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Applies to:
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Summary To minimize costs while collecting user feedback for dataset curation, use init_logger() to create a logger instance and only call logger.log() when users provide feedback (thumbs up/down). This approach avoids automatic tracing of every interaction and allows manual review before promoting logs to datasets for experiments.

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Configuration Steps

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Step 1: Initialize logger at startup

Create a logger instance at application startup instead of using traced decorators for automatic logging. 
import braintrust

logger = braintrust.init_logger(project="My App")

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Step 2: Log only on feedback events

Call logger.log() exclusively when user feedback is received to avoid logging every interaction. 
def on_feedback(user_input, agent_output, thumbs_up: bool):
    logger.log(
        input=user_input,
        output=agent_output,
        scores={"user_feedback": 1 if thumbs_up else 0},
        tags=["user-feedback"],
    )

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Step 3: Review and promote logs

Review logged interactions in the project Logs tab, add additional tags as needed, and promote entries to datasets for running experiments.

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Alternative: Direct Dataset Insertion

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Skip logging layer entirely

For maximum cost savings, insert directly into datasets when feedback is received without using the logging layer. 
import braintrust

dataset = braintrust.init_dataset(project="My App", name="user-feedback")

def on_feedback(user_input, agent_output, thumbs_up: bool):
    dataset.insert(
        input=user_input,
        expected=agent_output,
        metadata={"feedback": "positive" if thumbs_up else "negative"},
    )