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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A hub for various industry-specific schemas to be used with VLMs.
| Date | Stars |
|---|---|
| 2026-07-24 | 553 |
| 2026-07-25 | 553 |
| 2026-07-28 | 554 |
| 2026-07-30 | 554 |
| 2026-08-06 | 554 |
Today
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Momentum
0.0
growth rate 0.00%/day
<div align="center">
<p align="center" style="width: 100%;">
<img src="https://raw.githubusercontent.com/vlm-run/.github/refs/heads/main/profile/assets/vlm-black.svg" alt="VLM Run Logo" width="80" style="margin-bottom: -5px; color: #2e3138; vertical-align: middle; padding-right: 5px;"><br>
</p>
<h2>VLM Run Hub</h2>
<p align="center">
<a href="https://vlm.run"><b>Website</b></a> | <a href="https://app.vlm.run/"><b>Platform</b></a> | <a href="https://docs.vlm.run/"><b>Docs</b></a> | <a href="https://vlm.run/blog"><b>Blog</b></a> | <a href="https://discord.gg/4jgyECY4rq"><b>Discord</b></a> | <a href="vlmrun/hub/catalog.yaml"><b>Catalog</b></a>
</p>
<p align="center">
<a href="https://pypi.org/project/vlmrun-hub/"><img alt="PyPI Version" src="https://badge.fury.io/py/vlmrun-hub.svg"></a>
<a href="https://pypi.org/project/vlmrun-hub/"><img alt="PyPI Version" src="https://img.shields.io/pypi/pyversions/vlmrun-hub"></a>
<a href="https://www.pepy.tech/projects/vlmrun-hub"><img alt="PyPI Downloads" src="https://img.shields.io/pypi/dm/vlmrun-hub"></a><br>
<a href="https://github.com/vlm-run/vlmrun-hub/blob/main/LICENSE"><img alt="PyPi Downloads" src="https://img.shields.io/github/license/vlm-run/hub.svg"></a>
<a href="https://discord.gg/4jgyECY4rq"><img alt="Discord" src="https://img.shields.io/badge/discord-chat-purple?color=%235765F2&label=discord&logo=discord"></a>
<a href="https://twitter.com/vlmrun"><img alt="PyPi Version" src="https://img.shields.io/twitter/follow/vlmrun.svg?style=social&logo=twitter"></a>
</p>
<br>
</div>
Welcome to **VLM Run Hub**, a comprehensive repository of pre-defined [Pydantic](https://docs.pydantic.dev/latest/) schemas for extracting structured data from unstructured visual domains such as images, videos, and documents. Designed for [Vision Language Models (VLMs)](https://huggingface.co/blog/vlms) and optimized for real-world use cases, VLM Run Hub simplifies the integration of visual ETL into your workflows.
<table>
<tr>
<td> <b>Image</b> </td>
<td> <b>JSON</b> </td>
</tr>
<tr>
<td style="width: 40%;">
<img src="https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.us-drivers-license/dl3.jpg">
</td>
<td>
```json
{
"issuing_state": "MT",
"license_number": "0812319684104",
"first_name": "Brenda",
"middle_name": "Lynn",
"last_name": "Sample",
"address": {
"street": "123 MAIN STREET",
"city": "HELENA",
"state": "MT",
"zip_code": "59601"
},
"date_of_birth": "1968-08-04",
"gender": "F",
"height": "5'06\"",
"weight": 150.0,
"eye_color": "BRO",
"issue_date": "2015-02-15",
"expiration_date": "2023-08-04",
"license_class": "D"
}
```
</td>
</tr>
</table>
</details>
### 💡 Motivation
While vision models like OpenAI's [GPT-4o](https://openai.com/index/hello-gpt-4o/) and Anthropic's [Claude Vision](https://www.anthropic.com/claude) excel in exploratory tasks like "chat with images," they often lack practicality for automation and integration, where **strongly-typed**, **validated outputs** are crucial.
The **Structured Outputs API** (popularized by [GPT-4o](https://openai.com/index/introducing-structured-outputs-in-the-api/), [Gemini](https://ai.google.dev/gemini-api/docs/structured-output)) addresses this by constraining LLMs to return data in precise, strongly-typed formats such as [Pydantic](https://docs.pydantic.dev/latest/) models. This eliminates complex parsing and validation, ensuring outputs conform to expected types and structures. These schemas can be nested and include complex types like lists and dictionaries, enabling seamless integration with existing systems while leveraging the full capabilities of the model.
### 🧰 Why use this hub of pre-defined Pydantic schemas?
- 📚 **Easy to use:** [Pydantic](https://docs.pydantic.dev/latest/) is a well-understood and battle-tested data model for structured data.
- 🔋 **Batteries included:** Each schema in this repo has been validated across real-world industry use cases—from healthcareExcerpt of 17,042 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:41c06ba8b1be27f8, topic:multimodal, topic:vlm, readme:vision-language
matched fp:41c06ba8b1be27f8, topic:etl, readme:etl
matched fp:41c06ba8b1be27f8, topic:computer-vision