---
id: vlmrun-hub
version: "0.1.35"
license: Apache-2.0
license_treatment: permissive
maintenance: aging
---

# vlmrun-hub — VLM Run Hub for various industry-specific schemas

License: permissive · Maintenance: aging · Downloads: 174.2K/mo

## What it is and what it does

VLM Run Hub is a library of pre-built Pydantic data models designed to standardize the extraction of structured information from visual content—images, videos, and documents. Rather than writing custom parsing logic for each extraction task, you import a schema from the hub (e.g., Invoice, DriverLicense, MedicalInsuranceCard) and pass it to a Vision Language Model's structured output API. The model returns data conforming to that schema, which Pydantic automatically validates.

The library covers real-world domains: document processing (invoices, receipts, passports), accounting and finance (W2 forms, balance sheets), healthcare (insurance cards, pathology reports), retail (product catalogs), media (sports game states), and logistics. Each schema has been tested across industry use cases. The schemas are model-agnostic—use the same Invoice schema with OpenAI's GPT-4o, Anthropic's Claude Vision, or the VLM Run SDK without rewriting prompts.

Use it for:

- Extract invoice line items, dates, and customer details from receipt images for accounting automation.
- Parse driver's license or passport images into structured fields for identity verification workflows.
- Convert medical insurance cards into validated data for claims processing systems.
- Extract product information and pricing from retail catalogs or ecommerce images.
- Automate document classification and field extraction for document management systems.
- Build visual ETL pipelines that feed validated structured data directly into downstream databases or APIs.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides a catalog of pre-defined Pydantic schemas for extracting structured data from images, videos, and documents using Vision Language Models.

Yes, if you are building applications that extract structured data from images or documents using Vision Language Models. The pre-built schemas save significant development time and eliminate the need to design and validate extraction formats for common document types. The library is permissively licensed, has low install friction, and covers a broad range of real-world domains. Maintenance is aging but not stalled; evaluate whether the schema catalog meets your specific use cases before committing.

## Install

pip install vlmrun-hub
uv add vlmrun-hub
poetry add vlmrun-hub

## Installing vlmrun-hub

Before you install: Low install friction with a pure-Python wheel. Maintenance status is aging—last commit was 2025-12-15 and the project has 554 stars, suggesting active but not rapid development.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.

Quickstart:

pip install vlmrun-hub

from vlmrun.hub.schemas.document.invoice import Invoice
from pydantic import ValidationError

# Use with instructor + OpenAI or another VLM provider
# Schema validates extracted data automatically

Requires Python 3.9 or later; intended for use with a Vision Language Model provider (e.g., OpenAI, Anthropic, or VLM Run SDK).

Verify before relying:

- Whether the catalog of schemas is actively maintained and updated with new domains beyond those listed in the excerpt.
- Performance characteristics when validating large batches of extracted data or deeply nested schemas.
- Compatibility guarantees with specific VLM provider APIs (e.g., OpenAI Structured Outputs, Gemini, Anthropic).

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 174.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pydantic schemas for vision models, structured data extraction from images, VLM output validation, document extraction schemas, vision language model integration, image to JSON extraction, visual ETL schemas, vision-language-models, structured-extraction, document-processing

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