> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vlm.run/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

The **VLM Run API** is a unified platform for production-ready multimodal AI. Use it to extract structured data from documents, images, videos, and audio — or run complex multi-step workflows with visual agents.

* **Base URL**: `https://api.vlm.run/v1`
* **Authentication**: `Authorization: Bearer <VLMRUN_API_KEY>`
* **Models Supported**:
  * Requests: `vlm-1`
  * Agent Executions / Chat Completions: `vlmrun-orion-1:auto`, `vlmrun-orion-1:fast`, `vlmrun-orion-1:pro`

See [Ways to Use VLM Run](/ways-to-use-vlm-run) for a side-by-side comparison of Requests, Executions, Chat Completions, and the Chat UI.

<Tip>Access your [API keys](https://app.vlm.run/dashboard/settings/api_keys) in our dashboard.</Tip>

## Structured Extraction

Use the [Generate](/api-reference/v1/post-image-generate) endpoints to extract structured JSON from images, documents, audio, and video.

<CodeGroup>
  ```python Python theme={"theme":{"light":"github-light","dark":"dark-plus"}}
  from pathlib import Path
  from vlmrun.client import VLMRun
  from vlmrun.client.types import PredictionResponse

  # Initialize the client
  client = VLMRun(api_key="<VLMRUN_API_KEY>")

  # Document -> JSON
  response: PredictionResponse = client.document.generate(
      file=Path("path/to/document.pdf"),
      model="vlm-1",
      domain="document.invoice",
  )
  ```

  ```javascript Node.js SDK theme={"theme":{"light":"github-light","dark":"dark-plus"}}
  import { VLMRun } from "vlmrun";

  // Initialize the client
  const client = new VLMRun({
      apiKey: "<VLMRUN_API_KEY>",
  });

  // Upload a document
  const file = await client.files.upload({
    filePath: "path/to/invoice.pdf",
  });

  // Process a document using file ID
  const response = await client.document.generate({
    fileId: file.id,
    model: "vlm-1",
    domain: "document.markdown",
  });
  console.log(response);
  ```
</CodeGroup>

## Chat Completions & Agent Executions

Use the [Chat Completions](/api-reference/v1/post-chat-completions) endpoint for interactive multi-modal conversations, or the [Agent Executions](/api-reference/v1/get-agent-executions) endpoint for batch execution workflows.

<CodeGroup>
  ```python Python / VLMRun SDK theme={"theme":{"light":"github-light","dark":"dark-plus"}}
  from vlmrun.client import VLMRun

  # Initialize the VLM Run client
  client = VLMRun(api_key="<VLMRUN_API_KEY>")

  # Create a chat completion
  response = client.agent.completions.create(
      model="vlmrun-orion-1:auto",
      messages=[
          {
              "role": "user",
              "content": [
                  {"type": "text", "text": "What do you see in this image?" },
                  {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
              ]
          }
      ],
      max_tokens=1000
  )
  ```

  ```typescript TypeScript / VLMRun SDK theme={"theme":{"light":"github-light","dark":"dark-plus"}}
  import { VlmRun } from "vlmrun";

  // Initialize the VLM Run client
  const client = new VlmRun({
    apiKey: "<VLMRUN_API_KEY>"
  });

  // Create a chat completion
  const response = await client.agent.completions.create({
    model: "vlmrun-orion-1:auto",
    messages: [
      {
        role: "user",
        content: [
          { type: "text", text: "What do you see in this image?" },
          { type: "image_url", image_url: { url: "https://example.com/image.jpg" } }
        ]
      }
    ],
    max_tokens: 1000
  });
  console.log(response.choices[0].message.content);
  ```

  ```curl cURL theme={"theme":{"light":"github-light","dark":"dark-plus"}}
  curl -X POST https://api.vlm.run/v1/openai/chat/completions \
    -H "Authorization: Bearer $VLMRUN_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "vlmrun-orion-1:auto",
      "messages": [
        {
          "role": "user",
          "content": [
            { "type": "text", "text": "What do you see in this image?" },
            { "type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
          ]
        }
      ],
      "max_tokens": 1000
    }'
  ```
</CodeGroup>