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LangSmith supports logging and rendering images as part of traces. This is currently supported for multimodal LLM runs. In order to log images, use wrap_openai/ wrapOpenAI in Python or TypeScript respectively and pass an image URL or base64 encoded image as part of the input. 
from openai import OpenAI
from langsmith.wrappers import wrap_openai
client = wrap_openai(OpenAI())
response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "What's in this image?"},
          {
            "type": "image_url",
            "image_url": {
              "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
            },
          },
        ],
      }
    ],
)
print(response.choices[0])

import OpenAI from "openai";
import { wrapOpenAI } from "langsmith/wrappers";
// Wrap the OpenAI client to automatically log traces
const wrappedClient = wrapOpenAI(new OpenAI());
const response = await wrappedClient.chat.completions.create({
  model: "gpt-4-turbo",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image_url",
          image_url: {
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
          },
        },
      ],
    },
  ],
});
console.log(response.choices[0]);
The image will be rendered as part of the trace in the LangSmith UI.
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