-07-07T19:49:15.710611+00:00"
---
# pixeltable
> Unified multimodal backend for AI data apps
Pixeltable is a solution that integrates the functions of storing media, running models, indexing embeddings, and serving endpoints into one system, providing capabilities such as unified multimodal storage, import/export functionality, embedding services, model inference, and more.
## Facts
- Repository: https://github.com/pixeltable/pixeltable
- Homepage: https://docs.pixeltable.com
- Stars: 1,594 · Forks: 216 · Open issues: 39 · Watchers: 20
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-07T19:16:34+00:00
## Categories
- [Model Training](/categories/model-training.md)
- [Inference & Serving](/categories/inference-serving.md)
- [Vector Databases](/categories/vector-databases.md)
## Tags
data-science, ai, artificial-intelligence, feature-store, feature-engineering, database, chatbot, computer-vision
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## README (excerpt)
```text
[**Quick Start**](https://docs.pixeltable.com/overview/quick-start) |
[**Documentation**](https://docs.pixeltable.com/) |
[**CLI**](https://docs.pixeltable.com/platform/cli) |
[**Dashboard**](https://docs.pixeltable.com/platform/dashboard) |
[**llms-full.txt**](https://docs.pixeltable.com/llms-full.txt) |
[**Starter Kit**](https://github.com/pixeltable/pixeltable-starter-kit) |
[**AI Coding Skill**](https://github.com/pixeltable/pixeltable-skill) |
[**Discord**](https://discord.gg/QPyqFYx2UN)
## Make Building Multimodal AI Data Apps Dead Simple
**Pixeltable is the unified multimodal backend for AI data apps.** One Python API: store media, run models, index embeddings, serve endpoints, and version everything in a single system instead of gluing together blob storage, a vector DB, an orchestrator, and edge functions. Chunking, embeddings, agents, and serving run from computed columns on insert, not glue scripts you maintain separately. Transactions, caching, retries, and observability are built in. Extend with `@pxt.udf`, `@pxt.uda`, and `@pxt.query`.
## Core Capabilities
Expand any row for what Pixeltable replaces, a quick example, and doc links. Examples assume `import pixeltable as pxt`.
Store: unified multimodal interface
[`pxt.Image`](https://docs.pixeltable.com/platform/type-system), `pxt.Video`, `pxt.Audio`, `pxt.Document`, `pxt.Json`: one table for structured and media data with `destination=` for S3, GCS, Azure, R2, and more. Not S3 + Postgres + boto3 sync.
```python
t = pxt.create_table(
'media',
{
'img': pxt.Image,
'video': pxt.Video,
'audio': pxt.Audio,
'document': pxt.Document,
'metadata': pxt.Json,
},
)
```
[Type system](https://docs.pixeltable.com/platform/type-system) · [Tables & data](https://docs.pixeltable.com/tutorials/tables-and-data-operations) · [Cloud storage](https://docs.pixeltable.com/integrations/cloud-storage)
Import / export: I/O without glue scripts
`create_table(source=...)`, path/URL `insert()`, Hugging Face, `export_parquet()`, PyTorch, COCO, and more. Not per-format ETL scripts.
```python
# Create a table from a file, URL, or Hugging Face dataset
pxt.create_table('app/data', source='data.csv')
pxt.create_table('app/reviews', source=hf_dataset)
# Append rows into an existing table from a path or URL
t.insert('s3://my-bucket/new_rows.parquet')
# Export to analytics/ML formats
pxt.io.export_parquet(t, 'data.parquet')
pytorch_ds = t.to_pytorch_dataset('pt') # PyTorch DataLoader ready
coco_path = t.to_coco_dataset() # COCO annotations
```
[CSV import](https://docs.pixeltable.com/howto/cookbooks/data/data-import-csv) · [Hugging Face](https://docs.pixeltable.com/howto/cookbooks/data/data-import-huggingface) · [PyTorch export](https://docs.pixeltable.com/howto/cookbooks/data/data-export-pytorch) · [ML data wrangling](https://docs.pixeltable.com/use-cases/ml-data-wrangling)
Iterate: explode media into rows
`create_view()` with iterators splits documents into chunks, video into frames, audio into segments, and typed JSON lists into rows. Not FFmpeg/spaCy pipelines with child tables and foreign keys. For custom explode logic, use [`@pxt.iterator`](https://docs.pixeltable.com/platform/iterators#custom-iterators-with-pxtiterator).
```python
from pixeltable.functions.document import document_splitter
from pixeltable.functions.json imp
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/pixeltable-pixeltable`](/api/graphcanon/tools/pixeltable-pixeltable)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_