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BRAINTRUST

Trace, evaluate, and monitor production AI systems

ABOUT

Teams shipping LLM applications often lack a reliable way to trace prompt executions, compare outputs, and measure whether model or prompt changes improved quality. Braintrust centralizes traces, eval datasets, scoring, and experiment results so teams can debug failures, catch regressions, and monitor how AI workflows behave after deployment.

INSTALL
pip install braintrust npm install braintrust

INTEGRATION GUIDE

1. Trace agent runs and LLM calls to debug failures in production applications 2. Run offline and online evaluations against datasets before shipping prompt changes 3. Compare model, prompt, and tool-calling variants with structured scores and feedback 4. Monitor application behavior over time to catch regressions after deployment

TAGS

observabilityevaluationstracingllmagentspromptsmonitoring
Braintrust — AI Tool | Agentic AI For Good