Log media & attachments | Opik Documentation | Opik Documentation

Opik supports multimodal traces allowing you to track not just the text input and output of your LLM, but also images, videos and audio and any other media.

Logging Attachments

In the Python SDK, you can use the Attachment type to add files to your traces. Attachements can be images, videos, audio files or any other file that you might want to log to Opik.

Each attachment is made up of the following fields:

These attachements can then be logged to your traces and spans using The opik_context.update_current_span and opik_context.update_current_trace methods:

Using file paths

The most common way to log attachments is by providing a file path:

from opik import opik_context, track, Attachment

@track
def my_llm_agent(input):
    # LLM chain code
    # ...

# Update the trace with a file path
    opik_context.update_current_trace(
        attachments=[\
            Attachment(\
                data="<path to the image>",\
                content_type="image/png",\
            )\
        ]
    )

return "World!"

print(my_llm_agent("Hello!"))

Using raw bytes (file-like data)

You can also pass raw bytes directly to an attachment. This is useful when you have file content in memory (e.g., from an API response, generated content, or streaming data) and don’t want to write it to disk first:

from opik import opik_context, track, Attachment

@track
def process_image(image_bytes: bytes):
    # Process the image
    # ...

# Log the raw bytes as an attachment
    opik_context.update_current_trace(
        attachments=[\
            Attachment(\
                data=image_bytes,  # Raw bytes\
                file_name="processed_image.png",  # Required for bytes\
                content_type="image/png",\
            )\
        ]
    )

return "Image processed!"

# Example: Reading a file into memory and logging it
with open("image.png", "rb") as f:
    image_data = f.read()

print(process_image(image_data))

When using raw bytes, Opik automatically creates a temporary file for upload and cleans it up after the attachment is uploaded. If you don’t specify a content_type, Opik will try to infer it from the file_name or default to application/octet-stream.

Logging images from HTTP responses

A common use case is logging images fetched from external APIs or URLs:

import httpx
from opik import opik_context, track, Attachment

@track
def analyze_remote_image(image_url: str):
    # Fetch image from URL
    response = httpx.get(image_url)
    image_bytes = response.content
    content_type = response.headers.get("content-type", "image/jpeg")

# Log the fetched image as an attachment
    opik_context.update_current_trace(
        attachments=[\
            Attachment(\
                data=image_bytes,\
                file_name="remote_image.jpg",\
                content_type=content_type,\
            )\
        ]
    )

# Process the image...
    return "Image analyzed!"

# Analyze an image from a URL
result = analyze_remote_image("https://example.com/image.jpg")

Logging generated content

You can also log dynamically generated content like charts or reports:

from opik import opik_context, track, Attachment
import json

@track
def generate_report(data: dict):
    # Generate a JSON report
    report_bytes = json.dumps(data, indent=2).encode("utf-8")

opik_context.update_current_trace(
        attachments=[\
            Attachment(\
                data=report_bytes,\
                file_name="report.json",\
                content_type="application/json",\
            )\
        ]
    )

return "Report generated!"

Using the Opik client directly

You can also log attachments using the Opik client directly with both file paths and raw bytes:

import opik
from opik import Attachment

client = opik.Opik()

# Create a trace
trace = client.trace(
    name="my-trace",
    input={"query": "Process this data"},
    project_name="my-project",
)

# Log attachment with file path
span_with_file = client.span(
    trace_id=trace.id,
    name="file-attachment-span",
    attachments=[\
        Attachment(\
            data="/path/to/document.pdf",\
            content_type="application/pdf",\
        )\
    ],
)

# Log attachment with raw bytes
binary_data = b"Hello, this is binary content!"
span_with_bytes = client.span(
    trace_id=trace.id,
    name="bytes-attachment-span",
    attachments=[\
        Attachment(\
            data=binary_data,\
            file_name="data.bin",\
            content_type="application/octet-stream",\
        )\
    ],
)

client.flush()

The attachements will be uploaded to the Opik platform and can be both previewed and downloaded from the UI.

In order to preview the attachements in the UI, you will need to supply a supported content type for the attachment. We support the following content types:

Managing Attachments Programmatically

You can also manage attachments programmatically using the AttachmentClient:

import opik

opik_client = opik.Opik()
attachment_client = opik_client.get_attachment_client()

# Get list of attachments
attachments_details = attachment_client.get_attachment_list(
    project_name="my-project",
    entity_id="some-trace-uuid-7",
    entity_type="trace"
)

# Download an attachment
attachment_data = attachment_client.download_attachment(
    project_name="my-project",
    entity_type="trace",
    entity_id="some-trace-uuid-7",
    file_name="report.pdf",
    mime_type="application/pdf"
)

# Upload a new attachment
attachment_client.upload_attachment(
    project_name="my-project",
    entity_type="trace",
    entity_id="some-trace-uuid-7",
    file_path="/path/to/document.pdf"
)

Previewing base64 encoded images and image URLs

Opik automatically detects base64 encoded images and URLs logged to the platform, once an image is detected we will hide the string to make the content more readable and display the image in the UI. This is supported in the tracing view, datasets view and experiment view.

For example if you are using the OpenAI SDK, if you pass an image to the model as a URL, Opik will automatically detect it and display the image in the UI:

from opik.integrations.openai import track_openai
from openai import OpenAI

# Make sure to wrap the OpenAI client to enable Opik tracing
client = track_openai(OpenAI())

response = client.chat.completions.create(
  model="gpt-4o-mini",
  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",\
          },\
        },\
      ],\
    }\
  ],
  max_tokens=300,
)

print(response.choices[0])

Embedded Attachments

When you embed base64-encoded media directly in your trace/span input, output, or metadata fields, Opik automatically optimizes storage and retrieval for performance.

How It Works

For base64-encoded content larger than 250KB, Opik automatically extracts and stores it separately. This happens transparently - you don’t need to change your code.

When you retrieve your traces or spans later, the attachments are automatically included by default. For faster queries when you don’t need the attachment data, use the strip_attachments=true parameter.

Size Limits

Opik Cloud supports embedded attachments up to 100MB per field. This limit applies to individual string values in your input, output, or metadata fields.

Base64 encoding increases file size by about 33%. For example, a 75MB video becomes ~100MB when base64-encoded.

If you need to work with larger files:

  1. Use the Attachment API - Upload files separately using AttachmentClient (recommended for files >50MB). See Managing Attachments Programmatically

  2. Contact us - Get in touch if you need higher limits

  3. Self-host Opik - Configure your own limits. See the Self-hosting Guide

Best Practices

Downloading attachments

You can download attachments in two ways:

  1. From the UI: Hover over the attachments and click on the download icon
  2. Programmatically: Use the AttachmentClient as shown in the examples above.