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Braintrust Integration for LLM Agents

Overview

The Braintrust integration for our LLM agents provides comprehensive evaluation, monitoring, and tracing capabilities. This enables us to:

  1. Track and compare model performance across different versions and configurations
  2. Detect regressions early in the development process
  3. Ensure quality control through automated evaluations in CI/CD
  4. Gain insights into agent behavior through detailed tracing
  5. Standardize evaluation practices across our engineering teams

Architecture

Our integration is built around several key components:

  1. BraintrustArtifact System - A base class system for tracking versioned artifacts like prompts and scoring functions
  2. Evaluation Framework - Automated scoring pipelines for different agent types
  3. CI/CD Integration - Smart triggering of evaluations based on code changes
  4. Tracing Capabilities - Capturing LLM interactions for analysis and debugging

The architecture follows these key design principles: - Separation of concerns between agent logic and evaluation code - Versioned artifacts for reproducibility and tracking - Automated evaluation in the development workflow - Standardized scoring methodologies

Key Benefits

By integrating with Braintrust, we've gained:

  1. Objective Measurement - Standardized metrics across agent versions
  2. Development Velocity - Early detection of regressions and quick feedback
  3. Enhanced Collaboration - Shared understanding of performance through centralized dashboards
  4. Better Debugging - Detailed tracing of LLM interactions for issue resolution

Integration Components

BraintrustArtifact System

The BraintrustArtifact base class provides a foundation for tracking versioned artifacts in Braintrust:

  • Prompts - Versioned LLM prompts with metadata and model configuration
  • Scoring Functions - Standardized evaluation metrics with clear definitions
  • Projects - Organized by agent type (e.g., patient_matcher, protocol_parser)
  • Environments - Support for production and staging environments

Evaluation Framework

Our evaluation framework enables consistent assessment of LLM agent performance:

  • Standardized Scoring - Well-defined metrics for each agent type
  • Automated Evaluation Pipelines - Scripts for running evaluations locally or in CI/CD
  • Performance Tracking - Historical performance data across versions
  • Comparison Tools - Side-by-side comparison of different approaches

CI/CD Integration

The CI/CD integration ensures quality control throughout the development lifecycle:

  • Path-Based Triggering - Smart detection of which evaluations to run based on code changes
  • PR Comments - Automated evaluation results posted as comments on pull requests
  • Baseline Comparison - Performance compared to main branch for quick assessment
  • Dashboard Links - Direct access to detailed results in Braintrust

Tracing Capabilities

Tracing provides insights into LLM interactions for debugging and analysis:

  • Request/Response Logging - Full capture of inputs and outputs
  • Contextual Information - Metadata about the execution environment
  • Performance Metrics - Timing and token usage statistics
  • Failure Analysis - Tools for understanding errors and edge cases