Documentation Index
Fetch the complete documentation index at: https://laminar.sh/docs/llms.txt
Use this file to discover all available pages before exploring further.
Laminar automatically detects and stores image data sent to vision-capable models across supported integrations.
- Captures both Base64 data URLs and image URLs
- Works transparently in the background (no tracing-specific code required)
- Renders images inline in the trace view so you can debug multimodal runs
OpenAI Example (Base64 Image)
import OpenAI from 'openai';
import fs from 'node:fs';
import { Laminar, observe } from '@lmnr-ai/lmnr';
Laminar.initialize({
projectApiKey: process.env.LMNR_PROJECT_API_KEY,
instrumentModules: { OpenAI },
});
const openai = new OpenAI.OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const analyzeImage = (imagePath: string, userQuestion: string) =>
observe({ name: 'analyzeImage' }, async () => {
const base64 = fs.readFileSync(imagePath).toString('base64');
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [
{
role: 'user',
content: [
{ type: 'text', text: userQuestion },
{
type: 'image_url',
image_url: { url: `data:image/jpeg;base64,${base64}` },
},
],
},
],
max_tokens: 500,
});
return response.choices[0].message.content;
});
import base64
from openai import OpenAI
from lmnr import Laminar, observe
Laminar.initialize()
client = OpenAI()
@observe()
def analyze_image(image_path: str, user_question: str) -> str:
with open(image_path, "rb") as f:
base64_image = base64.b64encode(f.read()).decode("utf-8")
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": user_question},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
},
},
],
}
],
max_tokens=500,
)
return response.choices[0].message.content
Image URLs
If you reference an image by URL instead of uploading it, Laminar traces the URL and associates it with the LLM call (no extra tracing code needed).
OpenAI Example (Image URL)
import OpenAI from 'openai';
import { observe } from '@lmnr-ai/lmnr';
const openai = new OpenAI.OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const analyzeWebImage = (imageUrl: string, analysisPrompt: string) =>
observe({ name: 'analyzeWebImage' }, async () => {
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [
{
role: 'user',
content: [
{ type: 'text', text: analysisPrompt },
{ type: 'image_url', image_url: { url: imageUrl, detail: 'high' } },
],
},
],
});
return response.choices[0].message.content;
});
from openai import OpenAI
from lmnr import observe
client = OpenAI()
@observe()
def analyze_web_image(image_url: str, analysis_prompt: str) -> str:
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": analysis_prompt},
{
"type": "image_url",
"image_url": {"url": image_url, "detail": "high"},
},
],
}
],
)
return response.choices[0].message.content
In the Laminar platform, you can:
- View the actual images that were sent to the model
- Click on the image to view it in a larger view
- Correlate images with model responses for debugging
- Track image usage across different traces and sessions
Supported Integrations
Start here:
