vlmrun 0.5.7

Official Python SDK for VLM Run

VLM Run Python SDK

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The VLM Run Python SDK is the official Python SDK for VLM Run API platform, providing a convenient way to interact with our REST APIs.

🚀 Getting Started

Installation

pip install vlmrun

Installation with Optional Features

The package provides optional features that can be installed based on your needs:

• Chat with Orion via the CLI (see vlmrun chat)

  pip install "vlmrun[cli]"
  

• Video processing features (numpy, opencv-python):

  pip install "vlmrun[video]"
  

• Document processing features (pypdfium2):

  pip install "vlmrun[doc]"
  

• OpenAI SDK integration (for chat completions API):

  pip install "vlmrun[openai]"
  

• All optional features:

  pip install "vlmrun[all]"
  

Basic Usage

from PIL import Image
from vlmrun.client import VLMRun
from vlmrun.common.utils import remote_image

# Initialize the client
client = VLMRun(api_key="<your-api-key>")

# Process an image using local file or remote URL
image: Image.Image = remote_image("https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/invoice_1.jpg")
response = client.image.generate(
    images=[image],
    domain="document.invoice"
)
print(response)

# Or process an image directly from URL
response = client.image.generate(
    urls=["https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/invoice_1.jpg"],
    domain="document.invoice"
)
print(response)

OpenAI-Compatible Chat Completions

The VLM Run SDK provides OpenAI-compatible chat completions through the agent endpoint. This allows you to use the familiar OpenAI API with VLM Run's powerful vision-language models.

from vlmrun.client import VLMRun

client = VLMRun(
    api_key="your-key",
    base_url="https://api.vlm.run/v1"
)

response = client.agent.completions.create(
    model="vlmrun-orion-1",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

For async support:

import asyncio
from vlmrun.client import VLMRun

client = VLMRun(api_key="your-key", base_url="https://api.vlm.run/v1")

async def main():
    response = await client.agent.async_completions.create(
        model="vlmrun-orion-1",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

Installation: Install with OpenAI support using pip install vlmrun[openai]

CLI Chat with Skills

The vlmrun chat command supports skills — local directories containing a SKILL.md and optional assets that give the agent domain-specific expertise. Skills are sent inline with each request (no server-side upload required).

# Chat with an inline skill
vlmrun chat "Generate a youtube thumbnail for a video using the VLM Run brand colors" -k ./path/to/vlmrun-branding/

# Attach multiple skills (coming soon)
vlmrun chat "Analyze this invoice" -i invoice.pdf -k ./accounting-skills/ -k ./invoice-extraction/

To create a persistent server-side skill, use vlmrun skills upload ./my-skill/.

🔗 Quick Links

• 💬 Need help? Email us at [e-mail] or join our Discord
• 📚 Check out our Documentation
• 📣 Follow us on Twitter and LinkedIn