from openai import OpenAI

client = OpenAI(
    base_url="https://api.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="vlmrun-orion-2:auto",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Detect the car in this image. Return its bounding box and color."},
        {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg"}},
    ]}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.vlm.run/v1/openai",
  apiKey: process.env.VLMRUN_API_KEY,
});

const response = await client.chat.completions.create({
  model: "vlmrun-orion-2:auto",
  messages: [{ role: "user", content: [
    { type: "text", text: "Detect the car in this image. Return its bounding box and color." },
    { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg" } },
  ] }],
});
console.log(response.choices[0].message.content);

vlmrun chat "Detect the car in this image. Return its bounding box and color." -i car.jpg -m vlmrun-orion-2:auto

curl https://api.vlm.run/v1/openai/chat/completions \
  -H "Authorization: Bearer $VLMRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlmrun-orion-2:auto",
    "messages": [{"role": "user", "content": [
      {"type": "text", "text": "Detect the car in this image. Return its bounding box and color."},
      {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg"}}
    ]}]
  }'

from vlmrun.client import VLMRun

client = VLMRun(api_key="<VLMRUN_API_KEY>")

response = client.agent.completions.create(
    model="vlmrun-orion-2:auto",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Detect the car in this image. Return its bounding box and color."},
        {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg"}},
    ]}],
)

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  baseURL: "https://api.vlm.run/v1",
  apiKey: process.env.VLMRUN_API_KEY,
});

const response = await client.agent.completions.create({
  model: "vlmrun-orion-2:auto",
  messages: [{ role: "user", content: [
    { type: "text", text: "Detect the car in this image. Return its bounding box and color." },
    { type: "image_url", image_url: { url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg" } },
  ] }],
});
console.log(response.choices[0].message.content);

{
  "id": "chatcmpl-35942101-a661-4f51-8b8c-011f6c803dd0",
  "object": "chat.completion",
  "created": 1791255469,
  "model": "vlmrun-orion-2:auto",
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "\n\n**Car detected!**\n\n| Field | Value |\n|-------|-------|\n| **Label** | car |\n| **Confidence** | 0.98 |\n| **Bounding box (normalized xywh)** | x=0.055, y=0.333, w=0.874, h=0.442 |\n| **Color** | Mint green / turquoise |\n\nThe detected region is annotated in the image below:\n\n`img_b6ae94`"
      },
      "finish_reason": "stop",
      "index": 0
    }
  ],
  "usage": {
    "completion_tokens": 767,
    "prompt_tokens": 43739,
    "total_tokens": 44506,
    "credits_used": 2,
    "service_tier": "standard",
    "mode_multiplier": 1.0,
    "standard_cost_dollars": 0.015254,
    "cost_dollars": 0.015254,
    "savings_dollars": 0.0,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 116
    }
  },
  "system_fingerprint": "2026-05-19.0",
  "session_id": "da68e417-25d9-47cf-b3a3-22d3f75d749b",
  "service_tier": "standard"
}

{
  "detail": [
    {
      "type": "missing",
      "loc": ["body", "messages"],
      "msg": "Field required",
      "input": {
        "model": "vlmrun-orion-2:auto"
      }
    }
  ]
}