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Applies to:


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Summary

Issue: Traces with large conversational data (ie hundreds of runs with hundreds of thousands of tokens each) can cause slow UI loading and poor browsing experience. Cause: Large JSON payloads render inline, resulting in slow performance when opening traces. Resolution: Use JSONAttachment to move large data out of inline rendering while keeping essential metadata searchable.

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Implementation

Use JSONAttachment for large conversational data and keep essential metadata inline for indexing and filtering: Python: 
from braintrust import JSONAttachment

span.log(
    input={
        # Large data stored as attachment
        "transcript": JSONAttachment(large_conversation, filename="chat.json")
    },
    # Essential metadata stays inline for filtering/search
    metadata={
        "model": "gpt-4",
        "turns": 156,
        "summary": "Customer support chat about billing"
    }
)
TypeScript: 
import { JSONAttachment } from "braintrust";

span.log({
  input: {
    // Large data stored as attachment
    transcript: new JSONAttachment(largeConversation, { filename: "chat.json" })
  },
  // Essential metadata stays inline for filtering/search
  metadata: {
    model: "gpt-4",
    turns: 156,
    summary: "Customer support chat about billing"
  }
});
Key benefits:
  • UI for trace loads quickly, since large data as attachments don’t render inline
  • Full conversation still accessible via attachment viewer in UI
  • Essential metadata visible at a glance for filtering and search